SomnoMetry is intended for use for the diagnostic evaluation by a physician to assess sleep quality and as an aid for the diagnosis of sleep and respiratory-related sleep disorders in adults only. SomnoMetry is a software-only medical device to be used to analyze physiological signals and automatically score sleep study results, including the staging of sleep, AHI, and detection of sleepdisordered breathing events including obstructive apneas. It is intended to be used under the supervision of a clinician in a clinical environment. All automatically scored events are subject to verification by a qualified clinician.

Device Description

The SomnoMetry is an Artificial Intelligent/Machine Learning (AI/ML)-enabled Software as a Medical Device (SaMD) that automatically scores sleep study results by analyzing polysomnography (PSG) signals recorded during sleep studies. It is intended to be used under the supervision of a clinician in clinical environments to aid in the diagnosis of sleep and respiratory related sleep disorders.

All scored events that are analyzed, displayed, and summarized can be manually marked or edited by a qualified clinician during review and verification.

SomnoMetry consists of:

A web Application Programing Interface (API) to allow authenticated users to upload . PSG files to SomnoMetry Platform
A database to store the input, intermedium output, final output, and associated data ●
A database API to access the database and store/retrieve the output ●
A dashboard to display, retrieve, manage, edit, verify, and summarize the output
An AI/ML Engine using AI/ML algorithms/approaches to analyze PSG data ●
A reporting API to generate sleep reports

AI/ML Overview

Here's an analysis of the acceptance criteria and study that proves the device meets them, based on the provided text:

Device: SomnoMetry (Neumetry Medical Inc.)

Regulatory Class: Class II (Product Code: OLZ)
Intended Use: Diagnostic evaluation to assess sleep quality and aid in diagnosing sleep and respiratory-related sleep disorders in adults. Automatically scores sleep study results (sleep staging, AHI, obstructive apneas). Used under clinician supervision; all automatically scored events are subject to verification.

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly define 'acceptance criteria' in a numerical table for the SomnoMetry device against specific thresholds (e.g., "accuracy must be > 90%"). Instead, it frames the performance evaluation as demonstrating non-inferiority and substantial equivalence to a predicate device (EnsoSleep K162627) based on clinical performance metrics. The implicit acceptance criteria are that the SomnoMetry's performance for sleep staging and sleep apnea diagnosis is statistically equivalent to, or better than, the predicate device.

Table 1: Implicit Acceptance Criteria and SomnoMetry's Reported Performance (vs. Predicate)

