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The PVDF Effort Sensor is intended to measure and output respiratory effort signals from a patient for archival in a sleep study. The sensor is an accessory to a polysomnography system which records and conditions the physiological signals for analysis and display, such that the data may be analyzed by a qualified sleep clinician to aid in the diagnosis of sleep disorders.

The PVDF Effort Sensor is intended for use on both adults and children by healthcare professionals within a hospital, laboratory, clinic, or nursing home, or outside of a medical facility under the direction of a medical professional.

The PVDF Effort Sensor does not include or trigger alarms, and is not intended to be used alone as, or a critical component of,

• an alarm or alarm system;
• an apnea monitor or apnea monitoring system; or
• life monitor or life monitoring system.

The PV01 PVDF Effort Sensor is a respiratory effort monitoring accessory designed for use during sleep studies to assess breathing patterns by measuring chest and abdominal wall movement. The device functions as an accessory to polysomnography (PSG) systems, enabling qualified sleep clinicians to analyze respiratory data for the diagnosis of sleep disorders.

The sensor consists of two main components: a PVDF (polyvinylidene fluoride) sensor module and an elastic belt. The sensor module contains two plastic enclosures connected by a piezoelectric PVDF sensing element encased in a silicone laminate. The PVDF material generates a tiny voltage that is output through the lead wire to the sleep amplifier. The change in voltage as the tension on the PVDF film fluctuates corresponds to the breathing of the patient. Since the PVDF material generates voltage, the sensor does not require a battery or power from the amplifier. The output signal is processed by the sleep recording system for monitoring and post-study analysis.

The PV01 PVDF Effort Sensor is intended for prescription use only by healthcare professionals in hospitals, sleep laboratories, clinics, nursing homes, or in home environments under medical professional direction. The device is designed for use on both adult and children participating in sleep disorder studies. The sensor is intended to be worn over clothes and not directly on the patient's skin.

The 510(k) clearance letter for the PV01 PVDF Effort Sensor does not contain the specific details required to fully address all aspects of your request regarding acceptance criteria and the study proving the device meets them. This document is a regulatory approval letter, summarizing the basis for clearance, not a detailed study report.

However, based on the provided text, here's an attempt to extract and infer the information:

Overview of Device Performance Study

The PV01 PVDF Effort Sensor underwent "comprehensive verification and validation testing" including "functional and performance evaluations" and "validation studies" to confirm it meets design specifications and is safe and effective. Additionally, "comparative testing against the Reference Device" was performed.

This suggests that the performance evaluation primarily focused on:

1. Safety Tests: Compliance with UL 60601-1 standards to ensure electrical and liquid ingress safety.
2. Usability and Validation Test: Assessment of user experience and comfort during a simulated sleep study.
3. Performance Comparison Test: Electrical signal output comparison to a legally marketed predicate device under simulated breathing conditions.
4. Temperature Range Test: Verification of signal output performance at extreme operating temperatures.

Acceptance Criteria and Reported Device Performance

Based on the "Summary of Tests Performed" section, the following can be inferred:

Missing Information and Limitations:

The provided FDA 510(k) clearance letter is a high-level summary and does not contain the granular details typically found in a full study report. Therefore, most of the following requested information cannot be extracted directly from this document.

1. Sample size used for the test set and data provenance:

   • Test Set Size: Not specified for any of the performance tests. For the usability test, it mentions "Participants" (plural), but no number. For the performance comparison test, it states "Both devices were placed on a rig," implying a comparison, but no human subject or case count.
   • Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). The usability test mentions "participants," potentially implying prospective data collection, but this is a broad inference.

2. Number of experts used to establish the ground truth for the test set and their qualifications:

   • Not Applicable/Not Specified: The device is a "PVDF Effort Sensor" that measures and outputs respiratory effort signals. Its purpose is to provide raw physiological data for a "qualified sleep clinician to aid in the diagnosis of sleep disorders." The device itself does not provide a diagnosis or interpretation that would require expert ground truth labeling in the traditional sense of an AI diagnostic device (e.g., image-based AI). The performance assessment appears to be against expected signal characteristics and comparison to a known device, not against clinical ground truth established by experts.

3. Adjudication method for the test set:

   • Not Applicable/Not Specified: Given the nature of the device (a sensor outputting physiological signals) and the described tests, a formal adjudication process (like for interpreting medical images) is not mentioned or implied.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs. without AI assistance:

   • No: This type of study (MRMC for AI assistance) is not mentioned. The device is a sensor, not an AI interpretative tool designed to assist human readers directly. It provides raw data for clinicians to analyze.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

   • Partially Yes (for the sensor itself): The "Performance Comparison Test" and "Temperature Range Test" assess the device's signal output performance independently without a human in the loop for interpretation. The "Safety Tests" are also standalone tests on the device's physical and electrical properties.

6. The type of ground truth used:

   • Physiological Simulation / Device Output Comparison: For the "Performance Comparison Test," the ground truth was essentially the simulated breathing patterns produced by a "rig" and the expected output signals of a known predicate/reference device.
   • User Feedback / Self-Reported Metrics: For the "Usability and Validation Test," the ground truth was the participants' subjective feedback on comfort and ease-of-use, and the absence of reported use errors or adverse events.
   • Compliance with Standards: For "Safety Tests," the ground truth was compliance with the specified clauses of the UL 60601-1 standard.

7. The sample size for the training set:

   • Not Applicable/Not Specified: The PV01 PVDF Effort Sensor is described as a passive hardware sensor ("generates a tiny voltage," "does not require a battery or power from the amplifier") that measures physical movement. It is not an AI/ML algorithm that requires a "training set" in the computational sense.

8. How the ground truth for the training set was established:

   • Not Applicable: As stated above, there is no mention or implication of a training set as this is a hardware sensor, not an AI/ML algorithm.

In summary, the provided document gives a high-level overview of the acceptance criteria met for regulatory clearance, primarily focusing on safety, basic functional performance relative to another device, and usability. It does not delve into the detailed statistical methodology and independent ground truth establishment typical of AI/ML device studies.

