(254 days)
DeepRESP is an aid in the diagnosis of various sleep disorders where subjects are often evaluated during the initiation or follow-up of treatment of various sleep disorders. The recordings to be analyzed by DeepRESP can be performed in a hospital, patient home, or an ambulatory setting. It is indicated for use with adults (22 years and above) in a clinical environment by or on the order of a medical professional.
DeepRESP is intended to mark sleep study signals to aid in the identification of events and annotation of traces; automatically calculate measures obtained from recorded signals (e.g., magnitude, time, frequency, and statistical measures of marked events); infer sleep staging with arousals with EEG and in the absence of EEG. All output is subject to verification by a medical professional.
DeepRESP is a cloud-based software as a medical device (SaMD), designed to perform analysis of sleep study recordings, with and without EEG signals, providing data for the assessment and diagnosis of sleep-related disorders. Its algorithmic framework provides the derivation of sleep staging including arousals, scoring of respiratory events and key parameters such as the Apnea-Hypopnea Index (AHI).
DeepRESP is hosted on a serverless stack. It consists of:
- A web Application Programming Interface (API) intended to interface with a third-party client application, allowing medical professionals to access DeepRESP's analytical capabilities.
- Predefined sequences called Protocols that run data analyses, including artificial intelligence and rule-based models for the scoring of sleep studies, and a parameter calculation service.
- A Result storage using an object storage service to temporarily store outputs from the DeepRESP Protocols.
Here's a breakdown of the acceptance criteria and the study details for the DeepRESP device, based on the provided FDA 510(k) summary:
1. Table of Acceptance Criteria & Reported Device Performance:
The document doesn't explicitly state "acceptance criteria" as a separate table, but it compares DeepRESP's performance against manual scoring and predicate devices. I've extracted the performance metrics that effectively serve as acceptance criteria given the "non-inferiority" and "superiority" claims against established devices.
| Metric (Against Manual Scoring) | DeepRESP Performance (95% CI) | Equivalent Predicate Performance (Nox Sleep System K192469) (95% CI) | Superiority/Non-inferiority Claim | Relevant Study Type |
|---|---|---|---|---|
| Severity Classification (AHI ≥ 5) | ||||
| PPA% | 87.5 [86.2, 89.0] | 73.6 [PPA% reported for predicate] | Superiority | Type I/II |
| NPA% | 91.9 [87.4, 95.8] | 65.8 [NPA% reported for predicate] | Non-inferiority | Type I/II |
| OPA% | 87.9 [86.6, 89.3] | 73.0 [OPA% reported for predicate] | Superiority | Type I/II |
| Severity Classification (AHI ≥ 15) | ||||
| PPA% | 74.1 [72.0, 76.5] | 54.5 [PPA% reported for predicate] | Superiority | Type I/II |
| NPA% | 94.7 [93.2, 96.2] | 89.8 [NPA% reported for predicate] | Non-inferiority | Type I/II |
| OPA% | 81.5 [79.9, 83.3] | 67.2 [OPA% reported for predicate] | Superiority | Type I/II |
| Respiratory Events | ||||
| PPA% | 72.0 [70.9, 73.2] | 58.5 [PPA% reported for predicate] | Non-inferiority (Superiority for OPA claimed) | Type I/II |
| NPA% | 94.2 [94.0, 94.5] | 95.4 [NPA% reported for predicate] | Non-inferiority | Type I/II |
| OPA% | 87.2 [86.8, 87.5] | 81.7 [OPA% reported for predicate] | Superiority | Type I/II |
| Sleep State Estimation (Wake) | ||||
| PPA% | 95.4 [95.1, 95.6] | 56.7 [PPA% reported for predicate] | Non-inferiority | Type I/II |
| NPA% | 94.6 [94.4, 94.9] | 98.1 [NPA% reported for predicate] | Non-inferiority | Type I/II |
| OPA% | 94.8 [94.6, 95.0] | 89.8 [OPA% reported for predicate] | Non-inferiority | Type I/II |
| Arousal Events | ||||
| ArI ICC (against Sleepware G3 K202142) | 0.63 [ArI ICC] | 0.794 [ArI ICC for additional predicate] | Non-inferiority | Type I/II |
| PPA% | 62.2 [61.2, 63.1] | N/A (Manual for primary predicate) | N/A | Type I/II |
