A Deep Neuro-Fuzzy Method for ECG Big Data Analysis via Exploring Multimodal Feature Fusion
Pages 444 - 456
Abstract
In the realm of medical data processing, particularly in the diagnosis and monitoring of cardiac diseases, the analysis of electrocardiogram (ECG) signals represents a critical challenge, especially with the burgeoning volume of ECG Big Data. Traditional methods and existing research often fall short in effectively analyzing this data, limited by their inability to fully capture the complex and nonlinear patterns inherent in ECG signals. Addressing these limitations, in this article, we introduce a novel deep neuro-fuzzy model augmented with multimodal feature fusion. Our method ingeniously combines the power of neuro-fuzzy systems with the robust feature extraction capabilities of deep learning, specifically leveraging a Transformer-based architecture, to analyze both ECG signals and their corresponding spectral images. This multimodal fusion not only enriches the model's input data, providing a comprehensive understanding of cardiac signals, but also enhances the adaptability and accuracy of cardiac arrhythmia detection. We rigorously validate our approach on the MIT-BIH arrhythmia database, conducting a series of experiments, including performance evaluations and ablation studies, to highlight the significant contributions of the multimodal feature fusion and neuro-fuzzy module. The results achieve significant improvements in classification metrics: an accuracy of 98.46% and an F1 score of 99.1%. Moreover, we benchmark the Transformer's feature extraction performance against other architectures, such as ResNet. The results unequivocally demonstrate our model's superiority and illustrate the potential of integrated neuro-fuzzy and deep learning approaches in overcoming the current limitations of ECG signal analysis.
References
[1]
H. Blackburn, A. Keys, E. Simonson, P. Rautaharju, and S. Punsar, “The electrocardiogram in population studies: A classification system,” Circulation, vol. 21, no. 6, pp. 1160–1175, 1960.
[2]
N. Talpur, S. J. Abdulkadir, H. Alhussian, M.H. N. H. Aziz, and A. Bamhdi, “A comprehensive review of deep neuro-fuzzy system architectures and their optimization methods,” Neural Comput. Appl., pp. 1–39, 2022.
[3]
M. Yeganejou, S. Dick, and J. Miller, “Interpretable deep convolutional fuzzy classifier,” IEEE Trans. Fuzzy Syst., vol. 28, no. 7, pp. 1407–1419, Jul. 2019.
[4]
G. Srivastava, J. C.-W. Lin, D. Pamucar, and S. Kotsiantis, “Applications of fuzzy systems in data science and Big Data,” IEEE Trans. Fuzzy Syst., vol. 29, no. 1, pp. 1–3, Jan. 2021.
[5]
D. Lahat, T. Adali, and C. Jutten, “Multimodal data fusion: An overview of methods, challenges, and prospects,” Proc. IEEE, vol. 103, no. 9, pp. 1449–1477, Sep. 2015.
[6]
S. A. Shufni and M. Y. Mashor, “ECG signals classification based on discrete wavelet transform, time domain and frequency domain features,” in Proc. 2015 2nd Int. Conf. Biomed. Eng. (ICoBE), 2015, pp. 1–6.
[7]
Y. A. Altay and A. S. Kremlev, “Comparative analysis of ECG signal processing methods in the time-frequency domain,” in Proc. 2018 IEEE Conf. Russian Young Researchers Elect. Electron. Eng. (EIConRus), 2018, pp. 1058–1062.
[8]
V. Mazaheri and H. Khodadadi, “Heart arrhythmia diagnosis based on the combination of morphological, frequency and nonlinear features of ECG signals and metaheuristic feature selection algorithm,” Expert Syst. Appl., vol. 161, 2020, Art. no.
[9]
J. Wang et al., “Adversarial de-noising of electrocardiogram,” Neurocomputing, vol. 349, pp. 212–224, 2019.
[10]
A. Biran and A. Jeremic, “ECG based human identification using short time Fourier transform and histograms of fiducial QRS features,” in Proc. Biosignals, 2020, pp. 324–329.
[11]
Z. Wu, T. Lan, C. Yang, and Z. Nie, “A novel method to detect multiple arrhythmias based on time-frequency analysis and convolutional neural networks,” IEEE Access, vol. 7, pp. 170820–170830, 2019.
[12]
S. S. Abdeldayem and T. Bourlai, “Automatically detecting arrhythmia-related irregular patterns using the temporal and spectro-temporal textures of ECG signals,” in Proc. IEEE 2018 24th Int. Conf. Pattern Recognit. (ICPR), 2018, pp. 2301–2307.
[13]
M. K. Gautam and V. K. Giri, “A neural network approach and wavelet analysis for ECG classification,” in Proc. IEEE 2016 Int. Conf. Eng. Technol. (ICETECH), 2016, pp. 1136–1141.
