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The EarlySense Bed Sensing Unit is an accessory that is compatible with bedside units of EarlySense Systems (Models 2.0 and InSight) intended for continuous measurement of respiration rate, heart rate and movement, in an automatic contact-less manner. Environment of use for the accessory is defined as per compatible cleared bedside units labeling: EarlySense 2.0 - at home, in hospital or clinic setting and InSight - in hospital or clinic setting. The device is indicated for use in children, adolescents and adults. The operation of the EarlySense system has been studied in children (weight ≥10 Kg) and adults (weight

EarlySense is submitting a new model for its Bed Sensing Unit to be used with cleared EarlySense bedside unit models (EarlySense 2.0 -K131379 and EarlySense InSight -K152911) which are intended for contactless measurement of heart rate (HR), respiratory rate (RR) and motion. Similar to the cleared Bed Sensing units, the subject device is placed under the bed mattress and connected to supporting bedside unit, to allow contactless measurements of HR, RR and motion and detection of bed exit. The modification of the cleared sensing unit includes addition of two load cells elements intended to be used in the Bed Exit feature of the system. The cleared and subject device share exactly the same intended use, the same fundamental functionality and similar types of components, and the same fundamental principles and mode of operation. As also described above, the modification that includes addition of load cells is not considered to affect the system performance. The subject device is also compatible with the existing optional accessories of the cleared sensor, i.e., extension cable and solid metal plate. Utilization of extension cable is to allow placing the sensing unit under the mattress farther than 3 meters from compatible bedside units. The solid metal plate can be used for beds that have un-flat surface (such as grid like).

The provided text describes the EarlySense Bed Sensing Unit and its substantial equivalence to predicate devices, but it does not contain acceptance criteria or detailed results from a study proving the device meets those criteria in a format that would allow filling out all the requested fields.

The document states that the modification to the cleared sensing unit (addition of two load cell elements) did not affect the system performance. The performance data presented focuses on verifying that the outputs of the modified device are similar to the predicate device and within system algorithms' specifications, rather than explicitly stating and demonstrating achievement of specific, quantitative performance acceptance criteria for heart rate or respiration rate accuracy.

Therefore, I cannot provide a complete answer to all parts of your request. However, I can extract what is available regarding performance and testing.

Here's an analysis of what information is available and what is missing:

Information Available:

• Device Performance (Comparative): "Bench testing results showed that in all relating parameters the signals from the sensing unit were similar to the predicate sensor and all parameters were within system's algorithms specification. In addition, tests were performed to compare performance of the sensing unit to predicate sensor in providing 'time to alert' for bed exit notification. Controlled experiments showed that the time to alert for bed exit notifications are similar for both sensors, thus addition of the load cells does not affect system's performance and the sensing unit is substantially equivalent to the cleared sensor."

Information NOT Available (and thus cannot be filled in):

• Specific Acceptance Criteria: The document does not explicitly state quantitative acceptance criteria (e.g., "Accuracy of respiration rate shall be within X bpm of reference"). It only states that the modified device's signals were "similar" to the predicate and "within system's algorithms specification."
• Reported Device Performance (against specific criteria): Since specific criteria aren't provided, performance against them cannot be reported.
• Sample Size for Test Set: Not specified. The document mentions "controlled experiments" for bed exit, but no numbers.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective).
• Number of Experts for Ground Truth: Not specified.
• Qualifications of Experts: Not specified.
• Adjudication Method: Not specified.
• Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study: Not mentioned, and likely not applicable for a bed sensing unit measuring vital signs.
• Effect size of human readers with AI vs. without AI assistance: Not applicable.
• Standalone (algorithm only) performance: The performance data focuses on the new component (load cells) and its similarity to the predicate. The overall system (EarlySense System Models 2.0 and InSight) with the Bed Sensing Unit measures HR, RR, and movement, and its performance was likely established in prior 510(k)s (K131379 and K152911). This document asserts that the new bed sensing unit does not negatively impact that performance.
• Type of Ground Truth: Not explicitly stated, though for vital signs, it would typically be a reference device or manual measurement.
• Sample Size for Training Set: Not applicable for this submission, as the focus is on a hardware modification and its impact on signal characteristics and similarity to a predicate. It's not a new algorithm being trained.
• How Ground Truth for Training Set was Established: Not applicable.

