An optimized convolutional neural network for arrhythmia classification
<p>Electrocardiogram (ECG) is a practical medical test to diagnose arrhythmia. As a</p><p>crucial computational application in clinical practice, ECG automatic classification can</p><p>effectively detect the possible occurrence of...
Saved in:
| Main Author: | |
|---|---|
| Format: | thesis |
| Language: | zsm |
| Published: |
2022
|
| Subjects: | |
| Online Access: | https://ir.upsi.edu.my/detailsg.php?det=9598 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
oai:ir.upsi.edu.my:9598 |
|---|---|
| record_format |
uketd_dc |
| institution |
Universiti Pendidikan Sultan Idris |
| collection |
UPSI Digital Repository |
| language |
zsm |
| topic |
Q Science |
| spellingShingle |
Q Science Shan, Wei Chen An optimized convolutional neural network for arrhythmia classification |
| description |
<p>Electrocardiogram (ECG) is a practical medical test to diagnose arrhythmia. As a</p><p>crucial computational application in clinical practice, ECG automatic classification can</p><p>effectively detect the possible occurrence of cardiovascular disease. At present, the</p><p>main problems in the automated classification of ECG are due to (1) the complexity of</p><p>algorithms to capture heartbeats, (2) the complex changes of irregular heartbeats in</p><p>rhythm or morphology leading to difficulties in the ECG feature recognition, and (3)</p><p>the needs of large training samples and training time for a machine learning to achieve</p><p>the ideal recognition accuracy. Given the problems in ECG automatic classification,</p><p>this study proposes an effective automated classification approach for arrhythmia based</p><p>on a representative convolution neural network that can decode ECG source files and</p><p>identify heartbeats accurately based on the detection of QRS waveform from the ECG</p><p>records. A one-dimensional convolutional neural network (1D-CNN) is proposed to</p><p>accurately classify different types of arrhythmias by automatically extracting the</p><p>morphological features of ECG. The initial connection weights of 1D-CNN are</p><p>optimized based on differential evolution to improve its ECG classification. The</p><p>optimized 1D-CNN is evaluated against two arrhythmia databases, namely the MITBIH</p><p>and SCDH arrhythmia databases. Besides, a comparison is made between the</p><p>optimized and unoptimized 1D-CNN. The results show that the proposed model has</p><p>higher accuracy in heartbeat classification. Compared to the unoptimized 1D-CNN, the</p><p>accuracy improves by 0.6% and 3.1%, respectively. Besides, the optimized 1D-CNN</p><p>requires less training time, 9.22 seconds less with MIT-BIH and 10.35 seconds less with</p><p>SCDH based on ReLU active function and 10 epochs, as compared to the unoptimized</p><p>1D-CNN based on the same parameter settings. The training time of the optimized 1DCNN</p><p>decreased by 67.2% and 64.2% with MIT-BIH and SCDH, respectively.</p> |
| format |
thesis |
| qualification_name |
|
| qualification_level |
Doctorate |
| author |
Shan, Wei Chen |
| author_facet |
Shan, Wei Chen |
| author_sort |
Shan, Wei Chen |
| title |
An optimized convolutional neural network for arrhythmia classification |
| title_short |
An optimized convolutional neural network for arrhythmia classification |
| title_full |
An optimized convolutional neural network for arrhythmia classification |
| title_fullStr |
An optimized convolutional neural network for arrhythmia classification |
| title_full_unstemmed |
An optimized convolutional neural network for arrhythmia classification |
| title_sort |
optimized convolutional neural network for arrhythmia classification |
| granting_institution |
Universiti Pendidikan Sultan Idris |
| granting_department |
Fakulti Seni, Komputeran dan Industri Kreatif |
| publishDate |
2022 |
| url |
https://ir.upsi.edu.my/detailsg.php?det=9598 |
| _version_ |
1783730286873280512 |
| spelling |
