Deep Learning Autoencoder Study on ECG Signals
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Keywords
Arrhythmia, Electrocardiogram, Deep Learning, Autoencoder, Deep LSTM Autoencoder
Abstract
Arrhythmia refers to an irregular heart rhythm resulting from disruptions in the heart's electrical activity. To identify arrhythmias, an electrocardiogram (ECG) is commonly employed, as it can record the heart's electrical signals. However, ECGs may encounter interference from sources like electromagnetic waves and electrode motion. Several researchers have investigated the denoising of electrocardiogram signals for arrhythmia detection using deep autoencoder models. Unfortunately, these studies have yielded suboptimal results, indicated by low Signal-to-Noise Ratio (SNR) values and relatively large Root Mean Square Error (RMSE). This study addresses these limitations by proposing the utilization of a Deep LSTM Autoencoder to effectively denoise ECG signals for arrhythmia detection. The model's denoising performance is evaluated based on achieved SNR and RMSE values. The results of the denoising evaluations using the Deep LSTM Autoencoder on the AFDB dataset show SNR and RMSE values of 56.16 and 0.00037, respectively. Meanwhile, for the MITDB dataset, the corresponding values are 65.22 and 0.00018. These findings demonstrate significant improvement compared to previous research. However, it's important to note a limitation in this study—the restricted availability of arrhythmia datasets from MITDB and AFDB. Future researchers are encouraged to explore and acquire a more extensive collection of arrhythmia data to further enhance denoising performance.
References
[2] R. Liu et al., "An artificial intelligence-based risk prediction model of myocardial infarction," BMC Bioinformatics, vol. 23, no. 1, p. 217, 2022/06/07 2022.
[3] R. A. Cahya, C. Dewi, and B. Rahayudi, "Klasifikasi Aritmia Dari Hasil Elektrokardiogram Menggunakan Support Vector Machine Dengan Seleksi Fitur Menggunakan Algoritma Genetika," Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 3, pp. 1170-1178, 08/28 2017.
[4] S. Hori, T. Shouo, K. Gyohten, H. Ohki, T. Takami, and N. Sato, "Arrhythmia Detection Based on Patient-Specific Normal ECGs Using Deep Learning," in 2020 Computing in Cardiology, 2020, pp. 1-4.
[5] S. Nurmaini, A. Darmawahyuni, A. N. Sakti Mukti, M. N. Rachmatullah, F. Firdaus, and B. Tutuko, "Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification," Electronics, vol. 9, no. 1, p. 135, 2020.
[6] M. Thill, W. Konen, H. Wang, and T. Bäck, "Temporal convolutional autoencoder for unsupervised anomaly detection in time series," Applied Soft Computing, vol. 112, p. 107751, 2021/11/01/ 2021.
[7] S. G. Chickaramanna et al., "Classification of Arrhythmia Using Machine Learning Algorithm," Revue d'Intelligence Artificielle, vol. 36, no. 4, pp. 529-534, 2022.
[8] Y. Hou, R. Liu, M. Shu, and C. Chen, "An ECG denoising method based on adversarial denoising convolutional neural network," Biomedical Signal Processing and Control, vol. 84, p. 104964, 2023/07/01/ 2023.
[9] P. Singh and A. Sharma, "Attention-Based Convolutional Denoising Autoencoder for Two-Lead ECG Denoising and Arrhythmia Classification," IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-10, 2022.
[10] J. Park, Y. Seo, and J. Cho, "Unsupervised outlier detection for time-series data of indoor air quality using LSTM autoencoder with ensemble method," Journal of Big Data, vol. 10, no. 1, p. 66, 2023/05/17 2023.
[11] H. T. Chiang, Y. Y. Hsieh, S. W. Fu, K. H. Hung, Y. Tsao, and S. Y. Chien, "Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders," IEEE Access, vol. 7, pp. 60806-60813, 2019.
[12] S. A. Deevi, C. P. Kaniraja, V. D. Mani, D. Mishra, S. Ummar, and C. Satheesh, "HeartNetEC: a deep representation learning approach for ECG beat classification," Biomedical Engineering Letters, vol. 11, no. 1, pp. 69-84, 2021/02/01 2021.
[13] J. Shi et al., "New ECG Compression Method for Portable ECG Monitoring System Merged with Binary Convolutional Auto-Encoder and Residual Error Compensation," Biosensors, vol. 12, no. 7, p. 524, 2022.
[14] B. Marwan, F. Samann, and T. Schanze, "Denoising of ECG with single and multiple hidden layer autoencoders," Current Directions in Biomedical Engineering, vol. 8, no. 2, pp. 652-655, 2022.
[15] S. Śmigiel, "ECG Classification Using Orthogonal Matching Pursuit and Machine Learning," Sensors, vol. 22, no. 13, p. 4960, 2022.
[16] K. Fujiwara, S. Miyatani, A. Goda, M. Miyajima, T. Sasano, and M. Kano, "Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis," Sensors, vol. 21, no. 9, p. 3235, 2021.
[17] S. Modak, L. Y. Taha, and E. Abdel-Raheem, "A Novel Method of QRS Detection Using Time and Amplitude Thresholds With Statistical False Peak Elimination," IEEE Access, vol. 9, pp. 46079-46092, 2021.
[18] J. Hua, J. Rao, Y. Peng, J. Liu, and J. Tang, "Deep Compressive Sensing on ECG Signals with Modified Inception Block and LSTM," Entropy, vol. 24, no. 8, p. 1024, 2022.
[19] B.-H. Kim and J.-Y. Pyun, "ECG Identification For Personal Authentication Using LSTM-Based Deep Recurrent Neural Networks," Sensors, vol. 20, no. 11, p. 3069, 2020.
[20] J. Torres-Soto and E. A. Ashley, "Multi-task deep learning for cardiac rhythm detection in wearable devices," npj Digital Medicine, vol. 3, no. 1, p. 116, 2020/09/09 2020.
[21] Safrizal, F. Arnia, and R. Muharar, "Pengenalan Aksara Jawi Tulisan Tangan Menggunakan Freemen Chain Code (FCC), Support Vector Machine (SVM) dan Aturan Pengambilan Keputusan," Jurnal Nasional Teknik Elektro, vol. 5, no. 1, pp. 45-55, 03/04 2016.
[22] S. Bahri, K. Saddami, F. Arnia, and K. Muchtar, "Perbandingan Kinerja Support Vector Machine (SVM) Dalam Mengenali Wajah Menggunakan SURF DAN GLCM," Jurnal Nasional Teknik Elektro, vol. 8, no. 2, pp. 65 – 74, 06/26 2019.
[23] Y. Zhou, L. Li, Y. Liu, Z. Zhang, and T. Matsui, "Clustering analysis of acoustic emission signals in the monitoring of stone monuments: case of the freeze‒thaw deterioration of tuffs," Heritage Science, vol. 11, no. 1, p. 119, 2023/06/07 2023..
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