Summary of Available Information:

1. A table of acceptance criteria and the reported device performance

2. Sample sized used for the test set and the data provenance

• Sample Size: Not specified. The document mentions "controlled experiments" and "bench tests" but provides no participant or case numbers.
• Data Provenance: Not specified.

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

• Not specified.

4. Adjudication method for the test set

• Not specified.

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

• No MRMC study was reported. This type of study is not applicable given the nature of the device (a vital signs bed sensing unit).

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

• The performance data provided is related to the hardware component (Bed Sensing Unit) itself, assessing its signal characteristics compared to an earlier version. The system's algorithms process these signals, and the focus of this 510(k) is that the new hardware does not negatively impact the algorithmic performance already established. The document states, "Bench tests were performed to verify that the heart beat and respiration signals obtained from the subject device are similar to its predicate device, and to validate that the addition of load cells do not affect signal parameters as required by the EarlySense system's algorithms, to maintain specifications."

7. The type of ground truth used

• For heart rate and respiration rate signal similarity, the ground truth was effectively the predicate device's signals and the system's algorithms specifications. For bed exit "time to alert," the ground truth was implied to be direct observation in "controlled experiments."

8. The sample size for the training set

• Not applicable. This submission is for a hardware modification to an existing device, not a new algorithm training.

9. How the ground truth for the training set was established

• Not applicable.

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The EarlySense InSight System is intended for continuous measurement of respiration rate, heart rate and movement, in an automatic contact-less manner, in hospital or clinic setting. The system is indicated for use in children, adolescents and adults. The operation of the EarlySense has been studied in children (weight ≥ 10 Kg) and adults (weight

A traditional 510(k) is submitted to clear the new EarlySense InSight System. The EarlySense Insight System developed by EarlySense Ltd. is designed for continuous and contact-free measurement of Heart and Respiratory Rate. In addition, the System tracks body motion; monitors patient movement and can notify users when the patient exits the bed or the chair. The device does not have a display and is controlled from a remote control and display station by the user. The measured data on Heart Rate, Respiratory Rate, Movement and alerts are communicated to a remote station that can communicate with the EarlySense device according to predefined protocol, to be displayed to users. The Remote Control and Display Device (RCD) can be computer or central monitoring device that can communicate with the InSight Unit through predefined standard protocol through LAN or Wi-Fi and is capable to remotely access the InSight Unit and display the information received from InSight Unit. All the settings and changing of configurations is performed by the user, via a Remote Control and Display Device (RCD device). The EarlySense Insight monitoring device can notify the Remote Control and Display device, when monitored parameters excurse above or below predefined limits. The user (Healthcare practitioner) receives the information and should actively acknowledge the alert notifications and adjust monitoring parameters as required.

The EarlySense InSight device incorporates the following components:

• -EarlySense InSight Unit – placed at bedside (subject of this submission)
• EarlySense Bed Sensing Unit (K131379) placed under the mattress -
• Chair Sensing Unit (optional)(K133661)

Acceptance Criteria and Study for EarlySense InSight System

This response is based solely on the provided text.

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state acceptance criteria in a quantitative or pass/fail format. However, it describes that the device's performance was compared to a reference device and found to be "similar."

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

The document states that signals were tested from "children, adolescents and adults' populations," suggesting a diverse age range. However, it does not specify the exact sample size (number of individuals or data points) for the test set.

Data Provenance: The document does not explicitly state the country of origin. It describes "Non Clinical Testing" and refers to "signals from children, adolescents and adults' populations were tested." Given that the submitter is EarlySense Ltd. from Israel, it's possible the data originated there, but this is not confirmed. The study appears to be prospective benchmark testing given its description of "side by side performance testing" against a reference device.

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

The document does not specify the number of experts used or their qualifications to establish ground truth.

4. Adjudication Method for the Test Set

The document does not describe an adjudication method for the test set. The ground truth was established by a "reference device" rather than human experts.

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

No, an MRMC comparative effectiveness study was not done. The study described focuses on the standalone performance of the EarlySense InSight device against a reference device and its predicate, rather than comparing human reader performance with and without AI assistance.

6. If a Standalone Study Was Done

Yes, a standalone study was done. The "Non Clinical Testing" section explicitly describes "side by side performance testing of the InSight device vs. its predicate EarlySense 2.0. Signals from children, adolescents and adults' populations were tested. The accuracy of the InSight and EarlySense 2.0 in analyzing the signals and generating heart rate, respiration rate and motion measures were compared to results of reference device (Embla Sleep lab. System Medcare K024322)." This directly assesses the algorithm's performance without human intervention.