| NPA% | 89.3 [88.8, 89.7] | N/A (Manual for primary predicate) | N/A | Type I/II |
| OPA% | 81.4 [81.1, 81.7] | N/A (Manual for primary predicate) | N/A | Type I/II |
| Type III Severity Classification (AHI ≥ 5) | ||||
| PPA% | 93.1 [92.2, 93.9] | 82.4 [PPA% reported for predicate] | Superiority | Type III |
| NPA% | 81.1 [75.1, 86.6] | 56.6 [NPA% reported for predicate] | Non-inferiority | Type III |
| OPA% | 92.5 [91.7, 93.3] | 81.1 [OPA% reported for predicate] | Non-inferiority | Type III |
| Type III Respiratory Events | ||||
| PPA% | 75.4 [74.6, 76.1] | 58.5 [PPA% reported for predicate] | Superiority | Type III |
| NPA% | 87.8 [87.4, 88.1] | 95.4 [NPA% reported for predicate] | Non-inferiority | Type III |
| OPA% | 83.7 [83.4, 84.0] | 81.7 [OPA% reported for predicate] | Superiority | Type III |
| Type III Arousal Events | ||||
| ArI ICC (against Sleepware G3 K202142) | 0.76 [ArI ICC] | 0.73 [ArI ICC for additional predicate] | Non-inferiority | Type III |
2. Sample Size Used for the Test Set and Data Provenance:
- Type I/II Studies (EEG present): 2,224 sleep recordings
- Type III Studies (No EEG): 3,488 sleep recordings (including 2,213 Type I recordings and 1,275 Type II recordings, processed to utilize only Type III relevant signals).
- Provenance: Retrospective study. Data originated from sleep clinics in the United States, collected as part of routine clinical work for patients suspected of sleep disorders. The patient population showed diversity in age, BMI, and race/ethnicity (Caucasian or White, Black or African American, Other, Not Reported) and was considered representative of patients seeking medical services for sleep disorders in the United States.
3. Number of Experts and Qualifications for Ground Truth:
The document explicitly states that the studies used "manually scored sleep recordings" but does not specify the number of experts or their specific qualifications (e.g., "radiologist with 10 years of experience"). It implicitly relies on the quality of "manual scoring" from routine clinical work in US sleep clinics as the ground truth.
4. Adjudication Method for the Test Set:
The document does not describe any specific adjudication method (e.g., 2+1, 3+1). It refers to "manual scoring" as the established ground truth.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
No, a MRMC comparative effectiveness study was not reported. The study design was a retrospective data analysis comparing the algorithm's performance against existing manual scoring (ground truth) and established predicate devices. There is no information about human readers improving with AI vs. without AI assistance. The device is intended to provide automatic scoring subject to verification by a medical professional.
6. Standalone (Algorithm Only) Performance:
Yes, the study report describes the standalone performance of the DeepRESP algorithm. The reported PPA, NPA, OPA percentages, and ICC values represent the agreement of the automated scoring by DeepRESP compared to the manual ground truth. The device produces output "subject to verification by a medical professional," but the performance metrics provided are for the algorithmic output itself.
7. Type of Ground Truth Used:
The ground truth used was expert consensus (manual scoring). The document states "It used manually scored sleep recordings... The studies were done by evaluating the agreement in scoring and clinical indices resulting from the automatic scoring by DeepRESP compared to manual scoring."
8. Sample Size for the Training Set:
The document does not explicitly state the sample size used for the training set. The clinical validation study is described as a "retrospective study" used for validation, but details about the training data are not provided in this summary.
9. How the Ground Truth for the Training Set Was Established:
The document does not specify how the ground truth for the training set was established. It only describes the ground truth for the validation sets as "manually scored sleep recordings" from routine clinical work.
