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Comparative Performance Analysis of Time-Frequency Domain Images and Raw Signal Data for Classification of ECG Signals

Yıl 2024, Cilt: 12 Sayı: 2, 745 - 761, 29.04.2024

Turgut Özseven

https://doi.org/10.29130/dubited.1236072

Öz

ECG signals are one of the most common tools used to diagnose cardiovascular diseases. ECG signals are obtained by measuring electrical changes on the skin surface. Arrhythmias occurring in the heart are diagnosed because the expert evaluates ECG signals. This diagnosis depends on the experience of the specialist and is a subjective evaluation. With the widespread use of computer-aided diagnostic systems, evaluations dependent on the expert's experience are objectified, and support is provided to the physician for diagnosis. For computer-aided ECG classification, beats are detected from ECG signals, and arrhythmias are detected by analyzing the structure of these beats. In recent years, deep learning models have been successful in classifying ECG signals. The data to be used in the classification process is realized with the help of morphological features or images of the signal. The main objective of this study is to compare the classification performance of digital and visual heartbeat data for ECG signal classification. For this purpose, 1D-CNN and 2D-CNN architectures are used for the type of ECG signals. As inputs of the 1D-CNN model, numerical values of the heartbeat signal and hand-crafted features obtained from these numerical values were used. The inputs of the 2D-CNN model are the raw signal image, spectrogram, scalogram, Mel-spectrogram, GFCC, and CQT images, which are visual representations of the heartbeat signal. The results show that the numerical model of the ECG signal fails for classification, while the hand-crafted features provide 85.2% accuracy. The results obtained with the visual representation of the signal provided over 99% classification accuracy for all images. The highest success rate was 99.9% with the visualization of the raw signal. In line with these findings, the 2D-CNN architecture and the visual representation of the heartbeat signal were found to be the most suitable method for classifying ECG signals.

