A Smart Wearable for Real-Time Cardiac Disease Detection Using Beat-by-Beat ECG Signal Analysis with an Edge Computing AI Classifier

This paper introduces a novel edge-computing wearable device for real-time beat-by-beat electrocardiogram (ECG) classification. Early detection of heart disease can prevent and improve the patient's health and minimize the load on healthcare professionals. The proposed wearable integrates the pre-trained artificial intelligence (AI) model with the device firmware to detect abnormal ECG beats. The wearable contains a custom printed circuit board (PCB) connected to a commercial Bluetooth system on chip (SoC) to process ECG signals. The AI-integrated firmware is programmed into 32 bit ARM® Cortex™ based SoC. Data pre-processing, feature extraction, and inferencing are done on the SoC. The smart wearable is tested by passing the MIT-BIH test dataset through the wearable system as sample real-time data. The accuracy of the proposed device is assessed by testing normal and 4 abnormal ECG beats. The ANN model, after testing, provides an accuracy of 90.6 % with a precision of 95.6% and a recall of 95.1 %. The system consumes low power, a maximum of 128 m W, and offers a low latency of 6 ms for inferencing. The wearable is also tested on 5 subjects. The proposed wearable is smart, low-powered, and suitable for real-time regular cardiac monitoring. The performance indicates the device can effectively detect abnormal heart rhythms.