Key result
Single-lead ECG wearable sensor achieves ~94% accuracy across cardiac and biometric tasks.
Why the study?
Uploading wearable ECG signals to server-side devices poses privacy risks, while existing multi-tasking ECG processors rely on multi-beat windows and complex models that cause high latency and energy consumption, limiting deployment in resource-constrained wearables.
This single-lead wearable ECG sensor architecture enables low-latency (1.04 s), ultra-low-energy (0.19 µJ/task) on-device arrhythmia detection and biometric authentication with >94% accuracy, mitigating privacy risks of server-side processing.
Answers are grounded in this paper and cite their sources.
May enable privacy-preserving, low-latency QRS detection on wearables; leaves open clinical validation before routine cardiovascular use.
The wearable electrocardiogram (ECG) sensor has immense potential for cardiovascular healthcare monitoring. However, uploading ECG signals to server-side devices for diagnosis poses privacy risks. Multi-tasking ECG processors rely on multi-beat windows and complex models, leading to high latency and energy consumption that limit deployment in resource-constrained wearable devices. In this work, we present a single-lead ECG sensor for authenticated healthcare that performs QRS complex detection (Task 1), arrhythmia detection (Task 2), bio-identification (Task 3), and bio-authentication (Task 4). This sensor uses an invariant spectrum feature extractor on dual-mode single-beat ECG segments to derive normalized biometric features for these tasks, reducing the record latency to a single beat. A binary neural network (BNN) model is used for multi-task decision-making, achieving an accuracy of >94% across all tasks in the MIT-BIH database while reducing the model size to 5.725 kB. The sensor, implemented in 55-nm technology, occupies 0.50 mm2, with the digital processing core (DPC) occupying 0.27 mm2. Operating at a 0.85-V supply and 320-kHz clock, the DPC achieves a task latency of 1.04 s, representing a2×–12×reduction compared to state-of-the-art work, with inference energy of0.19~μJ/task (3.7×–26×reduction).
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Ji et al. (2026) studied Arrhythmia and cardiac biometric monitoring. Single-lead ECG wearable sensor with invariant spectrum feature extractor and binary neural network (BNN) model vs. State-of-the-art multi-tasking ECG processors was evaluated on Classification accuracy across QRS complex detection, arrhythmia detection, bio-identification, and bio-authentication. A single-lead ECG wearable sensor using a binary neural network achieved >94% accuracy across cardiac and biometric tasks with 1.04 s latency and 0.19 µJ/task energy.
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