oai:ir.upsi.edu.my:95982023-10-20 An optimized convolutional neural network for arrhythmia classification 2022 Shan, Wei Chen Q Science <p>Electrocardiogram (ECG) is a practical medical test to diagnose arrhythmia. As a</p><p>crucial computational application in clinical practice, ECG automatic classification can</p><p>effectively detect the possible occurrence of cardiovascular disease. At present, the</p><p>main problems in the automated classification of ECG are due to (1) the complexity of</p><p>algorithms to capture heartbeats, (2) the complex changes of irregular heartbeats in</p><p>rhythm or morphology leading to difficulties in the ECG feature recognition, and (3)</p><p>the needs of large training samples and training time for a machine learning to achieve</p><p>the ideal recognition accuracy. Given the problems in ECG automatic classification,</p><p>this study proposes an effective automated classification approach for arrhythmia based</p><p>on a representative convolution neural network that can decode ECG source files and</p><p>identify heartbeats accurately based on the detection of QRS waveform from the ECG</p><p>records. A one-dimensional convolutional neural network (1D-CNN) is proposed to</p><p>accurately classify different types of arrhythmias by automatically extracting the</p><p>morphological features of ECG. The initial connection weights of 1D-CNN are</p><p>optimized based on differential evolution to improve its ECG classification. The</p><p>optimized 1D-CNN is evaluated against two arrhythmia databases, namely the MITBIH</p><p>and SCDH arrhythmia databases. Besides, a comparison is made between the</p><p>optimized and unoptimized 1D-CNN. The results show that the proposed model has</p><p>higher accuracy in heartbeat classification. Compared to the unoptimized 1D-CNN, the</p><p>accuracy improves by 0.6% and 3.1%, respectively. Besides, the optimized 1D-CNN</p><p>requires less training time, 9.22 seconds less with MIT-BIH and 10.35 seconds less with</p><p>SCDH based on ReLU active function and 10 epochs, as compared to the unoptimized</p><p>1D-CNN based on the same parameter settings. The training time of the optimized 1DCNN</p><p>decreased by 67.2% and 64.2% with MIT-BIH and SCDH, respectively.</p> 2022 thesis https://ir.upsi.edu.my/detailsg.php?det=9598 https://ir.upsi.edu.my/detailsg.php?det=9598 text zsm closedAccess Doctoral Universiti Pendidikan Sultan Idris Fakulti Seni, Komputeran dan Industri Kreatif <p>Acharya, U. R., Fujita, H., Lih, O. S., Adam, M., Tan, J. H., & Chua, C. K. (2017). Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network. Knowledge-Based Systems, 132, 6271. https://doi.org/10.1016/j.knosys.2017.06.003</p><p>Acharya, U. R., Fujita, H., Lih, O. S., Hagiwara, Y., Tan, J. H., & Adam, M. (2017). Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Information Sciences, 405, 8190. https://doi.org/10.1016/j.ins.2017.04.012</p><p>Acharya, U. R., Fujita, H., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adam, M. (2017). Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Information Sciences, 415416, 190198. https://doi.org/10.1016/j.ins.2017.06.027</p><p>Acharya, U. R., Fujita, H., Oh, S. L., Raghavendra, U., Tan, J. H., Adam, M., Gertych, A., & Hagiwara, Y. (2018). Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network. Future Generation Computer Systems, 79, 952959. https://doi.org/10.1016/j.future.2017.08.039</p><p>Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adam, M., Gertych, A., & Tan, R. S. (2017). A deep convolutional neural network model to classify heartbeats. Computers in Biology and Medicine, 89, 389396. https://doi.org/10.1016/j.compbiomed.2017.08.022</p><p>Afonso, V. X., Tompkins, W. J., Nguyen, T. Q., & Luo, S. (1999). ECG beat detection using filter banks. IEEE Transactions on Biomedical Engineering, 46(2), 192202. https://doi.org/10.1109/10.740882</p><p>Altan, G., Kutlu, Y., & Yeniad, M. (2019). ECG based human identification using Second Order Difference Plots. Computer Methods and Programs in Biomedicine, 170, 8193. https://doi.org/10.1016/j.cmpb.2019.01.010</p><p>Ansari, S., Gryak, J., & Najarian, K. (2018). Noise Detection in Electrocardiography Signal for Robust Heart Rate Variability Analysis: A Deep Learning Approach. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018-July, 56325635. https://doi.org/10.1109/EMBC.2018.8513537</p><p>Arel, I., Rose, D. C., & Karnowski, T. P. (2010). Deep machine learning-a new frontier in artificial intelligence research. IEEE Computational Intelligence Magazine, 5(4), 1318.</p><p>Attia, Z. I., Kapa, S., Yao, X., Lopez-Jimenez, F., Mohan, T. L., Pellikka, P. A., Carter, R. E., Shah, N. D., Friedman, P. A., & Noseworthy, P. A. (2019). Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction. Journal of Cardiovascular Electrophysiology, 30(5), 668674. https://doi.org/10.1111/jce.13889</p><p>Azimi-Sadjadi, M. R., Citrin, S., & S. S. (1990). Supervised learning process of mufti-layer perceptron neural networks using fast least squares. Acoustics, Speech, and Signal Processing. 