7. The Type of Ground Truth Used

The ground truth used was based on a reference device: "Embla Sleep lab. System Medcare K024322." This is a medical device indicated for sleep laboratory use, implying a high standard for physiological measurements.

8. The Sample Size for the Training Set

The document does not specify the sample size used for the training set. It only mentions that the system's detection algorithms are "the same as those used in the predicate EarlySense 2.0 device" and that the predicate device likely used data for its development.

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

The document does not explicitly describe how the ground truth for the training set was established. It states that the algorithms are the same as those from the predicate EarlySense 2.0 device, which implies that the training was done for the predicate device, but the details of that process (e.g., specific ground truth methods or datasets) are not provided in this submission.

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The EarlySense Central Display System is intended to provide remote central monitoring and display of information as recorded by multiple EarlySense bedside units, on a central remote screen. The system can be used in hospital type and clinic environment.

The EarlySense Central Display System (CDS) is a system which includes EarlySense developed application software, installed on standard off-the-shelf PC computer with a computer screen. The system also uses standard off-the shelf communication and IT hardware.

The CDS is intended to communicate with multiple EarlySense Bedside monitoring devices (cleared as K131379 and K120465), in order to display the information as it is monitored on the bedside units on a central remote screen. The users can access the user interface of the individual bedside units via the CDS's screen and view or adjust bedside units' parameters, e.g. change settable parameters, such as alert thresholds.

The communication is performed through TCP/IP protocol, either via standard wired or via wireless LAN communication. The transmitted information from Bedside Unit to CDS and backwards includes alert information and physiological parameters (such as patient in / out of bed status, heart rate, respiration rate, motion rate and Sp02, if monitored at bedside unit, as well as room and bed number, etc.). The CDS can also format the alert information as obtained from Bedside units into a message that can be transmitted to external devices that can communicate with the CDS via standard TCP/IP port. In addition, a possibility to remotely view the CDS screen from a tablet or additional PC computer exists.

The accessories that can be possibly used with CDS system include:

• . Computer Screen
• . Keyboard and mouse
• Additional hallway LCD/LED screen .
• . External communication devices, like: pagers, etc.
• Tablet or additional PC computer (to remotely view the CDS computer . screen)

The provided text describes the EarlySense Central Display System (CDS) and its substantial equivalence to predicate devices, but it does not contain the detailed information necessary to answer most of the questions about acceptance criteria and a specific study proving the device meets those criteria.

The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to legally marketed predicate devices, rather than a detailed report of a performance study with specific acceptance criteria.

Here's what can be extracted and what is missing:

1. Table of acceptance criteria and the reported device performance:

This information is not provided in the document. The document states that the modified EarlySense CDS System was subject to "the whole range of verification and validation tests," including:

• Risk analysis
• Software Verification and Validation
• Performance Bench Testing

However, it does not specify what the acceptance criteria for these tests were, nor does it present the actual performance results in relation to such criteria. The conclusion simply states that the non-clinical tests demonstrate the device is "as safe, as effective, and performs as well as or better than the predicate devices."

Regarding a specific study proving the device meets acceptance criteria:

The document mentions "Performance Bench Testing" but provides no details on its methodology, sample size, or specific outcomes.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This information is not provided. The document mentions "Performance Bench Testing" but does not detail the sample size or data provenance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not provided. The mention of "Performance Bench Testing" does not include details on how ground truth was established or by whom. The device is a central display system for physiological parameters, not an AI diagnostic device that typically requires expert-established ground truth in the same way.

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

This information is not provided.

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

This information is not provided. The device is a central display system for monitoring, not an AI for interpretation or assisting human readers in a diagnostic task that would typically warrant a MRMC study.

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

The document implies "Performance Bench Testing" was done for the software algorithm as part of the system. The device itself is essentially a standalone software application displaying data from other bedside units. However, specific details of this testing (e.g., how "standalone" was measured) are not provided. The comparison is made against predicate devices which are also central display systems, implying a functional comparison rather than a human-in-the-loop versus algorithm-only comparison.

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

This information is not provided. For a central display system, the "ground truth" for performance testing would likely involve ensuring accurate transmission and display of physiological parameters from the bedside units. This would typically be verified against the direct output of the bedside units or simulated data, rather than expert consensus or pathology.