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The Somfit D is a single-use, non-invasive prescription device for home use with patients suspected to have sleep-related breathing disorders. The Somfit D is a diagnostic aid for the detection of sleep-related breathing disorders, sleep staging (REM, N1, N2, N3, Wake), and snoring level. The Somfit D system acquires electrical data from three frontal electrodes, tri-axial accelerometer data, acoustical and plethysmographic data. The Somfit D calculates and reports to clinicians EEG/EOG channels, Sleep Stages, SpO2, Peripheral Arterial Tonometry signal, pulse rate, and snoring level. The Somfit D calculates and reports to clinicians derived parameters such as Peripheral Arterial Tonometry-derived Apnea Hypopnea Index, Obstructive Desaturation Index; and hypnogram-derived indices such as time in each sleep stage. Somfit D data is not intended to be used as the sole or primary basis for diagnosing any sleep-related breathing disorder, prescribing treatment, or determining whether additional diagnostic assessment is warranted. The Somfit D is not intended for use as life support equipment, for example vital signs monitoring in intensive care unit. The Somfit D is a prescription device indicated for adult patients aged 21 years and over.

The Somfit D is a home-based sleep monitoring device which records signals from the patient's forehead. Somfit D is a wearable, low voltage, battery operated device which is attached to subject forehead via a self-adhesive and disposable skin electrode patch. The electrodes are placed on the anterior Prefrontal cortex (PFC) at the Fp1 and Fp2 positions according to the 10/20 EEG system. The device allows for recording of two frontal EEG signals, pulse rate, SPO2, PAT, PPG, motion, and snore. Somfit D uses a mobile phone application to acquire data wirelessly via Bluetooth BLE technology, then transfer into a secure cloud, for management, storage and post-processing. The software reports measured parameters in a format compatible with the American Academy of Sleep Medicine guidelines, including sleep time, ODI, pAHI and conventional graphical displays such as a hypnogram.

The provided FDA 510(k) clearance letter and Somfit D 510(k) Summary describe the acceptance criteria and the study that proves the device meets those criteria. However, it explicitly states that the Somfit D is substantially equivalent to the predicate device, Somfit (K231546), and therefore, the performance data for the Somfit D is derived from the studies conducted on the Somfit. The summary then refers to already submitted and approved studies for the Somfit device.

Here's a breakdown of the requested information based on the provided document:

Acceptance Criteria and Device Performance (Derived from Predicate Device, Somfit)

Table 1: Acceptance Criteria and Reported Device Performance

Study Details (Pertaining to the Predicate Device, Somfit, as explicitly stated for Somfit D equivalence)

1. Sample sizes used for the test set and the data provenance:

   • Oximeter Validation: Controlled desaturation study in accordance with ISO 80601-2-61. Specific sample size not specified in this document, but implied to be sufficient for standard compliance. Data provenance is implied to be from a "Hypoxia Lab." The document doesn't specify if it was retrospective or prospective, or country of origin, but clinical studies for FDA clearance are typically prospective to ensure controlled conditions.
   • Home Sleep Apnea Test Validation (pAHI, ODI, Sleep Staging): A "Multi-Site Clinical Study" was conducted. Specific sample sizes for each of these validations are not provided in this summary. The document states "clinical data for the purpose of the predicate device Somfit (K231546), already submitted and approved," indicating these details would be found in the original Somfit 510(k) submission. The provenance is from "Multi-Site Clinical Study," implying multiple locations, likely within the regulatory jurisdiction where the clearance was sought (e.g., US, Australia). It's implied to be prospective for validation purposes.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • This information is not explicitly stated in the provided 510(k) summary for any of the clinical validations (Oximeter, pAHI, ODI, Sleep Staging). It only mentions that the studies were "meaningful validations" and "concordance," implying comparison to a gold standard. For sleep staging, ground truth is typically established by certified polysomnography technologists and/or sleep physicians.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

   • This information is not explicitly stated in the provided 510(k) summary for any of the clinical validations.

4. If a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • No MRMC study is mentioned. The Somfit D (and its predicate Somfit) is described as a diagnostic aid that calculates and reports parameters to clinicians. It's not presented as an AI-assissted reading tool for human interpretation, but rather a device that quantifies specific physiological signals and derives standard indices. The human role is in interpretation of the reported data, not in an AI-assisted reading workflow.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   • Yes, the device (Somfit D, through its predicate Somfit) performs standalone algorithmic analysis to calculate:
     • Sleep Stages (REM, N1, N2, N3, Wake)
     • SpO2
     • Peripheral Arterial Tonometry (PAT) signal
     • Pulse rate
     • Snoring level
     • PAT-derived Apnea Hypopnea Index (pAHI)
     • Obstructive Desaturation Index (ODI)
     • Hypnogram-derived indices (e.g., time in each sleep stage)
   • The "meaningful validation" studies for these parameters (pAHI, ODI, Sleep Staging) suggest a comparison of the device's algorithmic outputs against established ground truth.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • Oximeter Validation: Performed in a "Hypoxia Lab," implying comparison to a highly accurate laboratory reference oximeter or blood gas analysis (gold standard for SpO2 measurements).
   • Home Sleep Apnea Test Validation (pAHI, ODI, Sleep Staging): While not explicitly stated, for HSAT devices, "ground truth" for pAHI and ODI is typically derived from comparison to full in-lab Polysomnography (PSG) data scored by a certified sleep technologist and/or interpreted by a board-certified sleep physician, which is considered the clinical gold standard. For sleep staging, the ground truth would be expert-scored PSG recordings.

7. The sample size for the training set:

   • This information is not provided in the summary. The summary focuses on the validation studies, which imply the device's algorithms were already developed and trained.

8. How the ground truth for the training set was established:

   • This information is not provided in the summary, as it pertains to the development phase rather than the validation phase described.

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The LuMon™ System is a noninvasive, non-radiation device that provides information on regional impedance variation within a cross-section of a patient's thorax. Graphical and numerical information is presented to the user as an adjunctive tool to other clinical information to support the user to assess a patient's respiratory condition.

The LuMon™ System is indicated for neonatal and infant patients with chest circumferences between 16.5 - 50 cm and adolescent through adult patients with chest circumferences between 76 - 128 cm who are breathing spontaneously or require mechanical ventilation in professional healthcare facilities.

Impedance-based respiratory rate monitoring is indicated for adults 22 years and older whose chest circumference is above 76 cm only.

The LuMon™ System also displays selected physiological and respiratory parameters from supported bedside devices.

The LuMon™ System does not measure regional ventilation of the lungs.

The LuMon™ System is a compact and lightweight Electrical Impedance Tomography (EIT) system that provides noninvasive monitoring of variations of regional air content/volume within a cross-section of the patient's thorax and patient respiration. It displays the results as real-time EIT images, waveforms, and derived parameters.

The system consists of a controller display unit, signal acquisition connector cable, and patient-applied conductive textile electrode belts. The system can connect to external bedside devices such as ventilators and monitoring devices to display contextual information for interpretation of EIT measurements.