Metric/Endpoint	Acceptance Criterion (Implicit: Non-inferiority/Substantial Equivalence to Predicate)	SomnoMetry Performance (Clinical Study Results)	Predicate Device Performance (Clinical Study Results)
Endpoint 1: Sleep Staging Performance
Wake (W)	Non-inferior to AASM gold standard (manual scoring)	True Label W: Predicted W = 92.7% (91.8, 93.6)*	Not directly comparable in this table; stated as "substantially equivalent to the predicate device."
Stage 1 (N1)	Non-inferior to AASM gold standard (manual scoring)	True Label N1: Predicted N1 = 47.1% (46.1, 48.8)*	Not directly comparable in this table; stated as "substantially equivalent to the predicate device."
Stage 2 (N2)	Non-inferior to AASM gold standard (manual scoring)	True Label N2: Predicted N2 = 88.3% (87.4, 89.1)*	Not directly comparable in this table; stated as "substantially equivalent to the predicate device."
Stage 3 (N3)	Non-inferior to AASM gold standard (manual scoring)	True Label N3: Predicted N3 = 80.8% (79.8, 81.7)*	Not directly comparable in this table; stated as "substantially equivalent to the predicate device."
REM (R)	Non-inferior to AASM gold standard (manual scoring)	True Label R: Predicted R = 94.5% (93.5, 95.5)*	Not directly comparable in this table; stated as "substantially equivalent to the predicate device."
*Note on Sleep Staging:	The confusion matrix shows how well the algorithm predicts each sleep stage given the true stage. Higher percentages on the diagonal (true W to predicted W, etc.) indicate better performance.
Endpoint 2: Sleep Apnea Diagnostic Agreement (AHI Thresholds)
Positive Agreement (PA)	No statistically significant differences from predicate device	All: AHI > 5: 90.6%; AHI ≥ 15: 89.1%; AHI ≥ 30: 83.3% REM: AHI ≥ 5: 85.6%; AHI ≥ 15: 80.0%; AHI ≥ 30: 78.8%	All: AHI > 5: 91%; AHI ≥ 15: 95%; AHI ≥ 30: N/A REM: AHI > 5: 83%; AHI ≥ 15: 79%; AHI ≥ 30: N/A
Negative Agreement (NA)	No statistically significant differences from predicate device	All: AHI > 5: 92.2%; AHI ≥ 15: 94.9%; AHI ≥ 30: 97.5% REM: AHI ≥ 5: 94.7%; AHI ≥ 15: 94.7%; AHI ≥ 30: 95.6%	All: AHI > 5: 76%; AHI ≥ 15: 98%; AHI ≥ 30: N/A REM: AHI > 5: 89%; AHI ≥ 15: 96%; AHI ≥ 30: N/A
Overall Agreement (OA)	No statistically significant differences from predicate device	All: AHI > 5: 91.2%; AHI ≥ 15: 92.8%; AHI ≥ 30: 95.6% REM: AHI ≥ 5: 88.9%; AHI ≥ 15: 88.9%; AHI ≥ 30: 92.4%	All: AHI > 5: 85%; AHI ≥ 15: 97%; AHI ≥ 30: N/A REM: AHI > 5: 86%; AHI ≥ 15: 92%; AHI ≥ 30: N/A
Likelihood Ratio (+)	No statistically significant differences from predicate device	All: AHI > 5: 11.62; AHI ≥ 15: 17.47; AHI ≥ 30: 33.32 REM: AHI ≥ 5: 16.15; AHI ≥ 15: 15.09; AHI ≥ 30: 17.91	All: AHI > 5: 3.76; AHI ≥ 15: 52.25; AHI ≥ 30: N/A REM: AHI > 5: 7.71; AHI ≥ 15: 22.0; AHI ≥ 30: N/A
Likelihood Ratio (-)	No statistically significant differences from predicate device	All: AHI > 5: 0.10; AHI ≥ 15: 0.11; AHI ≥ 30: 0.17 REM: AHI ≥ 5: 0.15; AHI ≥ 15: 0.21; AHI ≥ 30: 0.22	All: AHI > 5: 0.12; AHI ≥ 15: 0.05; AHI ≥ 30: N/A <brREM: AHI > 5: 0.19; AHI ≥ 15: 0.22; AHI ≥ 30: N/A

2. Sample Size and Data Provenance for Test Set

Sample Size: N = 201 adult subjects.
Data Provenance: Retrospective clinical data obtained from 2 AASM accredited Sleep Testing Facilities in California, USA.
- The data was verified to meet specified disease spectrum, medical condition, medication, and demographic requirements.
- Randomized sampling with proportionate allocation across each sleep apnea disease severity quantile (normative, mild, moderate, and severe sleep apnea) and sleep cycles was used.
- Age of subjects ranged from 20 to 84 years.
- No race/ethnicity information was collected.

3. Number of Experts and Qualifications for Ground Truth

The document explicitly states the ground truth was established by "manually scored PSG data" which aligns with the "AASM gold standard". While it doesn't specify the number of experts, it implies these were the standard scores provided by the AASM accredited facilities. The qualifications are implicitly that they are "qualified clinicians" following "American Academy of Sleep Medicine scoring manual and guidelines."

4. Adjudication Method for Test Set

The document does not describe a formal expert adjudication method (e.g., 2+1, 3+1). The ground truth is stated as "AASM gold standard of manually scored PSG data," which suggests a single, accepted manual score per PSG study generated by the sleep facility.

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

No, a MRMC comparative effectiveness study was not conducted. The study evaluated the standalone performance of the AI algorithm against the AASM gold standard and then compared this algorithm performance to the predicate device's algorithm performance. It did not assess how human readers improve with AI assistance versus without. The device's intended use note ("All automatically scored events are subject to verification by a qualified clinician") implies a human-in-the-loop workflow, but the study itself wasn't designed to measure the impact of AI assistance on human reader performance.

