Why the study?
Previous ECG monitoring products were limited by common signal loss, ergonomics, flexibility, and battery life.
A newly designed ultra-low-power ECG patch and cloud-based AI platform demonstrated high accuracy (up to 98.7%) for arrhythmia detection on a test dataset.
Demonstrates proof-of-concept for ultra-low-power, cloud-based ECG monitoring.
This paper reports a new device for electrocardiogram (ECG) signal monitoring and software for signal analysis and artificial intelligence (AI) assisted diagnosis. The hardware mitigates the signal loss common in previous products by enhancing the ergonomy, flexibility, and battery life. The power efficiency is optimized by design using switching converters, ultra-low-power components, and efficient signal processing. It enables 14-day of uninterrupted ECG monitoring and connectivity with a smartphone and microSD card storage. The software is implemented in Android app and web-based platforms via Internet of Things (IoT). This component provides cloud-based and local storage and uses AI for arrhythmia detection. The arrhythmia detection algorithm shows 98.7% accuracy using Artificial Neural Network and K-Nearest Neighbors methods, and 98.1% using Decision Tree method on test data set.
No takes yet. Share an insight, caveat, or question.
Bardia Baraeinejad (2021) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: