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Article

Resource-Constrained On-Chip AI Classifier for Beat-by-Beat Real-Time Arrhythmia Detection with an ECG Wearable System

Department of Computer Science, Texas Tech University, Lubbock, TX 79409, USA
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Author to whom correspondence should be addressed.
Electronics 2025, 14(13), 2654; https://doi.org/10.3390/electronics14132654
Submission received: 3 June 2025 / Revised: 27 June 2025 / Accepted: 28 June 2025 / Published: 30 June 2025

Abstract

The electrocardiogram (ECG) is one of the vital physiological signals for human health. Lightweight neural network (NN) models integrated into a low-resource wearable device can benefit the user with a low-power, real-time edge computing system for continuous and daily monitoring. This work introduces a novel edge-computing wearable device for real-time beat-by-beat ECG arrhythmia classification. The proposed wearable integrates the light AI model into a 32-bit ARM® Cortex-based custom printed circuit board (PCB). The work analyzes the performance of artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory (LSTM) models for real-time wearable implementation. The wearable is capable of real-time QRS detection and feature extraction from raw ECG data. The QRS detection algorithm offers high reliability with a 99.5% F1 score and R-peak position error (RPE) of 6.3 ms for R-peak-to-R-peak intervals. The proposed method implements a combination of top time series, spectral, and signal-specific features for model development. Lightweight, pretrained models are deployed on the custom wearable and evaluated in real time using mock data from the MIT-BIH dataset. We propose an LSTM model that provides efficient performance over accuracy, inference latency, and memory consumption. The proposed model offers 98.1% accuracy, with 98.2% sensitivity and 99.5% specificity while testing in real time on the wearable. Real-time inferencing takes 20 ms, and the device consumes as low as 5.9 mA of power. The proposed method achieves efficient performance in real-time testing, which indicates the wearable can be effectively used for real-time continuous arrhythmia detection.
Keywords: electrocardiogram; lightweight model; arrhythmia classification; wearable; real time electrocardiogram; lightweight model; arrhythmia classification; wearable; real time

Share and Cite

MDPI and ACS Style

Rahman, M.; Morshed, B.I. Resource-Constrained On-Chip AI Classifier for Beat-by-Beat Real-Time Arrhythmia Detection with an ECG Wearable System. Electronics 2025, 14, 2654. https://doi.org/10.3390/electronics14132654

AMA Style

Rahman M, Morshed BI. Resource-Constrained On-Chip AI Classifier for Beat-by-Beat Real-Time Arrhythmia Detection with an ECG Wearable System. Electronics. 2025; 14(13):2654. https://doi.org/10.3390/electronics14132654

Chicago/Turabian Style

Rahman, Mahfuzur, and Bashir I. Morshed. 2025. "Resource-Constrained On-Chip AI Classifier for Beat-by-Beat Real-Time Arrhythmia Detection with an ECG Wearable System" Electronics 14, no. 13: 2654. https://doi.org/10.3390/electronics14132654

APA Style

Rahman, M., & Morshed, B. I. (2025). Resource-Constrained On-Chip AI Classifier for Beat-by-Beat Real-Time Arrhythmia Detection with an ECG Wearable System. Electronics, 14(13), 2654. https://doi.org/10.3390/electronics14132654

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