ASCNet-ECG: Deep Autoencoder based Attention aware Skip Connection network for ECG filtering

ASCNet-ECG: Deep Autoencoder based Attention aware Skip Connection network for ECG filtering
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© 2023 by IJETT Journal
Volume-71 Issue-2
Year of Publication : 2023
Author : Raghavendra Badiger, M. Prabhakar
DOI : 10.14445/22315381/IJETT-V71I2P240

How to Cite?

Raghavendra Badiger, M. Prabhakar, "ASCNet-ECG: Deep Autoencoder based Attention aware Skip Connection network for ECG filtering ," International Journal of Engineering Trends and Technology, vol. 71, no. 2, pp. 382-398, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I2P240

Abstract
Currently, the telehealth monitoring field has gained huge attention due to its noteworthy use in day-to-day life. This advancement has led to an increase in the data collection of electrophysiological signals. Due to this advancement, electrocardiogram (ECG) signal monitoring has become a leading task in the medical field. ECG plays an important role in the medical field by analysing cardiac physiology and abnormalities. However, these signals are affected due to numerous varieties of noises, such as electrode motion, baseline wander and white noise etc., which affects the diagnosis accuracy. Therefore, filtering ECG signals became an important task. Currently, deep learning schemes are widely employed in signal-filtering tasks due to their efficient architecture of feature learning. This work presents a deep learning-based scheme for ECG signal filtering, which is based on the deep autoencoder module. According to this scheme, the data is processed through the encoder and decoder layer to reconstruct by eliminating noises. The proposed deep learning architecture uses a modified ReLU function to improve the learning of attributes because standard ReLU cannot adapt to huge variations. Further, a skip connection is also incorporated in the proposed architecture, which retains the key feature of the encoder layer while mapping these features to the decoder layer. Similarly, an attention model is also included, which performs channel and spatial attention, which generates the robust map by using channel and average pooling operations, resulting in improving the learning performance. The proposed approach is tested on a publicly available MIT-BIH dataset where different types of noise, such as electrode motion, baseline water and motion artifacts, are added to the original signal at varied SNR levels. The outcome of the proposed ASCNet is measured in terms of RMSE and SNR.

Keywords
ECG, Signal filtering, Deep auto encoder, Attention module, Deep learning, MIT-BIH