8. The sample size for the training set

This information is not provided. The device is described as a software application that displays data from other units and allows for parameter adjustment. It does not appear to be an AI/ML device that requires a "training set" in the conventional sense for learning patterns or making predictions. It's a display and control interface.

9. How the ground truth for the training set was established

This information is not provided and is likely not applicable, as the device is not described as an AI/ML system requiring a training set with established ground truth.

Summary of what is present:

• Device Description: The EarlySense Central Display System (CDS) is a software application installed on standard off-the-shelf PC hardware. It communicates with multiple EarlySense Bedside monitoring devices to remotely display physiological information (heart rate, respiration rate, motion rate, SpO2, in/out of bed status, etc.) and allows remote adjustment of bedside unit parameters.
• Purpose of Review: This is a 510(k) premarket notification for an updated software version (1.1.2.2) of the CDS, demonstrating substantial equivalence to predicate devices (EarlySense Central Display Station, K121885, and Connex Central Station, K132807).
• Performance Data Mentioned: The system underwent Risk analysis, Software Verification and Validation, and Performance Bench Testing.
• Conclusion: Non-clinical tests demonstrate the device is "as safe, as effective, and performs as well as or better than the predicate devices."

The document focuses on the regulatory pathway of substantial equivalence rather than a detailed scientific and clinical performance study report.

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The EarlySense Chair Sensing Unit is intended to be used for continuous measurement of Respiration Rate, Heart Rate and Movement, in an automatic contact-less manner, at home, hospital or clinic setting. The sensor is indicated for use in children, adolescents and adults. The operation of the EarlySense system has been studied in children (weight ≥ 10 Kg) and adults (weight

EarlySense is submitting a new accessory for contactless measurement of heart, respiratory rate and motion, the Chair Sensing Unit. The Chair Sensing Unit, similar to its predicate, the EarlySense Bed Sensing Unit cleared as part of previous EarlySense system submissions (K070375, K082465, K092062, K120465), is intended for continuous measurement of Heart Rate, Respiration Rate and motion while the patient is resting.

The EarlySense Chair sensing unit is comprised of the following components:

1. Sensor: that includes piezoelectric elements incorporated into a plate

2. A cushion made of foam into which the sensor is inserted into - so not to be touched by the patient.

The Chair sensing unit should be connected to a bedside unit that receives and analyzes the signals from the Chair sensing unit to measure and display heart rate, respiratory rate and motion.

The EarlySense Chair Sensing Unit is an accessory to the EarlySense System for continuous, contactless measurement of Respiration Rate, Heart Rate, and Movement. The 510(k) summary provided does not contain specific, quantitative acceptance criteria or detailed results of a clinical study that proves the device meets such criteria. Instead, it relies on bench testing and comparison to a predicate device to establish substantial equivalence.

Here's an analysis of the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

No specific quantitative acceptance criteria or detailed performance data (e.g., accuracy, precision) for the EarlySense Chair Sensing Unit are explicitly stated in the document. The document primarily focuses on establishing substantial equivalence to the predicate device through bench testing rather than reporting precise performance metrics against pre-defined targets.

The general conclusion from bench testing is that: "the signals as detected by the chair sensing unit are similar to those that are detected by the predicate EarlySense Bed Sensor and thus the performance of the Chair Sensing Unit is equivalent to the performance of the Bed Sensing Unit and the performance of the chair sensor is not affected by the new design that includes insertion of the sensor into a cushion (rather than insertion of the sensor under a bed mattress)."

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

The document mentions "Bench Testing" which involved "Performance tests were performed with signals simulating physiological patient signals". No information is provided regarding the sample size of these simulated signals or their provenance (e.g., country of origin, retrospective/prospective). This was a bench test, not human-subject testing.

3. Number of Experts Used to Establish Ground Truth and Qualifications

This information is not applicable as the study described is bench testing using simulated physiological signals, not human data requiring expert ground truth establishment.

4. Adjudication Method for the Test Set

Not applicable, as there was no human-subject test set requiring adjudication.

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

No MRMC comparative effectiveness study was mentioned or performed. The submission focuses on validating the device itself, not on evaluating its impact on human reader performance.