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March 14, 2025
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Nox Medical ehf % Hrishikesh Gadagkar Senior Principal Ram+ 2790 Mosside Blvd #800 Monroeville, Pennsylvania 15146
Re: K241960
Trade/Device Name: DeepRESP Regulation Number: 21 CFR 882.1400 Regulation Name: Electroencephalograph Regulatory Class: Class II Product Code: OLZ Dated: July 3, 2024 Received: July 3, 2024
Dear Hrishikesh Gadagkar:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food. Drug. and Cosmetic Act (that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device"
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(https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30. Design controls; 21 CFR 820.90. Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the OS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rue"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
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Sincerely,
Patrick Antkowiak -S
for Jay Gupta Assistant Director DHT5A: Division of Neurosurgical, Neurointerventional, and Neurodiagnostic Devices OHT5: Office of Neurological and Physical Medicine Devices Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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Indications for Use
510(k) Number (if known) K241960
Device Name DeepRESP
Indications for Use (Describe)
DeepRESP is an aid in the diagnosis of various sleep disorders where subjects are often evaluated during the initiation or follow-up of treatment of various sleep disorders. The recordings to be analyzed by DeepRESP can be performed in a hospital, patient home, or an ambulatory setting. It is indicated for use with adults (22 years and above) in a clinical environment by or on the order of a medical professional.
DeepRESP is intended to mark sleep study signals to aid in the identification of events and annotation of traces; automatically calculate measures obtained from recorded signals (e.g., magnitude, time, frequency, and statistical measures of marked events); infer sleep staging with arousals with EEG and in the absence of EEG. All output is subject to verification by a medical professional.
| Type of Use (Select one or both, as applicable) |
|---|
| ------------------------------------------------- |
X Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
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510(k) Summary DeepRESP – 510(k) Traditional
DATE PREPARED
March 13, 2025
MANUFACTURER AND 510(k) OWNER
Nox Medical ehf Katrinartuni 2, IS - 105 Reykjavik, Iceland Telephone: +354 570 7170 Official Contact: Kolbrun E Ottosdottir, Chief Compliance Officer
REPRESENTATIVE/CONSULTANT
Hrishikesh Gadagkar, Sr. Principal RQM+ 2790 Mosside Blvd #800, Monroeville, PA Telephone: +1 (410) 245-0501 Email: hgadagkar@rqmplus.com
DEVICE INFORMATION
| Proprietary Name/Trade Name: | DeepRESP |
|---|---|
| Common Name: | Electroencephalograph, Automatic Event Detection Softwarefor Polysomnograph with Electroencephalograph |
| Regulation Number: | 882.1400 |
| Class: | II |
| Product Code: | OLZ |
| Premarket Review: | Division of Neurosurgical, Neurointerventional and NeurodiagnosticDevices (DHT5A), Office of Neurological and Physical MedicineDevices (OHT5) |
| Review Panel: | Neurology |
PREDICATE DEVICE IDENTIFICATION
The DeepRESP is substantially equivalent to the following predicate:
| 510(k)Number | Predicate Device Name / Manufacturer | PrimaryPredicate | AdditionalPredicate |
|---|---|---|---|
| K192469 | Nox Sleep System / Nox Medical | x | |
| K202142 | Sleepware G3 / Respironics Inc | x |
DEVICE DESCRIPTION
DeepRESP is a cloud-based software as a medical device (SaMD), designed to perform analysis of sleep study recordings, with and without EEG signals, providing data for the assessment and diagnosis of sleep-related disorders. Its algorithmic framework provides the derivation of sleep staging including arousals, scoring of respiratory events and key parameters such as the Apnea-Hypopnea Index (AHI).
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Image /page/5/Picture/0 description: The image shows the text "nox medical" in a sans-serif font. The text is dark blue and appears to be a logo or brand name. The words are lowercase and evenly spaced.
510(k) Summarv DeepRESP – 510(k) Traditional
DeepRESP is hosted on a serverless stack. It consists of:
- A web Application Programming Interface (API) intended to interface with a third-party ● client application, allowing medical professionals to access DeepRESP's analytical capabilities.
- Predefined sequences called Protocols that run data analyses, including artificial . intelligence and rule-based models for the scoring of sleep studies, and a parameter calculation service.
- A Result storage using an object storage service to temporarily store outputs from the . DeepRESP Protocols.