[14]
S. Pal and M. Mitra, “Detection of ECG characteristic points using multiresolution wavelet analysis based selective coefficient method,” Measurement, vol. 43, no. 2, pp. 255–261, 2010.
[15]
U. Desai, R. J. Martis, C. G. Nayak, K. Sarika, and G. Seshikala, “Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques,” in Proc. 2015 Annu. IEEE India Conf. (INDICON), 2015, pp. 1–4.
[16]
E. Alickovic and A. Subasi, “Medical decision support system for diagnosis of heart arrhythmia using DWT and random forests classifier,” J. Med. Syst., vol. 40, no. 4, 2016, Art. no.
[17]
S. Raj and K. C. Ray, “Sparse representation of ECG signals for automated recognition of cardiac arrhythmias,” Expert Syst. Appl., vol. 105, pp. 49–64, 2018.
[18]
Z. I. Attia et al., “An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: A retrospective analysis of outcome prediction,” Lancet, vol. 394, no. 10201, pp. 861–867, 2019.
[19]
H. Zhu et al., “Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: A cohort study,” Lancet Digit. Health, vol. 2, no. 7, pp. e348–e357, 2020.
[20]
Q. Xie, S. Tu, G. Wang, Y. Lian, and L. Xu, “Discrete biorthogonal wavelet transform based convolutional neural network for atrial fibrillation diagnosis from electrocardiogram,” in Proc. Int. Joint Conf. Artif. Intell., 2020, pp. 4403–4409.
[21]
P. Xiong, Y. Xue, M. Liu, H. Du, H. Wang, and X. Liu, “Detection of inferior myocardial infarction based on densely connected convolutional neural network,” Sheng wu yi xue Gong Cheng xue za zhi= J. Biomed. Engineering= Shengwu Yixue Gongchengxue Zazhi, vol. 37, no. 1, pp. 142–149, 2020.
[22]
S. Al-Zaiti et al., “Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram,” Nature Commun., vol. 11, no. 1, 2020, Art. no.
[23]
A. Mostayed, J. Luo, X. Shu, and W. Wee, “Classification of 12-lead ECG signals with bi-directional LSTM network,” 2018, arXiv:1811.02090.
[24]
Q. Yao, R. Wang, X. Fan, J. Liu, and Y. Li, “Multi-class arrhythmia detection from 12-lead varied-length ECG using attention-based time-incremental convolutional neural network,” Inf. Fusion, vol. 53, pp. 174–182, 2020 .
[25]
S. Saadatnejad, M. Oveisi, and M. Hashemi, “LSTM-based ECG classification for continuous monitoring on personal wearable devices,” IEEE J. Biomed. Health Inform., vol. 24, no. 2, pp. 515–523, 2019.
[26]
J. Wang, R. Li, R. Li, B. Fu, C. Xiao, and D. Z. Chen, “Towards interpretable arrhythmia classification with human-machine collaborative knowledge representation,” IEEE Trans. Biomed. Eng., vol. 68, no. 7, pp. 2098–2109, Jul. 2021.
[27]
X. Xu, S. Jeong, and J. Li, “Interpretation of electrocardiogram (ECG) rhythm by combined CNN and BILSTM,” IEEE Access, vol. 8, pp. 125380–125388, 2020.
[28]
Y. Wang, G. Yang, S. Li, Y. Li, L. He, and D. Liu, “Arrhythmia classification algorithm based on multi-head self-attention mechanism,” Biomed. Signal Process. Control, vol. 79, 2023, Art. no.
[29]
Y. Zhang, J. Yi, A. Chen, and L. Cheng, “Cardiac arrhythmia classification by time–frequency features inputted to the designed convolutional neural networks,” Biomed. Signal Process. Control, vol. 79, 2023, Art. no.
[30]
L. Subramanyan and U. Ganesan, “A novel deep neural network for detection of atrial fibrillation using ECG signals,” Knowl.-Based Syst., vol. 258, 2022, Art. no.
[31]
S. Śmigiel, K. Pałczyński, and D. Ledziński, “ECG signal classification using deep learning techniques based on the PTB-XL dataset,” Entropy, vol. 23, no. 9, 2021, Art. no.
[32]
A. Rath, D. Mishra, G. Panda, S. C. Satapathy, and K. Xia, “Improved heart disease detection from ECG signal using deep learning based ensemble model,” Sustain. Comput.: Inform. Syst., vol. 35, 2022, Art. no.
[33]
Ö. Yildirim, “A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification,” Comput. Biol. Med., vol. 96, pp. 189–202, 2018.
