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A multi-label classification system for anomaly classification in electrocardiogram

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Abstract

Automatic classification of ECG signals has become a research hotspot, and most of the research work in this field is currently aimed at single-label classification. However, a segment of ECG signal may contain more than two cardiac diseases, and single-label classification cannot accurately judge all possibilities. Besides, single-label classification performs classification in units of segmented beats, which destroys the contextual relevance of signal data. Therefore, studying the multi-label classification of ECG signals becomes more critical. This study proposes a method based on the multi-label question transformation method-binary correlation and classifies ECG signals by constructing a deep sequence model. Binary correlation simplifies the learning difficulty of deep learning models and converts multi-label problems into multiple binary classification problems. The experimental results are as follows: F1 score is 0.767, Hamming Loss is 0.073, Coverage is 3.4, and Ranking Loss is 0.262. It performs better than existing work.

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References

  1. Hong S, Zhou Y, Shang J, Xiao C, Sun J. Opportunities and challenges of deep learning methods for electrocardiogram data: a systematic review. Comput Biol Med. 2020;122:103801.

    Article  Google Scholar 

  2. Sarki R, Ahmed K, Wang H, Zhang Y. Automated detection of mild and multi-class diabetic eye diseases using deep learning. Health Inf Sci Syst. 2020;8(1):1–9.

    Article  Google Scholar 

  3. Sun L, Zhou R, Peng D, Bouguettaya A, Zhang Y. Automatically building service-based systems with function relaxation. IEEE Transactions on Cybernetics; 2022.

  4. He J, Rong J, Sun L, Wang H, Zhang Y, Ma J. A framework for cardiac arrhythmia detection from iot-based ECGs. World Wide Web. 2020;23(5):2835–50.

    Article  Google Scholar 

  5. Pandey D, Yin X, Wang H, Zhang Y. Accurate vessel segmentation using maximum entropy incorporating line detection and phase-preserving denoising. Comput Vis Image Underst. 2017;155:162–72.

    Article  Google Scholar 

  6. Yu Q, Sun L. Lpclass: lightweight personalized sensor data classification in computational social systems. IEEE Transactions on Computational Social Systems; 2022.

  7. Sun L, Wu J. A scalable and transferable federated learning system for classifying healthcare sensor data. IEEE J Biomed Health Inf; 2022.

  8. Tan W, Zhou L, Li X, Yang X, Chen Y, Yang J. Analysis of segmentation of lung parenchyma based on deep learning methods. J X-Ray Sci Technol. 2021;29:945–59.

    Article  Google Scholar 

  9. Du J, Michalska S, Subramani S, Wang H, Zhang Y. Neural attention with character embeddings for hay fever detection from twitter. Health Inf Sci Syst. 2019;7(1):1–7.

    Article  Google Scholar 

  10. Petmezas G, Haris K, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, Katsaggelos AK, Maglaveras N. Automated atrial fibrillation detection using a hybrid CNN-ISTM network on imbalanced ECG datasets. Biomed Signal Process Control. 2021;63: 102194.

    Article  Google Scholar 

  11. Sun Z, Wang C, Zhao Y, Yan C. Multi-label ECG signal classification based on ensemble classifier. IEEE Access. 2020;8:117986–96.

    Article  Google Scholar 

  12. Huang H, Lv J, Pu Y, Wang Y, Zhu J. Multi-label diagnosis algorithm for arrhythmia diseases based on improved classifier chains. In: Fei M, Chen L, Ma S, Li X, editors. Intelligent life system modelling, image processing and analysis. New York: Springer; 2021. p. 94–103.

    Chapter  Google Scholar 

  13. Tan W, Zhou L, Li X, Yang X, Chen Y, Yang J. Automated vessel segmentation in lung CT and CTA images via deep neural networks. J X-Ray Sci Technol. 2021;2021:1–15.

    Google Scholar 

  14. Naz M, Shah JH, Khan MA, Sharif M, Raza M, Damaševičius R. From ECG signals to images: a transformation based approach for deep learning. PeerJ Comput Sci. 2021;7:386.

    Article  Google Scholar 

  15. Li H, Wang Y, Wang H, Zhou B. Multi-window based ensemble learning for classification of imbalanced streaming data. World Wide Web. 2017;20(6):1507–25.

    Article  Google Scholar 

  16. Yin J, Tang M, Cao J, Wang H, You M, Lin Y. Vulnerability exploitation time prediction: an integrated framework for dynamic imbalanced learning. World Wide Web. 2021;25:1–23.

    Google Scholar 

  17. Tithi SR, Aktar A, Aleem F, Chakrabarty A. ECG data analysis and heart disease prediction using machine learning algorithms. In: 2019 IEEE Region 10 Symposium (TENSYMP); 2019. pp. 819–824. https://doi.org/10.1109/TENSYMP46218.2019.8971374.

  18. Subramanian K, Prakash NK. Machine learning based cardiac arrhythmia detection from ECG signal. In: 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT); 2020. pp. 1137–1141. https://doi.org/10.1109/ICSSIT48917.2020.9214077.

  19. Singh V, Tewary S, Sardana V, Sardana HK. Arrhythmia detection - a machine learning based comparative analysis with mit-bih ecg data. In: 2019 IEEE 5th International Conference for Convergence in Technology (I2CT); 2019. pp. 1–5. https://doi.org/10.1109/I2CT45611.2019.9033665.

  20. Bulbul HI, Usta N, Yildiz M. Classification of ECG arrhythmia with machine learning techniques. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA); 2017. pp. 546–549. https://doi.org/10.1109/ICMLA.2017.0-104.

  21. Chakraborty A, Chatterjee S, Majumder K, Shaw RN, Ghosh AA. comparative study of myocardial infarction detection from ECG data using machine learning. In: Advanced computing and intelligent technologies. New York: Springer; 2022. p. 257–67.

    Chapter  Google Scholar 

  22. Dohare AK, Kumar V, Kumar R. Detection of myocardial infarction in 12 lead ECG using support vector machine. Appl Soft Comput. 2018;64:138–47.

    Article  Google Scholar 

  23. Celin S, Vasanth K. ECG signal classification using various machine learning techniques. J Med Syst. 2018;42(12):1–11.

    Article  Google Scholar 

  24. Li Y, Zhang Z, Zhou F, Xing Y, Li J, Liu C. Multi-label classification of arrhythmia for long-term electrocardiogram signals with feature learning. IEEE Trans Instrum Meas. 2021;70:1–11. https://doi.org/10.1109/TIM.2021.3077667.

    Article  Google Scholar 

  25. Jiang Z, Almeida TP, Schlindwein FS, Ng GA, Zhou H, Li X. Diagnostic of multiple cardiac disorders from 12-lead ECGs using graph convolutional network based multi-label classification. In: 2020 Computing in Cardiology; 2020. pp. 1–4. https://doi.org/10.22489/CinC.2020.135.

  26. Cai J, Sun W, Guan J, You I. Multi-ECGNet for ECG arrythmia multi-label classification. IEEE Access. 2020;8:110848–58. https://doi.org/10.1109/ACCESS.2020.3001284.

    Article  Google Scholar 

  27. Li Y, Zhang Z, Zhou F, Xing Y, Li J, Liu C. Multi-label feature selection for long-term electrocardiogram signals. In: 2020 International Conference on Sensing, Measurement Data Analytics in the Era of Artificial Intelligence (ICSMD); 2020. pp. 335–340. https://doi.org/10.1109/ICSMD50554.2020.9261669.

  28. Nejedly P, Ivora A, Viscor I, Halamek J, Jurak P, Plesinger F. Utilization of residual CNN-GRU with attention mechanism for classification of 12-lead ECG. In: IEEE 2020 Computing in Cardiology; 2020. pp. 1–4.

  29. Jia W, Xu X, Xu X, Sun Y, Liu X. Automatic detection and classification of 12-lead ECGs using a deep neural network. In: IEEE 2020 Computing in Cardiology; 2020. pp. 1–4.

  30. Cai W, Hu S, Yang J, Cao J. Automatic 12-lead ECG classification using deep neural networks. In: IEEE 2020 Computing in Cardiology; 2020. pp. 1–4.

  31. Bodini M, Rivolta MW, Sassi R. Classification of 12-lead ECG with an ensemble machine learning approach. In: 2020 Computing in Cardiology; 2020. pp. 1–4. https://doi.org/10.22489/CinC.2020.406.