1990 International Conference on. IEEE., 1990.</p><p>Baloglu, U. B., Talo, M., Yildirim, O., Tan, R. S., & Acharya, U. R. (2019). Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recognition Letters, 122, 2330. https://doi.org/10.1016/j.patrec.2019.02.016</p><p>Bappy, D. M., & Jeon, I. (2016). Combination of hybrid median filter and total variation minimisation for medical X-ray image restoration. IET Image Processing, 10(4), 261271. https://doi.org/10.1049/iet-ipr.2015.0054</p><p>Batouche, M., & Mennour, R. (2018). Novel scalable deep learning approaches for big data analytics applied to ECG processing. International Journal of Applied Metaheuristic Computing, 9(4), 3351. https://doi.org/10.4018/IJAMC.2018100102</p><p>Bengio, Y. (2013). Deep learning of representations: Looking forward. International Conference on Statistical Language and Speech Processing, 137.</p><p>Bernstam, E. V., Smith, J. W., & Johnson, T. R. (2010). What is biomedical informatics? Journal of Biomedical Informatics, 43(1), 104110. https://doi.org/10.1016/j.jbi.2009.08.006</p><p>Boureau, Y., Ponce, J., Fr, J. P., & Lecun, Y. (2010). A theoretical analysis of feature pooling in visual recognition. Proceedings of the 27th International Conference on Machine Learning, 111118. https://www.di.ens.fr/sierra/pdfs/icml2010b.pdf</p><p>Burbidge, R., Trotter, M., Buxton, B., & Holden, S. (2001). Drug design by machine learning: Support vector machines for pharmaceutical data analysis. Computers and Chemistry, 26(1), 514. https://doi.org/10.1016/S0097-8485(01)00094-8</p><p>Cai, J.-F., Dong, B., Osher, S., & Shen, Z. (2012). Image restoration: total variation, wavelet frames, and beyond. Journal of the American Mathematical Society, 25(4),10331089.</p><p>Castaldo, R., Montesinos, L., Melillo, P., James, C., & Pecchia, L. (2019). Ultra-short term HRV features as surrogates of short term HRV: A case study on mental stress detection in real life. BMC Medical Informatics and Decision Making, 19(1), 113. https://doi.org/10.1186/s12911-019-0742-y</p><p>Chandra, B. S., Sastry, C. S., & Jana, S. (2019). Robust Heartbeat Detection from Multimodal Data via CNN-Based Generalizable Information Fusion. IEEE Transactions on Biomedical Engineering, 66(3), 710717. https://doi.org/10.1109/TBME.2018.2854899</p><p>Changxing, Q., Yiming, B., & Yong, L. (2017). Improved BP neural network algorithm model based on chaos genetic algorithm. 2017 3rd IEEE International Conference on Control Science and Systems Engineering, ICCSSE 2017, 679682. https://doi.org/10.1109/CCSSE.2017.8088019</p><p>Chen, M., Wang, G., Xie, P., Sang, Z., Lv, T., Zhang, P., & Yang, H. (2018). Region Aggregation Network: Improving Convolutional Neural Network for ECG Characteristic Detection. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018-July, 25592562. https://doi.org/10.1109/EMBC.2018.8512789</p><p>Chen, T. E., Yang, S. I., Ho, L. T., Tsai, K. H., Chen, Y. H., Chang, Y. F., Lai, Y. H., Wang, S. S., Tsao, Y., & Wu, C. C. (2017). S1 and S2 heart sound recognition using deep neural networks. IEEE Transactions on Biomedical Engineering, 64(2), 372380. https://doi.org/10.1109/TBME.2016.2559800</p><p>Chen, W., & Shi, K. (2019). A deep learning framework for time series classification using Relative Position Matrix and Convolutional Neural Network. Neurocomputing, 359(xxxx), 384394. https://doi.org/10.1016/j.neucom.2019.06.032</p><p>Cornelia, G., & Romulus, R. (2005). ECG signals processing using Wavelets. University of Oradea: Electronics Department, Oradea, Romania.</p><p>Das, S., & Suganthan, P. N. (2011). Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, 15(1), 431. https://doi.org/10.1109/TEVC.2010.2059031</p><p>Dhawan, A. P. (2011). Medical image analysis. John Wiley & Sons.