The Eko Analysis Software is intended to provide support to the evaluation of patients ' heart sounds and ECG's. The software analyzes simultaneous ECG and heart sounds. The software will detect the presence of suspected murmurs in the heart sounds. The software also detects the presence of atrial fibrillation and normal sinus rhythm from the ECG signal. In addition, it calculates certain cardiac time intervals such as heart rate, QRS duration and EMAT. The software does not distinguish between different kinds of murmurs and does not identify other arrhythmias.

It is not intended as a sole means of diagnosis. The interpretations of heart sounds and ECG offered by the software are only significant when used in conjunction with physician over-read and is for use on adults (> 18 years).

Device Description

The Eko Analysis Software is a cloud-based software API that allows a user to upload synchronized ECG and heart sound/phonocardiogram (PCG) data for analysis. The software uses several methods to interpret the acquired signals including signal processing and artificial neural networks. The API can be electronically interfaced, and perform analysis with data transferred from multiple mobile or computer based applications.

The EAS software is only intended to be used in conjunction with data acquired using two previously-cleared physiological data acquisition devices (Eko DUO (K170874) and Eko CORE (K151319)). The software is designed to be used with companion mobile apps that are used during data acquisition. After analysis, results are returned through an interface to the mobile apps for display.

The algorithm consists of the following components:

Rhythm detection algorithm: A neural network model that uses ECG to detect normal sinus rhythm and atrial fibrillation.
Murmur detection algorithm: A neural network model that uses heart sounds to detect the presence of murmurs.
Heart rate analysis algorithm: A signal processing algorithm that uses ECG or heart sounds as appropriate to calculate heart rate. It also provides an alert if the measured heart rate is indicative of Bradycardia or Tachycardia.
QRS duration algorithm: A signal processing algorithm that measures the width of the QRS pulse on a single-channel ECG.
EMAT Interval algorithm: A signal processing algorithm that uses Q peak detection and S1 envelope detection to measure the Q-S1 interval, defined as electromechanical activation time or EMAT.

AI/ML Overview

Here's an analysis of the acceptance criteria and study details for the Eko Analysis Software, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The FDA 510(k) summary does not explicitly state pre-defined "acceptance criteria" in terms of specific thresholds for sensitivity, specificity, or error rates that the device had to meet for clearance. However, it presents the performance results of the device's algorithms, implying that these results were considered acceptable for demonstrating substantial equivalence.

Feature	Performance Metric(s)	Reported Device Performance	Implicit Acceptance (Interpretation)
Rhythm Detection	Sensitivity (Normal/AFib)	100% (95% CI: 93.8 - 100.0)	Excellent sensitivity for detected rhythms.
	Specificity (Normal/AFib)	96.2% (95% CI: 93.8 - 97.7)	Very good specificity for detected rhythms, showing low false positives among classified ECGs.
	% Classifiable ECG Recordings	74.3% (544/732)	A significant portion of recordings are classifiable, indicating functional utility.
Murmur Detection	Sensitivity	87.6% (95% CI: 84.2 – 90.5)	Good sensitivity for detecting murmurs.
	Specificity	87.8% (95% CI: 85.3 – 89.9)	Good specificity for murmur detection, showing a balance between true positives and true negatives.
Heart Rate Calculation	Heart Rate Error (MIT-BIH dataset)	1.14% (95% CI: 0.95 - 1.34)	Very low error rate for heart rate calculation.
	Bradycardia Detection Sensitivity	94.7% (95% CI: 89.8 - 97.3)	High sensitivity for identifying bradycardia.
	Bradycardia Detection Specificity	99.7% (95% CI: 99.4 - 99.8)	Excellent specificity for identifying bradycardia, suggesting very few false alarms.
	Tachycardia Detection Sensitivity	93.6% (95% CI: 90.9 - 95.6)	High sensitivity for identifying tachycardia.
	Tachycardia Detection Specificity	99.0% (95% CI: 98.7 - 99.3)	Excellent specificity for identifying tachycardia.
QRS Duration Calculation	Absolute Mean Error (ms)	9.25 (95% CI: 7.93 - 10.58)	The absolute mean error is quantified, providing a measure of accuracy. The acceptability implicitly relies on clinical relevance.
EMAT Calculation	Absolute Error (Physionet 2016 dataset)	1.68% (95% CI: 1.06 - 2.30)	The absolute error is quantified, providing a measure of accuracy. The acceptability implicitly relies on clinical relevance.

