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Commercially Available Heart Rate Monitor Repurposed for Automatic Arrhythmia Detection with Snapshot
Chiara Martini1, Bernardo Di Maria2, Claudio Reverberi3
1Department of Radiology, Parma University Hospital, Via Gramsci 14, 43125 Parma, Italy.
Insights
This study introduces a new wearable device that automatically detects arrhythmias and records ECGs. The customized system shows high accuracy in simulations, offering a promising tool for cardiac monitoring.
Area of Science:
- Cardiology
- Biomedical Engineering
- Medical Devices
Background:
- Opportunistic arrhythmia screening using ECG has limited diagnostic yield due to the intermittent nature of arrhythmias.
- Previous work validated a Bluetooth heart rate sensor and smartphone app (RITMIA™) for atrial fibrillation detection.
- Limitations in opportunistic screening lead to high rates of missed diagnoses.
Purpose of the Study:
- To test an upgraded cardiac monitoring system with on-sensor algorithm processing and ECG acquisition capabilities.
- To evaluate a customized, ultra-low weight device for standalone automatic arrhythmia detection and ECG recording.
- To assess the feasibility and diagnostic accuracy of the enhanced system in simulated arrhythmia conditions.
Main Methods:
- Reprogrammed a sports heart rate monitor (Movensense HR+) to run the RITMIA algorithm in real-time.
- Utilized RR interval analysis for arrhythmia detection.
- Triggered brief, on-board, single-lead ECG recordings upon arrhythmia detection.
Main Results:
- The customized device successfully detected all simulated types of arrhythmias.
- The system correctly triggered visually interpretable ECG tracings for detected arrhythmias.
- Initial data indicates high diagnostic accuracy and feasibility of the device.
Conclusions:
- The customized device demonstrates capability for automatic arrhythmia detection and ECG recording.
- The system shows promise as an effective tool for cardiac monitoring.
- Further human studies are required to confirm real-life accuracy.
Abstract:
The usefulness of opportunistic arrhythmia screening strategies, using an electrocardiogram (ECG) or other methods for random "snapshot" assessments is limited by the unexpected and occasional nature of arrhythmias, leading to a high rate of missed diagnosis. We have previously validated a cardiac monitoring system for AF detection pairing simple consumer-grade Bluetooth low-energy (BLE) heart rate (HR) sensors with a smartphone application (RITMIA™, Heart Sentinel srl, Italy). In the current study, we test a significant upgrade to the above-mentioned system, thanks to the technical capability of new HR sensors to run algorithms on the sensor itself and to acquire, and store on-board, single-lead ECG strips. We have reprogrammed an HR monitor intended for sports use (Movensense HR+) to run our proprietary RITMIA algorithm code in real-time, based on RR analysis, so that if any type of arrhythmia is detected, it triggers a brief retrospective recording of a single-lead ECG, providing tracings of the specific arrhythmia for later consultation. We report the initial data on the behavior, feasibility, and high diagnostic accuracy of this ultra-low weight customized device for standalone automatic arrhythmia detection and ECG recording, when several types of arrhythmias were simulated under different baseline conditions. Conclusions: The customized device was capable of detecting all types of simulated arrhythmias and correctly triggered a visually interpretable ECG tracing. Future human studies are needed to address real-life accuracy of this device.
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