6. Standalone (Algorithm Only) Performance Study

Yes, a standalone (algorithm only) performance study was performed. The "retrospective clinical performance testing" evaluated the SomnoMetry AI/ML algorithms directly, comparing their output to the AASM gold standard. The results presented in Table 2 (Confusion Matrix for Sleep Staging) and Table 3 (Sleep Apnea Diagnostic Agreement) are for the algorithm's performance.

7. Type of Ground Truth Used

The type of ground truth used was expert consensus / AASM (American Academy of Sleep Medicine) gold standard based on "manually scored PSG data." This is considered the clinical standard for sleep study interpretation.

8. Sample Size for the Training Set

The document does not specify the sample size for the training set. It only describes the test set (N=201).

9. How Ground Truth for the Training Set Was Established

The document does not describe how the ground truth for the training set was established. It focuses solely on the clinical evaluation (test set) and states that data was obtained from AASM accredited facilities. It can be inferred that similar "manually scored PSG data" would have been used for training, given the expertise required for such scoring and the device's reliance on AASM guidelines.

Summary

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September 21, 2022

Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.

Neumetry Medical Inc Paul Chen CEO 47102 Mission Falls Ct., Suite 210 Fremont, California 94539

Re: K221179

Trade/Device Name: SomnoMetry Regulation Number: 21 CFR 882.1400 Regulation Name: Electroencephalograph Regulatory Class: Class II Product Code: OLZ Dated: August 22, 2022 Received: August 22, 2022

Dear Paul Chen:

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 (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 located 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.

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

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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 803) for devices or postmarketing safety reporting (21 CFR 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 (OS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 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).

Sincerely,

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

Submission Number (if known)

K221179

Device Name

SomnoMetry (V 1.0)

Indications for Use (Describe)

Type of Use (Select one or both, as applicable)

Prescription Use (Part 21 CFR 801 Subpart D)

Over-The-Counter Use (21 CFR 801 Subpart C)

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Image /page/3/Picture/0 description: The image shows the logo for Neumetry. The logo consists of two overlapping triangles, one blue and one yellow. The word "NEUMETRY" is written in blue below the triangles. The font is sans-serif and the letters are capitalized.

510(K) Summary

Prepared in accordance with 21 CFR 807.92

1. Submitter

Name:	Neumetry Medical Inc.
Contact Person:	Paul Chenpaul@neumetry.com
Address & Phone:	47102 Mission Falls Ct, Suite 210Fremont, CA -4539
Phone:	925 997 9560
Date:	September 19, 2022

2. Subject Device

Device Name:	SomnoMetry
Model Number:	V1.0
Common Name:	Automatic Event Detection Software for Polysomnograph WithElectroencephalograph
Regulation Number:	21 CFR 882.1400
Regulation Name:	Electroencephalograph
Regulatory Class:	II
Product Code:	OLZ
Review Panel:	Neurology

3. Predicate Device

EnsoData Inc., EnsoSleep K162627 This predicate has not been subject to a design-related recall. No reference devices were used in this submission.

4. Device Description

All scored events that are analyzed, displayed, and summarized can be manually marked or edited by a qualified clinician during review and verification.

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Image /page/4/Picture/0 description: The image shows the logo for Neumetry. The logo consists of two overlapping shapes, one in blue and one in orange, that resemble stylized mountains or peaks. Below the shapes, the word "NEUMETRY" is written in a stylized, futuristic font, also in blue. The logo appears to be for a company or organization named Neumetry.

The SomnoMetry SaMD employs a broad array of signal processing, data indexing, conventional machine learning and deep learning algorithms/approaches in PSG physiological signals to derive actionable clinical insights.

