Classification of Arrhythmic and Normal Signals using Continuous Wavelet Transform (CWT) and Long Short-Term Memory (LSTM)

Yunidar Yunidar; Melinda Melinda; Ulul Azmi; Nurlida Bashir; Cut Nanda Nurbadriani; Zulfikar Taqiuddin

doi:10.22219/kinetik.v9i2.1917

Issue

Vol. 9, No. 2, May 2024

Issue Published : May 31, 2024

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Classification of Arrhythmic and Normal Signals using Continuous Wavelet Transform (CWT) and Long Short-Term Memory (LSTM)

https://doi.org/10.22219/kinetik.v9i2.1917

Yunidar Yunidar

Universitas Syiah Kuala

Melinda Melinda

Department of Electrical Engineering and Computer, University of Syiah Kuala

https://www.scopus.com/redirect.uri?url=https://orcid.org/0000-0001-9082-6639&authorId=53264423700&origin=AuthorProfile&orcId=0000-0001-9082-6639&category=orcidLink

Ulul Azmi

Universitas Syiah Kuala

Nurlida Bashir

Universiti Sains Islam Malaysia

Cut Nanda Nurbadriani

Universitas Syiah Kuala

Zulfikar Taqiuddin

Universitas Syiah Kuala

Corresponding Author(s) : Melinda Melinda

melinda@unsyiah.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 9, No. 2, May 2024
Article Published : May 27, 2024

Abstract

An electrocardiogram (ECG) can detect heart abnormalities through signals from the rhythm of the human heartbeat. One of them is arrhythmia disease, which is caused by an improper heartbeat and causes failure of blood pumping. In reading ECG signals, a common problem encountered is the uncertainty of the prediction results. An accurate and efficient heart defect classification system is needed to help patients and healthcare providers carry out appropriate therapy or treatment. Several studies have developed algorithms that are more effective in Machine Learning (ML) in automatically providing initial screening of patients' heart conditions. This study proposed the Long Short-Term Memory (LSTM) method as a classifier of heart conditions experienced by humans and Continuous Wavelet Transform (CWT) as a feature extractor to eliminate noise during data collection. CWT and LSTM methods are believed to perform well in feature extraction and classification of ECG signals. The dataset used in this study was taken from the MIT-BIH Arrhythmia Database. The results of this study successfully extracted ECG signals using CWT, thus improving the understanding of ECG characteristics. This research also succeeded in classifying ECG signals using the LSTM method, which obtained an accuracy training value of 98.4% and an accuracy testing value of 94.42 %.

Keywords

Electrocardiogram LSTM Arrhythmia Continuous Wavelet Transform Long Short-Term Memory

Yunidar, Y., Melinda, M., Azmi, U., Bashir, N., Nurbadriani, C. N., & Taqiuddin, Z. (2024). Classification of Arrhythmic and Normal Signals using Continuous Wavelet Transform (CWT) and Long Short-Term Memory (LSTM). Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 9(2), 129-138. https://doi.org/10.22219/kinetik.v9i2.1917

