ECG Monitoring System Integrated With IR-UWB Radar Based on CNN

In the demand for protecting the increasing aged groups from heart attacks, the improvement of the mobile electrocardiogram (ECG) monitoring systems becomes significant. The limitations of the arrhythmia classification in these systems are the lack of ability to cope with motion state and the low accuracy in new users' data. This paper proposes a system which applies the impulse radio ultra wideband radar data as additional information to assist the arrhythmia classification of ECG recordings in the slight motion state. Besides, this proposed system employs a cascade convolutional neural network to achieve an integrated analysis of ECG recordings and radar data. The experiments are implemented in the Caffe platform and the result reaches an accuracy of 88.89% in the slight motion state. It turns out that this proposed system keeps a stable accuracy of classification for normal and abnormal heartbeats in the slight motion state.

[1]  Xia Liu,et al.  Ccdd: an Enhanced Standard ECG Database with its Management and Annotation Tools , 2012, Int. J. Artif. Intell. Tools.

[2]  C.D. Nugent,et al.  Evaluation of electrocardiogram beat detection algorithms: patient specific versus generic training , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  A. Lazaro,et al.  Vital signs monitoring using impulse based UWB signal , 2011, 2011 41st European Microwave Conference.

[4]  Moncef Gabbouj,et al.  Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Biomedical Engineering.

[5]  S.V. Samkov,et al.  Algorithm of signal processing in ultra-wideband radar designed for remote measuring parameters of patient's cardiac activity , 2004, 2004 Second International Workshop Ultrawideband and Ultrashort Impulse Signals (IEEE Cat. No.04EX925).

[6]  Ting Chen,et al.  Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach , 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[7]  Zhanpeng Jin,et al.  Enabling Smart Personalized Healthcare: A Hybrid Mobile-Cloud Approach for ECG Telemonitoring , 2014, IEEE Journal of Biomedical and Health Informatics.

[8]  Ataollah Ebrahimzadeh,et al.  Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features , 2010, Biomed. Signal Process. Control..

[9]  Moncef Gabbouj,et al.  A Generic and Robust System for Automated Patient-Specific Classification of ECG Signals , 2009, IEEE Transactions on Biomedical Engineering.

[10]  David Girbau,et al.  ANALYSIS OF VITAL SIGNS MONITORING USING AN IR-UWB RADAR , 2010 .

[11]  Alireza Ahmadian,et al.  An accurate and robust algorithm for detection of heart and respiration rates using an impulse based UWB signal , 2009, 2009 International Conference on Biomedical and Pharmaceutical Engineering.

[12]  Jonathan Li,et al.  SAR Image Denoising via Clustering-Based Principal Component Analysis , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Yu-Liang Hsu,et al.  ECG arrhythmia classification using a probabilistic neural network with a feature reduction method , 2013, Neurocomputing.

[14]  Jun Dong,et al.  Deep learning research on clinical electrocardiogram analysis , 2015 .

[15]  Xiaojun Cao,et al.  Ubiquitous WSN for Healthcare: Recent Advances and Future Prospects , 2014, IEEE Internet of Things Journal.

[16]  G. Manoharan,et al.  Use of frequency analysis on the ECG for the prognosis of low energy cardioversion treatment of atrial fibrillation , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Bernd Schleicher,et al.  Vital signs monitoring with a UWB radar based on a correlation receiver , 2010, Proceedings of the Fourth European Conference on Antennas and Propagation.

[18]  Reza Ebrahimpour,et al.  Classification of ECG arrhythmia by a modular neural network based on Mixture of Experts and Negatively Correlated Learning , 2013, Biomed. Signal Process. Control..

[19]  Min Chen,et al.  WE-CARE: An Intelligent Mobile Telecardiology System to Enable mHealth Applications , 2014, IEEE Journal of Biomedical and Health Informatics.

[20]  M. P. S. Chawla,et al.  A comparative analysis of principal component and independent component techniques for electrocardiograms , 2009, Neural Computing and Applications.

[21]  Jing Li,et al.  Simulation and signal processing of UWB radar for human detection in complex environment , 2012, 2012 14th International Conference on Ground Penetrating Radar (GPR).

[22]  C. Van Hoof,et al.  Motion artifact removal using cascade adaptive filtering for ambulatory ECG monitoring system , 2012, 2012 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[23]  Philip de Chazal,et al.  A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval Features , 2006, IEEE Transactions on Biomedical Engineering.

[24]  Jing Li,et al.  Advanced Signal Processing for Vital Sign Extraction With Applications in UWB Radar Detection of Trapped Victims in Complex Environments , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  B. Levitas,et al.  Detection and separation of several human beings behind the wall with UWB Radar , 2008, 2008 International Radar Symposium.

[26]  Wei Hu,et al.  Noncontact Accurate Measurement of Cardiopulmonary Activity Using a Compact Quadrature Doppler Radar Sensor , 2014, IEEE Transactions on Biomedical Engineering.

[27]  Marko Helbig,et al.  Simultaneous electrical and mechanical heart activity registration by means of synchronized ECG and M-sequence UWB sensor , 2016, 2016 10th European Conference on Antennas and Propagation (EuCAP).

[28]  Shailja Shukla,et al.  ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifier , 2013 .