SomnoMetry consists of:

A web Application Programing Interface (API) to allow authenticated users to upload . PSG files to SomnoMetry Platform
A database to store the input, intermedium output, final output, and associated data ●
A database API to access the database and store/retrieve the output ●
A dashboard to display, retrieve, manage, edit, verify, and summarize the output
An AI/ML Engine using AI/ML algorithms/approaches to analyze PSG data ●
A reporting API to generate sleep reports

SomnoMetry works in the following sequence:

Upload PSG data via the upload API (SSL encrypted)
Analyze PSG data with SomnoMetry AI/ML Engine ●
The output of the AI/ML Engine is stored in the database
The database API queries the database and retrieves output
The output of SomnMetry can be reviewed, retrieved, managed, edited, and verified via the dashboard
Generate sleep reports via the dashboard ●

5. Indications For Use

SomnoMetry is intended for use for the diagnostic evaluation by a physician to assess sleep quality and as an aid for the diagnosis of sleep and respiratory-related sleep disorders in adults only. SomnoMetry is a software-only medical device to be used to analyze physiological signals and automatically score sleep study results, including the staging of sleep, AHI, and detection of sleep-disordered breathing events including obstructive apneas. It is intended to be used under the supervision of a clinician in a clinical environment. All automatically scored events are subject to verification by a qualified clinician.

6. Summary of Technological Characteristics

The SomnoMetry software employs a broad array of signal processing, data indexing, conventional machine learning and deep learning algorithms/approaches in PSG signals to score the sleep study results automatically which has similar intended use and indications for use as the predicate device. The main difference in the intended use is that the predicate and subject devices use different algorithms. The two devices have similar technological characteristics: both algorithms automatically process and score sleep staging and respiratory events, and both SomnoMetry's AI/ML and the predicate's automated algorithms have optimized to ensure the accuracy and precision in scoring. The AI/ML algorithms do not introduce any new risks or unexpected results. Clinical performance testing confirmed that the SomnoMetry's AI/ML

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Image /page/5/Picture/0 description: The image shows the logo for Neumetry. The logo consists of two overlapping shapes, one in blue and one in yellow. The blue shape is on the left and the yellow shape is on the right. The word "NEUMETRY" is written in blue below the shapes. The font is a futuristic-looking sans-serif font.

algorithms performances are substantially equivalent to those by the device predicate's automated algorithms.

Elements	Predicate Device	Subject Device
	EnsoSleep K162627	SomnoMetry
Classification	OLZ, Automated Event DetectionSoftware for Polysomnograph withElectroencephalograph	OLZ, Automated Event DetectionSoftware for Polysomnograph withElectroencephalograph
Indications foruse	EnsoSleep is intended for use for thediagnostic evaluation by a physicianto assess sleep quality and as an aidfor the diagnosis of sleep andrespiratory related sleep disorders inadults only. EnsoSleep is a software-only medical device to be usedunder the supervision of a clinicianto analyze physiological signals andautomatically score sleep studyresults, including the staging ofsleep, detection of arousals, legmovements, and sleep disorderedbreathing events includingobstructive apneas. Allautomatically scored events aresubject to verification by a qualifiedclinician. Central apneas, mixedapneas, and hypopneas must bemanually marked within records.	SomnoMetry is intended for use for thediagnostic evaluation by a physician toassess sleep quality and as an aid for thediagnosis of sleep and respiratory-related sleep disorders in adults only.SomnoMetry is a software-only medicaldevice to be used to analyzephysiological signals and automaticallyscore sleep study results, including thestaging of sleep, AHI, and detection ofsleep-disordered breathing eventsincluding obstructive apneas. It isintended to be used under thesupervision of a clinician in a clinicalenvironment. All automatically scoredevents are subject to verification by aqualified clinician.
Intended Use	Analyze pre-recorded physiologicaldata acquired during sleep.	Analyze pre-recorded physiological dataacquired during sleep and deriveactionable clinical insights.
Patientpopulation	Adults only	Adults only
Scoring rules	American Academy of SleepMedicine scoring manual andguidelines.	American Academy of Sleep Medicinescoring manual and guidelines.
Environmentof use	Physician office (data analysis andreporting). No limitation on wheredata are acquired.	Physician office. No limitation on wheredata are acquired.
Score sleepstaging	Yes	Yes
Score sleepdisorder	Yes	Yes
respiratoryevents
Automaticallyinitiates sleepstudy scoring	Yes	Yes
Algorithmdescription	Automated algorithms are applied tothe raw signals in order to deriveadditional signals and interpret theraw and derived signal information.	A broad array of signal processing, dataindexing, conventional machinelearning and deep learningalgorithms/approaches are applied to theraw signals to derive actionable clinicalinsights.
PhysicalCharacteristics	Operates on any PC with Windows7 and 8 operating system platforms.	Web-based software operates in thecloud with Windows, Mac OS, or Linux
Cybersecurity	Authentication controls,authorization controls,cryptographic controls, accesscontrols, checksum controls,software distribution controls,intrusion detection system controls,network and systems controls, anddatabase controls.	User authentication with strongpassword, authorization, end to end SSLencryption, access controls, checksum,network and database controls, intrusionprevention system, and anonymization.