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References

S. Chatterjee, R. S. Thakur, R. N. Yadav, L. Gupta, and D. K. Raghuvanshi, “Review of noise removal techniques in ECG signals,” IET Signal Processing, vol. 14, no. 9. Institution of Engineering and Technology, pp. 569–590, Dec. 01, 2020. https://doi.org/10.1049/iet-spr.2020.0104
M. Badr, S. Al-Otaibi, N. Alturki, and T. Abir, “Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks,” Comput Intell Neurosci, vol. 2022, 2022. https://doi.org/10.1155/2022/1094830
S. Balasubramanian and M. S. Naruka, “A noise removal methodology for effective ecg enhancement in heart disease prediction & analysis,” Int J Health Sci (Qassim), May 2022. https://doi.org/10.53730/ijhs.v6nS1.7813
Q. Bi, K. E. Goodman, J. Kaminsky, and J. Lessler, “What is machine learning? A primer for the epidemiologist,” Am J Epidemiol, vol. 188, no. 12, pp. 2222–2239, Dec. 2019. https://doi.org/10.1093/aje/kwz189
B. Mahesh, “Machine Learning Algorithms-A Review,” International Journal of Science and Research, 2018. https://doi.org/10.21275/ART20203995
A. Sherstinsky, “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network,” Physica D, vol. 404, Mar. 2020. https://doi.org/10.1016/j.physd.2019.132306
G. Kłosowski, T. Rymarczyk, D. Wójcik, S. Skowron, T. Cieplak, and P. Adamkiewicz, “The use of time-frequency moments as inputs of lstm network for ecg signal classification,” Electronics (Switzerland), vol. 9, no. 9, pp. 1–22, Sep. 2020. https://doi.org/10.3390/electronics9091452
J. Gao, H. Zhang, P. Lu, and Z. Wang, “An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset,” J Healthc Eng, vol. 2019, 2019. https://doi.org/10.1155/2019/6320651
A. Sharma, N. Garg, S. Patidar, R. San Tan, and U. R. Acharya, “Automated pre-screening of arrhythmia using hybrid combination of Fourier–Bessel expansion and LSTM,” Comput Biol Med, vol. 120, May 2020. https://doi.org/10.1016/j.compbiomed.2020.103753
M. A. Khan and Y. Kim, “Cardiac arrhythmia disease classification using LSTM deep learning approach,” Computers, Materials and Continua, vol. 67, no. 1, pp. 427–443, 2021. https://doi.org/10.32604/cmc.2021.014682
X. Song et al., “Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model,” J Pet Sci Eng, vol. 186, Mar. 2020. https://doi.org/10.1016/j.petrol.2019.106682
T. Wang, C. Lu, Y. Sun, M. Yang, C. Liu, and C. Ou, “Automatic ECG classification using continuous wavelet transform and convolutional neural network,” Entropy, vol. 23, no. 1, pp. 1–13, Jan. 2021. https://doi.org/10.3390/e23010119
R. A. Alharbey, S. Alsubhi, K. Daqrouq, and A. Alkhateeb, “The continuous wavelet transform using for natural ECG signal arrhythmias detection by statistical parameters,” Alexandria Engineering Journal, vol. 61, no. 12, pp. 9243–9248, Dec. 2022. https://doi.org/10.1016/j.aej.2022.03.016
J. Zheng, J. Zhang, S. Danioko, H. Yao, H. Guo, and C. Rakovski, “A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients,” Sci Data, vol. 7, no. 1, Dec. 2020. https://doi.org/10.1038/s41597-020-0386-x
Z. F. M. Apandi, R. Ikeura, and S. Hayakawa, “Arrhythmia Detection Using MIT-BIH Dataset: A Review,” in 2018 International Conference on Computational Approach in Smart Systems Design and Applications, ICASSDA 2018, Institute of Electrical and Electronics Engineers Inc., Sep. 2018. https://doi.org/10.1109/ICASSDA.2018.8477620
T. I. Toma and S. Choi, “A Parallel Cross Convolutional Recurrent Neural Network for Automatic Imbalanced ECG Arrhythmia Detection with Continuous Wavelet Transform,” Sensors, vol. 22, no. 19, Oct. 2022. https://doi.org/10.3390/s22197396
R. Cartas-Rosado et al., “Continuous wavelet transform based processing for estimating the power spectrum content of heart rate variability during hemodiafiltration,” Biomed Signal Process Control, vol. 62, Sep. 2020. https://doi.org/10.1016/j.bspc.2020.102031
P. S. Muhuri, P. Chatterjee, X. Yuan, K. Roy, and A. Esterline, “Using a long short-term memory recurrent neural network (LSTM-RNN) to classify network attacks,” Information (Switzerland), vol. 11, no. 5, May 2020. https://doi.org/10.3390/INFO11050243
J. Zhu, H. Chen, and W. Ye, “A Hybrid CNN-LSTM Network for the Classification of Human Activities Based on Micro-Doppler Radar,” IEEE Access, vol. 8, pp. 24713–24720, 2020. https://doi.org/10.1109/ACCESS.2020.2971064
Ş. Öztürk and U. Özkaya, “Gastrointestinal tract classification using improved LSTM based CNN,” Multimed Tools Appl, vol. 79, no. 39–40, pp. 28825–28840, Oct. 2020. https://doi.org/10.1007/s11042-020-09468-3
M. M. Rahman, Y. Watanobe, and K. Nakamura, “Source Code Assessment and Classification Based on Estimated Error Probability Using Attentive LSTM Language Model and Its Application in Programming Education”. https://doi.org/10.3390/app10080000
J. Amin, M. Sharif, M. Raza, T. Saba, R. Sial, and S. A. Shad, “Brain tumor detection: a long short-term memory (LSTM)-based learning model,” Neural Comput Appl, vol. 32, no. 20, pp. 15965–15973, Oct. 2020. https://doi.org/10.1007/s00521-019-04650-7
D. Skrobek et al., “Prediction of sorption processes using the deep learning methods (long short-term memory),” Energies (Basel), vol. 13, no. 24, Dec. 2020. https://doi.org/10.3390/en13246601
J. Xu, Y. Zhang, and D. Miao, “Three-way confusion matrix for classification: A measure driven view,” Inf Sci (N Y), vol. 507, pp. 772–794, Jan. 2020. https://doi.org/10.1016/j.ins.2019.06.064
M. Fahmy Amin, “Confusion Matrix in Three-class Classification Problems: A Step-by-Step Tutorial,” Journal of Engineering Research, vol. 7, no. 1, pp. 0–0, Mar. 2023. https://doi.org/10.21608/erjeng.2023.296718

