Compressive Sampling Based Multi-Spectrum Deep Learning for Sub-Nyquist Pacemaker ECG Analysis

Automatic electrocardiogram (ECG) analysis for pacemaker patients is crucial for monitoring cardiac conditions and the effectiveness of cardiac resynchronization treatment. However, under the condition of energy-saving remote monitoring, the low-sampling-rate issue of an ECG device can lead to the miss detection of pacemaker spikes as well as incorrect analysis on paced rhythm and non-paced arrhythmias. To solve the issue, this paper proposed a novel system that applies the compressive sampling (CS) framework to sub-Nyquist acquire and reconstruct ECG, and then uses multi-dimensional feature-based deep learning to identify paced rhythm and non-paced arrhythmias. Simulation testing results on ECG databases and comparison with existing approaches demonstrate its effectiveness and outstanding performance for pacemaker ECG analysis.

[1]  Earl C Herleikson,et al.  A software-based pacemaker pulse detection and paced rhythm classification algorithm. , 2002, Journal of electrocardiology.

[2]  Kuldeep Singh Rajput,et al.  Spectro-Temporal Feature Based Multi-Channel Convolutional Neural Network for ECG Beat Classification , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[3]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[4]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Deanna Needell,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.

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

[7]  U. Rajendra Acharya,et al.  ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform , 2013, Biomed. Signal Process. Control..

[8]  Andrew Y. Ng,et al.  Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks , 2017, ArXiv.

[9]  T. Manigandan,et al.  Analysis and detection R-peak detection using Modified Pan-Tompkins algorithm , 2014, 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies.

[10]  Jiann-Shiun Yuan,et al.  ECG Arrhythmia Classification Using Transfer Learning from 2- Dimensional Deep CNN Features , 2018, 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[11]  Razvan Pascanu,et al.  Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks , 2013, ECML/PKDD.

[12]  R. J. Harper,et al.  The paced electrocardiogram: issues for the emergency physician. , 2001, The American journal of emergency medicine.