Key result
An ultra-low power analog ECG processor based on stochastic resonance achieved an average F1 score of 99.88% for QRS complex detection over the MIT-BIH Arrhythmia database.
The proposed ultra-low power analog ECG processor achieves high QRS detection accuracy (F1=99.88%) while consuming only 2.2 nW, demonstrating significant potential for energy-efficient real-time cardiac monitoring.
Enables ultra-low-power analog ECG designs; leaves open prospective clinical validation before practice adoption.
This paper presents an ultra-low power electrocardiogram (ECG) processor that can detect QRS-waves in real time as the data streams in. The processor performs out-of-band noise suppression via a linear filter, and in-band noise suppression via a nonlinear filter. The nonlinear filter also enhances the QRS-waves by facilitating stochastic resonance. The processor identifies the QRS-waves on noise-suppressed and enhanced recordings using a constant threshold detector. For energy-efficiency and compactness, the processor exploits current-mode analog signal processing techniques, which significantly reduces the design complexity when implementing the second-order dynamics of the nonlinear filter. The processor is designed and implemented in TSMC 65 nm CMOS technology. In terms of detection performance, the processor achieves an average <tex-math notation="LaTeX">F1 = 99.88%</tex-math> over the MIT-BIH Arrhythmia database and outperforms all previous ultra-low power ECG processors. The processor is the first that is validated against noisy ECG recordings of MIT-BIH NST and TELE databases, where it achieves better detection performances than most digital algorithms run on digital platforms. The design has a footprint of 0.08 mm 2 and dissipates 2.2 nW when supplied by a single 1V supply, making it the first ultra-low power and real-time processor that facilitates stochastic resonance.
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Güngör et al. (2023) studied Arrhythmia (ECG QRS detection). Ultra-low power analog ECG processor based on stochastic resonance vs. Previous ultra-low power ECG processors and digital algorithms was evaluated on QRS complex detection sensitivity (F1 score). An ultra-low power analog ECG processor based on stochastic resonance achieved an average F1 score of 99.88% for QRS complex detection over the MIT-BIH Arrhythmia database.
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