[34]
G. Bortolan and W. Pedrycz, “Fuzzy descriptive models: An interactive framework of information granulation [ECG data],” IEEE Trans. Fuzzy Syst., vol. 10, no. 6, pp. 743–755, Dec. 2002.
[35]
R. Xiao et al., “Integrating multimodal information in machine learning for classifying acute myocardial infarction,” Physiol. Meas., vol. 44, no. 4, 2023, Art. no.
[36]
R. Sharma, C. F. Eick, and N. V. Tsekos, “SM2N2: A stacked architecture for multimodal data and its application to myocardial infarction detection,” in Proc. 2020 Stat. Atlases Comput. Models Heart. MMs EMIDEC Challenges: 11th Int. Workshop, STACOM 2020, Held Conjunction MICCAI, Lima, Peru, Oct. 2020, pp. 342–350.
[37]
M. R. Schwob, A. Dempsey, F. Zhan, J. Zhan, and A. Mehmood, “Robust multimodal heartbeat detection using hybrid neural networks,” IEEE Access, vol. 8, pp. 82201–82214, 2020.
[38]
M. Zarrabi et al., “A system for accurately predicting the risk of myocardial infarction using PCG, ECG and clinical features,” Biomed. Eng.: Appl., Basis Commun., vol. 29, no. 03, 2017, Art. no.
[39]
R. Xiao, C. Ding, X. Hu, and J. Zégre-Hemsey, “Ml for MI-integrating multimodal information in machine learning for predicting acute myocardial infarction,” medRxiv, pp. 2022–10, 2022.
[40]
Z. Ahmad, A. Tabassum, L. Guan, and N. M. Khan, “ECG heartbeat classification using multimodal fusion,” IEEE Access, vol. 9, pp. 100615–100626, 2021.
[41]
T. Phan et al., “Multimodality multi-lead ECG arrhythmia classification using self-supervised learning,” in Proc. 2022 IEEE-EMBS Int. Conf. Biomed. Health Inform. (BHI), 2022, pp. 1–4.
[42]
A. Vaswani et al., “Attention is all you need,” in Proc. Adv. Neural Inf. Process. Syst., 2017, vol. 30, pp. 1–11.
[43]
Z. Liu et al., “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proc. IEEE/CVF Int. Conf. Comput. Vis., 2021, pp. 10012–10022.
[44]
R. J. Martis, U. R. Acharya, K. Mandana, A. K. Ray, and C. Chakraborty, “Cardiac decision making using higher order spectra,” Biomed. Signal Process. Control, vol. 8, no. 2, pp. 193–203, 2013.
[45]
U. R. Acharya et al., “A deep convolutional neural network model to classify heartbeats,” Comput. Biol. Med., vol. 89, pp. 389–396, 2017.
[46]
S. Kiranyaz, T. Ince, and M. Gabbouj, “Real-time patient-specific ECG classification by 1-D convolutional neural networks,” IEEE Trans. Biomed. Eng., vol. 63, no. 3, pp. 664–675, Mar. 2016.
[47]
S. L. Oh, E. Y. Ng, R. S. Tan, and U. R. Acharya, “Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats,” Comput. Biol. Med., vol. 102, pp. 278–287, 2018.
[48]
P. Novotna, T. Vicar, M. Ronzhina, J. Hejc, and J. Kolarova, “Deep-learning premature contraction localization in 12-lead ECG from whole signal annotations,” in Proc. IEEE Comput. Cardiol., 2020, pp. 1–4.
[49]
H. I. Fawaz et al., “InceptionTime: Finding AlexNet for time series classification,” Data Mining Knowl. Discov., vol. 34, no. 6, pp. 1936–1962, 2020.
[50]
N. Talpur, S. J. Abdulkadir, and M. H. Hasan, “A deep learning based neuro-fuzzy approach for solving classification problems,” in Proc. IEEE 2020 Int. Conf. Comput. Intell. (ICCI), 2020, pp. 167–172.
[51]
P. De Chazal, M. O'Dwyer, and R. B. Reilly, “Automatic classification of heartbeats using ECG morphology and heartbeat interval features,” IEEE Trans. Biomed. Eng., vol. 51, no. 7, pp. 1196–1206, Jul. 2004.
[52]
M. Tan and Q. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proc. Int. Conf. Mach. Learn., PMLR, 2019, pp. 6105–6114.
[53]
L. Chen, C. Lian, Z. Zeng, B. Xu, and Y. Su, “Cross-modal multiscale multi-instance learning for long-term ECG classification,” Inf. Sci., vol. 643, 2023, Art. no.
[54]
Association for the Advancement of Medical Instrumentation and others, “ANSI/AAMI EC57: 2012. Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms,” Amer. Nat. Std., 2013.
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