Image /page/5/Figure/6 description: The image is a dataflow diagram titled "Figure 1 - DeepRESP dataflow". The diagram shows the flow of data between different components, including the EMR Repository, Client application, DeepRESP API, DeepRESP Protocols, and DeepRESP Result storage. The EMR Repository and Client application are connected by a two-way arrow, indicating that data can flow in both directions. The Client application is connected to the DeepRESP API, which is connected to the DeepRESP Protocols, which is connected to the DeepRESP Result storage.
INDICATIONS FOR USE DeepRESP is an aid in the diagnosis of various sleep disorders where subjects are often evaluated during the initiation or follow-up of treatment of various sleep disorders. The recordings to be analyzed by DeepRESP can be performed in a hospital, patient home, or an ambulatory setting. It is indicated for use with adults (22 years and above) in a clinical environment by or on the order of a medical professional.
DeepRESP is intended to mark sleep study signals to aid in the identification of events and annotation of traces; automatically calculate measures obtained from recorded signals (e.g., magnitude, time, frequency, and statistical measures of marked events); infer sleep staging with arousals with EEG and in the absence of EEG. All output is subject to verification by a medical professional.
COMPARISON OF TECHNOLOGICAL CHARACTERISTICS
Nox Medical believes that the DeepRESP is substantially equivalent to the primary predicate device based on the information summarized here:
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510(k) Summary DeepRESP – 510(k) Traditional
| Trade/Device Name | Subject Device: | Primary Predicate Device: | Additional Predicate Device: |
|---|---|---|---|
| DeepRESP | Nox Sleep System | Sleepware G3 | |
| Manufacturer | Nox Medical | Nox Medical | Respironics, Inc. |
| 510(k) Number | K241960 | K192469 | K202142 |
| Regulation Number | 21 CFR 882.1400 | 21 CFR 882.1400 | 21 CFR 882.1400 |
| Regulation Name | Electroencephalograph | Electroencephalograph | Electroencephalograph |
| Regulatory Class | Class II | Class II | Class II |
| Product Code(s) | OLZ | Primary: OLZSubsequent: KZM | OLZ |
| Intended Use | DeepRESP is a cloud-based artificial intelligence-enabled software application used for analysis(automatic scoring), retrieval, and summarizationof data recorded with sleep monitoring devices.The device uses an algorithm to categorize sleep-related events that help aid in the diagnosis ofsleep-related disorders. The results of the analyzeddata are then transferred to another software formanual scoring, display and reporting | The Nox Sleep System is used as an aid in thediagnosis of different sleep disorders and for theassessment of sleep.The Nox Sleep System is used to measure, record,display, organize, analyze, summarize, and retrievephysiological parameters during sleep and wake.TheNox Sleep System allows the user to decide on thecomplexity of the study by varying the number andtypes of physiological signals measured.The Nox Sleep System allows for generation ofuser/pre-defined reports based on subject´s data.The user of the Nox Sleep System are medicalprofessionals who have received training in the areasof hospital/clinical procedures, physiologicalmonitoring of human subjects, or sleep disorderinvestigation.The intended environments are hospitals, institutions,sleep centers, sleep clinics, or other testenvironments, including the patient's home. | Sleepware G3 is a software application used foranalysis (automatic and manual scoring), display,retrieval, summarization, report generation, andnetworking of data received from monitoring devicesused to categorize sleep related events that help aidin the diagnosis of sleep-related disorders.The optional Somnolyzer software application isintended to mark sleep study signals in order to aidin the identification of events and annotation oftraces; automatically calculate measures obtainedfrom recorded signals (e.g., magnitude, time,frequency, and statistical measures of markedevents); and infer sleep staging in the absence ofEEG. All output subject to verification by a qualifieduser. |