Table 1 : Technological Characteristics Comparison

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Image /page/6/Picture/0 description: The image is a logo for Neumetry. The logo consists of two overlapping shapes, one blue and one yellow, that resemble the letter "M". Below the shapes is the word "NEUMETRY" in blue, stylized font. The logo is simple and modern, and the colors are bright and eye-catching.

7. Performance Data

Neumetry conducted the necessary non-clinical testing and clinical evaluations on the SomnoMetry with past results supporting the determination of substantial equivalence. Performance testing and activities that were conducted included the followings:

. Software verification and validation which included software code reviews, automated testing, acceptance testing and labeling review.
Study that utilized retrospective clinical data to demonstrate automatic scoring sleep ● study results.
. Design traceability that confirms all requirement tracing is complete from design inputs to verification/validation and that all risk controls are implemented.
Design verification testing which confirmed that all software requirements are developed as expected
Design validation testing which simulated the intended use to confirm that the end-to-end ● functionality of the SomnoMetry in conjunction with the AI/ML algorithms meets the design requirements
A cybersecurity and data security testing were conducted to verify that data and patient ● protected health information security measures are thoroughly included in the design of the software.

Non-clinical Testing

Safety and performance of the SomnoMetry have been verified and validated through software testing and analytical validation. Software development and testing were performed in

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Image /page/7/Picture/0 description: The image shows the logo for Neumetry. The logo consists of two overlapping shapes, one in blue and one in yellow. The shapes are similar to the letter "M". Below the shapes is the word "NEUMETRY" in a stylized font. The word is also in blue.

accordance with "IEC 62304:2006/A1:2015, Medical Device Software – Software life cycle processes". Risk has been assessed in accordance with "ISO 14971:2007, Medical Devices -Application of Risk Management to Medical Devices". During software testing, all pre-defined acceptance criteria for the SomnoMetry were met and all software test cases passed. The same verification and validation methodology, risk assessment and acceptance criterion were used for predicate device.

Retrospective Clinical Performance Testing - Clinical Evaluation

The clinical evaluation was provided in this submission in accordance with "Software as a Medical Device (SaMD): Clinical Evaluation - Guidance for Industry and Food and Drug Administration Staff" (December 2017). The clinical evaluation included a total of 201 polysomnography (PSG) files obtained from 2 AASM accredited Sleep Testing Facilities in California as the ground truths to validate the SomnoMetry AI/ML algorithms.

An archived collection of retrospective diagnostic clinical PSG subject data was obtained from the 2 AASM Accredited Sleep Testing Facilities and were verified to meet the specified disease spectrum, medical condition, medication, and demographic requirements.

To construct the final study sample from the archived collection population, a randomized sampling with proportionate allocation across each sleep apnea disease severity quantile (normative, mild, moderate, and severe sleep apnea) and sleep cycles was used to construct a valid sample of N=201 adult subjects. Age of subjects from 20 to 84 was selected. No race/ethnicity information was collected in either AASM Accredited Sleep Test Facility.

