Key result
An energy-efficient analog electrocardiogram processor achieved an average QRS detection sensitivity of 99.63% and positive predictivity of 99.47% using the MIT-BIH database.
The proposed analog ECG processor achieves high QRS detection sensitivity and positive predictivity with ultra-low power consumption.
An energy-efficient electrocardiogram (ECG) processor for real-time QRS detection is presented. The proposed algorithm is based on the Pan-Tompkins algorithm and it is implemented in the analog domain leveraging ultra-low power analog electronics biased in subthreshold. Operational transconductance amplifiers with ∼100 mV linear range are used in almost all of the processing blocks, while squaring is performed on current signals. Additionally, instead of adaptive thresholding, a fixed-level thresholding is performed, thereby eliminating the need for additional blocks such as memory and threshold update. The processor is designed in 65 nm TSMC CMOS technology and has a footprint of 0.078 mm 2 . When supplied by a 1 V supply, the processor consumes 1.2 nW. Using the recordings in the MIT-BIH database, the processor achieves an average QRS detection sensitivity of 99.63% and positive predictivity of 99.47%.
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Güngör et al. (2021) studied Electrocardiogram QRS detection. Analog Electrocardiogram Processor was evaluated on Average QRS detection sensitivity and positive predictivity. An energy-efficient analog electrocardiogram processor achieved an average QRS detection sensitivity of 99.63% and positive predictivity of 99.47% using the MIT-BIH database.
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