| Indications for Use | DeepRESP is an aid in the diagnosis of varioussleep disorders where subjects are often evaluatedduring the initiation or follow-up of treatment ofvarious sleep disorders. The recordings to be | The Nox Sleep System is used as an aid in thediagnosis of different sleep disorders and for theassessment of sleep.The Nox Sleep System is used to measure, record, | Sleepware G3 is a software application used foranalysis (automatic and manual scoring), display,retrieval, summarization, report generation, andnetworking of data received from monitoring devices |
| analyzed by DeepRESP can be performed in ahospital, patient home, or an ambulatorysetting. It is indicated for use with adults (22years and above) in a clinical environment by or onthe order of a medical professional.DeepRESP is intended to mark sleep study signalsto aid in the identification of events and annotationof traces; automatically calculate measuresobtained from recorded signals (e.g., magnitude,time, frequency, and statistical measures ofmarked events); infer sleep staging with arousalswith EEG and in the absence of EEG. All output issubject to verification by a medical professional. | display, organize, analyze, summarize, and retrievephysiological parameters during sleep and wake.The Nox Sleep System allows the user to decide onthe complexity of the study by varying the numberand types of physiological signals measured.The Nox Sleep System allows for generation ofuser/pre-defined reports based on subject 's data.The user of the Nox Sleep System are medicalprofessionals who have received training in the areasof hospital/clinical procedures, physiologicalmonitoring of human subjects, or sleep disorderinvestigation.The intended environments are hospitals, institutions,sleep centers, sleep clinics, or other testenvironments, including the patient's home. | used to categorize sleep related events that help aidin the diagnosis of sleep-related disorders. It isindicated for use with adults (18 and older) andinfant patients (one year old or less) in a clinicalenvironment by or on the order of a physician.The optional Somnolyzer scoring algorithms are foruse with adults (18 and older) to generate an outputthat is ready for review and interpretation by aphysician. Cardio-Respiratory Sleep Staging (CReSS)is an additional functionality of Somnolyzer whichuses standard Home Sleep Apnea Test HSAT signals(in the absence of EEG signals) to infer sleep stage. | |
| Use Environment | Physician office. No limitation on where data areacquired. | Physician office. No limitation on where data areacquired. | Physician office. No limitation on where data areacquired. |
| Patient Population | 22 years and older | 22 years and older | 18 years and older |
| Type of Use | Prescription Use only | Prescription Use only | Prescription Use only |
| Signals analyzed | EEG, Chin EMG, EOG, Abdomen and Thorax RIP,SpO2, Nasal Pressure, Body position | EEG, Chin EMG, EOG Abdomen and Thorax RIP,SpO2; Cannula flow; Body position | EEG, ECG, EOG, EMG waveforms; SpO2; respiratoryeffort; airflow; heart/pulse rate; snoring loudness;body movement and position. |
| Sleep Scoringwith EEG | Automatic ready for review by medical professional | Automatic | Automatic ready for review and interpretation by aphysician |
| Manual through a 3rd party application. | Manual | Manual | |
| Sleep ScoringWithout EEG | Automatic ready for review by medical professional | Not available | Automatic ready for review and interpretation by aphysician |
| Score Sleep DisorderRespiratory Events | Automatic ready for review by medical professional | Automatic | Automatic ready for review and interpretation by aphysician |
| Manual through a 3rd party application. | Manual | Manual | |
| Score Arousal Eventswith EEG | Automatic ready for review by medical professional | Manual | Automatic ready for review and interpretation by aphysician |