References

S. Chatterjee, R. S. Thakur, R. N. Yadav, L. Gupta, and D. K. Raghuvanshi, “Review of noise removal techniques in ECG signals,” IET Signal Processing, vol. 14, no. 9. Institution of Engineering and Technology, pp. 569–590, Dec. 01, 2020. https://doi.org/10.1049/iet-spr.2020.0104

M. Badr, S. Al-Otaibi, N. Alturki, and T. Abir, “Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks,” Comput Intell Neurosci, vol. 2022, 2022. https://doi.org/10.1155/2022/1094830

S. Balasubramanian and M. S. Naruka, “A noise removal methodology for effective ecg enhancement in heart disease prediction & analysis,” Int J Health Sci (Qassim), May 2022. https://doi.org/10.53730/ijhs.v6nS1.7813

Q. Bi, K. E. Goodman, J. Kaminsky, and J. Lessler, “What is machine learning? A primer for the epidemiologist,” Am J Epidemiol, vol. 188, no. 12, pp. 2222–2239, Dec. 2019. https://doi.org/10.1093/aje/kwz189

B. Mahesh, “Machine Learning Algorithms-A Review,” International Journal of Science and Research, 2018. https://doi.org/10.21275/ART20203995

A. Sherstinsky, “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network,” Physica D, vol. 404, Mar. 2020. https://doi.org/10.1016/j.physd.2019.132306

G. Kłosowski, T. Rymarczyk, D. Wójcik, S. Skowron, T. Cieplak, and P. Adamkiewicz, “The use of time-frequency moments as inputs of lstm network for ecg signal classification,” Electronics (Switzerland), vol. 9, no. 9, pp. 1–22, Sep. 2020. https://doi.org/10.3390/electronics9091452

J. Gao, H. Zhang, P. Lu, and Z. Wang, “An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset,” J Healthc Eng, vol. 2019, 2019. https://doi.org/10.1155/2019/6320651

A. Sharma, N. Garg, S. Patidar, R. San Tan, and U. R. Acharya, “Automated pre-screening of arrhythmia using hybrid combination of Fourier–Bessel expansion and LSTM,” Comput Biol Med, vol. 120, May 2020. https://doi.org/10.1016/j.compbiomed.2020.103753

M. A. Khan and Y. Kim, “Cardiac arrhythmia disease classification using LSTM deep learning approach,” Computers, Materials and Continua, vol. 67, no. 1, pp. 427–443, 2021. https://doi.org/10.32604/cmc.2021.014682

X. Song et al., “Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model,” J Pet Sci Eng, vol. 186, Mar. 2020. https://doi.org/10.1016/j.petrol.2019.106682

