Normally Off ECG SoC With Non-Volatile MCU and Noise Tolerant Heartbeat Detector

This paper describes an electrocardiograph (ECG) monitoring SoC using a non-volatile MCU (NVMCU) and a noise-tolerant instantaneous heartbeat detector. The novelty of this work is the combination of the non-volatile MCU for normally off computing and a noise-tolerant-QRS (heartbeat) detector to achieve both low-power and noise tolerance. To minimize the stand-by current of MCU, a non-volatile flip-flop and a 6T-4C NVRAM are used. Proposed plate-line charge-share and bit-line non-precharge techniques also contribute to mitigate the active power overhead of 6T-4C NVRAM. The proposed accurate heartbeat detector uses coarse-fine autocorrelation and a template matching technique. Accurate heartbeat detection also contributes system-level power reduction because the active ratio of ADC and digital block can be reduced using heartbeat prediction. Measurement results show that the fully integrated ECG-SoC consumes 6.14 μA including 1.28- μA non-volatile MCU and 0.7- μA heartbeat detector.

[1]  M Hogaki,et al.  An adaptive correlation ratemeter: a new method for Doppler fetal heart rate measurements. , 1978, Ultrasonics.

[2]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[3]  C. Li,et al.  Detection of ECG characteristic points using wavelet transforms. , 1995, IEEE transactions on bio-medical engineering.

[4]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[5]  Shoichi Masui,et al.  Design and applications of ferroelectric nonvolatile SRAM and flip-flop with unlimited read/program cycles and stable recall , 2003, Proceedings of the IEEE 2003 Custom Integrated Circuits Conference, 2003..

[6]  Pablo Laguna,et al.  A wavelet-based ECG delineator: evaluation on standard databases , 2004, IEEE Transactions on Biomedical Engineering.

[7]  Paul S. Addison,et al.  Continuous Wavelet Transform Modulus Maxima Analysis of the Electrocardiogram: Beat Characterisation and Beat-to-Beat Measurement , 2005, Int. J. Wavelets Multiresolution Inf. Process..

[8]  H.L. Chan,et al.  Heartbeat Detection Using Energy Thresholding and Template Match , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[9]  Julien Penders,et al.  Robust beat detector for ambulatory cardiac monitoring , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  J. Huisken,et al.  Low-power robust beat detection in ambulatory cardiac monitoring , 2009, 2009 IEEE Biomedical Circuits and Systems Conference.

[11]  Refet Firat Yazicioglu,et al.  A low power ECG signal processor for ambulatory arrhythmia monitoring system , 2010, 2010 Symposium on VLSI Circuits.

[12]  Refet Firat Yazicioglu,et al.  ECG Signal Compression and Classification Algorithm With Quad Level Vector for ECG Holter System , 2010, IEEE Transactions on Information Technology in Biomedicine.

[13]  Masatoshi Sekine,et al.  Non-contact heart rate detection using periodic variation in Doppler frequency , 2011, 2011 IEEE Sensors Applications Symposium.

[14]  Yutaka Hata,et al.  Systems Health care , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[15]  Ray-Jade Chen,et al.  A micropower biomedical signal processor for mobile healthcare applications , 2011, IEEE Asian Solid-State Circuits Conference 2011.

[16]  Ray-Jade Chen,et al.  A sub-100µW multi-functional cardiac signal processor for mobile healthcare applications , 2012, 2012 Symposium on VLSI Circuits (VLSIC).

[17]  Shintaro Izumi,et al.  Instantaneous Heart Rate detection using short-time autocorrelation for wearable healthcare systems , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  Refet Firat Yazicioglu,et al.  A 20µW intra-cardiac signal-processing IC with 82dB bio-impedance measurement dynamic range and analog feature extraction for ventricular fibrillation detection , 2013, 2013 IEEE International Solid-State Circuits Conference Digest of Technical Papers.

[19]  Ray-Jade Chen,et al.  A 48.6-to-105.2µW machine-learning assisted cardiac sensor SoC for mobile healthcare monitoring , 2013, 2013 Symposium on VLSI Circuits.

[20]  Chacko John Deepu,et al.  An ECG-SoC with 535nW/channel lossless data compression for wearable sensors , 2013, 2013 IEEE Asian Solid-State Circuits Conference (A-SSCC).

[21]  Hiroshi Nakajima,et al.  A 6.14µA normally-off ECG-SoC with noise tolerant heart rate extractor for wearable healthcare systems , 2014, 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings.

[22]  Refet Firat Yazicioglu,et al.  A Configurable and Low-Power Mixed Signal SoC for Portable ECG Monitoring Applications , 2011, IEEE Transactions on Biomedical Circuits and Systems.

[23]  Brian P. Otis,et al.  A single-chip encrypted wireless 12-lead ECG smart shirt for continuous health monitoring , 2014, 2014 Symposium on VLSI Circuits Digest of Technical Papers.

[24]  Shintaro Izumi,et al.  Noise tolerant QRS detection using template matching with short-term autocorrelation , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[25]  Yoshinobu Ichida,et al.  Highly Reliable Non-volatile Logic Circuit Technology and Its Application , 2014 .

[26]  Hiroshi Nakajima,et al.  A Wearable Healthcare System With a 13.7 $\mu$ A Noise Tolerant ECG Processor , 2015, IEEE Transactions on Biomedical Circuits and Systems.

[27]  Yoshimoto Masahiko,et al.  A 2.4 pJ Ferroelectric-Based Non-Volatile Flip-Flop with 10-Year Data Retention Capability , 2015 .