| Manual through a 3rd party application. | Manual | Manual | |
| Score Arousal Eventswithout EEG | Automatic ready for review by medical professional | Not available | Automatic ready for review and interpretation by aphysician |
| Automatic ready for review by medical professional | Automatic | Automatic ready for review and interpretation by aphysician | |
| Score Body Position | Manual through a 3rd party application. | Manual | Manual |
| Manual Review ofAutomatic Scoring | Yes | Yes | Yes |
| Sleep StudyReporting | Yes | Yes | Yes |
| HardwareComponents | Not included | Included | Not included |
| PhysicalCharacteristics | API-based software operates in the cloud. | Operates on any PC with Windows 10. | Operates on Windows 10 PC, and Microsoft SurfacePro (for offline review). |
| Performance Testing | IEC 62304:2006/ A1:2015ISO 14971:2019ANSI/AAMI SW96:2023 | IEC 62304:2006ISO 14971:2007 | IEC 62304:2006/ A1:2015ISO 14971:2007 |
| Clinical performanceType I/II studies | Severity classificationAHI ≥ 5PPA% [95% CI]: 87.5NPA% [95% CI]: 91.9OPA% [95% CI]: 87.9AHI ≥ 15PPA% [95% CI]: 74.1NPA% [95% CI]: 94.7OPA% [95% CI]: 81.5 | Severity classificationAHI ≥ 5PPA% [95% CI]: 73.6NPA% [95% CI]: 65.8OPA% [95% CI]: 73.0AHI ≥ 15PPA% [95% CI]: 54.5NPA% [95% CI]: 89.8OPA% [95% CI]: 67.2 | N/A |
| Sleep stagesWakePPA% [95% CI]: 95.4NPA% [95% CI]: 94.6OPA% [95% CI]: 94.8REM | Sleep stagesWakePPA% [95% CI]: 56.7NPA% [95% CI]: 98.1OPA% [95% CI]: 89.8REM | N/A | |
| PPA% [95% CI]: 84.3NPA% [95% CI]: 98.3OPA% [95% CI]: 96.3 | PPA% [95% CI]: 74.8NPA% [95% CI]: 95.1OPA% [95% CI]: 92.1 | ||
| N1PPA% [95% CI]: 42.8NPA% [95% CI]: 89.7OPA% [95% CI]: 87.1 | N1PPA% [95% CI]: 9.2NPA% [95% CI]: 98.2OPA% [95% CI]: 93.3 | ||
| N2PPA% [95% CI]: 74.2NPA% [95% CI]: 82.8OPA% [95% CI]: 78.7 | N2PPA% [95% CI]: 80.3NPA% [95% CI]: 73.4OPA% [95% CI]: 76.7 | ||
| N3PPA% [95% CI]: 43.1NPA% [95% CI]: 98.5OPA% [95% CI]: 91.6 | N3PPA% [95% CI]: 82.4NPA% [95% CI]: 91.7OPA% [95% CI]: 90.5 | ||
| Respiratory eventsPPA% [95% CI]: 72.0NPA% [95% CI]: 94.2OPA% [95% CI]: 87.2 | Respiratory eventsPPA% [95% CI]: 58.5NPA% [95% CI]: 95.4OPA% [95% CI]: 81.7 | N/A | |
| Arousal eventsArI ICC [95% CI]: 0.63 | N/A | Arousal eventsArI ICC [95% CI]: 0.794 | |
| Clinical performanceType III studies | Severity classificationAHI ≥ 5PPA% [95% CI]: 93.1NPA% [95% CI]: 81.1OPA% [95% CI]: 92.5AHI ≥ 15 | Severity classificationAHI ≥ 5PPA% [95% CI]: 82.4NPA% [95% CI]: 56.6OPA% [95% CI]: 81.1AHI ≥ 15 | N/A |
| NPA% [95% CI]: 92.3OPA% [95% CI]: 84.7 | NPA% [95% CI]: 89.3OPA% [95% CI]: 67.9 | ||
| Sleep states | Sleep states | ||
| WakePPA% [95% CI]: 76.2NPA% [95% CI]: 96.8OPA% [95% CI]: 92.7 | WakePPA% [95% CI]: 56.8NPA% [95% CI]: 98.0OPA% [95% CI]: 89.9 | ||
| NREMPPA% [95% CI]: 94.5NPA% [95% CI]: 79.0OPA% [95% CI]: 89.2 | NREMPPA% [95% CI]: 93.8NPA% [95% CI]: 70.0OPA% [95% CI]: 85.7 | N/A | |
| REMPPA% [95% CI]: 79.1NPA% [95% CI]: 98.1OPA% [95% CI]: 95.4 | REMPPA% [95% CI]: 74.7NPA% [95% CI]: 95.1OPA% [95% CI]: 92.2 | ||
| Respiratory events | Respiratory events | ||
| PPA% [95% CI]: 75.4NPA% [95% CI]: 87.8OPA% [95% CI]: 83.7 | PPA% [95% CI]: 58.5NPA% [95% CI]: 95.4OPA% [95% CI]: 81.7 | N/A | |
| Arousal events | Arousal events | ||
| ArI ICC [95% CI]: 0.76 | N/A | ArI ICC [95% CI]: 0.73 |
Copyright © 2024 Nox Medical ehf
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510(k) Summarv DeepRESP – 510(k) Traditional
SUMMARY OF NON-CLINICAL TESTING
Software verification and validation testing were performed per IEC 62304:2006/A1:2015 Medical device software – Software life cycle processes, IEC 82304-1:2017 Health Software – general requirements for product safety and the FDA quidance General Principles of Software Validation to demonstrate safety and performance based on current industry standards and that we met the intended use and user needs. Relative documentation was prepared as recommended in FDA's Guidance for Industry and FDA Staff, Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices.
