Ultra-Low Power CAN Detection and VA Prediction

In this chapter, an ECG processor on-chip for full ECG feature extraction and cardiac autonomic neuropathy (CAN) is presented. Absolute value curve length transform (ACLT) is performed for QRS detection, whereas full feature extraction (detecting QRSon, QRSoff, P-, and T-waves) is achieved by low-pass differentiation. Proposed QRS detector attains a sensitivity of 99.37% and predictivity of 99.38%. Extracted RR interval along with QT interval enables CAN severity detector. CAN is cardiac arrhythmia usually seen in diabetic patients and have prevalent effect in sudden cardiac death. In this chapter, the first hardware real-time implementation of the CAN severity detector is proposed. Detection is based on RR variability and QT variability analysis. RR variability metrics are based on mean RR interval and RMSSD of RR interval. The proposed architecture is implemented in 65 nm technology, and it consumes only 75 nW at 0.6 V, when operating at 250 Hz. Ultra-low power dissipation of the system enables it to be integrated into wearable healthcare devices.

[1]  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.

[2]  Mohammed Ismail,et al.  Ultra-Low Power, Secure IoT Platform for Predicting Cardiovascular Diseases , 2017, IEEE Transactions on Circuits and Systems I: Regular Papers.

[3]  P. Caminal,et al.  Automatic wave onset and offset determination in ECG signals: Validation with the CSE database , 1992, Proceedings Computers in Cardiology.

[4]  Mohammed Ismail,et al.  Low-Power ECG-Based Processor for Predicting Ventricular Arrhythmia , 2016, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[5]  Herbert F. Jelinek,et al.  Multiscale Renyi Entropy and Cardiac Autonomic Neuropathy , 2015, 2015 20th International Conference on Control Systems and Computer Science.

[6]  Herbert F. Jelinek,et al.  An innovative Multi-disciplinary Diabetes Complications Screening Program in a Rural Community: A Description and Preliminary Results of the Screening , 2006 .

[7]  Marimuthu Palaniswami,et al.  Heart rate independent QT variability component can detect subclinical cardiac autonomic neuropathy in diabetes , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[8]  Herbert F. Jelinek,et al.  Evaluation of normalised Renyi entropy for classification of cardiac autonomic neuropathy , 2014, 2014 8th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO).

[9]  Shuenn-Yuh Lee,et al.  Low-Power Wireless ECG Acquisition and Classification System for Body Sensor Networks , 2015, IEEE Journal of Biomedical and Health Informatics.

[10]  David Atienza,et al.  A Modular Low-Complexity ECG Delineation Algorithm for Real-Time Embedded Systems , 2018, IEEE Journal of Biomedical and Health Informatics.

[11]  Ming Liu,et al.  A 410-nW efficient QRS processor for mobile ECG monitoring in 0.18-μm CMOS , 2016, 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[12]  V. Spallone,et al.  Diagnosis of Cardiovascular Autonomic Neuropathy in Diabetes , 1997, Diabetes.

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

[14]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[15]  R. H. Mitchell,et al.  Power spectral analysis of the electrocardiogram in diabetic children , 1992, Diabetologia.

[16]  Soo-Won Kim,et al.  Design of Wavelet-Based ECG Detector for Implantable Cardiac Pacemakers , 2013, IEEE Transactions on Biomedical Circuits and Systems.

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

[18]  H. Calkins,et al.  Beat-to-beat QT interval variability: novel evidence for repolarization lability in ischemic and nonischemic dilated cardiomyopathy. , 1997, Circulation.

[19]  Marimuthu Palaniswami,et al.  QT Variability Index Changes With Severity of Cardiovascular Autonomic Neuropathy , 2012, IEEE Transactions on Information Technology in Biomedicine.

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

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

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

[23]  D. Ewing,et al.  Diagnosis and management of diabetic autonomic neuropathy. , 1982, British medical journal.

[24]  Jemal H. Abawajy,et al.  Enhancing Predictive Accuracy of Cardiac Autonomic Neuropathy Using Blood Biochemistry Features and Iterative Multitier Ensembles , 2016, IEEE Journal of Biomedical and Health Informatics.

[25]  Jun Zhou,et al.  A 457 nW Near-Threshold Cognitive Multi-Functional ECG Processor for Long-Term Cardiac Monitoring , 2014, IEEE Journal of Solid-State Circuits.

[26]  Herbert F. Jelinek,et al.  Principal component analysis of heart rate variability data in assessing cardiac autonomic neuropathy , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[27]  Marimuthu Palaniswami,et al.  Association of cardiac autonomic neuropathy with alteration of sympatho-vagal balance through heart rate variability analysis. , 2010, Medical engineering & physics.

[28]  William P. Marnane,et al.  Novel Real-Time Low-Complexity QRS Complex Detector Based on Adaptive Thresholding , 2015, IEEE Sensors Journal.

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

[30]  M. P. Tarvainen,et al.  Renyi entropy in identification of cardiac autonomic neuropathy in diabetes , 2012, 2012 Computing in Cardiology.

[31]  D. Ewing,et al.  The Value of Cardiovascular Autonomic Function Tests: 10 Years Experience in Diabetes , 1985, Diabetes Care.

[32]  David Blaauw,et al.  An Injectable 64 nW ECG Mixed-Signal SoC in 65 nm for Arrhythmia Monitoring , 2015, IEEE Journal of Solid-State Circuits.

[33]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[34]  R. Freeman,et al.  Diabetic autonomic neuropathy. , 2003, Diabetes care.

[35]  Mohammed Ismail,et al.  A Nano-Watt ECG Feature Extraction Engine in 65-nm Technology , 2018, IEEE Transactions on Circuits and Systems II: Express Briefs.