A 410-nW efficient QRS processor for mobile ECG monitoring in 0.18-μm CMOS

This paper proposes a low power and efficient QRS processor for real-time and continuous mobile ECG monitoring. The QRS detection algorithm is based on the harr wavelet transform. In order to reduce power consumption, an optimized FIR filter structure is proposed. To improve accuracy, R position modification (RPM) has been designed. Fabricated with the 0.18-μm N-well CMOS 1P6M technology, power consumption of this chip is only 410 nW in 1 V voltage supply, which is much lower than that of previous work. Validated by all 48 databases in the MIT-BIH arrhythmia database, sensitivity (Se) and positive prediction (Pr) are 99.60% and 99.77% respectively.

[1]  Amit Acharyya,et al.  A Low-Complexity ECG Feature Extraction Algorithm for Mobile Healthcare Applications , 2013, IEEE Journal of Biomedical and Health Informatics.

[2]  S. Cerutti,et al.  ECG fiducial points detection through wavelet transform , 1995, Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society.

[3]  Jiyang Gao,et al.  Design of a computer-aided visual system for Total Hip Replacement surgery , 2015, 2015 IEEE International Symposium on Circuits and Systems (ISCAS).

[4]  Hanjun Jiang,et al.  CMOS wireless stress sensor IC with 256-cell sensing array for ultra-thin applications , 2016 .

[5]  S.N. Tandon,et al.  Using wavelet transforms for ECG characterization. An on-line digital signal processing system , 1997, IEEE Engineering in Medicine and Biology Magazine.

[6]  S. Mallat.  A wavelet tour of signal processing , 1998 .

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

[8]  R. Orglmeister,et al.  The principles of software QRS detection , 2002, IEEE Engineering in Medicine and Biology Magazine.

[9]  Feng Wan,et al.  A 0.83-$\mu {\rm W}$ QRS Detection Processor Using Quadratic Spline Wavelet Transform for Wireless ECG Acquisition in 0.35- $\mu{\rm m}$ CMOS , 2012, IEEE Transactions on Biomedical Circuits and Systems.

[10]  A. Josko.  Discrete Wavelet Transform In Automatic ECG Signal Analysis , 2007, 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007.

[11]  Jérôme Boudy,et al.  Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation , 2007, EURASIP J. Adv. Signal Process..

[12]  P. Stein,et al.  Heart Rate Variability: Measurement and Clinical Utility , 2005, Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc.

[13]  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.

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

[15]  Yuanjin Zheng,et al.  A real-time ECG QRS detection ASIC based on wavelet multiscale analysis , 2009, 2009 IEEE Asian Solid-State Circuits Conference.

[16]  A. Cohen,et al.  Wavelets: the mathematical background , 1996, Proc. IEEE.

[17]  T. Olmez,et al.  ECG waveform classification using the neural network and wavelet transform , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.

[18]  Rui Paulo Martins,et al.  A 0.83-µW QRS Detection Processor Using Quadratic Spline Wavelet Transform for Wireless ECG Acquisition in 0.35-µm CMOS , 2012, IEEE Trans. Biomed. Circuits Syst..

[19]  Willis J. Tompkins,et al.  Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database , 1986, IEEE Transactions on Biomedical Engineering.

[20]  Ming Liu,et al.  Novel wavelet neural network algorithm for continuous and noninvasive dynamic estimation of blood pressure from photoplethysmography , 2016, Science China Information Sciences.

[21]  Hanjun Jiang,et al.  A 410-nW Efficient QRS Processor for Mobile ECG Monitoring in 0.18-μm CMOS , 2017, IEEE Transactions on Biomedical Circuits and Systems.

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

[23]  Ying Zhang,et al.  Atrial fibrillatory signal estimation using blind source extraction algorithm based on high-order statistics , 2008, Science in China Series F: Information Sciences.

[24]  Reza Lotfi,et al.  A Level-Crossing Based QRS-Detection Algorithm for Wearable ECG Sensors , 2014, IEEE Journal of Biomedical and Health Informatics.

[25]  W.J. Tompkins,et al.  ECG beat detection using filter banks , 1999, IEEE Transactions on Biomedical Engineering.

[26]  Ming Liu,et al.  A low-complexity ECG processing algorithm based on the Haar wavelet transform for portable health-care devices , 2014, Science China Information Sciences.

[27]  S.M. Szilagyi,et al.  Wavelet transform and neural-network-based adaptive filtering for QRS detection , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[28]  Jiyang Gao,et al.  A Visual-Aided Wireless Monitoring System Design for Total Hip Replacement Surgery , 2015, IEEE Transactions on Biomedical Circuits and Systems.

[29]  Hu Xiaohui,et al.  A flexible capacitive tactile sensor array with micro structure for robotic application , 2014 .