6. Standalone Performance Study (Algorithm Only)

The testing described (bench testing with simulated signals) serves as a standalone performance evaluation of the device's ability to detect and transmit signals, and the system's software to analyze these signals. The document states: "The system's detection algorithms differentiate between large body movements, breathing movements and the cardioballistic effect, and thus compute the continuous heart and respiration rates and the body movement." However, detailed performance metrics (e.g., accuracy against a known true value) are not provided, nor is a specific "standalone study" with quantifiable results reported. The performance is largely inferred as being "equivalent" to the predicate device.

7. Type of Ground Truth Used

For the bench testing, the "ground truth" was likely supplied by the "signals simulating physiological patient signals" themselves, which implies a known input with which the device's output could be compared. The nature or source of these simulated signals is not detailed.

8. Sample Size for the Training Set

No information is provided about a training set since the document describes validation activities for a device accessory, not the development of a new algorithm that would typically require a training set. The underlying algorithms for HR, RR, and movement were likely developed and validated with the predicate device.

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

Not applicable, as no training set is mentioned for the validation of this accessory. The existing algorithms from the predicate device (K120465) are utilized.

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The EarlySense 2.0 System is intended for continuous measurement of respiration rate, heart rate and movement, in an automatic contact-less manner, at home, hospital or clinic setting. The system is indicated for use in children, adolescents and adults. The operation of the EarlySense has been studied in children (weight ≥ 10 Kg) and adults (weight

The modified EarlySense System 2.0 is similar to cleared (EverOn™1.0S) System (K120465) except for the hardware and software changes that are described below.

The cleared (K120465) EarlySense (EverOn™1.0S) System is comprised of the following components:

1. A Bed Sensing Unit based on piezoelectric sensing

2. A Bedside Unit incorporating a medical grade power supply and the following modules:

A Signal Sampling Module consisting of amplifiers, filters and analog to a. digital converter as well as a microprocessor designated to transfer the sampled data to the Signal Processing Module, and

b. A Signal Processing and Display Module.

3. An Optional Oximetry OEM module with communication interface and compatible marketed sensors.

The EarlySense System 2.0 is a modified version of the EarlySense (EverOn™1.0S) System (K120465). The changes include modifications to the Bed Sensing Unit (adding an accelerometer, piezoelectric polymer sensor, and base plate handles) and the Bedside Unit (dimensional changes, replacement of power supply, speaker's buzzer, communication card, fan, additional USB and sensor's connectors, and software update to version 1.0.3).

Acceptance Criteria and Device Performance:

The provided 510(k) summary does not explicitly list numerical acceptance criteria or detail the specific performance metrics achieved by the EarlySense System 2.0. Instead, it states that the device was subject to various verification and validation tests and that the modifications "did not affect its performance, effectiveness or safety." The submission aimed to demonstrate substantial equivalence to the predicate device (K120465).

The performance claims rely on the device maintaining the same functionality and performance as its predicate, which was already cleared. The performance criteria for the predicate device would be relevant here, but are not provided in this document.

Studies and Ground Truth Information:

The document focuses on demonstrating that the modifications did not negatively impact the performance of the device and thus, it remains substantially equivalent to the cleared predicate. It does not present new clinical studies with detailed acceptance criteria and performance results in the same way a de novo submission might.

Here's an analysis of the provided information concerning studies:

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The EarlySense (EverOn) Central Display Station (CDS) is intended to provide secondary display of the information as displayed on multiple individual bed side monitoring units, on a central remote screen. The EarlySense (EverOn) CDS is not intended to replace any part of the bed-side patient monitoring procedures. The system can be used in hospitals or hospital type and clinic environment.

The EarlySense Central Display Station (CDS) is intended to communicate with multiple EarlySense Bedside monitoring devices and remotely display, on a central screen, the information as displayed on the individual Bedside monitoring units. The communication can be performed either via standard wired or via wireless LAN communication. The transmitted information from Bedside to CDS includes alert information and physiological parameters. The CDS can also format the alert information as obtained from Bedside units into a message that can be transmitted to external devices that can communicate with the CDS via standard TCP/IP port. Data and report files (.CSV and .PDF) generated at the Bedside units can be retrieved via the CDS by the user and can be downloaded and for sent from the CDS. The CDS can also collectively generate, for all Bedside units connected to a CDS, unified tabulated reports that indicate the settings/alerts of each bedside unit. The CDS includes standard hardware (PC, communication and IT hardware), EarlySense develops the application software which is used on the system's PC computer (Central Display Station), equipped with Linux Debian operating system.