</p><p>Ding, S., Zhang, Y., Chen, J., & Jia, W. (2013). Research on using genetic algorithms to optimize Elman neural networks. Neural Computing and Applications, 23(2),293297. https://doi.org/10.1007/s00521-012-0896-3</p><p>Ehteram, M., Salih, S. Q., & Yaseen, Z. M. (2020). Efficiency evaluation of reverse osmosis desalination plant using hybridized multilayer perceptron with particle swarm optimization. Environmental Science and Pollution Research, 27(13), 1527815291. https://doi.org/10.1007/s11356-020-08023-9</p><p>Erdenebayar, U., Kim, H., Park, J. U., Kang, D., & Lee, K. J. (2019). Automatic prediction of atrial fibrillation based on convolutional neural network using a short-term normal electrocardiogram signal. Journal of Korean Medical Science, 34(7), 110. https://doi.org/10.3346/jkms.2019.34.e64</p><p>Escalona-Morn, M. A., Soriano, M. C., Fischer, I., & Mirasso, C. R. (2015). Electrocardiogram classification using reservoir computing with logistic regression. IEEE Journal of Biomedical and Health Informatics, 19(3), 892898. https://doi.org/10.1109/JBHI.2014.2332001</p><p>Fan, X., Yao, Q., Cai, Y., Miao, F., Sun, F., & Li, Y. (2018). Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation from Single Lead Short ECG Recordings. IEEE Journal of Biomedical and Health Informatics, 22(6), 17441753. https://doi.org/10.1109/JBHI.2018.2858789</p><p>Fujita, H., & Cimr, D. (2019). Computer Aided detection for fibrillations and flutters using deep convolutional neural network. Information Sciences, 486, 231239. https://doi.org/10.1016/j.ins.2019.02.065</p><p>Galloway, C. D., Valys, A. V., Shreibati, J. B., Treiman, D. L., Petterson, F. L., Gundotra, V. P., Albert, D. E., Attia, Z. I., Carter, R. E., Asirvatham, S. J., Ackerman, M. J., Noseworthy, P. A., Dillon, J. J., & Friedman, P. A. (2019). Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia from the Electrocardiogram. JAMA Cardiology, 4(5), 428436. https://doi.org/10.1001/jamacardio.2019.0640</p><p>Ghosh, S., Das, S., Vasilakos, A. V., & Suresh, K. (2012). On convergence of differential evolution over a class of continuous functions with unique global optimum. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42(1), 107124. https://doi.org/10.1109/TSMCB.2011.2160625</p><p>Golrizkhatami, Z., & Acan, A. (2018). ECG classification using three-level fusion of different feature descriptors. Expert Systems with Applications, 114, 5464. https://doi.org/10.1016/j.eswa.2018.07.030</p><p>Grau, J., Ben-Gal, I., Posch, S., & Grosse, I. (2006). VOMBAT: Prediction of transcription factor binding sites using variable order Bayesian trees. Nucleic Acids Research, 34(WEB. SERV. ISS.), 529533. https://doi.org/10.1093/nar/gkl212</p><p>Greenwald, S. D. (1986). The development and analysis of a ventricular fibrillation detector. Massachusetts Institute of Technology.</p><p>Guo, H., Tao, H., Salih, S. Q., & Yaseen, Z. M. (2020). Optimized parameter estimation of a PEMFC model based on improved Grass Fibrous Root Optimization Algorithm. Energy Reports, 6, 15101519. https://doi.org/10.1016/j.egyr.2020.06.001</p><p>Hadi, S. J., Tombul, M., Salih, S. Q., Al-Ansari, N., & Yaseen, Z. M. (2020). The Capacity of the Hybridizing Wavelet Transformation Approach with Data-Driven Models for Modeling Monthly-Scale Streamflow. IEEE Access, 8, 101993102006. https://doi.org/10.1109/ACCESS.2020.2998437</p><p>Hamilton, P. S., & Tompkins, W. J. (1986). Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database. IEEE Transactions on Biomedical Engineering, BME-33(12), 11571165. https://doi.org/10.1109/TBME.1986.325695</p><p>Hammad, M., Liu, Y., & Wang, K. (2019). Multimodal biometric authentication systems using convolution neural network based on different level fusion of ECG and fingerprint. IEEE Access, 7(c), 2552725542. https://doi.org/10.1109/ACCESS.2018.2886573</p><p>Hammad, M., Maher, A., Wang, K., Jiang, F., & Amrani, M. (2018). Detection of abnormal heart conditions based on characteristics of ECG signals. Measurement: Journal of the International Measurement Confederation, 125, 634644. https://doi.org/10.1016/j.measurement.2018.05.033</p><p>Hammad, M., & Wang, K. (2019). Parallel score fusion of ECG and fingerprint for human authentication based on convolution neural network. Computers and Security, 81, 107122. https://doi.org/10.1016/j.cose.2018.11.003</p><p>Hammad, M., Zhang, S., & Wang, K. (2019). A novel two-dimensional ECG feature extraction and classification algorithm based on convolution neural network for human authentication. Future Generation Computer Systems, 101, 180196. https://doi.org/10.1016/j.future.2019.06.008</p><p>Hanbay, K. (2019). Deep neural network based approach for ECG classification using hybrid differential features and active learning. IET Signal Processing, 13(2), 165175. https://doi.org/10.1049/iet-spr.2018.5103</p><p>He, J., Li, K., Liao, X., Zhang, P., & Jiang, N. (2019). Real-Time Detection of Acute Cognitive Stress Using a Convolutional Neural Network from Electrocardiographic Signal. IEEE