Anahtar Kelimeler

ECG deep learning heartbeat classification

Kaynakça

  • [1] O. M. A. Ali, S. W. Kareem, and A. S. Mohammed, “Evaluation of Electrocardiogram Signals Classification Using CNN, SVM, and LSTM Algorithm: A review,” presented at 8th International Engineering Conference on Sustainable Technology and Development (IEC), pp. 185–191, 2022.
  • [2] “World health statistics 2018: monitoring health for the SDGs, sustainable development goals - RELACSIS | OPS/OMS,” Pan American Health Organization / World Health Organization, 2018. https://www3.paho.org/relacsis/index.php/es/noticias-relacsis/906-report-world-health-statistics-2018-monitoring-health-for-the-sdgs-sustainable-development-goals (accessed Jan. 12, 2023).
  • [3] E. J. Benjamin et al., “Heart disease and stroke statistics—2019 update: a report from the American Heart Association,” Circulation, vol. 139, no. 10, pp. e56–e528, 2019.
  • [4] X. Liu, H. Wang, Z. Li, and L. Qin, “Deep learning in ECG diagnosis: A review,” Knowledge-Based Systems, vol. 227, p. 107187, 2021.
  • [5] U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, M. Adam, A. Gertych, and R. San Tan, “A deep convolutional neural network model to classify heartbeats,” Computers in Biology and Medicine, vol. 89, pp. 389–396, 2017.
  • [6] G. De Lannoy, D. François, J. Delbeke, and M. Verleysen, “Weighted conditional random fields for supervised interpatient heartbeat classification,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 1, pp. 241–247, 2011.
  • [7] H. V. Pipberger, R. J. Arms, and F. W. Stallmann, “Automatic screening of normal and abnormal electrocardiograms by means of a digital electronic computer,” Proceedings of the Society for Experimental Biology and Medicine, vol. 106, no. 1, pp. 130–132, 1961.
  • [8] H. Malik, U. Bashir, and A. Ahmad, “Multi-classification neural network model for detection of abnormal heartbeat audio signals,” Biomedical Engineering Advances, vol. 4, p. 100048, 2022.
  • [9] M. Cao, T. Zhao, Y. Li, W. Zhang, P. Benharash, and R. Ramezani, “ECG heartbeat classification using deep transfer learning with convolutional neural network and STFT technique,” arXiv preprint arXiv:2206.14200, 2022.
  • [10] M. Degirmenci, M. A. Ozdemir, E. Izci, and A. Akan, “Arrhythmic heartbeat classification using 2d convolutional neural networks,” Irbm, vol. 43, no. 5, pp. 422–433, 2022.
  • [11] G. Yao, X. Mao, N. Li, H. Xu, X. Xu, Y. Jiao, and J. Ni, “Interpretation of electrocardiogram heartbeat by CNN and GRU,” Computational and Mathematical Methods in Medicine, vol. 2021, 2021.
  • [12] S. Aziz, S. Ahmed, and M.-S. Alouini, “ECG-based machine-learning algorithms for heartbeat classification,” Scientific reports, vol. 11, no. 1, pp. 1–14, 2021.
  • [13] S. Zhou, and B. Tan, “Electrocardiogram soft computing using hybrid deep learning CNN-ELM,” Applied soft computing, vol. 86, p. 105778, 2020.
  • [14] T. Wang, C. Lu, W. Ju, and C. Liu, “Imbalanced heartbeat classification using EasyEnsemble technique and global heartbeat information,” Biomedical Signal Processing and Control, vol. 71, p. 103105, 2022.
  • [15] D. Zhang, H. Zhou, F. Li, L. Zhang, and J. Wang, “A reparameterization multifeature fusion CNN for arrhythmia heartbeats classification,” Computational and Mathematical Methods in Medicine, vol. 2022, 2022.
  • [16] A. Tyagi and R. Mehra, “Intellectual heartbeats classification model for diagnosis of heart disease from ECG signal using hybrid convolutional neural network with GOA,” SN Applied Sciences, vol. 3, no. 2, pp. 1–14, 2021.
  • [17] Y. Xu, S. Zhang, Z. Cao, Q. Chen, and W. Xiao, “Extreme learning machine for heartbeat classification with hybrid time-domain and wavelet time-frequency features,” Journal of Healthcare Engineering, vol. 2021, 2021.
  • [18] E. Jing, H. Zhang, Z. Li, Y. Liu, Z. Ji, and I. Ganchev, “ECG heartbeat classification based on an improved ResNet-18 model,” Computational and Mathematical Methods in Medicine, vol. 2021, 2021.
  • [19] E. Essa and X. Xie, “An ensemble of deep learning-based multi-model for ECG heartbeats arrhythmia classification,” IEEE access, vol. 9, pp. 103452–103464, 2021.
  • [20] E. Maghawry, T. F. Gharib, R. Ismail, and M. J. Zaki, “An efficient heartbeats classifier based on optimizing convolutional neural network model,” IEEE access, vol. 9, pp. 153266–153275, 2021.
  • [21] S. K. Pandey, R. R. Janghel, and V. Vani, “Patient specific machine learning models for ECG signal classification,” Procedia Computer Science, vol. 167, pp. 2181–2190, 2020.
  • [22] S. L. Oh, E. Y. Ng, R. San Tan, and U. R. Acharya, “Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats,” Computers in biology and medicine, vol. 102, pp. 278–287, 2018.
  • [23] R. J. Martis, U. R. Acharya, and L. C. Min, “ECG beat classification using PCA, LDA, ICA and discrete wavelet transform,” Biomedical Signal Processing and Control, vol. 8, no. 5, pp. 437–448, 2013.