The provided FDA 510(k) clearance letter and summary for the LuMon™ System contains information regarding its acceptance criteria and the studies conducted to demonstrate its performance. However, some specific details commonly found in a comprehensive study report (e.g., exact sample size for each clinical study, number of experts for ground truth, adjudication methods beyond "clinician-scored") are not explicitly stated in this high-level summary.

Based on the provided text, here's a structured response addressing your request:

Acceptance Criteria and Device Performance for LuMon™ System

The LuMon™ System underwent extensive non-clinical (bench and pre-clinical) and clinical testing to demonstrate its safety and effectiveness. The acceptance criteria are implicitly defined by the performance characteristics presented in the comparison tables and the successful attainment of stated accuracies and correlations.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are generally established as equivalent to or better than the predicate/reference devices, or as meeting pre-defined tolerances for specific parameters.

• Data Provenance: Not explicitly stated regarding country of origin for clinical data. The studies are described as "pre-clinical" and "clinical," with no indication of being retrospective. "Clinical testing was performed to support safety and effectiveness" generally implies prospective data collection for regulatory purposes.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Not specified.
• Qualifications of Experts: For the Respiration Rate study, the reference standard was "Clinician-scored EtCO2 capnogram." This implies medical professionals were involved in establishing the ground truth, but their specific qualifications (e.g., types of physicians, years of experience) are not detailed.

4. Adjudication Method for the Test Set

• For Respiration Rate Ground Truth: "Clinician-scored EtCO2 capnogram" implies expert review. However, the exact adjudication method (e.g., 2+1, 3+1, majority vote, independent reads with reconciliation) is not specified.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• It is not explicitly stated that a specific MRMC study was conducted to compare human readers with and without AI assistance.
• The device is presented as an "adjunctive tool to other clinical information to support the user," meaning it assists clinicians rather than replacing them. Its effectiveness is shown through its ability to provide accurate EIT data and respiratory rate, which clinicians would then integrate into their assessment. The summary focuses on the device's accuracy relative to reference standards or predicate devices, not on direct human-AI performance comparison studies.

6. Standalone (Algorithm Only) Performance

• Yes, the performance characteristics listed in the tables (e.g., R-squared correlations for EIT-CT, accuracy for RRi against reference standards, SNR, voltage accuracy) represent the standalone performance of the algorithm and the device. The clinical studies compare the device's output itself to established medical standards or other modalities, distinct from how a human user might interpret or use that output.

7. Type of Ground Truth Used

• Pre-clinical (Regional Impedance Distribution): Differential CT changes in aeration (healthy and injured lungs, one- and two-sided intubation) and "established physiological changes" were used as ground truth.
• Clinical (Global Volume Changes): Body plethysmograph traces and Ventilator flow-sensed volumes were used as ground truth.
• Clinical (Regional Impedance Distribution): The Timpel Enlight 2100 predicate comparison was used for ground truth.
• Clinical (Respiration Rate): Clinician-scored EtCO2 capnogram was used as ground truth.

8. Sample Size for the Training Set

• The information provided is a 510(k) summary, which typically focuses on validation. The sample size for the training set is not provided in this document. Training data details are usually proprietary and not disclosed in 510(k) summaries unless directly relevant to the regulatory pathway or substantial equivalence claim.

9. How Ground Truth for the Training Set Was Established

• The document does not specify how ground truth was established for the training set. Similar to the training set size, details about the training data and its ground truth establishment are generally considered proprietary and are not typically included in a public 510(k) summary. The summary focuses on the independent test data performance.

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The Respiree Cardio-Respiratory Monitor is a respiratory monitor intended for hospitals and hospital-type facilities in non-ICU settings and home settings.

The Respiree Cardio-Respiratory Monitor is indicated for the non-invasive spot checking of respiration rate (RR) for adult patients.

The Respiree Cardio-Respiratory Monitor System comprised of the following devices:

• Respiree Cardio-Respiratory Monitor
• Respiree Gateway and accessories (Antenna, charging cable)
• Respiree Dashboard

The Respiree Cardio-Respiratory Monitor is a wearable respiratory monitor. For measurement of respiration rate (RR), the device is affixed to the chest using a disposable adhesive patch with a hook-and-loop fastener to attach to the monitor. The device uses a vertical-cavity surface-emitting diode to emit optical light directed toward the skin. An integrated photodetector in a nearby position senses the diffused collected light. An adaptive signal processing method is used to enhance the device respiratory rate measurements by splitting the signal processing optimizations across different respiratory rate bands.

The monitor is powered by a 3.7V rechargeable, lithium-ion battery and is charged using the gateway provided. The Respiree Cardio-Respiratory Monitor transmits respiration rate raw data to the gateway via AES 256 encrypted Bluetooth wireless technology, and the latter uploads the data to the fixed secured cloud server either via Wi-Fi or LTE.

The Respiree Dashboard is a web application user interface that enable healthcare professional to access recorded respiration rate information for spot patient monitoring. The data from the Respiree Cardio- respiratory Monitor are intended for use by healthcare professionals as an aid to diagnosis and treatment. The device is not intended for use on critical care patients.

The provided FDA 510(k) clearance letter and summary for the Respiree Cardio-Respiratory Monitor System (K250934) indicate that clinical studies were not required for this specific submission, as there was "no change in the respiration rate software algorithm cleared in the previous version of the device (K223681)." This implies that the performance data for the respiration rate measurement itself was established in a prior submission (K223681).

Therefore, I cannot extract specific details about new clinical studies for K250934 that would directly prove the device meets acceptance criteria for respiration rate measurement within this document. The document primarily focuses on demonstrating substantial equivalence based on the updated hardware, expanded use environment (home setting), and data presentation methods, leveraging the previous clearance for the core measurement accuracy.

However, I can infer information about the acceptance criteria for the respiration rate measurement and the reported device performance based on the comparison table with the predicate devices. The other requested information (sample size, experts, adjudication, MRMC, standalone, ground truth, training set details) is typically found in the clinical study report itself, which is not part of this 510(k) summary for K250934.

Inference from K250934 Document (based on predicate comparison):

1. Table of Acceptance Criteria and Reported Device Performance (Inferred from Predicate Comparison)

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The monitors are intended to be used for monitoring, storing, recording, and reviewing of, and to generate alarms for, multiple physiological parameters of adults and pediatrics (including neonates). The monitors are intended for use by trained healthcare professionals in hospital environments.