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

Rhythm Detection Test Set:
- Proprietary EKO ECG dataset: 732 ECG recordings from 139 patients.
  - Provenance: Retrospective. Collected using Eko DUO (732 recordings from 139 patients) and Eko CORE (1445 recordings from 236 patients) devices. Geographic origin not explicitly stated, but proprietary datasets from "individual volunteers" suggest it could be a local or multi-center collection by Eko Devices Inc.
- Publicly available databases: MIT-BIH Arrhythmia Database, MIT-BIH Arrhythmia Noise Stress Database, AHA Database, NST Database, Physionet QT Database, PhysioNet 2016 Database.
  - Provenance: Retrospective. International, well-established public reference datasets.
Murmur Detection Test Set:
- Eko Heart Sound Database: Data collected using both Eko CORE and Eko DUO devices.
  - Provenance: Retrospective. Similar to the EKO ECG dataset, proprietary data collected from "individual volunteers." The combined total number of patients/recordings from both devices is 139 + 236 = 375 patients and 732 + 1445 = 2177 recordings for the proprietary dataset. It's unclear if the "Eko Heart Sound Database" is precisely the same as the "EKO ECG dataset" or a subset/superset, but the description points to the same underlying proprietary data collection.
Heart Rate Calculation Test Set:
- Publicly available datasets: Same as Rhythm Detection (MIT-BIH, etc.).
- Proprietary EKO ECG dataset: Same as Rhythm Detection (732 ECG recordings from 139 patients).
QRS Duration Calculation Test Set:
- Publicly available PhysioNet QT database.
EMAT Calculation Test Set:
- Publicly available Physionet 2016 database.
- Proprietary Eko ECG dataset: Same as Rhythm Detection (732 ECG recordings from 139 patients).

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

The document does not explicitly state the number of experts or their specific qualifications (e.g., "radiologist with 10 years of experience") used to establish the ground truth for the test sets.

For publicly available datasets (like MIT-BIH, PhysioNet), the ground truth is typically established by multiple cardiologists or electrophysiologists based on extensive review and annotation, often with published consensus guidelines. The qualifications of these annotators are generally high, representing expert cardiac clinicians/researchers.
For the proprietary Eko datasets, the document does not specify how the ground truth was established, who established it, or their qualifications. It mentions "retrospective analysis," which usually implies that an expert (or panel of experts) reviewed the recordings and clinical data to determine the presence of conditions (e.g., AFib, murmur) for ground truth labeling.

4. Adjudication Method for the Test Set

The document does not describe a specific adjudication method (e.g., 2+1, 3+1) for establishing the ground truth for any of the test sets, either for public or proprietary data. For public databases, consensus annotations are the typical method. For proprietary data, this information is not provided.

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

No mention of a Multi-Reader Multi-Case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance is present in the document. The study focuses purely on the standalone performance of the algorithms.

6. Standalone (Algorithm Only) Performance Study

Yes, the document exclusively describes standalone performance studies. It reports the performance metrics (sensitivity, specificity, error rates) of the Eko Analysis Software's algorithms directly on the test datasets, independent of human interaction or a human-in-the-loop workflow.

7. Type of Ground Truth Used

The ground truth used appears to be expert consensus or expert-annotated data.

For publicly available databases, ground truth is typically derived from expert annotations and established clinical criteria.
For proprietary datasets, the nature of the metrics (e.g., sensitivity/specificity for rhythm and murmur detection) strongly implies that a human expert (or panel) reviewed the ECG and heart sound recordings to classify them as having or not having AFib, normal sinus rhythm, or a murmur, which served as the reference standard.