  • [24] Y. H. Hu, W. J. Tompkins, J. L. Urrusti, and V. X. Afonso, “Applications of artificial neural networks for ECG signal detection and classification,” Journal of electrocardiology, vol. 26, pp. 66–73, 1993.
  • [25] N. Izeboudjen and A. Farah, “A new neural network system for arrhythmia’s classification,” NC, vol. 98, pp. 23–25, 1998.
  • [26] J.-S. Wang, W.-C. Chiang, Y.-L. Hsu, and Y.-T. C. Yang, “ECG arrhythmia classification using a probabilistic neural network with a feature reduction method,” Neurocomputing, vol. 116, pp. 38–45, 2013.
  • [27] C. V. Banupriya and S. Karpagavalli, “Electrocardiogram beat classification using probabilistic neural network,” Int. J. Comput. Appl.(IJCA), vol. 1, no. 7, pp. 31–37, 2014.
  • [28] N. Acır, “Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm,” Neural computing & applications, vol. 14, no. 4, pp. 299–309, 2005.
  • [29] M. Moavenian and H. Khorrami, “A qualitative comparison of artificial neural networks and support vector machines in ECG arrhythmias classification,” Expert Systems with Applications, vol. 37, no. 4, pp. 3088–3093, 2010.
  • [30] M. H. Song, J. Lee, S. P. Cho, K. J. Lee, and S. K. Yoo, “Support vector machine based arrhythmia classification using reduced features,” Artificial Intelligence in Medicine, vol.44, no:1, pp. 51-64, 2005.
  • [31] A. B. A. Qayyum, T. Islam, and M. A. Haque, “ECG heartbeat classification: A comparative performance analysis between one and two dimensional convolutional neural network,” in 2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON), 2019, pp. 93–96.
  • [32] C. Chen, Z. Hua, R. Zhang, G. Liu, and W. Wen, “Automated arrhythmia classification based on a combination network of CNN and LSTM,” Biomedical Signal Processing and Control, vol. 57, p. 101819, 2020.
  • [33] H. Shi, C. Qin, D. Xiao, L. Zhao, and C. Liu, “Automated heartbeat classification based on deep neural network with multiple input layers,” Knowledge-Based Systems, vol. 188, p. 105036, 2020.
  • [34] Q. Xie, S. Tu, G. Wang, Y. Lian, and L. Xu, “Feature enrichment based convolutional neural network for heartbeat classification from electrocardiogram,” IEEE Access, vol. 7, pp. 153751–153760, 2019.
  • [35] X. Xu, and H. Liu, “ECG heartbeat classification using convolutional neural networks’, IEEE Access, vol. 8, pp. 8614–8619, 2020.
  • [36] T. F. Romdhane, H. Alhichri, R. Ouni, and M. Atri, “Electrocardiogram heartbeat classification based on a deep convolutional neural network and focal loss,” Computers in Biology and Medicine, vol. 123, p. 103866, 2020.
  • [37] K. Yadav, S. Tiwari, A. Jain, and A. K. Y. Dafhalla, “Deep learning based cardiovascular disease diagnosis system from heartbeat sound,” International Journal of Speech Technology, pp. 1-12, 2021.
  • [38] W. Ullah, I. Siddique, R. M. Zulqarnain, M. M. Alam, I. Ahmad, and U. A. Raza, “Classification of arrhythmia in heartbeat detection using deep learning,” Computational Intelligence and Neuroscience, pp. 1–13, 2021.
  • [39] Y. Liang, S. Yin, Q. Tang, Z. Zheng, M. Elgendi, and Z. Chen, “Deep learning algorithm classifies heartbeat events based on electrocardiogram signals,” Frontiers in Physiology, vol. 11, 2020.
  • [40] G. B. Moody, and R. G. Mark, “The impact of the MIT-BIH arrhythmia database,” IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 3, pp. 45–50, 2001.
  • [41] Ö. Yakut, S. Solak, and E. Bolat, “IIR based digital filter design for denoising the ECG signal,” Journal Of Polytechnic, vol. 21, no. 1, 2018.
  • [42] Z. Dokur, “Yapay sinir ağları ve genetik algoritmalar kullanılarak EKG vurularının sınıflandırılması,” Fen Bilimleri Enstitüsü, İstanbul Teknik Üniversitesi, İstanbul, Türkiye, 2023.
  • [43] J. K. Das, A. Ghosh, A. K. Pal, S. Dutta, and A. Chakrabarty, “Urban sound classification using convolutional neural network and long short term memory based on multiple features,” in 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS), 2020.
  • [44] Z. Huang, C. Liu, H. Fei, W. Li, J. Yu, and Y. Cao, “Urban sound classification based on 2-order dense convolutional network using dual features,” Applied Acoustics, vol. 164, p. 107243, 2020.
  • [45] M. A. Kızrak ve B. Bolat, “Derin öğrenme ile kalabalık analizi üzerine detaylı bir araştırma,” Bilişim Teknolojileri Dergisi, vol. 11, no. 3, pp. 263–286, 2018.
  • [46] A.-M. Šimundić, “Measures of Diagnostic Accuracy: Basic Definitions,” EJIFCC, vol. 19, no. 4, pp. 203–211, 2009.
  • [47] P. Eusebi, “Diagnostic Accuracy Measures,” Cerebrovascular Diseases, vol. 36, no. 4, pp. 267–272, 2013.
  • [48] H. Xie, H. Liu, S. Zhou, T. Gao, and M. Shu, “A lightweight 2-D CNN model with dual attention mechanism for heartbeat classification,” Applied Intelligence, 2022.
  • [49] A. M. Alqudah, S. Qazan, L. Al-Ebbini, H. Alquran, and I. A. Qasmieh, “ECG heartbeat arrhythmias classification: a comparison study between different types of spectrum representation and convolutional neural networks architectures,” J Ambient Intell Human Comput, vol. 13, no. 10, pp. 4877–4907, 2022.

