پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شمارهها | 702 |
| تعداد مقالات | 10,131 |
| تعداد مشاهده مقاله | 71,399,332 |
| تعداد دریافت فایل اصل مقاله | 63,119,071 |
Inception based GAN for ECG arrhythmia classification | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، Special Issue، اسفند 2021، صفحه 1585-1594 اصل مقاله (417.29 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.5831 | ||
| نویسندگان | ||
| Neerajkumar S. Sathawane* ؛ Ulhaskumar Gokhale؛ Dinesh Padole؛ Sanjay Wankhede | ||
| GHRCOE, Nagpur, India | ||
| تاریخ دریافت: 17 مرداد 1400، تاریخ بازنگری: 06 آبان 1400، تاریخ پذیرش: 22 آبان 1400 | ||
| چکیده | ||
| Cardiovascular diseases are the world's principal reason for death, accounting it about 17.9 million people per year, as reported by World Health Organization(WHO). Arrhythmia is often a heart disease that is interpreted by a variation in the linearity of the heartbeat. The goal of this study would be to develop a new deep learning technique to accurately interpret arrhythmia utilizing a one-second segment. This paper introduces a novel method for automatic GAN-based arrhythmia classification. The input ECG signal is derived from the fusion of well known Physionet dataset from MIT-BIH and some Hospital ECG databases. The ECG segment over time is used to detect 15 different classes of arrhythmias. The GAN network uses an attention-based generator to learn local essential features and to maintain data integrity for both time and frequency domains. Among these, the highest accuracy obtained is 98\%. It can be inferred from the results that the proposed approach is smart enough to make meaningful predictions and produces excellent performance on the related metrics. | ||
| کلیدواژهها | ||
| Electrocardiogram؛ ECG classification؛ Inception؛ GAN؛ Generative adversarial network | ||
| مراجع | ||
|
[1] 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, Biomed. Eng. Lett. 11 (2021) 69—84. [2] J. Ferretti, V. Randazzo, G. Cirrincione and E. Pasero, 1-D convolutional neural network for ECG arrhythmia classification, Prog. Artific. Intell. Neural Syst. 184 (2021) 269–279, [3] S.I. Haider and M. Alhussein, Detection and classification of baseline-wander noise in ECG signals using discrete wavelet transform and decision tree classifier, Elektron. Elektrotechnika 25(4)(2019) 47—57. [4] B.M. Mathunjwa, Y. Tsong Lin, Ch. Hung Lin, M. F. Abbod and J. Shing Shieh, ECG arrhythmia classification by using a recurrence plot and convolutional neural network, Biomed. Signal Proces. Control. 64 (102262) (2021. [5] F. Meneguitti Dias, H. L.M. Monteiro, Th. Wulfert Cabral, R. Naji, M. Kuehni and E. Jos´e da S. Luz, Arrhythmia classification from single-lead ECG signals using the inter-patient paradigm, Comput. Meth. Prog. Biomed. 202(105948) (2021). [6] S.K. Mohapatra and M.N. Mohanty, Convolutional Neural Network Based Arrhythmia Classification with Selective Features from Empirical Mode Decomposition, Proc. Second Int. Conf. Inf. Manag. Machine Intell. Lecture Notes in Networks and Systems, Singapore, 166 (2021) 375–383. [7] S.K. Pandey and R.R. Janghel, Automated detection of arrhythmia from electrocardiogram signal based on new convolutional encoded features with bidirectional long short-term memory network classifier, Phys. Eng. Sci. Med (2021). [8] G. Petmezas, K. Haris, L. Stefanopoulos, V. Kilintzis, A. Tzavelis, J. A Rogers, A. K Katsaggelos and N. Maglaveras, Automated Atrial Fibrillation Detection using a Hybrid CNN-LSTM Network on Imbalanced ECG Datasets, Biomed. Signal Proces. Control. 63(102194) (2021). [9] A. Peimankar and S. Puthusserypady, DENS-ECG: A deep learning approach for ECG signal delineation, Expert Syst. Appl. 165 (113911) (2021). [10] M. Rashed-Al-Mahfuz, M.A. Moni, P. Lio, S M. Shariful Islam, S. Berkovsky, M. Khushi and J.M.W. Quinn, Deep convolutional neural networks based ECG beats classification to diagnose cardiovascular conditions, Biomed. Eng. Lett. 11 (2021) 147—162. [11] S. Sakib, M. M. Fouda and Z. M. Fadlullah, A rigorous analysis of biomedical edge computing: An arrhythmia classification use-case Lleveraging deep learning, 2020 IEEE Int. Conf. Internet Things Intell. Syst. BALI, Indonesia, (2021) 136–141. [12] U. Satija, B. Ramkumar and M.S. Manikandan, A review of signal processing techniques for electrocardiogram signal quality assessment, IEEE Rev. Biomed. Engin. 11(1) (2018) 36-–52. [13] X. Wang, Y. Zhou, M. Shu, Y. Wang and A. Dong, ECG baseline wander correction and denoising based on sparsity, IEEE Access, 7 (2019) 31573-–31585. [14] [https://www.physionet.org/physiobank/database/mitdb/ ”PhysioBank,” vol. 2004: Physionet.]. [15] [https://en.wikipedia.org/]. [16] News : [https://www.who.int/health-topics/cardiovascular-diseasestab=tab 1]. | ||
|
آمار تعداد مشاهده مقاله: 44,696 تعداد دریافت فایل اصل مقاله: 50,365 |
||