Access, 7(c), 4271042717. https://doi.org/10.1109/ACCESS.2019.2907076</p><p>Holsinger, W. P., Kempner, K. M., & Miller, M. H. (1971). A QRS Preprocessor Based on Digital Differentiation. IEEE Transactions on Biomedical Engineering, BME-18(3), 212217. https://doi.org/10.1109/TBME.1971.4502834</p><p>Hu, Z., Xiong, S., Su, Q., & Zhang, X. (2013). Sufficient conditions for global convergence of differential evolution algorithm. Journal of Applied Mathematics, 2013. https://doi.org/10.1155/2013/193196</p><p>Huaixuan, W., Lei, Z., Chujun, F., & Yongxia, Y. (2019). ECG Signal Denoising Based on Adaptive Wavelet Threshold Function. Computing Technology and Automation, 38(2), 2019.</p><p>Hwang, B., You, J., Vaessen, T., Myin-Germeys, I., Park, C., & Zhang, B. T. (2018). Deep ECGNet: An optimal deep learning framework for monitoring mental stress using ultra short-term ECG signals. Telemedicine and E-Health, 24(10), 753772. https://doi.org/10.1089/tmj.2017.0250</p><p>Jahmunah, V., Oh, S. L., Wei, J. K. E., Ciaccio, E. J., Chua, K., San, T. R., & Acharya, U. R. (2019). Computer-aided diagnosis of congestive heart failure using ECG signals A review. Physica Medica, 62(May), 95104. https://doi.org/10.1016/j.ejmp.2019.05.004</p><p>Ji, Y., Zhang, S., & Xiao, W. (2019). Electrocardiogram classification based on faster regions with convolutional neural network. Sensors (Switzerland), 19(11). https://doi.org/10.3390/s19112558</p><p>Jiao Y., ZeFeng L., C. C. (2018). Evaluation and Analysis of ECG Signal Denoising. INFORMATION &COMMUNICATIONS, 183(3), 2018.</p><p>Jing, W., Yaseen, Z. M., Shahid, S., Saggi, M. K., Tao, H., Kisi, O., Salih, S. Q., Al-Ansari, N., & Chau, K. W. (2019). Implementation of evolutionary computing models for reference evapotranspiration modeling: short review, assessment and possible future research directions. Engineering Applications of Computational Fluid Mechanics, 13(1), 811823. https://doi.org/10.1080/19942060.2019.1645045</p><p>Jolm C R. (2015). The Image Processing Handbook Seventh Edition. Boca Raton. CCR Press.</p><p>Joseph, A. N. (2012). Biomedical computing: Digitizing Life in the United States. Johns Hopkins University Press.</p><p>Kiranyaz, S., Ince, T., & Gabbouj, M. (2016). Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks. IEEE Transactions on Biomedical Engineering, 63(3), 664675. https://doi.org/10.1109/TBME.2015.2468589</p><p>Kiranyaz, S., Ince, T., Hamila, R., & Gabbouj, M. (2015). Convolutional Neural Networks for patient-specific ECG classification. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2015-Novem, 26082611. https://doi.org/10.1109/EMBC.2015.7318926</p><p>Koleejan, C., Xue, B., & Zhang, M. (2015). Code coverage optimisation in genetic algorithms and particle swarm optimisation for automatic software test data generation. 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, 12041211. https://doi.org/10.1109/CEC.2015.7257026</p><p>Labati, R. D., Muoz, E., Piuri, V., Sassi, R., & Scotti, F. (2019). Deep-ECG: Convolutional Neural Networks for ECG biometric recognition. Pattern Recognition Letters, 126, 7885. https://doi.org/10.1016/j.patrec.2018.03.028</p><p>Leema, N., Nehemiah, H. K., & Kannan, A. (2016). Neural network classifier optimization using Differential Evolution with Global Information and Back Propagation algorithm for clinical datasets. Applied Soft Computing Journal, 49, 834844. https://doi.org/10.1016/j.asoc.2016.08.001</p><p>Leonid I. Rudin 1, S. O. and E. F. 2. (1992). Nonlinear total variation based noise removal algorithms. Physica D Nonlinear Phenomena, 60(14), 259268. http://linkinghub.elsevier.com/retrieve/pii/016727899290242F%0Apapers3://publication/doi/10.1016/0167-2789(92)90242-F</p><p>Lesk, A. (2019). Introduction to bioinformatics. Oxford University Press.</p><p>Li, C., Zheng, C., & Tai, C. (1995). Detection of ECG characteristic points using wavelet transforms. IEEE Transactions on Biomedical Engineering, 42(1), 2128.</p><p>Li, D., Li, X., Zhao, J., & Bai, X. (2019). Automatic staging model of heart failure based on deep learning. Biomedical Signal Processing and Control, 52, 7783. https://doi.org/10.1016/j.bspc.2019.03.009</p><p>Li, K., Pan, W., Li, Y., Jiang, Q., & Liu, G. (2018). A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal. Neurocomputing, 294, 94101. https://doi.org/10.1016/j.neucom.2018.03.011</p><p>Li, Z., Feng, X., Wu, Z., Yang, C., Bai, B., & Yang, Q. (2019). Classification of atrial fibrillation recurrence based on a convolution neural network with SVM architecture. IEEE Access, 7(1), 7784977856. https://doi.org/10.1109/ACCESS.2019.2920900</p><p>Lipman, D. J., & Pearson, W. R. (1985). Rapid and sensitive protein similarity searches. Science, 227(4693), 14351441.