EKG Sinyallerinin Sınıflandırılmasında Zaman-Frekans Domenindeki Görüntülerin ve Ham Sinyal Verilerinin Karşılaştırmalı Performans Analizi

Yıl 2024, Cilt: 12 Sayı: 2, 745 - 761, 29.04.2024

Turgut Özseven

https://doi.org/10.29130/dubited.1236072

Öz

EKG sinyalleri kardiyovasküler hastalıkların klinik tanısı için kullanılan en yaygın araçlardan birisidir. Cilt yüzeyindeki elektriksel değişimlerin ölçülmesi ile EKG sinyalleri elde edilmektedir. EKG sinyallerinin uzmanın değerlendirmesi sonucu kalpte oluşan aritmiler teşhis edilmektedir. Bu teşhis uzmanın deneyimine bağlı olup subjektif bir değerlendirmedir. Bilgisayar destekli tanı sistemlerinin yaygınlaşması ile uzmanın deneyimine bağımlı değerlendirmeler objektifleşmekte ve hekime tanı için destek sağlanmaktadır. Bilgisayar destekli EKG sınıflandırma için EKG sinyallerinden atımlardan tespit edilmekte ve bu atımların yapısı incelenerek aritmiler tespit edilmektedir. Son yıllarda derin öğrenme modellerindeki yüksek başarı EKG sinyallerinin de sınıflandırılması için kullanılmaya başlanmıştır. Sınıflandırma sürecinde kullanılacak veri sinyalin morfolojik özellikleri veya görüntüsü yardımıyla gerçekleştirilmektedir. Bu çalışmanın temel amacı, EKG sinyallerinin sınıflandırılması için sayısal ve görsel kalp ritmi verilerinin sınıflandırma performanslarının karşılaştırılmasıdır. Bu amaçla, EKG sinyallerinin sınıflandırılması için 1D-CNN ve 2D-CNN mimarileri kullanılmıştır. 1D-CNN modelinin girdileri olarak kalp ritmi sinyalinin sayısal değerleri ve bu sayısal değerlerden elde edilen öznitelikler kullanılmıştır. 2D-CNN modelinin girdisi kalp ritmi sinyallinin görsel olarak temsilini içeren ham sinyal görüntüsü, spektrogram, skalogram, mel-spektrogram, GFCC ve CQT görüntüleridir. Elde edilen sonuçlar, EKG sinyallerinin sayısal temsilinin sınıflandırma için başarısız olduğunu, hand-crafted özniteliklerin %85.2 doğruluk sağladığını göstermiştir. Sinyalin görsel temsili ile elde edilen sonuçlar tüm görüntüler için %99 üzerinde sınıflandırma doğruluğu sağlamıştır. Bunlar içerisindeki en yüksek başarı ise sinyalin ham halinin görselleştirilmesi ile %99.9 olarak elde edilmiştir. Elde edilen bu bulgular doğrultusunda, EKG sinyallerinin sınıflandırılması için en uygun yöntemin 2D-CNN mimarisi ve kalp ritmi sinyalinin görsel temsili olduğunu göstermiştir.