The provided text is a 510(k) summary for the EarlySense Central Display Station (CDS). It describes the device, its intended use, and argues for substantial equivalence to a predicate device. However, it does not include detailed acceptance criteria or a study proving the device meets specific performance criteria in the way a clinical or technical performance study would.

The document states: "Performance testing, inclusive of software verification and Full load bench testing was performed to verify that all modifications introduced in the device as compared to its predicate device did not raise any new safety and effectiveness issues." This sentence indicates that some testing was done, but it does not describe the specific acceptance criteria or the reported device performance in a quantitative manner.

Therefore, much of the requested information cannot be extracted directly from this 510(k) summary. I will answer based on the information available and indicate where information is not provided.

Acceptance Criteria and Study Information for EarlySense Central Display Station (CDS)

Based on the provided 510(k) summary, the device is a Central Display Station (CDS) that remotely displays information from bedside monitoring units. The primary claim for this submission is substantial equivalence to a predicate device (EverOn Central Display Station (CDS) cleared under K110521) after an operating system change (Windows XP to Linux Debian) and minor GUI modifications.

1. A table of acceptance criteria and the reported device performance

Note: The 510(k) summary describes functional modifications and states that performance testing was conducted to ensure no new safety or effectiveness issues. However, it does not provide specific, quantifiable acceptance criteria (e.g., "display latency less than X milliseconds," "data accuracy within Y%") or detailed performance data (e.g., "average latency was Z milliseconds"). The acceptance criteria are largely inferred from the claim of substantial equivalence and the scope of modifications.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This information is not provided in the 510(k) summary. The document mentions "Performance testing, inclusive of software verification and Full load bench testing" but does not detail the sample size or provenance of any data used for these tests. Given the nature of a CDS (secondary display), the "test set" would likely refer to the configurations and scenarios tested rather than a patient data set for clinical performance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not provided. The device is a display and communication system, not an interpretive AI device requiring expert ground truth for diagnostic accuracy. The testing would likely involve software engineers and potentially clinical users validating display accuracy and functionality.

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

This information is not provided. As explained above, the nature of the device's function does not typically involve expert adjudication in the way an imaging or diagnostic AI would.

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

An MRMC comparative effectiveness study was not done. This device is a display and communication system, and not an AI or diagnostic tool designed to directly improve human reader performance in interpreting medical cases. No "AI assistance" is mentioned as a feature of the device.

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

This device does not contain an "algorithm" in the sense of a diagnostic or interpretive AI. It is a display and communication system that provides information for human-in-the-loop patient monitoring. Therefore, a standalone effectiveness study of an algorithm is not applicable and was not done.

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

The concept of "ground truth" (e.g., for diagnostic accuracy) is not applicable in the context of this device. The "truth" would refer to the accurate and timely display of physiological parameters and alerts as generated by the bedside monitoring units. The testing would verify that the CDS accurately reflects the source data.

8. The sample size for the training set

This device is not an AI or machine learning system that requires a "training set." Therefore, this information is not applicable and not provided.

9. How the ground truth for the training set was established

As there is no training set for an AI algorithm, this information is not applicable and not provided.

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The EarlySense (EverOn) System is intended for continuous measurement of respiration rate, heart rate and movement, in an automatic contact-less manner, at home, hospital or clinic setting. The system is indicated for use in children, adolescents and adults. The operation of the EarlySense has been studied in children (weight ≥ 10 Kg) and adults (weight

The EarlySense (EverOn) System consists of the following main components:

• A piezoelectric Sensing Unit placed under the mattress or mattress pad.
• Bedside Unit with Proprietary recording and data analysis software
• OEM Oximetry Module (optional)
  The EarlySense (EverOn) System is designed for continuous and contact-less monitoring of respiration rate, heart rate and movement. The under mattress Sensing Unit includes a piezoelectric sensor, which converts mechanical movements into an electric signal. The principles and the mode of operation for the capability of contactless monitoring of the EarlySense (EverOn) is identical to the cleared EverOn 1.0 (K092062). The EarlySense interfaces with a pulse oximeter OEM module (optional feature) and can therefore also monitor oxygen saturation. The oximetry module is connected externally via UART (RS232/TTL) to the Bedside unit. A compatible oximetry sensor is then attached to the patient's finger and the monitor begins to continuously display oximetry data (e.g. SpO2).
  The Bedside Unit, processes inputted signals, displays the patient's parameters, and generates alerts (respiration rate, heart rate, movement, and SpO2) as per set thresholds when needed. The continuously accumulated data from the monitored period are displayed on the bedside unit and communicated, via wired or via wireless LAN communication to a dedicated may options of SerilySense Central Display Station (CDS) - cleared under K100376 and K110521). Analysis of the results may be performed either on-line during the monitoring session, or later, off-line. Data for each patient is recorded and can therefore also be retrieved and presented.

Here's a breakdown of the acceptance criteria and study information for the EarlySense (EverOn™) System, based on the provided 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary focuses primarily on showing substantial equivalence to predicate devices and adherence to general safety and performance standards. It does not explicitly state specific quantitative acceptance criteria for respiration rate, heart rate, movement, or SpO2, nor does it provide detailed performance values for these metrics. Instead, the summary emphasizes that a "set of software and bench testing was performed to demonstrate the performance and accuracy" and that the device "performs according to its specifications described in the Software Requirements Specifications (SRS)."

However, given the context of a 510(k) for an oximetry module addition and an existing cleared device, we can infer the type of performance that would be required for the added oximetry function based on typical regulatory expectations for pulse oximeters. For the existing functions (respiration rate, heart rate, movement), the claim is that its operation is "identical to the cleared EverOn 1.0 (K092062)."

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

The summary does not provide specific sample sizes for particular performance tests. It mentions "bench testing" and "software verification and validation testing." No information about human subject test sets (sample size, country of origin, retrospective/prospective) is provided beyond the existing claim of equivalence to the predicate device for respiration rate, heart rate, and movement. The indication for use states, "The operation of the EarlySense has been studied in children (weight ≥ 10 Kg) and adults (weight

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The EverOn Central Display Station (CDS) is intended to provide secondary display of the information as displayed on multiple individual bed side monitoring units, on a central remote screen. The EverOn CDS is not intended to replace any part of the bed-side patient monitoring procedures. The system can be used in hospitals or hospital type and clinic environment.

The EverOn Central Display Station (CDS) is intended to communicate with multiple EverOn bed-side monitoring devices and remotely display the information as displayed on multiple individual bed side monitoring units, on a central screen. The communication can be performed either via standard wired or via wireless LAN communication.

The transmitted information from Bed-side to CDS includes alert information and physiological parameters. The CDS can also format the alert information as obtained from bed-side units into a message that can be transmitted to external devices that can communicated with the CDS via standard TCP/IP port. Data and report files (.CSV and .PDF) generated at the bed-side units can be retrieved via the CDS by the user and can be downloaded and / or sent for printing from the CDS. The CDS includes standard hardware (PC, communication and IT hardware). EarlySense develops the application software which is used on the system's PC computer (Central Display Station).

The provided document is a 510(k) summary for the EverOn Central Display Station (CDS) with wireless communication capability. This document focuses on demonstrating substantial equivalence to a predicate device and certifying compliance with relevant standards, rather than providing detailed performance data from a clinical study with acceptance criteria in the typical sense for a diagnostic algorithm.

Therefore, many of the requested points regarding acceptance criteria, sample sizes, ground truth establishment, expert qualifications, and specific study types (MRMC, standalone) are not applicable or not explicitly detailed in this type of regulatory submission.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

This document does not present a table of acceptance criteria and reported device performance in the manner typically seen for diagnostic algorithms. Instead, the "performance data" section focuses on demonstrating the safety and effectiveness of the modification (wireless communication) to an already cleared device and showing compliance with established standards.

The acceptance criteria are implicitly those of the standards met and the successful completion of verification and validation testing, hazard analysis, and full load bench testing.

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

• Test Set Sample Size: Not explicitly stated. The full load bench testing implies a robust simulation or real-world test, but the number of bed-side units or data points is not
  quantified.
• Data Provenance: Not applicable in the context of a clinical test set. The testing was conducted by EarlySense Ltd.

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

Not applicable. The performance evaluation was engineering-focused (software verification, bench testing, hazard analysis) rather than clinical. There's no mention of experts establishing clinical ground truth for a test set.

4. Adjudication Method for the Test Set

Not applicable. This testing did not involve human interpretation or adjudication in the context of clinical ground truth.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not performed. This type of study is not relevant for a device like the EverOn CDS, which is a display and communication system, not a diagnostic algorithm that assists human readers in interpretation.