</p><p>Liu, S., Wu, G., Liu, H., & Zhang, X. (2017). Image restoration approach using a joint sparse representation in 3D-transform domain. Digital Signal Processing: A Review Journal, 60, 307323. https://doi.org/10.1016/j.dsp.2016.10.008</p><p>Liu, W., Huang, Q., Chang, S., Wang, H., & He, J. (2018). Multiple-feature-branch convolutional neural network for myocardial infarction diagnosis using electrocardiogram. Biomedical Signal Processing and Control, 45, 2232. https://doi.org/10.1016/j.bspc.2018.05.013</p><p>Liu, X. (2016). Weighted total generalised variation scheme for image restoration. IET Image Processing, 10(1), 8088. https://doi.org/10.1049/iet-ipr.2015.0013</p><p>Mangoud, M. A., Elragal, H. M., & Alshara, M. T. (2014). Design of time modulated concentric circular and concentric hexagonal antenna array using hybrid enhanced particle swarm optimisation and differential evolution algorithm. IET Microwaves, Antennas and Propagation, 8(9), 657665. https://doi.org/10.1049/iet-map.2013.0132</p><p>Marinho, L. B., Nascimento, N. de M. M., Souza, J. W. M., Gurgel, M. V., Rebouas Filho, P. P., & de Albuquerque, V. H. C. (2019). A novel electrocardiogram feature extraction approach for cardiac arrhythmia classification. Future Generation Computer Systems, 97, 564577. https://doi.org/10.1016/j.future.2019.03.025</p><p>Martis, R. J., Acharya, U. R., & Adeli, H. (2014). Current methods in electrocardiogram characterization. Computers in Biology and Medicine, 48(1), 133149. https://doi.org/10.1016/j.compbiomed.2014.02.012</p><p>Mathews, S. M., Kambhamettu, C., & Barner, K. E. (2018). A novel application of deep learning for single-lead ECG classification. Computers in Biology and Medicine, 99, 5362. https://doi.org/10.1016/j.compbiomed.2018.05.013</p><p>Meertens, H., Van Herk, M., & Weeda, J. (1988). An inverse filter for digital restoration of portal images. Physics in Medicine and Biology, 33(6), 687702. https://doi.org/10.1088/0031-9155/33/6/003</p><p>Osher, S., Burger, M., Goldfarb, D., Xu, J., & Yin, W. (2005). An iterative regularization method for total variation-based image restoration. Multiscale Modeling and Simulation, 4(2), 460489. https://doi.org/10.1137/040605412</p><p>Pal, S., & Mitra, M. (2012). Increasing the accuracy of ECG based biometric analysis by data modelling. Measurement: Journal of the International Measurement Confederation, 45(7), 19271932. https://doi.org/10.1016/j.measurement.2012.03.005</p><p>Papyan, V., & Elad, M. (2016). Multi-scale patch-based image restoration. IEEE Transactions on Image Processing, 25(1), 249261. https://doi.org/10.1109/TIP.2015.2499698</p><p>Penghui, L., Ewees, A. A., Beyaztas, B. H., Qi, C., Salih, S. Q., Al-Ansari, N., Bhagat, S. K., Yaseen, Z. M., & Singh, V. P. (2020). Metaheuristic optimization algorithms hybridized with artificial intelligence model for soil temperature prediction: Novel model. IEEE Access, 8, 5188451904. https://doi.org/10.1109/ACCESS.2020.2979822</p><p>Poh, M. Z., Poh, Y. C., Chan, P. H., Wong, C. K., Pun, L., Leung, W. W. C., Wong, Y. F., Wong, M. M. Y., Chu, D. W. S., & Siu, C. W. (2018). Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms. Heart, 18. https://doi.org/10.1136/heartjnl-2018-313147</p><p>Polat, K., & Gnes, S. (2007). Detection of ECG Arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine. Applied Mathematics and Computation, 186(1), 898906. https://doi.org/10.1016/j.amc.2006.08.020</p><p>Pourbabaee, B., Roshtkhari, M. J., & Khorasani, K. (2018). Deep Convolutional Neural Networks and Learning ECG Features for Screening Paroxysmal Atrial Fibrillation Patients. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(12), 20952104. https://doi.org/10.1109/TSMC.2017.2705582</p><p>Qiao Li et al. (2018). Deep learning in the cross-time-frequency domain for sleep staging from a single lead electrocardiogram. Physiological Measurement, 111, 013.</p><p>R. Storn, K. P. (1997). Differential Evolution-A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11(3), 341359. https://doi.org/10.1071/AP09004</p><p>Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., & Shpanskaya, K. (2017). Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. ArXiv Preprint ArXiv:1711.05225.