Anahtar Kelimeler

EKG derin öğrenme kalp ritmi sınıflandırma

Kaynakça

  • [1] O. M. A. Ali, S. W. Kareem, and A. S. Mohammed, “Evaluation of Electrocardiogram Signals Classification Using CNN, SVM, and LSTM Algorithm: A review,” presented at 8th International Engineering Conference on Sustainable Technology and Development (IEC), pp. 185–191, 2022.
  • [2] “World health statistics 2018: monitoring health for the SDGs, sustainable development goals - RELACSIS | OPS/OMS,” Pan American Health Organization / World Health Organization, 2018. https://www3.paho.org/relacsis/index.php/es/noticias-relacsis/906-report-world-health-statistics-2018-monitoring-health-for-the-sdgs-sustainable-development-goals (accessed Jan. 12, 2023).
  • [3] E. J. Benjamin et al., “Heart disease and stroke statistics—2019 update: a report from the American Heart Association,” Circulation, vol. 139, no. 10, pp. e56–e528, 2019.
  • [4] X. Liu, H. Wang, Z. Li, and L. Qin, “Deep learning in ECG diagnosis: A review,” Knowledge-Based Systems, vol. 227, p. 107187, 2021.
  • [5] U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, M. Adam, A. Gertych, and R. San Tan, “A deep convolutional neural network model to classify heartbeats,” Computers in Biology and Medicine, vol. 89, pp. 389–396, 2017.
  • [6] G. De Lannoy, D. François, J. Delbeke, and M. Verleysen, “Weighted conditional random fields for supervised interpatient heartbeat classification,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 1, pp. 241–247, 2011.
  • [7] H. V. Pipberger, R. J. Arms, and F. W. Stallmann, “Automatic screening of normal and abnormal electrocardiograms by means of a digital electronic computer,” Proceedings of the Society for Experimental Biology and Medicine, vol. 106, no. 1, pp. 130–132, 1961.
  • [8] H. Malik, U. Bashir, and A. Ahmad, “Multi-classification neural network model for detection of abnormal heartbeat audio signals,” Biomedical Engineering Advances, vol. 4, p. 100048, 2022.
  • [9] M. Cao, T. Zhao, Y. Li, W. Zhang, P. Benharash, and R. Ramezani, “ECG heartbeat classification using deep transfer learning with convolutional neural network and STFT technique,” arXiv preprint arXiv:2206.14200, 2022.
  • [10] M. Degirmenci, M. A. Ozdemir, E. Izci, and A. Akan, “Arrhythmic heartbeat classification using 2d convolutional neural networks,” Irbm, vol. 43, no. 5, pp. 422–433, 2022.
  • [11] G. Yao, X. Mao, N. Li, H. Xu, X. Xu, Y. Jiao, and J. Ni, “Interpretation of electrocardiogram heartbeat by CNN and GRU,” Computational and Mathematical Methods in Medicine, vol. 2021, 2021.
  • [12] S. Aziz, S. Ahmed, and M.-S. Alouini, “ECG-based machine-learning algorithms for heartbeat classification,” Scientific reports, vol. 11, no. 1, pp. 1–14, 2021.
  • [13] S. Zhou, and B. Tan, “Electrocardiogram soft computing using hybrid deep learning CNN-ELM,” Applied soft computing, vol. 86, p. 105778, 2020.
  • [14] T. Wang, C. Lu, W. Ju, and C. Liu, “Imbalanced heartbeat classification using EasyEnsemble technique and global heartbeat information,” Biomedical Signal Processing and Control, vol. 71, p. 103105, 2022.
  • [15] D. Zhang, H. Zhou, F. Li, L. Zhang, and J. Wang, “A reparameterization multifeature fusion CNN for arrhythmia heartbeats classification,” Computational and Mathematical Methods in Medicine, vol. 2022, 2022.
  • [16] A. Tyagi and R. Mehra, “Intellectual heartbeats classification model for diagnosis of heart disease from ECG signal using hybrid convolutional neural network with GOA,” SN Applied Sciences, vol. 3, no. 2, pp. 1–14, 2021.
  • [17] Y. Xu, S. Zhang, Z. Cao, Q. Chen, and W. Xiao, “Extreme learning machine for heartbeat classification with hybrid time-domain and wavelet time-frequency features,” Journal of Healthcare Engineering, vol. 2021, 2021.
  • [18] E. Jing, H. Zhang, Z. Li, Y. Liu, Z. Ji, and I. Ganchev, “ECG heartbeat classification based on an improved ResNet-18 model,” Computational and Mathematical Methods in Medicine, vol. 2021, 2021.
  • [19] E. Essa and X. Xie, “An ensemble of deep learning-based multi-model for ECG heartbeats arrhythmia classification,” IEEE access, vol. 9, pp. 103452–103464, 2021.
  • [20] E. Maghawry, T. F. Gharib, R. Ismail, and M. J. Zaki, “An efficient heartbeats classifier based on optimizing convolutional neural network model,” IEEE access, vol. 9, pp. 153266–153275, 2021.
  • [21] S. K. Pandey, R. R. Janghel, and V. Vani, “Patient specific machine learning models for ECG signal classification,” Procedia Computer Science, vol. 167, pp. 2181–2190, 2020.
  • [22] S. L. Oh, E. Y. Ng, R. San Tan, and U. R. Acharya, “Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats,” Computers in biology and medicine, vol. 102, pp. 278–287, 2018.
  • [23] R. J. Martis, U. R. Acharya, and L. C. Min, “ECG beat classification using PCA, LDA, ICA and discrete wavelet transform,” Biomedical Signal Processing and Control, vol. 8, no. 5, pp. 437–448, 2013.
  • [24] Y. H. Hu, W. J. Tompkins, J. L. Urrusti, and V. X. Afonso, “Applications of artificial neural networks for ECG signal detection and classification,” Journal of electrocardiology, vol. 26, pp. 66–73, 1993.