8. Sample Size for the Training Set

The document does not provide a specific sample size for the training set. It mentions that the algorithms use "artificial neural networks" and that testing was carried out using "retrospective analysis on a combination of publicly available (...) and proprietary datasets." While these datasets were used for validation, the size and composition of the training datasets are not described.

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

The document does not describe how the ground truth for the training set was established. Given that neural networks were used, the training data would also have required labeled ground truth. It is reasonable to infer that if external/public datasets were used for validation, they likely also formed part of or informed the training process, and proprietary data collected by Eko would also have been used for training, with ground truth established similarly to the validation data (i.e., expert review/consensus, though not explicitly stated for training).

Summary

{0}------------------------------------------------

Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left, there is a symbol representing the Department of Health & Human Services - USA. To the right of this symbol, there is a blue square with the letters "FDA" in white. Next to the blue square, the words "U.S. FOOD & DRUG ADMINISTRATION" are written in blue.

January 15, 2020

Eko Devices Inc % Yarmela Pavlovic Partner Manatt, Phelps & Phillips, LLP One Embarcadero Center, 30th Floor San Francisco, California 94111

Re: K192004

Trade/Device Name: Eko Analysis Software Regulation Number: 21 CFR 870.2300 Regulation Name: Cardiac Monitor (Including Cardiotachometer And Rate Alarm) Regulatory Class: Class II Product Code: MWI, DOD, DPS Dated: December 18, 2019 Received: December 18, 2019

Dear Yarmela Pavlovic:

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

{1}------------------------------------------------

Page 2

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 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 (QS) 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 medical devices and radiation-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,

Stephen Browning Assistant Director Division of Cardiac Electrophysiology, Diagnostics and Monitoring Devices Office of Cardiovascular Devices Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

{2}------------------------------------------------

510(k) Number (if known) K192004

Device Name

Eko Analysis Software (EAS) Indications for Use (Describe)

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

冈 Prescription Use (Part 21 CFR 801 Subpart D)

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

CONTINUE ON A SEPARATE PAGE IF NEEDED.

This section applies only to requirements of the Paperwork Reduction Act of 1995.

DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.

The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:

Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.qov

"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."

{3}------------------------------------------------

510(k) SUMMARY

Eko Devices, Inc.'s Eko Analysis Software

Submitter

Eko Devices Inc. 2600 10th Street, Suite #260, Berkeley, CA - 94710

Contact Person: Subramaniam Venkatraman, CTO Phone: 844-356-3384 Email: contact@ekohealth.com

Date Prepared: January 8, 2020

Name of Device: Eko Analysis Software (EAS) Common or Usual Name: CardiacAl

Classification Name: Cardiac monitor Regulatory Class: Class II Product Code: MWI, DQD, DPS

Predicate Devices

Dictum Health Inc., IDM100 (K170798) Diacoustic Medical Devices (Pty) Ltd, SensiCardiac Mobi (K131044) Inovise Medical. AUDICOR 200 (K073545) physIQ Inc, physIQ Heart Rhythm Module (K180234)

Device Description

The algorithm consists of the following components:

Rhythm detection algorithm: A neural network model that uses ECG to detect normal sinus rhythm and atrial fibrillation.
Murmur detection algorithm: A neural network model that uses heart sounds to detect the ● presence of murmurs.
Heart rate analysis algorithm: A signal processing algorithm that uses ECG or heart ● sounds as appropriate to calculate heart rate. It also provides an alert if the measured heart rate is indicative of Bradycardia or Tachycardia.

{4}------------------------------------------------

QRS duration algorithm: A signal processing algorithm that measures the width of the ● QRS pulse on a single-channel ECG.
EMAT Interval algorithm: A signal processing algorithm that uses Q peak detection and ● S1 envelope detection to measure the Q-S1 interval, defined as electromechanical activation time or EMAT.