The monitored physiological parameters include: ECG, respiration (RESP), temperature (TEMP), functional oxygen saturation of arterial hemoglobin (SpO₂), pulse rate (PR), non-invasive blood pressure (NIBP), invasive blood pressure (IBP), carbon dioxide (CO2), and cardiac output (C.O.).

The arrhythmia detection and ST Segment analysis are intended for adult patients.

The NIBP monitoring supports iCUFS algorithm and iFAST algorithm. The iCUFS algorithm is intended for adult, pediatric and neonatal patients. The iFAST algorithm is intended for adult and pediatric patients (≥3 years of age). Both measurement algorithms are also intended for use with pregnant women, including pre-eclamptic patients. NIBP MAP is not applicable to pregnant women.

The Spot Temp with T2A module can only measure temperature of adult and pediatric (> 1 year of age) patients.

The monitors are not intended for MRI environments.

The cardiac output (C.O.) is only intended for adult patients.

The CX&UX series Patient Monitor including CX10/CX12/CX15/UX10/UX12/UX15 can perform long-time continuous monitoring of multiple physiological parameters. Also, it is capable of storing, displaying, analyzing and controlling measurements, and it will indicate alarms in case of abnormalities so that doctors and nurses can respond to the patient's situation as appropriate.

Minor differences from the predicate device are limited to some modifications of monitoring parameter specifications. These updates do not change the fundamental scientific technology of the cleared predicate device and thus do not raise any questions about the safety and effectiveness of the subject device.

The provided FDA 510(k) clearance letter details the device's technical specifications and comparisons to predicate devices, along with the non-clinical performance data and adherence to various IEC and ISO standards. However, it explicitly states: "Clinical data: The subject device did not require new clinical studies to support substantial equivalence."

This means that the submission for this Patient Monitor device (CX10, CX12, CX15, UX10, UX12, UX15) relies on demonstrating substantial equivalence to a legally marketed predicate device (Edan Instruments, Inc., Patient Monitor Model iX10, iX12, iX15, K232962) through non-clinical performance testing and software verification/validation, rather than new clinical trials or studies involving human patients.

Therefore, the requested information regarding acceptance criteria and studies that prove the device meets acceptance criteria through clinical performance (e.g., sample size for test set, expert involvement, MRMC studies, ground truth establishment for test/training sets, effect size of human reader improvement with AI) cannot be extracted from this document, as such clinical studies were explicitly not required for this 510(k) submission.

The document focuses on demonstrating that the new device's technical specifications and performance are similar to the predicate device, and that it complies with relevant safety and performance standards through bench testing.

Here's what can be extracted from the provided text regarding acceptance criteria and the type of study performed, specifically focusing on the non-clinical aspects:

Device: Patient Monitor (CX10, CX12, CX15, UX10, UX12, UX15)

The acceptance criteria for this device are implicitly tied to its performance meeting the standards and accuracy specifications of the predicate device and relevant international standards. Since no new clinical studies were conducted, the "proof" comes from non-clinical bench testing and software validation.

1. Table of Acceptance Criteria and Reported Device Performance (Non-Clinical/Bench Testing)

2. Sample Size Used for the Test Set and Data Provenance:

• Sample Size: Not applicable in terms of human subjects or patient data test sets, as "new clinical studies" were not required. The "test set" refers to bench testing and functional system-level validation. The specific number of test cycles or a detailed breakdown of test cases for bench testing is not provided in this summary.
• Data Provenance: The data primarily originates from Edan Instruments Inc. (Shenzhen, Guangdong, China) through internal engineering and quality assurance processes for non-clinical bench testing and software validation. It is not patient data, so concepts like "retrospective or prospective" do not apply.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:

• Not applicable for clinical ground truth: Since no clinical studies were performed requiring human interpretation or diagnosis for a test set, no medical experts (e.g., radiologists) were used to establish ground truth in this context.
• Internal experts: Bench testing and software validation would have involved engineers and quality assurance professionals, whose qualifications are implicit in the quality system (21 CFR Part 820) but not specified in detail here.

4. Adjudication Method for the Test Set:

• Not applicable: Adjudication methods (e.g., 2+1, 3+1) are relevant for clinical studies involving multiple readers. This was not a clinical study. Bench testing relies on established technical specifications and standard compliance.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done:

• No: No MRMC study was performed as no new clinical studies were required or conducted. Therefore, there's no effect size of human readers improving with AI assistance. The device is a patient monitor, not an AI-assisted diagnostic tool.

6. If a Standalone (i.e. algorithm only without human-in-the loop performance) was Done:

• Yes (for the technical components): The "performance testing-Bench" effectively represents a standalone evaluation of the device's functional components (ECG, NIBP, SpO2, etc.) and software against defined technical specifications and standards. The "software verification and validation testing" also represents a standalone evaluation of the algorithm and software functions. The specific algorithms (e.g., iCUFS, iFAST for NIBP, arrhythmia analysis logic) are tested independently for their accuracy against known inputs or reference standards as part of bench testing.

7. The Type of Ground Truth Used:

• Technical/Reference Standards: For the bench testing, the "ground truth" would be derived from:
  • Reference standards/simulators: Calibrated medical equipment, physiological simulators, and test signals (e.g., known ECG waveforms, simulated blood pressure readings, temperature standards) are used to provide the "true" values against which the device's measurements are compared.
  • Defined specifications: The device's internal design specifications and the requirements of the referenced IEC/ISO standards serve as the "ground truth" for compliance testing.
• Not clinical ground truth: No expert consensus, pathology, or outcomes data from real patients were used for establishing ground truth for this submission.

8. The Sample Size for the Training Set:

• Not applicable: The device is a patient monitor, not a machine learning/AI algorithm that typically undergoes a distinct "training" phase with a large dataset. Its functionality is based on established physiological measurement principles and programmed algorithms. Any internal calibration or algorithm refinement would be part of the product development process, not a dedicated "training set" in the AI/ML sense.

9. How the Ground Truth for the Training Set Was Established:

• Not applicable: As there was no "training set" in the context of an AI/ML model, the concept of establishing ground truth for it does not apply to this 510(k) submission.

In summary, this 510(k) clearance relies on demonstrating that the new Patient Monitor is substantially equivalent to a previously cleared predicate device, primarily through robust non-clinical bench testing and software validation, proving compliance with established medical device standards and functional specifications. No new clinical studies with patient data were required or conducted for this specific submission.

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The monitor B105M, B125M, B155M, B105P and B125P are portable multi-parameter patient monitors intended to be used for monitoring, recording, and to generate alarms for multiple physiological parameters of adult, pediatric, and neonatal patients in a hospital environment and during intra-hospital transport.