  • [25] N. Izeboudjen and A. Farah, “A new neural network system for arrhythmia’s classification,” NC, vol. 98, pp. 23–25, 1998.
  • [26] J.-S. Wang, W.-C. Chiang, Y.-L. Hsu, and Y.-T. C. Yang, “ECG arrhythmia classification using a probabilistic neural network with a feature reduction method,” Neurocomputing, vol. 116, pp. 38–45, 2013.
  • [27] C. V. Banupriya and S. Karpagavalli, “Electrocardiogram beat classification using probabilistic neural network,” Int. J. Comput. Appl.(IJCA), vol. 1, no. 7, pp. 31–37, 2014.
  • [28] N. Acır, “Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm,” Neural computing & applications, vol. 14, no. 4, pp. 299–309, 2005.
  • [29] M. Moavenian and H. Khorrami, “A qualitative comparison of artificial neural networks and support vector machines in ECG arrhythmias classification,” Expert Systems with Applications, vol. 37, no. 4, pp. 3088–3093, 2010.
  • [30] M. H. Song, J. Lee, S. P. Cho, K. J. Lee, and S. K. Yoo, “Support vector machine based arrhythmia classification using reduced features,” Artificial Intelligence in Medicine, vol.44, no:1, pp. 51-64, 2005.
  • [31] A. B. A. Qayyum, T. Islam, and M. A. Haque, “ECG heartbeat classification: A comparative performance analysis between one and two dimensional convolutional neural network,” in 2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON), 2019, pp. 93–96.
  • [32] C. Chen, Z. Hua, R. Zhang, G. Liu, and W. Wen, “Automated arrhythmia classification based on a combination network of CNN and LSTM,” Biomedical Signal Processing and Control, vol. 57, p. 101819, 2020.
  • [33] H. Shi, C. Qin, D. Xiao, L. Zhao, and C. Liu, “Automated heartbeat classification based on deep neural network with multiple input layers,” Knowledge-Based Systems, vol. 188, p. 105036, 2020.
  • [34] Q. Xie, S. Tu, G. Wang, Y. Lian, and L. Xu, “Feature enrichment based convolutional neural network for heartbeat classification from electrocardiogram,” IEEE Access, vol. 7, pp. 153751–153760, 2019.
  • [35] X. Xu, and H. Liu, “ECG heartbeat classification using convolutional neural networks’, IEEE Access, vol. 8, pp. 8614–8619, 2020.
  • [36] T. F. Romdhane, H. Alhichri, R. Ouni, and M. Atri, “Electrocardiogram heartbeat classification based on a deep convolutional neural network and focal loss,” Computers in Biology and Medicine, vol. 123, p. 103866, 2020.
  • [37] K. Yadav, S. Tiwari, A. Jain, and A. K. Y. Dafhalla, “Deep learning based cardiovascular disease diagnosis system from heartbeat sound,” International Journal of Speech Technology, pp. 1-12, 2021.
  • [38] W. Ullah, I. Siddique, R. M. Zulqarnain, M. M. Alam, I. Ahmad, and U. A. Raza, “Classification of arrhythmia in heartbeat detection using deep learning,” Computational Intelligence and Neuroscience, pp. 1–13, 2021.
  • [39] Y. Liang, S. Yin, Q. Tang, Z. Zheng, M. Elgendi, and Z. Chen, “Deep learning algorithm classifies heartbeat events based on electrocardiogram signals,” Frontiers in Physiology, vol. 11, 2020.
  • [40] G. B. Moody, and R. G. Mark, “The impact of the MIT-BIH arrhythmia database,” IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 3, pp. 45–50, 2001.
  • [41] Ö. Yakut, S. Solak, and E. Bolat, “IIR based digital filter design for denoising the ECG signal,” Journal Of Polytechnic, vol. 21, no. 1, 2018.
  • [42] Z. Dokur, “Yapay sinir ağları ve genetik algoritmalar kullanılarak EKG vurularının sınıflandırılması,” Fen Bilimleri Enstitüsü, İstanbul Teknik Üniversitesi, İstanbul, Türkiye, 2023.
  • [43] J. K. Das, A. Ghosh, A. K. Pal, S. Dutta, and A. Chakrabarty, “Urban sound classification using convolutional neural network and long short term memory based on multiple features,” in 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS), 2020.
  • [44] Z. Huang, C. Liu, H. Fei, W. Li, J. Yu, and Y. Cao, “Urban sound classification based on 2-order dense convolutional network using dual features,” Applied Acoustics, vol. 164, p. 107243, 2020.
  • [45] M. A. Kızrak ve B. Bolat, “Derin öğrenme ile kalabalık analizi üzerine detaylı bir araştırma,” Bilişim Teknolojileri Dergisi, vol. 11, no. 3, pp. 263–286, 2018.
  • [46] A.-M. Šimundić, “Measures of Diagnostic Accuracy: Basic Definitions,” EJIFCC, vol. 19, no. 4, pp. 203–211, 2009.
  • [47] P. Eusebi, “Diagnostic Accuracy Measures,” Cerebrovascular Diseases, vol. 36, no. 4, pp. 267–272, 2013.
  • [48] H. Xie, H. Liu, S. Zhou, T. Gao, and M. Shu, “A lightweight 2-D CNN model with dual attention mechanism for heartbeat classification,” Applied Intelligence, 2022.
  • [49] A. M. Alqudah, S. Qazan, L. Al-Ebbini, H. Alquran, and I. A. Qasmieh, “ECG heartbeat arrhythmias classification: a comparison study between different types of spectrum representation and convolutional neural networks architectures,” J Ambient Intell Human Comput, vol. 13, no. 10, pp. 4877–4907, 2022.
Toplam 49 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Turgut Özseven 0000-0002-6325-461X