SomnoMetry SaMD performance was evaluated across the following 2 experimental endpoints:

. Endpoint 1: As SomnoMetry is intended to assist clinicians with the assessment of sleep quality, therefore device performance for sleep staging scoring must be validated.
Endpoint 2: As SomnoMetry is intended to assist clinicians with scoring sleep disordered . respiratory events used in diagnostic evaluation, therefore device performance for diagnosing sleep apnea must be validated.

Table 2 confusion matrix with conditional probabilities shows that the performance of SomnoMetry is non-inferior compared to AASM gold standard of manually scored PSG data. The results confirm that clinical performance achieved by the SomnoMetry for sleep staging is substantially equivalent to the predicate device.

	W	1	2	3	R
Subjects #	167	167	167	167	167
Event #	28789	5791	47881	12888	13733
W	92.7(91.8, 93.6)	29.8(28.6, 31.1)	3.0(2.9, 3.1)	0(0, 0)	4.6(3.6, 5.6)
1	0(0, 0)	47.1(46.1, 48.8)	0(0, 0)	0(0, 0)	0(0, 0)

Table 2: Confusion Matrix

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2	3	R
6.0(5.9, 6.1)	20.2(19.0, 21.4)	94.5(93.5, 95.5)	11.5(10.5, 12.5)	14.3(13.4, 15.2)
0(0, 0)	0(0, 0)	2.0(2.0, 2.0)	88.3(87.4, 89.1)	0(0, 0)
1.0(1.0, 1.0)	2.4(1.4, 3.4)	1.0(1.0, 1.0)	0(0, 0)	80.8(79.8, 81,7)

True Labels

Table 3 shows the performance results in sleep apnea diagnostic agreement for endpoint 2 by SomnoMetry and the predicate device, SomnoMetry's performances showed no statistically significant differences from the predicate device's performance. The results confirm that the clinical performance achieved by SomnoMetry for diagnosing sleep apnea is substantially equivalent to the predicate device.

Table 3: Diagnosing Sleep Apnea Clinical Performance Comparison

Sleep ApneaDiagnostic AgreementClinical PerformanceComparisons	The Subject Device SomnoMetry					The Predicate DeviceEnsoSleep
Bootstrapped point-estimate of medianpercent agreement(%) with 95%percentile bootstrapconfident interval(R=1000 resamples)and likelihood ratiopairs	All			REM			All		REM
	AHI > 5	AHI ≥15	AHI ≥30	AHI ≥ 5	AHI >15	AHI ≥30	AHI > 5	AHI ≥15	AHI > 5	AHI ≥15
Sample Size (N)	167	167	167	167	167	167	72	72	72	72
Positive Agreement(PA)	90.6%(90.2%,91.0%)	89.1%(88.6%,89.6%)	83.3%(82.2%,84.4%)	85.6%(85.1%,86.1%)	80.0%(79.4%,80.6%)	78.8%(77.8%,79.8%)	91%(82%.98%)	95%(83%.100%)	83%(72%.94%)	79%(56%.94%)
Negative Agreement(NA)	92.2%(91.7%,92.7%)	94.9%(94.6%,95.2%)	97.5%(97.3%,97.7%)	94.7%(94.4%,95.0%)	94.7%(94.4%,95.1%)	95.6%(95.4%,95.8%)	76%(61%.90%)	98%(94%.100%)	89%(79%.97%)	96%(90%,100%)
Overall Agreement(OA)	91.2%(90.9%,91.5%)	92.8%(92.5%,93.1%	95.6%(95.4%,95.8%)	88.9%(88.5%,89.3%)	88.9%(88.7%,89.3%)	92.4%(92.1%,92.8%)	85%(77%.92%)	97%(93%.100%)	86%(79%.93%)	92%(85%.97%)
Likelihood Ratio (+)	11.62	17.47	33.32	16.15	15.09	17.91	3.76	52.25	7.71	22.0
Likelihood Ratio (-)	0.10	0.11	0.17	0.15	0.21	0.22	0.12	0.05	0.19	0.22

Table 3a - Younger group age under 65, N=96 subjects

510(k) Summary

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Image /page/9/Picture/0 description: The image shows the logo for Neumetry. The logo consists of two overlapping shapes that resemble mountains. One shape is blue, and the other is yellow. Below the shapes, the word "NEUMETRY" is written in a blue, stylized font.

