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The SimpleSENSE Platform is intended for use at home, or at a healthcare facility, under the direction of a licensed medical professional, to record, display and store the following physiological data: a) 2 leads of Electrocardiogram; b) Respiration rate measured through thoracic impedance; c) Heart Sounds; d) Activity including posture; and e) other validated data sources. The SimpleSENSE Platform is intended for use when the licensed medical professional decides to evaluate the physiologic signals of adult patients as an aid to diagnosis and treatment. The SimpleSENSE Platform is intended to be used by patients at rest and not performing any activities or movements. ECG recordings are indicated for the manual assessment of cardiac rhythm disturbances. The SimpleSENSE Platform does not produce alarms and is not intended for active patient monitoring (real-time). The SimpleSENSE Platform is not intended for use as life supporting equipment on high-risk patients such as critical care patients. The SimpleSENSE Platform is not intended for use in the presence of a pacemaker.

The Nanowear SimpleSENSE Platform is the next generation diagnostic monitoring technology that captures electrocardiographic (ECG) signals, respiration rate though thoracic impedance, heart sounds, activity including posture and movement with sensors embedded on a wearable textile garment. The signals are stored and wirelessly transmitted to a smartphone, which is then forwarded to a medical professional for review. The garment is designed to be unobtrusive to everyday activity and provide an easier and more efficient means of capturing ECG data from patients. The device consists of a combination of: The SimpleSENSE Garment: an integrated network of nanosensor electrodes for measuring ECG and respiratory rate from thoracic impedance. A MEMS microphone for measuring heart sounds. The SimpleSENSE Signal Acquisition Unit (SAU): data acquisition, storage, and transmission to a phone running iOS or Android. An accelerometer to measure activity. The SimpleSENSE Mobile Application: mobile application to start/stop a recording, logging symptoms, and data transmission. SimpleSENSE Web Server: allows initiation of a test, storage, and review of prescribed test data by a medical professional.

The provided text describes Nanowear Inc.'s K212160 submission for the SimpleSENSE Platform, an upgraded version of their previously cleared SimpleSENSE System (K201669). The submission is a Special 510(k), which means it primarily focuses on changes to an already cleared device, and thus omits much of the original performance data. Therefore, the information regarding acceptance criteria and performance testing is limited to what was specifically re-evaluated or added for the new device.

Here's an analysis based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly provide a table of acceptance criteria and reported device performance in the format requested. As this is a Special 510(k), the focus is on demonstrating that changes made to the device do not negatively affect safety or efficacy. The "Performance Testing" section (Section 11) lists the tests conducted for the new SimpleSENSE Platform.

However, based on the type of tests performed, we can infer general acceptance criteria for the new or modified components.

Note: The document explicitly states that performance data and verification/validation activities for aspects not affected by product changes were omitted from this Special 510(k). These include core functionalities like multiparametric data capture, ECG sensor performance, electrical current requirements for thoracic impedance, MEMS microphone testing, etc. For those, it relies on the predicate device's clearance (K201669).

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

The document does not specify a sample size for the test set used for the software and firmware verification activities. It also does not specify the data provenance (e.g., country of origin, retrospective/prospective) for these tests. This information would typically be found in detailed test reports, which are not included in this summary.

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

The document does not provide any information about the number or qualifications of experts used to establish ground truth for the test set, as the performance testing primarily focuses on software and firmware verification rather than clinical outcomes or diagnostic accuracy requiring expert interpretation.

4. Adjudication method for the test set

The document does not describe any adjudication method for the test set. Given the nature of the tests (software and firmware verification), a formal adjudication process involving multiple experts is unlikely to have been part of these specific engineering tests.

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

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study. The SimpleSENSE Platform's indications for use state that "ECG recordings are indicated for the manual assessment of cardiac rhythm disturbances" and that the device "does not produce alarms and is not intended for active patient monitoring (real-time)." There is no indication that AI or automated interpretation is part of the device's functionality that would necessitate such a study. The device provides raw physiological data for a medical professional to review.

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

The document does not describe any standalone (algorithm-only) performance testing. The device is intended to record and transfer physiological data for medical professionals to review; it does not explicitly claim automated diagnostic algorithms that would undergo standalone performance evaluation.

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

For the software and firmware verification tests, the "ground truth" would be the expected functional behavior of the software and firmware as per design specifications and requirements. This is not medical ground truth (like pathology or expert consensus) but rather engineering ground truth.

8. The sample size for the training set

The document does not mention a training set sample size. Since the described performance testing is for software/firmware verification and not for machine learning model development, a training set as typically understood in AI/ML contexts would not be relevant here.

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

Not applicable, as no training set for a machine learning model is mentioned or implied by the described performance testing.

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