Key result
An ultra-low-power ECG patch and cloud-based platform achieved 98.7% accuracy for arrhythmia detection using Artificial Neural Network and K-Nearest Neighbors methods on a test data set.
Why the study?
Previous ECG monitoring products commonly suffered from signal loss, prompting the need for enhanced ergonomy, flexibility, and battery life.
A novel ultra-low-power ECG patch and cloud-based AI platform demonstrated high accuracy (up to 98.7%) for arrhythmia detection on a test dataset.
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.
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Baraeinejad et al. (2021) studied Arrhythmia. Ultra-low-power ECG patch and smart cloud-based platform with AI was evaluated on Arrhythmia detection accuracy. An ultra-low-power ECG patch and cloud-based platform achieved 98.7% accuracy for arrhythmia detection using Artificial Neural Network and K-Nearest Neighbors methods on a test data set.
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