The monitor B105M, B125M, B155M, B105P and B125P are intended for use under the direct supervision of a licensed health care practitioner.

The monitor B105M, B125M, B155M, B105P and B125P are not Apnea monitors (i.e., do not rely on the device for detection or alarm for the cessation of breathing). These devices should not be used for life sustaining/supporting purposes.

The monitor B105M, B125M, B155M, B105P and B125P are not intended for use during MRI.

The monitor B105M, B125M, B155M, B105P and B125P can be stand-alone monitors or interfaced to other devices via network.

The monitor B105M, B125M, B155M, B105P and B125P monitor and display: ECG (including ST segment, arrhythmia detection, ECG diagnostic analysis and measurement), invasive blood pressure, heart/pulse rate, oscillometric non-invasive blood pressure (systolic, diastolic and mean arterial pressure), functional oxygen saturation (SpO2) and pulse rate via continuous monitoring (including monitoring during conditions of clinical patient motion or low perfusion), temperature with a reusable or disposable electronic thermometer for continual monitoring Esophageal/Nasopharyngeal/Tympanic/Rectal/Bladder/Axillary/Skin/Airway/Room/Myocardial/Core/Surface temperature, impedance respiration, respiration rate, airway gases (CO2, O2, N2O, anesthetic agents, anesthetic agent identification and respiratory rate), Cardiac Output (C.O.), Entropy, neuromuscular transmission (NMT) and Bispectral Index (BIS).

The monitor B105M, B125M, B155M, B105P and B125P are able to detect and generate alarms for ECG arrhythmias: Asystole, Ventricular tachycardia, VT>2, Ventricular Bradycardia, Accelerated Ventricular Rhythm, Ventricular Couplet, Bigeminy, Trigeminy, "R on T", Tachycardia, Bradycardia, Pause, Atrial Fibrillation, Irregular, Multifocal PVCs, Missing Beat, SV Tachy, Premature Ventricular Contraction (PVC), Supra Ventricular Contraction (SVC) and Ventricular fibrillation.

The proposed monitors B105M, B125M, B155M, B105P and B125P are new version of multi-parameter patient monitors developed based on the predicate monitors B105M, B125M, B155M, B105P and B125P (K213490) to provide additional monitored parameter Bispectral Index (BIS) by supporting the additional optional E-BIS module (K052145) which used in conjunction with Covidien BISx module (K072286).

In addition to the added parameter, the proposed monitors also offer below several enhancements:

• Provided data connection with GE HealthCare anesthesia devices to display the parameters measured from anesthesia devices (Applicable for B105M, B125M and B155M).
• Modified Early Warning Score calculation provided.
• Separated low priority alarms user configurable settings from the combined High/Medium/Low priority options.
• Provided additional customized notification tool to allow clinician to configure the specific notification condition of one or more physiological parameters measured by the monitor. (Applicable for B105M, B125M and B155M).
• Enhanced User Interface in Neuromuscular Transmission (NMT), Respiration Rate and alarm overview.
• Provided Venous Stasis to assist venous catheterization with NIBP cuff inflation.
• Supported alarm light brightness adjustment.
• Supported alarm audio pause by gesture (Not applicable for B105M and B105P).
• Supported automatic screen brightness adjustment.
• Supported network laser printing.
• Continuous improvements in cybersecurity

The proposed monitors B105M, B125M, B155M, B105P and B125P retain equivalent hardware design based on the predicate monitors and removal of the device Trim-knob to better support cleaning and disinfecting while maintaining the same primary function and operation.

Same as the predicate device, the five models (B105M, B125M, B155M, B105P and B125P) share the same hardware platform and software platform to support the data acquisition and algorithm modules. The differences between them are the LCD screen size and configuration options. There is no change from the predicate in the display size.

As with the predicate monitors B105M, B125M, B155M, B105P and B125P (K213490), the proposed monitors B105M, B125M, B155M, B105P and B125P are multi-parameter patient monitors, utilizing an LCD display and pre-configuration basic parameters: ECG, RESP, NIBP, IBP, TEMP, SpO2, and optional parameters which include CO2 and Gas parameters provided by the E-MiniC module (K052582), CARESCAPE Respiratory modules E-sCO and E-sCAiO (K171028), Airway Gas Option module N-CAiO (K151063), Entropy parameter provided by the E-Entropy module (K150298), Cardiac Output parameter provided by the E-COP module (K052976), Neuromuscular Transmission (NMT) parameter provided by E-NMT module (K051635) and thermal recorder B1X5-REC.

The proposed monitors B105M, B125M, B155M, B105P and B125P are not Apnea monitors (i.e., do not rely on the device for detection or alarm for the cessation of breathing). These devices should not be used for life sustaining/supporting purposes. Do not attempt to use these devices to detect sleep apnea.

As with the predicate monitors B105M, B125M, B155M, B105P and B125P (K213490), the proposed monitors B105M, B125M, B155M, B105P and B125P also can interface with a variety of existing central station systems via a cabled or wireless network which implemented with identical integrated WiFi module. (WiFi feature is disabled in B125P/B105P).

Moreover, same as the predicate monitors B105M, B125M, B155M, B105P and B125P (K213490), the proposed monitors B105M, B125M, B155M, B105P and B125P include features and subsystems that are optional or configurable, and it can be mounted in a variety of ways (e.g., shelf, countertop, table, wall, pole, or head/foot board) using existing mounting accessories.

The provided FDA 510(k) clearance letter and summary for K242562 (Monitor B105M, Monitor B125M, Monitor B155M, Monitor B105P, Monitor B125P) do not contain information about specific acceptance criteria, reported device performance metrics, or details of a study meeting those criteria for any of the listed physiological parameters or functionalities (e.g., ECG or arrhythmia detection).

Instead, the documentation primarily focuses on demonstrating substantial equivalence to a predicate device (K213490) by comparing features, technology, and compliance with various recognized standards and guidance documents for safety, EMC, software, human factors, and cybersecurity.

The summary explicitly states: "The subject of this premarket submission, the proposed monitors B105M/B125M/B155M/B105P/B125P did not require clinical studies to support substantial equivalence." This implies that the changes introduced in the new device versions were not considered significant enough to warrant new clinical performance studies or specific quantitative efficacy/accuracy acceptance criteria beyond what is covered by the referenced consensus standards.