	All			REM
	AHI ≥ 5	AHI ≥ 15	AHI ≥ 30	AHI ≥ 5	AHI ≥ 15	AHI ≥ 30
Sample size	96	96	96	96	96	96
PositiveAgreement (PA)	87.9%(87.3%, 88.5%)	85.7%(84.7%, 86.7%)	100%(100%, 100%)	85.6%(85.1%, 86.3%)	84.6%(83.9%, 85.3%)	81.0%(79.8%, 82.2%)
NegativeAgreement(NA)	88.1%(87.3%, 88.9%)	91.7%(91.2%, 92.2%)	97.8%(97.6%, 97.9%)	93.9%(93.3%, 94.6%)	93.0%(92.5%, 93.4%)	97.3%(97.0%, 97.6%)
Overall (OA)	88.0%(87.5%, 88.5%)	89.9%(89.5%, 90.2%)	98.0%(97.8%, 98.2%)	89.1%(88.7%, 89.5%)	89.8%(89.4%, 90.2%)	94.1%(93.8%, 94.4%)
Likelihood ratio(+)	7.39	10.33	45.45	14.03	12.09	30.0
Likelihood ratio(-)	0.14	0.16	0	0.15	0.17	0.20

Table 3b - Older group age over 65, N=71 subjects.

	All			REM
	AHI >= 5	AHI >= 15	AHI >= 30	AHI >= 5	AHI >= 15	AHI >= 30
Sample size	71	71	71	71	71	71
PositiveAgreement (PA)	93.1%(92.6%, 93.6%)	91.4%(90.8%, 92.0%)	73.3%(71.7%, 74.9%)	85.2%(84.5%, 85.9%)	75.0%(73.9%, 76.1%)	74.9%(73.0%, 76.9%)
NegativeAgreement(NA)	100%(100%, 100%)	100%(100%, 100%)	96.9%(96.6%, 97.2%)	94.4%(93.6%, 95.2%)	97.2%(96.8%, 97.6%)	93.3%(92.8%, 93.8%)
Overall (OA)	95.3%(95.0%, 95.6%)	96.6%(96.4%, 96.8%)	93.1%(92.8%, 93.4%)	88.4%(88.0%, 88.8%)	88.6%(88.2%, 88.9%)	90.0%(89.5%, 90.5%)
Likelihood ratio(+)	+∞	+∞	23.65	15.21	26.79	11.19
Likelihood ratio(-)	0	0	0.28	0.16	0.26	0.27

Table 3a and Table 3b show that there is no significant difference between the two subgroups. The subgroup performance matrixes demonstrated the generalizability of the SomnoMetry AI algorithm.

In the retrospective diagnostic clinical study, the final study results and statistical analysis were reported for each endpoint. The subject SomnoMetry SaMD performance showed no statistically significant differences from the predicate device performance. The results confirm that the clinical performance delivered by SomnoMetry is substantially equivalent to the predicate device across all endpoints evaluated.

The results of the software and performance testing validate that the SomnoMetry meets its requirements, performs as intended, and is as safe and effective as the predicate device. No new or different questions of safety or effectiveness have been raised.

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Image /page/10/Picture/0 description: The image shows the logo for Neumetry. The logo consists of two overlapping shapes, one in blue and one in yellow. The shapes are similar to an upside down "V". Below the shapes is the word "NEUMETRY" in a stylized font. The word is in blue.

8. Conclusion

The SomnoMetry is as safe and effective as the predicate device. The SomnoMetry has the same intended use and the indications for use fall within the scope of that for the predicate device. Many of the technological characteristics are the same for the subject and predicate devices. Any differences in technological characteristics between the subject and predicate devices have been addressed through software verification/validation testing and performance testing and do not raise any new or different questions of safety or effectiveness.

Based upon the results of the software verification and validation testing, and a clinical evaluation process consisting of valid clinical association identification, analytical validation, and clinical validation, it was determined the SomnoMetry was substantially equivalent to the predicate device.

Regulation Number and Section

§ 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).