A stacked contractive denoising auto-encoder for ECG signal denoising

As a primary diagnostic tool for cardiac diseases, electrocardiogram (ECG) signals are often contaminated by various kinds of noise, such as baseline wander, electrode contact noise and motion artifacts. In this paper, we propose a contractive denoising technique to improve the performance of current denoising auto-encoders (DAEs) for ECG signal denoising. Based on the Frobenius norm of the Jacobean matrix for the learned features with respect to the input, we develop a stacked contractive denoising auto-encoder (CDAE) to build a deep neural network (DNN) for noise reduction, which can significantly improve the expression of ECG signals through multi-level feature extraction. The proposed method is evaluated on ECG signals from the bench-marker MIT-BIH Arrhythmia Database, and the noises come from the MIT-BIH noise stress test database. The experimental results show that the new CDAE algorithm performs better than the conventional ECG denoising method, specifically with more than 2.40 dB improvement in the signal-to-noise ratio (SNR) and nearly 0.075 to 0.350 improvements in the root mean square error (RMSE).

[1]  N.V. Thakor,et al.  Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection , 1991, IEEE Transactions on Biomedical Engineering.

[2]  Changchun Bao,et al.  Wiener filtering based speech enhancement with Weighted Denoising Auto-encoder and noise classification , 2014, Speech Commun..

[3]  A. A. Armoundas,et al.  ECG denoising and fiducial point extraction using an extended Kalman filtering framework with linear and nonlinear phase observations , 2016, Physiological measurement.

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

[5]  Steve Renals,et al.  Convolutional Neural Networks for Distant Speech Recognition , 2014, IEEE Signal Processing Letters.

[6]  Feng Wu,et al.  Background Prior-Based Salient Object Detection via Deep Reconstruction Residual , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[8]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[9]  Malay Kishore Dutta,et al.  Optimized Noise Canceller for ECG Signals , 2011 .

[10]  Celia Shahnaz,et al.  Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains , 2012, Biomed. Signal Process. Control..

[11]  Prabin Kumar Bora,et al.  Electrocardiogram signal denoising using non-local wavelet transform domain filtering , 2015, IET Signal Process..

[12]  Christian Jutten,et al.  A Nonlinear Bayesian Filtering Framework for ECG Denoising , 2007, IEEE Transactions on Biomedical Engineering.

[13]  M. Z. U. Rahman,et al.  Efficient and Simplified Adaptive Noise Cancelers for ECG Sensor Based Remote Health Monitoring , 2012, IEEE Sensors Journal.

[14]  Sheng-Fu Liang,et al.  A novel application of the S-transform in removing powerline interference from biomedical signals , 2009, Physiological measurement.

[15]  M. Awal,et al.  An adaptive level dependent wavelet thresholding for ECG denoising , 2014 .

[16]  I. Daut,et al.  Wavelet based distortion measurement and enhancement of ECG signal , 2012, 2012 International Conference on Biomedical Engineering (ICoBE).

[17]  George C M Siontis,et al.  Prognostic significance of ambulatory ECG monitoring for ventricular arrhythmias. , 2013, Progress in cardiovascular diseases.

[18]  Pascal Vincent,et al.  A Connection Between Score Matching and Denoising Autoencoders , 2011, Neural Computation.

[19]  Wenhao Huang,et al.  Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning , 2014, IEEE Transactions on Intelligent Transportation Systems.

[20]  Philip Constantinou,et al.  Noise-Assisted Data Processing With Empirical Mode Decomposition in Biomedical Signals , 2011, IEEE Transactions on Information Technology in Biomedicine.

[21]  Samit Ari,et al.  ECG signal enhancement using S-Transform , 2013, Comput. Biol. Medicine.

[22]  G. Prats-Boluda,et al.  Development of a portable wireless system for bipolar concentric ECG recording , 2015 .

[23]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[24]  Rafi Ahamed Shaik,et al.  Efficient sign based normalized adaptive filtering techniques for cancelation of artifacts in ECG signals: Application to wireless biotelemetry , 2011, Signal Process..

[25]  Manuel Blanco-Velasco,et al.  ECG signal denoising and baseline wander correction based on the empirical mode decomposition , 2008, Comput. Biol. Medicine.

[26]  A. Kadish,et al.  Uncertainty principle of signal-averaged electrocardiography. , 2000, Circulation.