Intended Use / Indications for Use

The Eko Analysis Software is intended to provide support to the physician in the evaluation of patients' heart sounds and ECG's. The software analyzes simultaneous ECG and heart sounds. The software will detect the presence of suspected murmurs in the heart sounds. The software also detects the presence of atrial fibrillation and normal sinus rhythm from the ECG signal. In addition, it calculates certain cardiac time intervals such as heart rate. QRS duration and EMAT. The software does not distinguish between different kinds of murmurs and does not identify other arrhythmias.

Summary of Technological Characteristics

EAS combines the features of multiple predicate devices into a single combined software package. The intended use of the subject product (i.e., analysis of physiological data) is the same as that of all of the predicate devices and the indications for use and technological characteristics are either identical or very similar between the subject device and each relevant predicate. Any differences in specific analyzed parameters (indications for use) do not raise different questions of safety or effectiveness in comparison to the predicates. While some of the predicate devices feature additional technological capabilities (e.g., the SensiCardiac predicate additional features differentiation between pathologic and innocent murmur, while the subject device and predicate both differentiate between the presence of murmur and no murmur), this does not raise different questions of safety or effectiveness because in all cases the subject device features are a subset of those cleared for the predicates.

A table comparing the key features of the subject and predicate devices is provided below.

{5}------------------------------------------------

K192004
Page 3 of 5

	Eko AnalysisSoftware	IDM100	Sensi Cardiac MobiDiagnostic HeartMurmurApplication	AUDICOR 200	physIQ Heart RhythmModule (version 1.0)
510KNumber	K182119	K170798	K131044	K073545	K180234
PatientPopulation	Adult patients	Neonate (up to 28days); pediatric (29days to 12 years,excepted asnoted); adolescent(13-17 years); adult(18 years andolder)	Adult and pediatricpatients	Patients over 18 yearsof age	Adult patients
IntendedUser	Physicians	Physicians andpatients	Physicians	Physician	Physician or otherqualified medicalprofessionals
StandardsMet	IEC 60601-1IEC 60601-1-2IEC 60601-2-47IED 60601-2-25ANSI/AAMI EC57	ANSI/AAMI EC53EN/IEC 60601-2-25EN/IEC 60601-2-51	IEC 60601-1IEC 60601-1-2	EN 60601-1EN60601-1-2IEC 60601-2-25IEC 60601-2-51	ANSI/AAMI EC57
DeviceClassification	MWI, DQD, DPS	MWI	DQD, DQC	DPS, DQD, MLO	DPS
Prescribed	Prescription Only	Prescription Only	Prescription Only	Prescription Only	Prescription Only
Components	Software Only	Software +Hardware	Software Only	Software + Hardware	Software Only
Interface	Applicationprogramminginterface (API)				Callable applicationprogramming interface(API)
Display	No primary display	Yes	No primary display	Yes on Audicor-enabled laptop	No primary display
Physiological Inputs	Heart sounds andECG data	Heart sounds, ECGdata, SpO2, NIBP	Heart sounds	Heart sounds and ECGdata	Heart sounds and ECGdata
MurmurDetection	Yes (classification)	No	Yes (classification)	No	No
A-fibdetection	Yes (classification)	No, ECGacquisition only.	No	No	Yes (classification)
EMATCalculation	Yes	No	No	Yes	No
Heart RateCalculation	Yes	Yes	Yes	Yes	Yes
QRSdurationCalculation	Yes	No	No	Yes	Yes

{6}------------------------------------------------

Performance Data - Nonclinical Testing

The Eko Analysis Software was the subject of software verification and validation testing, consistent with the principles outlined in FDA's General Principles of Software Validation; Final Guidance for Industry and FDA Staff.