</p><p>Ribas Ripoll, V. J., Wojdel, A., Romero, E., Ramos, P., & Brugada, J. (2016). ECG assessment based on neural networks with pretraining. Applied Soft Computing Journal, 49, 399406. https://doi.org/10.1016/j.asoc.2016.08.013</p><p>Rubin, J., Parvaneh, S., Rahman, A., Conroy, B., & Babaeizadeh, S. (2018). Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings. Journal of Electrocardiology, 51(6), S18S21. https://doi.org/10.1016/j.jelectrocard.2018.08.008</p><p>Sadrawi, M., Lin, C. H., Lin, Y. T., Hsieh, Y., Kuo, C. C., Chien, J. C., Haraikawa, K., Abbod, M. F., & Shieh, J. S. (2017). Arrhythmia evaluation in wearable ECG devices. Sensors (Switzerland), 17(11), 114. https://doi.org/10.3390/s17112445</p><p>Sahoo, S., Kanungo, B., Behera, S., & Sabut, S. (2017). Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities. Measurement: Journal of the International Measurement Confederation, 108, 5566. https://doi.org/10.1016/j.measurement.2017.05.022</p><p>Salem, M., Taheri, S., & Yuan, J. S. (2018). ECG Arrhythmia Classification Using Transfer Learning from 2- Dimensional Deep CNN Features. 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings, 14. https://doi.org/10.1109/BIOCAS.2018.8584808</p><p>Salih, S. Q., & Alsewari, A. R. A. (2020). A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer. Neural Computing and Applications, 32(14), 1035910386. https://doi.org/10.1007/s00521-019-04575-1</p><p>Savalia, S., & Emamian, V. (2018). Cardiac arrhythmia classification by multi-layer perceptron and convolution neural networks. Bioengineering, 5(2). https://doi.org/10.3390/bioengineering5020035</p><p>Sayadi, O., & Shamsollahi, M. B. (2007). Multiadaptive bionic wavelet transform: Application to ECG denoising and baseline wandering reduction. Eurasip Journal on Advances in Signal Processing, 2007(c). https://doi.org/10.1155/2007/41274</p><p>Sayantan, G., Kien, K. P., & Kadambari, V. K. (2018). Classification of ECG beats using deep belief network and active learning. Medical and Biological Engineering and Computing, 56(10), 18871898. https://doi.org/10.1007/s11517-018-1815-2</p><p>Shahbazi, F., & Asl, B. M. (2015). Generalized discriminant analysis for congestive heart failure risk assessment based on long-term heart rate variability. Computer Methods and Programs in Biomedicine, 122(2), 191198. https://doi.org/10.1016/j.cmpb.2015.08.007</p><p>Shen, Z., & Cheng, L. (2016). Coupled image restoration model with non-convex non-smooth lp wavelet frame and total variation regularisation. IET Image Processing, 10(11), 926935. https://doi.org/10.1049/iet-ipr.2015.0787</p><p>Shepovalnikov, R. A., Nemirko, A. P., Kalinichenko, A. N., & Abramchenko, V. V. (2006). Investigation of time, amplitude, and frequency parameters of a direct fetal ECG signal during labor and delivery. Pattern Recognition and Image Analysis, 16(1), 7476. https://doi.org/10.1134/S1054661806010238</p><p>Shiyovich, A., Wolak, A., Yacobovich, L., Grosbard, A., & Katz, A. (2010). Accuracy of diagnosing atrial flutter and atrial fibrillation from a surface electrocardiogram by hospital physicians: Analysis of data from internal medicine departments. American Journal of the Medical Sciences, 340(4), 271275. https://doi.org/10.1097/MAJ.0b013e3181e73fcf</p><p>Shortliffe, E. H., Cimino, & J. J. (2014). Biomedical informatics: computer applications in health care and biomedicine. In Springer.</p><p>Singh, S., Pandey, S. K., Pawar, U., & Janghel, R. R. (2018). Classification of ECG Arrhythmia using Recurrent Neural Networks. Procedia Computer Science, 132(Iccids), 12901297. https://doi.org/10.1016/j.procs.2018.05.045</p><p>Smith, S. W., Walsh, B., Grauer, K., Wang, K., Rapin, J., Li, J., Fennell, W., & Taboulet, P. (2019). A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation. Journal of Electrocardiology, 52, 8895. https://doi.org/10.1016/j.jelectrocard.2018.11.013</p><p>SONG Xi-guo, D. Q. (2004). ON the Format of MIT-BIH Arrhythmia Database. Chinese Journal of Medical Physics, 53(9), 16891699.