Therefore, I cannot provide the requested information from the given text:

1. A table of acceptance criteria and the reported device performance: This information is not present. The document lists numerous standards and tests performed, but not specific performance metrics or acceptance thresholds.
2. Sample size used for the test set and the data provenance: Not explicitly stated for performance evaluation, as clinical studies were not required. The usability testing mentioned a sample size of 16 US clinical users, but this is for human factors, not device performance.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable, as detailed performance studies requiring expert ground truth are not described.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance: Not applicable. This device is a patient monitor, not an AI-assisted diagnostic tool that would typically involve human readers.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: The document describes "Bench testing related to software, hardware and performance including applicable consensus standards," which implies standalone testing against known specifications or simulated data. However, specific results or detailed methodologies for this type of testing are not provided beyond the list of standards.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not explicitly stated for performance assessment. For the various parameters (ECG, NIBP, SpO2, etc.), it would typically involve reference equipment or validated methods as per the relevant IEC/ISO standards mentioned.
8. The sample size for the training set: Not applicable, as this is not an AI/ML device that would require explicit training data in the context of this submission.
9. How the ground truth for the training set was established: Not applicable.

In summary, the provided document focuses on demonstrating that the new monitors are substantially equivalent to their predicate through feature comparison, adherence to recognized standards, and various non-clinical bench tests (e.g., hardware, alarms, EMC, environmental, reprocessing, human factors, software, cybersecurity). It does not contain the detailed performance study results and acceptance criteria typically found for novel diagnostic algorithms or AI-driven devices.

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Onera STS 2 measures and records multiple physiological parameters from a patient during a sleep study, which are used by clinicians to decide on the diagnosis of sleep disorders.

Onera STS 2 is intended to be used on a patient who has been prescribed a polysomnography study by a healthcare professional. The device is designed to be used under the direction of a physician or trained technician but applied by a layperson.

The recorded data will be made available to the healthcare professional to assist in the diagnosis of sleep disorders.

The device is intended to be used for adults.

The device is not a life supporting physiological monitor.

Onera Sleep Test System 2 (Onera STS 2) is a hardware, wearable system for measuring physiological signals during a sleep study. The device can be used in the home (Home Healthcare Environment) as well as in Professional Healthcare Facilities.

The device measures EEG, EOG, EMG, ECG, respiratory signals, cannula based respiratory flow, oxygen saturation, activity, position, ambient light level and sound pressure level.

The ECG signal is not intended for diagnosis of cardiac disorders, except for the manual determination of arrhythmia during polysomnography studies.

The Onera STS 2 does not provide any automated output like heart rate, assessments of arrhythmia, heart rate variability, or other related heart rate measurement functions.

Onera STS 2 consists of five Sensors applied on the forehead, upper chest area, abdomen and lower leg area (both left and right leg).

The Sensors should be placed on intact skin. During the night measurement it is not needed to inspect the application sites. The Sensors encrypt the recorded signals and upload the measurement data during the night to the Patient App installed on the patient's phone via Bluetooth. The Patient App securely transfers the data to the cloud interface for further processing once all data has been uploaded. The output of the system is represented by a file in EDF format which contains the recorded (physiological) signals. EDF files can be read by any application software that accepts such files as input.

It is not possible to relocate the Sensors during a sleep study since the Sensor is single-use and for one sleep study only.

Here's a summary of the acceptance criteria and study information for the Onera STS 2, based on the provided FDA 510(k) clearance letter:

Onera STS 2: Acceptance Criteria and Performance Study Summary

1. Table of Acceptance Criteria and Reported Device Performance

The provided document primarily focuses on the SpO2 measurement accuracy as a key performance metric with specific acceptance criteria.

Note: While other parameters are listed as "Identical" to the predicate, specific numerical acceptance criteria for those parameters are not explicitly stated in this document beyond their qualitative equivalence.

2. Sample Size and Data Provenance for the Test Set

• Sample Size: 12 healthy subjects
  • 9 male, 3 female
  • Aged between 23 and 46 years old
• Data Provenance: The study was conducted in an "independent research laboratory." The country of origin is not explicitly stated in the provided text. The study involved "induced hypoxia," indicating a prospective, controlled experimental design.

3. Number and Qualifications of Experts for Ground Truth

• The document does not mention the use of experts to establish ground truth for the SpO2 accuracy test.
• The ground truth for SpO2 was established by "laboratory co-oximeter" measurements of arterial hemoglobin oxygen (SaO2) values from blood samples.

4. Adjudication Method for the Test Set

• The document does not describe any adjudication method. The SpO2 accuracy was determined by direct comparison of the device's SpO2 measurements to SaO2 values from a laboratory co-oximeter.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not conducted or described in this document. The device primarily measures physiological parameters, and the study focused on the accuracy of these measurements rather than human reader interpretation with or without AI assistance.

6. Standalone (Algorithm Only) Performance Study

• Yes, a standalone performance study was conducted for SpO2 measurement accuracy. The device's SpO2 readings were directly compared to reference SaO2 values without human intervention in the SpO2 measurement process itself. The Onera STS 2 is described as measuring and recording parameters, with the output as an EDF file to be read by other software. The SpO2 accuracy assessment is specifically for the device's measurement capability.

7. Type of Ground Truth Used

• Objective Measurement (Laboratory Co-oximeter): For SpO2 accuracy, the ground truth was established by arterial hemoglobin oxygen (SaO2) values determined from blood samples using a laboratory co-oximeter, which is considered a gold standard for blood oxygen saturation.

8. Sample Size for the Training Set

• The document does not provide information regarding the sample size for a training set. This is likely because the performance study described (SpO2 accuracy) is a validation of the device's sensor capabilities, not an evaluation of a machine learning algorithm that would typically require a training set. The device outputs raw physiological signals in EDF format for clinicians to interpret, rather than providing automated diagnoses based on an internal algorithm.

9. How the Ground Truth for the Training Set Was Established

• As no training set is mentioned or implied for the device's core functionality (measuring and recording parameters for clinician interpretation), this information is not applicable and not provided in the document.

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The RTM Vital Signs RTMsense is indicated for use by healthcare professionals in healthcare facilities, such as post-operative care and general wards, to monitor breathing in adult (at least 22 years old) patients.

RTMsense is a non-invasive system that graphically displays respiratory function against time and reports respiratory rate.

RTMsense measurements are used as an adjunct to other clinical information sources.

The RTMsense Respiratory Monitoring System is a single use wearable device consisting of a wearable trachea sound sensor (TSS) and software that continuously measures a patient's respiratory rate by analyzing the sounds of air flow within the proximal trachea during inhalation and exhalation. The acoustic signal is transmitted wirelessly to a Lenovo Tablet, and the respiratory measurement values are displayed on the tablet after analysis of the acoustic data by a proprietary software algorithm.