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

The device itself is a display system, not a standalone diagnostic algorithm. The performance testing described (software verification, bench testing) could be considered "standalone" in the sense that it evaluates the system's function independently without continuous human interaction during the test, but it is not an AI algorithm.

7. The Type of Ground Truth Used

The "ground truth" for this device's performance evaluation was defined by:

• Engineering specifications and requirements (for software verification and validation).
• Expected communication protocols and display accuracy (for bench testing).
• Industry standards for safety, risk management, and software development (EN/IEC 60601-1-4, ISO 14971-1, IEC 62304, IEC 60812).

8. The Sample Size for the Training Set

Not applicable. The EverOn CDS is a software-driven display and communication system, not an AI/Machine Learning algorithm that requires a training set.

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

Not applicable, as no training set was used.

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The EverOn 1.0 system is intended for continuous measurement of respiration rate, heart rate and movement, in an automatic contact-less manner, at home, hospital or clinic setting. The system is indicated for use in children, adolescents and adults. The operation of the EverOn has been studied in children (weight ≥10 Kg) and adults (weight ≤111 Kg) during sleep and resting condition.

The EverOn 1.0 System is designed for continuous and contact-less monitoring of respiration rate, heart rate and movement. The system automatically starts measuring whenever the patient is in bed. The EverOn can provide alert notification (audible and visible) if either parameters exceed predefined thresholds set by the user. The EverOn can also provide patient out-of-bed (Bed Exit) alert notification to the user. The EverOn System consists of the following main components: A Sensing Unit placed under the mattress or mattress pad. A Control Unit (Bedside Unit). Proprietary recording and data analysis software The under mattress Sensing Unit includes a piezoelectric sensor, which converts mechanical movements into an electric signal. The Control Unit receives the electric signals, processes them and finally calculates, logs, displays the patient's parameters, and generates alerts as per set thresholds. when needed.

Here's an analysis of the provided text regarding the acceptance criteria and study for the EarlySense EverOn 1.0 System:

Acceptance Criteria and Study for EarlySense EverOn 1.0 System

The documentation focuses on demonstrating substantial equivalence to predicate devices, particularly regarding the system's ability to detect movement. The primary acceptance criteria provided relate to the accuracy of movement detection.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document doesn't explicitly state the 'acceptance criteria' in numerical thresholds (e.g., "accuracy must be >= 90%"). Instead, it presents the achieved accuracy values as evidence of meeting performance requirements and demonstrating equivalence.

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

The document does not explicitly state the numerical sample size (number of patients) used for the clinical testing in either the adult or children populations. It mentions that clinical testing "was performed among both children and adult populations" without providing specific numbers.

• Data Provenance: The document does not specify the country of origin for the clinical study data. It is implied to be part of the regulatory submission from EarlySense Ltd. (Israel). The study appears to be prospective as it describes the system's accuracy being "compared to two reference methods: Predicate and Gold Standard" during clinical testing.

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

The document does not provide information on the number of experts used or their qualifications to establish the ground truth. It simply refers to "Gold Standard" as one of the reference methods for comparison.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method used for the test set.

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

There is no mention of a Multi-Reader Multi-Case (MRMC) comparative effectiveness study being performed, nor any effect size regarding human reader improvement with or without AI assistance. The study focuses on the device's standalone performance in detecting movement against reference methods.

6. Standalone Performance Study

Yes, a standalone performance study was done. The reported accuracy percentages (e.g., "Overall Accuracy - Adults 95.6%") represent the performance of the EverOn 1.0 System (algorithm only, as it's a contact-less monitoring device) in detecting movement compared to the gold standard and a predicate device. This is a direct measurement of the algorithm's performance without human intervention in the interpretation process of the movement detection itself.

7. Type of Ground Truth Used

The types of ground truth used were:

• Gold Standard: The document refers to a "Gold Standard" without specifying its exact nature (e.g., expert observation, concurrent established medical device, video recording with manual annotation).
• Predicate Device (Compass): The Compass F10 system was used as a reference device, indicating its measurements were considered a form of ground truth for comparison.

8. Sample Size for the Training Set

The document does not provide any information regarding the sample size used for the training set.

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

The document does not provide any information on how the ground truth for the training set (if any was used for algorithm development) was established. The submission focuses on the validation of the final device.

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