A novel analog-to-information converter architecture demonstrates high efficiency (>98%) for QRS detection with low power requirements, showing promise for long-term wearable ECG monitoring.
May support ultra-low-power wearable ECG devices; hypothesis-generating pending hardware validation and clinical testing.
This paper illustrates an architectural design of a novel variable input-feature correlated asynchronous sampling and time-encoded digitization approach for source compression and direct feature extraction from physiological signals. The complete architecture represents an analog-to-information (A2I) converter, designed for ultra-low-power mixed-signal very-large-scale integrated implementation. The device will be suitable for long-term wearable monitoring of physiological signals, such as electrocardiogram (ECG). We show representative case studies on QRS detection in an ECG signal utilizing the proposed A2I converter to prove the functionality of the design. Simulation results show large source compression in the ECG signal and more than 98% efficiency in the detection of the Q, R, and S waves for challenging ECG waveforms, all with extremely low-power and storage requirements.
No takes yet. Share an insight, caveat, or question.
Agarwal et al. (2011) studied this question.
Synapse has enriched 3 closely related papers on similar clinical questions. Consider them for comparative context: