Key result
Wearable ECG patch detects congestive heart failure with ~99% accuracy using on-chip machine learning.
Why the study?
Remote monitoring systems typically rely on cloud-based data analytics that require continuous ECG transmission and stable internet, increasing latency and wireless energy consumption. This can delay clinical decision-making and timely intervention in time-sensitive cardiovascular disease scenarios.
Does a wearable single-lead ECG patch with an on-chip logistic regression model accurately classify congestive heart failure versus normal sinus rhythm in real time?
Does a wearable single-lead ECG patch with an on-chip logistic regression model accurately classify congestive heart failure versus normal sinus rhythm in real time?
This study demonstrates the feasibility of a cloud-free, single-lead wearable ECG patch using an on-device lightweight machine learning model to classify congestive heart failure and normal sinus rhythm in real time with high accuracy and sub-millisecond inference time.
Background Visual interpretation of ECG signals by experienced cardiologist, has played a central role in the assessment of cardiovascular diseases (CVDs).1, 2 ECG signal’s Morphological contain rich temporal information that can support early pathological changes of heart.3 Congestive heart failure (CHF) is associated with alterations in T-wave and reduced QRS amplitude.3,4 On the other hand, Remote monitoring (RM) is recognised as frontier in healthcare management. However, RM systems rely on cloud-based data analytics, requiring continuous transmission of ECG data5, which increases latency and wireless energy consumption and require stable internet. These may delay clinical decision-making and timely intervention, particularly in time-sensitive CVDs scenairo. Purpose This study aim to develop an intelligent ECG patch with an on-chip signal processing and classification pipeline for real-time classification of CHF and normal sinus rhythm (NSR). Methods Wearable ECG Patch: We designed a fully customisable single-lead wearable ECG patch incorporating a clinical-grade ECG analog front end, and an ESP32 microcontroller. The firmware is developed to support continuous ECG acquisition and analysis. Machine learning (ML) model: We used the publicly available CHF database from PhysioNet, 15 severe CHF patients and a NSR (had no significant arrhythmias) database with 18 subjects to develop light weight AI model. We extracted 3,300 ECG segments of 5 s (1,500 CHF NYHA Class 3–4; 1,800 NSR) and extracted six features. The identified features were split into 80% and 20% for training and testing. Finally, ML model were deployed onto EPS32 controller for classification purpose. Results AI Enable Wearable ECG Patch The developed device can capture ECG signal (P, QRS, and T wave) at 256Hz samples per seconds. The results showed that, out of the six extracted features, Peak-to-Average Power Ratio (PAPR) and Hjorth mobility were the most important features. Further, we found logistic regression as a lightweight model that classifies CHF and NSR with 99.26% accuracy, 99.18% sensitivity, and 99.36% specificity on unseen datasets, achieving a total latency of ~1.11 ms per 5 s segment, with model inference time below 0.6 ms. The trained model was deployed onto the ESP32 microcontroller to enable real-time classification of CHF and NSR. Figure 1 depicts the signal processing and classification pipeline used to determine the outcome and Figure 2 illustrates the ECG deviceholded by a participants and mobile App. Conclusion We have devleoped an intelligent single-lead wearable ECG patch and deployed a trained model on the ESP32 to allow on-device processing and classification of CHF and NSR in real time. The preliminarily finding illustrate the feasibility of low-power, on-device cardiac disease classification and lay the groundwork for the development of continuous, home-based HF management solutions in the future.Signal processing and classification pipelineECG device and smartphone-based app.
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Singh et al. (2026) studied Congestive heart failure (n=33). Cloud-free intelligent single-lead wearable ECG patch with on-chip logistic regression model vs. Normal sinus rhythm vs congestive heart failure reference labels was evaluated on Real-time classification accuracy of congestive heart failure and normal sinus rhythm. An intelligent wearable ECG patch using on-chip logistic regression classified congestive heart failure and normal sinus rhythm with 99.26% accuracy, 99.18% sensitivity, and 99.36% specificity.
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