SUMMARY OF CLINICAL TESTING
The clinical performance of DeepRESP was validated by a retrospective study. It used manually scored sleep recordings, originating from sleep clinics in the United States, performed as part of routine clinical work matching indicated use of the study device and patient population.
The performance of DeepRESP with regards to scoring of sleep recordings with electroencephalography (EEG) (i.e. Type I and II), and sleep recordings without EEG (i.e. Type III) was validated. Two separate studies were conducted, one to validate DeepRESP with scoring of Type I-II recordings and a second one with scoring of Type III recordings. The studies were done by evaluating the agreement in scoring and clinical indices resulting from the automatic scoring by DeepRESP compared to manual scoring. The performance of DeepRESP was compared to the agreement of the automatic scoring of the predicate device to manual scoring. The same collection of sleep recordings and same manual scoring were used when comparing the automatic scoring of DeepRESP and the predicate device. The study method was a retrospective data study comparing paired differences.
In both studies DeepRESPs performance with regards to sleep state estimation and respiratory event scoring was validated against the Nox Sleep System (K192469). For evaluation for performance of arousal scoring, Sleepware G3 (K202142) was used as a comparator.
For validating scoring performance for Type I recordings, a total of 2,224 Type I recordings were used. For validating scoring performance for Type III recordings, a total of 3,488 sleep recordings were used, including 2,213 Type I recordings and 1,275 Type II recordings. The Type I and II recordings were processed as Type III recordings by utilizing only the subset of signals from them that are common to Type III recordings.
The recording collection used for validation of Type I scoring consisted of 46% Females, had individuals in all age groups (22-35, 36-45, 46-55, 56-65, 65+), and all BMI groups (<25, 25-30, <30). The recording collection used for validation of Type III scoring consisted of 33% females, had individuals in all age groups (22-35, 36-45, 46-55, 56-65, 65+), and all BMI groups (<25, 25-30, <30). The Type I and II sleep recordings were collected as part of standard clinical care for patients suspected of suffering from sleep disorders. The patients came from urban, suburban, and rural areas with a high-level of race/ethnicity diversity (Caucasian or White, Black or African American, Other, Not Reported). The patient population was therefore considered as representative of patients seeking medical services for sleep disorders in the United States.
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510(k) Summarv DeepRESP – 510(k) Traditional
Results:
In the retrospective study intended to validate DeepRESP for Type I recordings:
- -In comparison to the primary predicate device with regards to severity classification of sleep recordings, with AHI ≥5 and AHI ≥15 as severity thresholds, the study device showcased non-inferiority against the primary predicate device. Additionally, for OPA (overall percentage agreement) and PPA (positive percentage agreement) the study device achieved superiority for AHI ≥15 and AHI ≥5.
- -In comparison to the primary predicate device with regards to overall respiratory event scoring the study device showcased non-inferiority for all endpoints, additionally achieving superiority for OPA.
- -In comparison to the primary predicate device with reqards to sleep state estimation the study device showcased non-inferiority for PPA, NPA, and OPA.
- -In comparison to the additional predicate device with regards to arousal event scoring, the study device achieved non-inferiority.
The performance agreement of DeepRESP for type I studies compared to manual scoring can be seen in table 1.