Performance Data - Clinical Testing

The algorithms in this submission have been validated using retrospective analysis on a combination of publicly available (MIT-BIH Arrhythmia Database, MIT-BIH Arrhythmia Noise Stress Database, AHA Database, NST Database, Physionet QT Database, and PhysioNet 2016 Database) and proprietary datasets captured with the Eko CORE and Eko DUO. In the proprietary datasets, the Eko CORE and Eko DUO were used to capture 15 second long heart sound and ECG recordings from chests of individual volunteers. A total of 732 recording were captured from 139 patients using the Eko DUO and 1445 recordings were captured from 236 patients using the Eko CORE. In the Eko DUO dataset 54.7% of patients were female and all patients were over the age of 18, with the largest percentage being 61 to 80 years of age. Additionally, 79.9% were white, while 9.4% were Asian and the remainder were Black or African American, American Indian or Alaskan Native, Native Hawaiian or Pacific Islander or Hispanic/Latino. In the Eko CORE dataset, 47.9% of patients were female. Additionally, 83.9% were white, 7.6% were black or African American and the remainder were Asian (4.2%), Hispanic or Latino (2.1%) or other/unknown. Patients were all over the age of 18 with the largest percentage being 51 to 80 years of age.

A brief description of the testing provided for each individual analysis algorithm is provided below along with performance results from the most relevant datasets:

Rhythm Detection

Testing was carried out on publicly available databases as well as the EKO ECG dataset. When the device was tested with the EKO ECG dataset, 74.3% (544/732) of ECG recordings were classified as either Normal or Atrial Fibrillation. Sensitivity and specificity measured in the classifiable ECGs were 100% (95% Cl: 93.8 - 100.0) and 96.2% (95% Cl: 93.8 -97.7), respectively.

Murmur Detection

Testing was carried out on the Eko Heart Sound Database comprised of data collected using both the Eko CORE and Eko DUO devices. Sensitivity and specificity in the Eko Heart Sound Database were 87.6% (95% C1: 84.2 – 90.5) and 87.8% (95% C1: 85.3 – 89.9), respectively.

Heart Rate Calculation

Testing was carried out on publicly available datasets as well as the EKO ECG dataset (described above). Bradycardia and Tachycardia detection accuracy was also measured in the publicly available datasets. Heart rate error measured in the MIT-BIH dataset was 1.14% (95% Cl: 0.95 - 1.34). Bradycardia detection had a sensitivity and specificity of 94.7% (95%

{7}------------------------------------------------

Cl: 89.8 - 97.3) and 99.7% (95% Cl: 99.4 - 99.8) respectively. Tachycardia detection had a sensitivity and specificity of 93.6% (95% C1: 90.9 - 95.6) and 99.0% (95% C1: 98.7 - 99.3) respectively.

QRS Duration Calculation

Testing was carried out using the publicly available PhysioNet QT database. Absolute Mean Error (ms) for calculating QRS duration was 9.25 (95% Cl: 7.93 - 10.58).

EMAT Calculation

Testing was carried out on publicly available Physionet 2016 database, as well as the Eko ECG dataset. Absolute Error in the Physionet 2016 dataset was 1.68% (95% Cl: 1.06 -2.30).

Given the data described above, the algorithms performed as expected. Based on the clinical performance the Eko Analysis Software has a safety and effectiveness profile that is similar to the predicate devices.

Conclusions

The Eko Analysis Software is as safe and effective as the predicate devices. The Eko Analysis Software has the same intended uses and similar indications, technological characteristics, and principles of operation as its predicate device. The minor differences in indications do not alter the intended diagnostic use of the device and do not raise different questions of safety and effectiveness when used as labeled. In addition, the minor technological differences between the Eko Analysis Software and its predicate devices raise no new issues of safety or effectiveness. Performance data demonstrate that the Eko Analysis Software is as safe and effective as the predicate devices. Thus, the Eko Analysis Software is substantially equivalent.

Regulation Number and Section

§ 870.2300 Cardiac monitor (including cardiotachometer and rate alarm).

(a)
Identification. A cardiac monitor (including cardiotachometer and rate alarm) is a device used to measure the heart rate from an analog signal produced by an electrocardiograph, vectorcardiograph, or blood pressure monitor. This device may sound an alarm when the heart rate falls outside preset upper and lower limits.(b)
Classification. Class II (performance standards).