  32. Sun L, Wang Y, Qu Z, Xiong NN. Beatclass: a sustainable ECG classification system in iot-based ehealth. IEEE Internet Things J. 2021;9:7178–95.

    Article  Google Scholar 

  33. Wang Y, Sun L, Subramani S. Cab: classifying arrhythmias based on imbalanced sensor data. KSII Trans Internet Inf Syst (TIIS). 2021;15(7):2304–20.

    Google Scholar 

  34. Sun L, Yu Q, Peng D, Subramani S, Wang X. Fogmed: a fog-based framework for disease prognosis based medical sensor data streams. CMC-Comput Mater Continua. 2021;66(1):603–19.

    Article  Google Scholar 

  35. Sun Z, Wang C, Zhao Y, Yan C. Multi-label ECG signal classification based on ensemble classifier. IEEE Access. 2020;8:117986–96. https://doi.org/10.1109/ACCESS.2020.3004908.

    Article  Google Scholar 

  36. Wong AW, Salimi A, Hindle A, Kalmady SV, Kaul P. Multilabel 12-lead electrocardiogram classification using beat to sequence autoencoders. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2021. pp. 1270–1274 . https://doi.org/10.1109/ICASSP39728.2021.9414934.

  37. Baydoun M, Safatly L, Abou Hassan OK, Ghaziri H, El Hajj A, Isma’eel H. High precision digitization of paper-based ECG records: a step toward machine learning. IEEE J Transl Eng Health and Med. 2019;7:1–8. https://doi.org/10.1109/JTEHM.2019.2949784.

    Article  Google Scholar 

  38. ; Salem M, Taheri S, Yuan J. ECG arrhythmia classification using transfer learning from 2- dimensional deep cnn features. In: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS); 2018. pp. 1–4. https://doi.org/10.1109/BIOCAS.2018.8584808.

  39. Satija U, Ramkumar B, Manikandan MS. A new automated signal quality-aware ECG beat classification method for unsupervised ECG diagnosis environments. IEEE Sens J. 2019;19(1):277–86. https://doi.org/10.1109/JSEN.2018.2877055.

    Article  Google Scholar 

  40. Liu TY, Lin KJ, Wu HC. ECG data encryption then compression using singular value decomposition. IEEE J Biomed Health Inform. 2017;22(3):707–13.

    Article  Google Scholar 

  41. Qiu H, Qiu M, Lu Z. Selective encryption on ECG data in body sensor network based on supervised machine learning. Inf Fusion. 2020;55:59–67.

    Article  Google Scholar 

  42. Ibaida A, Abuadbba A, Chilamkurti N. Privacy-preserving compression model for efficient iomt ECG sharing. Comput Commun. 2021;166:1–8.

    Article  Google Scholar 

  43. Raheja N, Manocha AK. Iot based ECG monitoring system with encryption and authentication in secure data transmission for clinical health care approach. Biomed Signal Process Control. 2022;74: 103481.

    Article  Google Scholar 

  44. Hsu PY, Hsu PH, Lee TH, Liu HL. Multi-label arrhythmia classification from 12-lead electrocardiograms. In: IEEE 2020 Computing in Cardiology; 2020. p. 1–4.

  45. Jiang Z, Almeida TP, Schlindwein FS, Ng GA, Zhou H, Li X. Diagnostic of multiple cardiac disorders from 12-lead ECGs using graph convolutional network based multi-label classification. In: IEEE 2020 Computing in Cardiology; 2020. pp. 1–4.

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Authors and Affiliations

  1. Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China

    Chenyang Li, Le Sun & Shangwe Charmant Nicolas

  2. Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China

    Chenyang Li, Le Sun & Shangwe Charmant Nicolas

  3. School of Computer Science and Network Engineering, Guangzhou University, Guangzhou, China

    Dandan Peng

  4. Information Technology Discipline, Victoria University, Melbourne, Australia

    Sudha Subramani

Authors
  1. Chenyang Li
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  2. Le Sun
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  4. Sudha Subramani
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  5. Shangwe Charmant Nicolas
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Correspondence to Le Sun.

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Li, C., Sun, L., Peng, D. et al. A multi-label classification system for anomaly classification in electrocardiogram. Health Inf Sci Syst 10, 19 (2022). https://doi.org/10.1007/s13755-022-00192-w

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