References
[1] [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases
[2] Carmine Izzo et al., “The Role of Oxidative Stress in Cardiovascular Aging and Cardiovascular Diseases,” Life, vol. 11, no. 1, p. 60, 2021. Crossref, https://doi.org/10.3390%2Flife11010060
[3] Donald Lloyd-Jones et al., “Heart Disease and Stroke Statistics—2009 Update: A Report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee,” Circulation, vol. 119, no. 3, pp. E21-E181, 2009. Crossref, https://doi.org/10.1161/circulationaha.108.191261
[4] Md. Shahbaz Alam, Heart Detection and Monitoring by IOT, Recent Trends in Communication and Electronics, 1st edition, 2021.
[5] Yuwei Zhang et al., “Wearable Fetal ECG Monitoring System From Abdominal Electrocardiography Recording,” Biosensors, vol. 12, no. 7, p. 475, 2022. Crossref, https://doi.org/10.3390%2Fbios12070475
[6] Taiyang Wu, Jean-Michel Redouté, and Mehmet Yuce, “A Wearable, Low-Power, Real-Time ECG Monitor for Smart T-Shirt and IoT Healthcare Applications,” Advances in Body Area Networks I, Springer, Cham, pp. 165-173, 2019.
[7] Christopher Beach et al., “An Ultra Low Power Personalizable Wrist Worn ECG Monitor Integrated with IoT Infrastructure,” IEEE Access, vol. 6, pp. 44010-44021, 2018. Crossref, https://doi.org/10.1109/ACCESS.2018.2864675
[8] Apurva Kulkarni et al., "Analysis of ECG Signals," SSRG International Journal of Electronics and Communication Engineering, vol. 3, no. 4, pp. 15-17, 2016. Crossref, https://doi.org/10.14445/23488549/IJECE-V3I4P104
[9] Elisa Spanò, Stefano Di Pascoli, and Giuseppe Iannaccone, “Low-Power Wearable ECG Monitoring System for Multiple-Patient Remote Monitoring,” IEEE Sensors Journal, vol. 16, no. 13, pp. 5452-5462, 2016. Crossref, https://doi.org/10.1109/JSEN.2016.2564995
[10] Wahyu Caesarendra et al., “An Embedded System Using Convolutional Neural Network Model for Online and Real-Time ECG Signal Classification and Prediction,” Diagnostics, vol. 12, no. 4, p. 795. Crossref, https://doi.org/10.3390/diagnostics12040795
[11] Karan Singh Parmar, Aman Kumar, and Uppal Kalita “ECG Signal Based Automated Hypertension Detection Using Fourier Decomposition Method and Cosine Modulated Filter Banks,” Biomedical Signal Processing and Control, vol. 76, p. 103629, 2022. Crossref, https://doi.org/10.1016/j.bspc.2022.103629
[12] Sarah Ayashm et al., “Analysis of ECG Signal by Using an FCN Network for Automatic Diagnosis of Obstructive Sleep Apnea,” Circuits, Systems, and Signal Processing, vol. 41, pp. 1-16, 2022. Crossref, https://doi.org/10.1007/s00034-022-02091-7
[13] Ramkumar. M, “Auto-Encoder and Bidirectional Long Short-Term Memory Based Automated Arrhythmia Classification for ECG Signal,” Biomedical Signal Processing and Control, vol. 77, p. 103826, 2022. Crossref, https://doi.org/10.1016/j.bspc.2022.103826
[14] Samson D.Yusuf et al., "Analysis of Butterworth Filter for Electrocardiogram De-Noising Using Daubechies Wavelets," SSRG International Journal of Electronics and Communication Engineering, vol. 7, no. 4, pp. 8-13, 2020. Crossref, https://doi.org/10.14445/23488549/IJECE-V7I4P103
[15] Yeldos A. Altay, Artem S. Kremlev, and Alexey A. Margun, “ECG Signal Filtering Approach for Detection of P, QRS, T Waves and Complexes in Short Single-Lead Recording,” 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (Eiconrus), pp. 1135-1140, IEEE.
[16] Rasti-Meymandi, A, and Ghaffari, A, “A Deep Learning-Based Framework for ECG Signal Denoising Based on Stacked Cardiac Cycle Tensor,” Biomedical Signal Processing and Control, pp. 1135-1140, 2019. Crossref, https://doi.org/10.1109/EIConRus.2019.8657104
[17] Nasser Mourad, “ECG Denoising Based on Successive Local Filtering,” Biomedical Signal Processing and Control, vol. 73, 2022. Crossref, https://doi.org/10.1016/j.bspc.2021.103431
[18] Mohamed Sraitih, and Younes Jabrane, “A Denoising Performance Comparison Based on ECG Signal Decomposition and Local Means Filtering,” Biomedical Signal Processing and Control, vol. 69, p. 102903, 2021. Crossref, https://doi.org/10.1016/j.bspc.2021.102903
[19] Shubham Srivastava et al., "ECG Pattern Analysis using Artificial Neural Network," SSRG International Journal of Electronics and Communication Engineering, vol. 7, no. 5, pp. 1-4, 2020. Crossref, https://doi.org/10.14445/23488549/IJECE-V7I5P101
[20] Francisco P. Romero et al., “Deepfilter: An ECG Baseline Wander Removal Filter Using Deep Learning Techniques,” Biomedical Signal Processing and Control, vol. 70, p. 102992, 2021. Crossref, https://doi.org/10.1016/j.bspc.2021.102992
[21] Guy J. J. Warmerdam et al., “Hierarchical Probabilistic Framework for Fetal R-Peak Detection, Using ECG Waveform and Heart Rate Information,” IEEE Transactions on Signal Processing, vol. 66, no.16, pp. 4388-4397, 2018. Crossref, https://doi.org/10.1109/TSP.2018.2853144
[22] Tae Wuk Bae et al., “An Adaptive Median Filter Based on Sampling Rate for R-Peak Detection and Major-Arrhythmia Analysis,” Sensors, vol. 20, no. 21, p. 6144, 2020. Crossref, https://doi.org/10.3390/s20216144
[23] Muhammad Uzair Zahid et al., “Robust R-Peak Detection in Low-Quality Holter ECGs Using 1D Convolutional Neural Network,” IEEE Transactions on Biomedical Engineering, vol. 69, no. 1, pp. 119-128, 2022. Crossref, https://doi.org/10.1109/TBME.2021.3088218
[24] [Online]. Available: https://physionet.org/content/mitdb/1.0.0/
[25] Ruixia Liu, Minglei Shu, and Changfang Chen, “ECG Signal Denoising and Reconstruction Based on Basis Pursuit,” Applied Sciences, vol. 11, no. 4, p. 1-15, 2021. Crossref, https://doi.org/10.3390/app11041591
[26] Bingxin Xu, “An ECG Denoising Method Based on the Generative Adversarial Residual Network,” Computational and Mathematical Methods in Medicine, vol. 2021, 2021. Crossref, https://doi.org/10.1155/2021/5527904
[27] Ngoc Thang Bui et al., “Real-Time Filtering and ECG Signal Processing Based On Dual-Core Digital Signal Controller System,” IEEE Sensors Journal, vol. 20, no. 12, pp. 6492-6503, 2020. Crossref, https://doi.org/10.1109/JSEN.2020.2975006
[28] Haemwaan Sivaraks, and Chotirat Ann Ratanamahatana, “Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery,” Computational and Mathematical Methods in Medicine, vol. 2015, pp. 1-20, Crossref, https://doi.org/10.1155/2015/453214
[29] Hamed Danandeh Hesar, and Maryam Mohebbi, “An Adaptive Kalman Filter Bank for ECG Denoising,” IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 1, pp. 13–21, 2021. Crossref, https://doi.org/10.1109/JBHI.2020.2982935