# Feasibility testing of a novel AI-enabled, cloud-based ECG diagnostic solution to enable fast and affordable diagnosis in long-term continuous ambulatory ECG monitoring

> **NIH NIH R41** · ZBEATS, INC. · 2022 · $259,613

## Abstract

PROJECT SUMMARY. The proposed observational study is to evaluate the feasibility of a novel ECG monitoring
system leveraging concurrent AI and cloud technologies in long-term continuous monitoring (LTCM) in the
clinical environment. It does not intend to use any data or information from the investigational solution to interfere,
intervene or affect any clinical decisions made for the participants. Among nearly 2M per year syncope or
TIA/stroke patients, 12-15% are cardiac-arrhythmia associated, which usually carries higher risk for long-term
disability and even mortality than other-etiologies patients. Proper risk stratification and early initiation of
appropriate preventative treatment can result in significant reduction of the cardiac related diseases and their
associated mortality. Although LTCM has been proven to be able to detect arrhythmia with high diagnostic yield,
the current standard of care has major market pains: 1) days-to-weeks of delay to deliver final report for offline
extended Holter; 2) low accuracy in stream arrhythmia detection for online Mobile Cardiac Telemetry; and 3)
physicians do not have access to patients’ ECG data. ZBeats’ solution is aiming to improve today’s standard of
care by addressing technology accessibility and affordability. ZBPro™, ZBeats’ alpha prototype was validated
against our proprietary dataset as well as public datasets required in ANSI/AAMI EC57, demonstrating
algorithms, data transmission and visualization work well as expected. In this Phase I study, the feasibility will
be tested in the clinical environment by completing the following specific aims (SA): SA1: setup data collection
systems and provide training to clinical personnel prior to recruitment. SA2: Conduct patients’ acceptability
evaluation by enrolling 60-75 patients to wear the device for up to 7 days. SA3: Evaluate the arrhythmia-capturing
capability by conducting physician’s satisfaction questionnaires after reviewing the reports generated from the
study system. SA4: Conduct data analysis and start designing the protocol for Phase II study. This proposal will
undergo collaboration among ZBeats, Stony Brook University Hospital and Lankenau Medical Center. The long-
term goal is to dramatically improve the current standard of care in LTCM by reducing the time to detection of
life-threatening arrhythmia from weeks to minutes for cardiac-related high-risk patients, increase the streaming
detection accuracy and reducing the total costs by leveraging AI algorithms, cloud infrastructure and a low-cost
flexible-material patch. This cost reduction will lead to more general medical use cases, such as telehealth &
Remote Patient Monitoring (RPM) to benefit broader population.

## Key facts

- **NIH application ID:** 10545691
- **Project number:** 1R41HL160317-01A1
- **Recipient organization:** ZBEATS, INC.
- **Principal Investigator:** Bin Fang
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $259,613
- **Award type:** 1
- **Project period:** 2022-09-12 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10545691

## Citation

> US National Institutes of Health, RePORTER application 10545691, Feasibility testing of a novel AI-enabled, cloud-based ECG diagnostic solution to enable fast and affordable diagnosis in long-term continuous ambulatory ECG monitoring (1R41HL160317-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10545691. Licensed CC0.

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