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A 65-nm 0.35-V 7.1-μW memory-less adaptive PCG processor for wearable long-term cardiac monitoring

In this paper, an energy-efficient phonocardiogram (PCG) signal processor is proposed for wearable long-term cardiac monitoring. To achieve high energy efficiency, the proposed PCG processor employs pipelined and memory-less adaptive architecture to implement wavelet-domain statistic based real-time de-noising, heart sound peak detection, false peak filtering, S1/S2 peak identification and cardiac feature extraction. The statistic threshold based memory-less adaptive processing scheme is developed to reduce computation and storage requirement, and therefore energy consumption significantly. At circuit level, sub/near-threshold minimum-energy design technique is adopted to enable processor to operate at the minimum energy point to maximize the energy efficiency. At the optimum operating point of 0.35 V and 2.1 MHz, the fabricated 65-nm PCG processor consumes 7.1 μW and 272 nJ when performing real-time S1/S2 detection of a 20-s heart sound signals with 99.2%/89.9% accuracy.