Yayımlanma Tarihi 29 Nisan 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 12 Sayı: 2

Kaynak Göster

APA Özseven, T. (2024). Comparative Performance Analysis of Time-Frequency Domain Images and Raw Signal Data for Classification of ECG Signals. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, 12(2), 745-761. https://doi.org/10.29130/dubited.1236072
AMA Özseven T. Comparative Performance Analysis of Time-Frequency Domain Images and Raw Signal Data for Classification of ECG Signals. DÜBİTED. Nisan 2024;12(2):745-761. doi:10.29130/dubited.1236072
Chicago Özseven, Turgut. “Comparative Performance Analysis of Time-Frequency Domain Images and Raw Signal Data for Classification of ECG Signals”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi 12, sy. 2 (Nisan 2024): 745-61. https://doi.org/10.29130/dubited.1236072.
EndNote Özseven T (01 Nisan 2024) Comparative Performance Analysis of Time-Frequency Domain Images and Raw Signal Data for Classification of ECG Signals. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 12 2 745–761.
IEEE T. Özseven, “Comparative Performance Analysis of Time-Frequency Domain Images and Raw Signal Data for Classification of ECG Signals”, DÜBİTED, c. 12, sy. 2, ss. 745–761, 2024, doi: 10.29130/dubited.1236072.
ISNAD Özseven, Turgut. “Comparative Performance Analysis of Time-Frequency Domain Images and Raw Signal Data for Classification of ECG Signals”. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 12/2 (Nisan 2024), 745-761. https://doi.org/10.29130/dubited.1236072.
JAMA Özseven T. Comparative Performance Analysis of Time-Frequency Domain Images and Raw Signal Data for Classification of ECG Signals. DÜBİTED. 2024;12:745–761.
MLA Özseven, Turgut. “Comparative Performance Analysis of Time-Frequency Domain Images and Raw Signal Data for Classification of ECG Signals”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, c. 12, sy. 2, 2024, ss. 745-61, doi:10.29130/dubited.1236072.
Vancouver Özseven T. Comparative Performance Analysis of Time-Frequency Domain Images and Raw Signal Data for Classification of ECG Signals. DÜBİTED. 2024;12(2):745-61.
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