| PPA% [95% CI] | NPA % [95% CI] | OPA % [95% CI] | |
|---|---|---|---|
| AHI ≥5 | 87.5 [86.2, 89.0] | 91.9 [87.4, 95.8] | 87.9 [86.6, 89.3] |
| AHI ≥15 | 74.1 [72.0, 76.5] | 94.7 [93.2, 96.2] | 81.5 [79.9, 83.3] |
| AHI ≥30 | 66.8 [63.6, 70.1] | 97.9 [97.2, 98.6] | 86.7 [85.3, 88.0] |
| Respiratory events | 72.0 [70.9, 73.2] | 94.2 [94.0, 94.5] | 87.2 [86.8, 87.5] |
| Wake | 95.4 [95.1, 95.6] | 94.6 [94.4, 94.9] | 94.8 [94.6, 95.0] |
| NREM | 92.3 [92.0, 92.6] | 92.7 [92.4, 92.9] | 92.4 [92.2, 92.6] |
| N1 | 42.8 [41.6, 43.8] | 89.7 [89.3, 90.1] | 87.1 [86.7, 87.5] |
| N2 | 74.2 [73.4, 74.9] | 82.8 [82.3, 83.3] | 78.7 [78.3, 79.1] |
| N3 | 43.1 [41.4, 44.8] | 98.5 [98.4, 98.7] | 91.6 [91.3, 91.9] |
| REM | 84.3 [83.6, 85.0] | 98.3 [98.2, 98.4] | 96.3 [96.2, 96.4] |
| Arousal events | 62.2 [61.2, 63.1] | 89.3 [88.8, 89.7] | 81.4 [81.1, 81.7] |
Table 1. Performance agreement of DeepRESP compared to manual soring. Scoring of Type I recordings.
In the retrospective study intended to validate DeepRESP for Type III recordings:
- -In comparison to the primary predicate device with regards to severity classification of sleep recordings, with AHI ≥5 and AHI ≥15 as severity thresholds, the study device showcased superiority for PPA for all thresholds. In the case of NPA, and OPA, the study device showcased non-inferiority compared to the primary predicate device.
- In comparison to the primary predicate device with regards to overall respiratory event scoring the study device showcased non-inferiority for NPA, achieving superiority for OPA and PPA.
- In comparison to the primary predicate device with regards to sleep state estimation the study device showcased non-inferiority for PPA, NPA, and OPA.
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510(k) Summarv DeepRESP – 510(k) Traditional
- In comparison to the additional predicate device with regards to arousal event scoring, the study device achieved non-inferiority.
The performance agreement of DeepRESP for Type III studies compared to manual scoring can be seen in table 2.
| PPA% [95% CI] | NPA % [95% CI] | OPA % [95% CI] | |
|---|---|---|---|
| AHI ≥5 | 93.1 [92.2, 93.9] | 81.1 [75.1, 86.6] | 92.5 [91.7, 93.3] |
| AHI ≥15 | 82.1 [80.6, 83.5] | 92.3 [90.5, 94.0] | 84.7 [83.5, 85.8] |
| AHI ≥30 | 75.9 [73.7, 78.0] | 96.8 [96.0, 97.6] | 87.2 [86.2, 88.4] |
| Respiratory events | 75.4 [74.6, 76.1] | 87.8 [87.4, 88.1] | 83.7 [83.4, 84.0] |
| Wake | 76.2 [75.5, 77.0] | 96.8 [96.6, 97.0] | 92.7 [92.5, 92.9] |
| NREM | 94.5 [94.2, 94.7] | 79.0 [78.4, 79.6] | 89.2 [88.9, 89.4] |
| REM | 79.1 [78.2, 79.9] | 98.1 [98.0, 98.2] | 95.4 [95.2, 95.5] |
| Arousal events | 66.8 [65.8, 67.6] | 86.8 [86.4, 87.1] | 81.8 [81.5, 82.0] |
Table 2. Performance agreement for DeepRESP compared to manual scoring of Type III recordings.
The validation results demonstrate that the predefined clinical performance criteria of DeepRESP against the primary predicate device and additional predicate device have been met for studies with and without EEG signals. DeepRESP is therefore considered as deemed effective in its intended purpose, i.e. categorising sleep related events, that help aid in the diagnosis of sleep related disorders.
CONCLUSION
Based on the verification and validation testing performed in accordance with IEC 62304:2006/A1:2015, it can be concluded that the subject device does not raise new issues of safety or effectiveness compared to the primary predicate device. The DeepRESP device is demonstrated to be substantially equivalent to the primary predicate device.
§ 882.1400 Electroencephalograph.
(a)
Identification. An electroencephalograph is a device used to measure and record the electrical activity of the patient's brain obtained by placing two or more electrodes on the head.(b)
Classification. Class II (performance standards).