T. Wang, C. Lu, Y. Sun, M. Yang, C. Liu, and C. Ou, “Automatic ECG classification using continuous wavelet transform and convolutional neural network,” Entropy, vol. 23, no. 1, pp. 1–13, Jan. 2021. https://doi.org/10.3390/e23010119

R. A. Alharbey, S. Alsubhi, K. Daqrouq, and A. Alkhateeb, “The continuous wavelet transform using for natural ECG signal arrhythmias detection by statistical parameters,” Alexandria Engineering Journal, vol. 61, no. 12, pp. 9243–9248, Dec. 2022. https://doi.org/10.1016/j.aej.2022.03.016

J. Zheng, J. Zhang, S. Danioko, H. Yao, H. Guo, and C. Rakovski, “A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients,” Sci Data, vol. 7, no. 1, Dec. 2020. https://doi.org/10.1038/s41597-020-0386-x

Z. F. M. Apandi, R. Ikeura, and S. Hayakawa, “Arrhythmia Detection Using MIT-BIH Dataset: A Review,” in 2018 International Conference on Computational Approach in Smart Systems Design and Applications, ICASSDA 2018, Institute of Electrical and Electronics Engineers Inc., Sep. 2018. https://doi.org/10.1109/ICASSDA.2018.8477620

T. I. Toma and S. Choi, “A Parallel Cross Convolutional Recurrent Neural Network for Automatic Imbalanced ECG Arrhythmia Detection with Continuous Wavelet Transform,” Sensors, vol. 22, no. 19, Oct. 2022. https://doi.org/10.3390/s22197396

R. Cartas-Rosado et al., “Continuous wavelet transform based processing for estimating the power spectrum content of heart rate variability during hemodiafiltration,” Biomed Signal Process Control, vol. 62, Sep. 2020. https://doi.org/10.1016/j.bspc.2020.102031

P. S. Muhuri, P. Chatterjee, X. Yuan, K. Roy, and A. Esterline, “Using a long short-term memory recurrent neural network (LSTM-RNN) to classify network attacks,” Information (Switzerland), vol. 11, no. 5, May 2020. https://doi.org/10.3390/INFO11050243

J. Zhu, H. Chen, and W. Ye, “A Hybrid CNN-LSTM Network for the Classification of Human Activities Based on Micro-Doppler Radar,” IEEE Access, vol. 8, pp. 24713–24720, 2020. https://doi.org/10.1109/ACCESS.2020.2971064

Ş. Öztürk and U. Özkaya, “Gastrointestinal tract classification using improved LSTM based CNN,” Multimed Tools Appl, vol. 79, no. 39–40, pp. 28825–28840, Oct. 2020. https://doi.org/10.1007/s11042-020-09468-3

M. M. Rahman, Y. Watanobe, and K. Nakamura, “Source Code Assessment and Classification Based on Estimated Error Probability Using Attentive LSTM Language Model and Its Application in Programming Education”. https://doi.org/10.3390/app10080000

J. Amin, M. Sharif, M. Raza, T. Saba, R. Sial, and S. A. Shad, “Brain tumor detection: a long short-term memory (LSTM)-based learning model,” Neural Comput Appl, vol. 32, no. 20, pp. 15965–15973, Oct. 2020. https://doi.org/10.1007/s00521-019-04650-7

D. Skrobek et al., “Prediction of sorption processes using the deep learning methods (long short-term memory),” Energies (Basel), vol. 13, no. 24, Dec. 2020. https://doi.org/10.3390/en13246601

J. Xu, Y. Zhang, and D. Miao, “Three-way confusion matrix for classification: A measure driven view,” Inf Sci (N Y), vol. 507, pp. 772–794, Jan. 2020. https://doi.org/10.1016/j.ins.2019.06.064

M. Fahmy Amin, “Confusion Matrix in Three-class Classification Problems: A Step-by-Step Tutorial,” Journal of Engineering Research, vol. 7, no. 1, pp. 0–0, Mar. 2023. https://doi.org/10.21608/erjeng.2023.296718

Issue

Vol. 9, No. 2, May 2024

Classification of Arrhythmic and Normal Signals using Continuous Wavelet Transform (CWT) and Long Short-Term Memory (LSTM)

Corresponding Author(s) : Melinda Melinda

Abstract

Keywords

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