The RTMsense software application has three parts: firmware on the TSS, a web-based application on the Lenovo tablet, and a cloud-based proprietary software algorithm. The TSS securely transmits acoustic data wirelessly to the local, Bluetooth low energy enabled Lenovo tablet. The tablet uses a web-based application to securely transmit the acoustic data to the cloud for analysis in RTM's proprietary cloud-based algorithm. The web application retrieves the processed data from the algorithm to display respiratory rate on the tablet.

The device will be used by healthcare professionals in healthcare facilities such as post-operative care or general wards. The RTMsense respiratory measurements are used as an adjunct to other clinical information sources.

The TSS is held in place by a flexible wearable carrier adhered to the patient's proximal trachea with commercially available medical grade adhesive. The TSS contains the audio sensor, onboard processing, wireless communications technology, and Lithium-ion coin cell rechargeable battery. A custom charger is provided to charge the battery.

The provided FDA 510(k) clearance letter and summary for the RTM Sense (A-0001) device details several aspects of its performance and validation. However, it does not explicitly provide a table of acceptance criteria for specific metrics, instead focusing on overall "passing" of predefined performance criteria. The information regarding ground truth establishment for the training set, number and qualifications of experts, and adjudication methods is also limited.

Based on the provided text, here's an attempt to reconstruct the information:

Overview of RTM Sense (A-0001) Performance Study

The RTM Sense (A-0001) is a non-invasive respiratory monitoring system that continuously measures a patient's respiratory rate by analyzing tracheal sounds. The device, intended for use by healthcare professionals in healthcare facilities, underwent non-clinical and clinical performance testing to demonstrate its safety and effectiveness and establish substantial equivalence to predicate devices.

1. Acceptance Criteria and Reported Device Performance

While explicit acceptance criteria are not presented in a table format within the document, the "Clinical Performance Testing" section describes primary endpoints that serve as de facto acceptance criteria. The results indicate that the device met these criteria.

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The Happy Health Home Sleep Test is a Software as a Medical Device that uses data from wearable devices to record, analyze, display, export, and store biophysical parameters to aid in evaluating sleep‐related breathing disorders of adult patients suspected of sleep apnea. The device is intended for use on individuals who are 22 years of age or older in clinical and home settings under the direction of a trained healthcare provider.

The Happy Health Home Sleep Test is a Software as a Medical Device that uses data from wearable devices to record, analyze, display, export, and store biophysical parameters to aid in evaluating sleep-related breathing disorders of adult patients suspected of sleep apnea.

The device is intended for use on individuals who are 22 years of age or older in clinical and home settings under the direction of a trained healthcare provider. The device is intended to process input data streams received from an external hardware device (i.e., a smart ring, K240236) and uses these signals to determine various sleep parameters that may be used and interpreted by a clinician in diagnosing sleep disorders such as sleep apnea.

The input physiologic signals from the external device are:

• Acceleration / Movement
• Photoplethysmography (PPG)

The external hardware device (K240236) includes a PPG sensor and accelerometer embedded within a housing to capture the above physiological signals. The K240236 device is worn on the finger and is indicated for continuous data collection of the above signals. Data from the external hardware device is transmitted over a secure API to the subject device for analysis.

The device then uses a set of algorithms to compute the following outputs:

• Happy Health Apnea Hypopnea Index (hAHI)
• Total Sleep Time

The outputs are available for a clinician to review as a report, accessible through a web-based viewer application.

The provided FDA 510(k) clearance letter and summary for the Happy Health Home Sleep Test give a good overview of the device's performance testing. Here's a structured breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly list "acceptance criteria" as a separate section with specific thresholds that were agreed upon before the study. Instead, it presents performance metrics of the Happy Health Home Sleep Test and compares them to the predicate and reference devices, implying these metrics are the basis for demonstrating substantial equivalence. For clarity, I'm inferring the acceptance criteria from the "Equivalent" column in the comparison tables and the detailed performance results.

2. Sample Size and Data Provenance

• Test Set Sample Size: 90 subjects.
• Data Provenance:
  • Country of Origin: Not explicitly stated, but the study was conducted at "two sleep labs", implying a clinical setting within the country of submission (likely USA, given FDA submission).
  • Retrospective or Prospective: The wording "Data from a total of 90 subjects referred to the sleep clinic by a physician was manually scored" suggests the data was collected prospectively for the purpose of the study. The phrasing "A clinical study was performed to evaluate the performance..." also indicates a planned, prospective study.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Not explicitly stated how many experts were involved in the manual scoring. The text only mentions "manually scored in accordance with the American Academy of Sleep Medicine (AASM) guidelines."
• Qualifications of Experts: Not explicitly stated, but implied to be qualified sleep technicians/physicians capable of AASM-compliant scoring.

4. Adjudication Method for the Test Set

The adjudication method for reconciling discrepant manual scores (if multiple scorers were used) is not specified in the provided text. It simply states "manually scored." If only one scorer per patient, no adjudication would be needed.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

There is no indication that a multi-reader multi-case (MRMC) comparative effectiveness study was done to evaluate how human readers improve with AI vs. without AI assistance. The study focuses solely on the device's standalone performance compared to manual PSG scoring.

6. Standalone (Algorithm Only) Performance

Yes, a standalone performance study was done. The entire clinical testing section details the performance of the "Happy Health Home Sleep Test" algorithm (hAHI and TST) compared to manually scored Polysomnography (PSG) data, without human-in-the-loop assistance.

7. Type of Ground Truth Used

The primary ground truth used was expert consensus / manual scoring of Polysomnography (PSG) data in accordance with American Academy of Sleep Medicine (AASM) guidelines. This is the gold standard for sleep studies.

8. Sample Size for the Training Set

The sample size for the training set is not provided in this document. The clinical study details describe the test set used for validation.

9. How the Ground Truth for the Training Set Was Established

The document does not provide information on how the ground truth for the training set was established. It only discusses the ground truth for the clinical validation (test) set.

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The Empatica Health Monitoring Platform is a wearable device and paired mobile and cloud-based software platform intended to be used by trained healthcare professionals or researchers for retrospective remote monitoring of physiologic parameters in ambulatory individuals 18 years of age and older in home-healthcare environments. As the platform does not provide real-time alerts related to variation of physiologic parameters, users should use professional judgment in assessing patient clinical stability and the appropriateness of using a monitoring platform designed for retrospective review.

The device is intended for continuous data collection supporting intermittent retrospective review of the following physiological parameters:

• Pulse Rate,
• Blood Oxygen Saturation under no-motion conditions,
• Respiratory Rate under no motion conditions,
• Peripheral Skin Temperature,
• Electrodermal Activity,
• Activity associated with movement during sleep

The Empatica Health Monitoring Platform can be used to analyze circadian rhythms and assess activity in any instance where quantifiable analysis of physical motion is desirable.

The Empatica Health Monitoring Platform is not intended for SpO2 monitoring in conditions of motion or low perfusion.

The Empatica Health Monitoring Platform is intended for peripheral skin temperature monitoring, where monitoring temperature at the wrist is clinically indicated.

The Empatica Health Monitoring Platform is not intended for Respiratory Rate monitoring in motion conditions. This device does not detect apnea and should not be used for detecting or monitoring cessation of breathing.

The Empatica Health Monitoring Platform is not intended for Pulse Rate monitoring in patients with chronic cardiac arrhythmias, including atrial fibrillation and atrial/ventricular bigeminy and trigeminy, and is not intended to diagnose or analyze cardiac arrhythmias. The Empatica Health Monitoring Platform is not a substitute for an ECG monitor, and should not be used as the sole basis for clinical decision-making.

The Empatica Health Monitoring Platform is a wearable device and software platform composed by:

• A wearable medical device called EmbracePlus,
• A mobile application running on smartphones called "Care App",
• A cloud-based software platform named "Care Portal".

The EmbracePlus is worn on the user's wrist and continuously collects raw data via specific sensors. These data are wirelessly transmitted via Bluetooth Low Energy to a paired mobile device where the Care App is up and running. The data received are analyzed by one of the Care App software modules, EmpaDSP, which computes the user physiological parameters. Based on the version of the Care App installed, the user can visualize a subset of these physiological parameters. The Care App is also responsible for transmitting, over cellular or WiFi connection sensors' raw data, device information, Care App-specific information, and computed physiological parameters to the Empatica Cloud. On the Empatica Cloud, these data are stored, further analyzed, and accessible by healthcare providers or researchers via a specific cloud-based software called Care Portal.

The Empatica Health Monitoring Platform is intended for retrospective remote monitoring of physiological parameters in ambulatory adults in home-healthcare environments. It is designed to continuously collect data to support intermittent monitoring of the following physiological parameters and digital biomarkers by trained healthcare professionals or researchers: Pulse Rate (PR), Respiratory Rate (RR), blood oxygen saturation (SpO2), peripheral skin temperature (TEMP), and electrodermal activity (EDA). Activity sensors are used to detect sleep periods and to monitor the activity associated with movement during sleep.

The provided FDA 510(k) clearance letter and its attachments describe the acceptance criteria and study that proves the Empatica Health Monitoring Platform (EHMP) meets those criteria, specifically concerning a new Predetermined Change Control Plan (PCCP) for the SpO2 quality indicator (QI) algorithm.

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria are outlined for the proposed modification to the SpO2 Quality Indicator (QI) algorithm. The reported device performance is presented as a statement of equivalence to the predicate device, implying that the acceptance criteria are met, as the 510(k) was cleared.

Note: For the pre-existing functionalities (Pulse Rate, Respiratory Rate, Peripheral Skin Temperature, Electrodermal Activity, Activity and Sleep), the document states that "no changes to the computation... compared with the cleared version" have been introduced, implying their previous acceptance criteria were met and remain valid.

2. Sample Sizes and Data Provenance

• Test Set Sample Size: Not explicitly stated for the SpO2 algorithm modification. The document only mentions "enhancing the development dataset with new samples" for the ML-based algorithm and clinical testing was "conducted in accordance with ISO 80601-2-61... and ... FDA Guidelines for Pulse Oximeters." These standards typically require a certain number of subjects and data points, but the exact numbers are not provided in this public summary.
• Data Provenance: Not specified in the provided document. It does not mention the country of origin, nor whether the data was retrospective or prospective.

3. Number and Qualifications of Experts for Ground Truth

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The document states the platform is "intended to be used by trained healthcare professionals or researchers," and later discusses "professional users" and "clinical interpretation," implying that the ground truth for clinical studies would likely involve such experts, but their specific roles, numbers, and qualifications for establishing ground truth are not detailed.

4. Adjudication Method for the Test Set

The adjudication method for establishing ground truth for the test set is not explicitly mentioned in the provided document.

5. Multi Reader Multi Case (MRMC) Comparative Effectiveness Study

There is no mention of a Multi Reader Multi Case (MRMC) comparative effectiveness study being conducted, nor any effect size regarding human readers improving with AI vs. without AI assistance. The device is for "retrospective remote monitoring" by healthcare professionals, implying an AI-driven data collection/analysis with human review, but not necessarily human-AI collaboration in real-time diagnostic interpretation that an MRMC study would evaluate.

6. Standalone (Algorithm Only) Performance

The acceptance criteria for the SpO2 QI algorithm include "Bench testing conducted using a functional tester to simulate a range of representative signal quality issues." This falls under standalone performance, as it tests the algorithm's ability to discriminate data quality without direct human input. Clinical testing also evaluates the algorithm's accuracy (Arms error) in comparison to an established standard, which is also a standalone performance measure.

7. Type of Ground Truth Used

• For the SpO2 QI ML algorithm: The ground truth for low-quality and high-quality data discrimination seems to be an internal standard/reference based on the "FDA-cleared SpO2 algorithm" and potentially expert labeling of data quality during the "enhancing the development dataset."
• For the SpO2 Accuracy (Arms Error): The ground truth for SpO2 values would be established in accordance with ISO 80601-2-61, which typically involves comparing the device's readings against a laboratory co-oximeter or a reference pulse oximeter for arterial oxygen saturation.

8. Sample Size for the Training Set

The document mentions "enhancing the development dataset with new samples" for the ML-based algorithm but does not specify the sample size for the training set.

9. How Ground Truth for Training Set was Established

The ground truth for training the ML-based SpO2 QI algorithm was established by "enhancing the development dataset with new samples." It also mentions performing "feature extraction and engineering on window lengths spanning a 10-30-second range." While it doesn't explicitly state the methodology, given the context of a "binary output" (high/low quality), it implies a labeling process, likely by human experts or based on predefined criteria derived from the previous FDA-cleared algorithm's performance on various data types. For the SpO2 accuracy, the ground truth would typically be established by a reference method consistent with the mentioned ISO standard and FDA guidance.

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