</p><p>Sufi, F., & Khalil, I. (2011). Diagnosis of cardiovascular abnormalities from compressed ECG: A data mining-based approach. IEEE Transactions on Information Technology in Biomedicine, 15(1), 3339. https://doi.org/10.1109/TITB.2010.2094197</p><p>Swapna, G., Soman, K. P., & Vinayakumar, R. (2018a). Automated detection of cardiac arrhythmia using deep learning techniques. Procedia Computer Science, 132, 11921201. https://doi.org/10.1016/j.procs.2018.05.034</p><p>Swapna, G., Soman, K. P., & Vinayakumar, R. (2018b). Automated detection of diabetes using CNN and CNN-LSTM network and heart rate signals. Procedia Computer Science, 132(Iccids), 12531262. https://doi.org/10.1016/j.procs.2018.05.041</p><p>Swapna, G., Vinayakumar, R., & Soman, K. P. (2018). Diabetes detection using deep learning algorithms. ICT Express, 4(4), 243246. https://doi.org/10.1016/j.icte.2018.10.005</p><p>Tan, J. H., Hagiwara, Y., Pang, W., Lim, I., Oh, S. L., Adam, M., Tan, R. S., Chen, M., & Acharya, U. R. (2018). Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals. Computers in Biology and Medicine, 94, 1926. https://doi.org/10.1016/j.compbiomed.2017.12.023</p><p>Tang L X, Zhao Y, L. J. Y. (2014). An Improved Differential Evolution Algorithm for Practical Dynamic Scheduling in Steelmaking-continuous Casting Production. IEEE Trans-Actions on Evolutionary Computation.</p><p>Uhlen, M., Oksvold, P., Fagerberg, L., Lundberg, E., Jonasson, K., Forsberg, M., Zwahlen, M., Kampf, C., Wester, K., Hober, S., Wernerus, H., Bjrling, L., & Ponten, F. (2010). Towards a knowledge-based Human Protein Atlas. Nature Biotechnology, 28(12), 12481250. https://doi.org/10.1038/nbt1210-1248</p><p>Urtnasan, E., Park, J. U., & Lee, K. J. (2018). Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram. Physiological Measurement, 39(6). https://doi.org/10.1088/1361-6579/aac7b7</p><p>Wan X., Tang, W., Zhang L., & Wu. M. (2016). An Improved Cubic Spline Electrocardiogram Baseline Drift An Improved Cubic Spline. Journal of Biomedical Engineering, 22(2), 2016.</p><p>Wang, E. K., Xi, L., Sun, R., Wang, F., Pan, L., Cheng, C., Dimitrakopoulou-Srauss, A., Zhe, N., & Li, Y. (2019). A new deep learning model for assisted diagnosis on electrocardiogram. Mathematical Biosciences and Engineering, 16(4), 24812491. https://doi.org/10.3934/mbe.2019124</p><p>Wang, Q., Gao, Q., Gao, X., & Nie, F. (2017). Angle principal component analysis. IJCAI International Joint Conference on Artificial Intelligence, 0, 29362942. https://doi.org/10.24963/ijcai.2017/409</p><p>Wang, Y., Deepu, C. J., & Lian, Y. (2011). A computationally efficient QRS detection algorithm for wearable ECG sensors. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 56415644. https://doi.org/10.1109/IEMBS.2011.6091365</p><p>Wolterink, J. M., Hamersvelt, R. W. va., Viergever, M. A., Leiner, T., & Igum, I. (2019). Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier. Medical Image Analysis, 51, 4660. https://doi.org/10.1016/j.media.2018.10.005</p><p>Wooff D. (2004). Logistic Regression: a Self learning Text. Journal of the Royal Statistical Society: Series A (Statistics in Society), 151(1), 11. https://doi.org/10.1111/j.1467-985x.1988.tb00001.x</p><p>Xia, Y., & Xie, Y. (2019). A novel wearable electrocardiogram classification system using convolutional neural networks and active learning. IEEE Access, 7(c), 79898001. https://doi.org/10.1109/ACCESS.2019.2890865</p><p>Xia, Y., Zhang, J., Ye, Q., Cheng, N., Lu, Y., & Zhang, D. (2018). Evaluation of deep convolutional neural networks for detection of freezing of gait in Parkinsons disease patients. Biomedical Signal Processing and Control, 46, 221230. https://doi.org/10.1016/j.bspc.2018.07.015</p><p>Xiao, B., Xu, Y., Bi, X., Zhang, J., & Ma, X. (2020). Heart sounds classification using a novel 1-D convolutional neural network with extremely low parameter consumption. Neurocomputing, 392(xxxx), 153159. https://doi.org/10.1016/j.neucom.2018.09.101</p><p>Xiao, R., Xu, Y., Pelter, M. M., Mortara, D. W., & Hu, X. (2018). A deep learning approach to examine ischemic ST changes in ambulatory ECG recordings. AMIA Summits on Translational Science Proceedings, 2018, 256.</p><p>Xu, Y., & Goodacre, R. (2018). On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning. Journal of Analysis and Testing, 2(3), 249262. https://doi.org/10.1007/s41664-018-0068-2</p><p>Xue, Y., Zhuang, Y., Ni, T., Ni, S., & Wen, X. (2014). Self-adaptive learning based discrete differential evolution algorithm for solving CJWTA problem. Journal of Systems Engineering and Electronics, 25(1), 5968. https://doi.org/10.1109/JSEE.2014.00007</p><p>Yang, T., Yu, L., Jin, Q., Wu, L., & He, B. (2018). Localization of Origins of Premature Ventricular Contraction by Means of Convolutional Neural Network from 12-Lead ECG. IEEE Transactions on Biomedical Engineering, 65(7), 16621671. https://doi.org/10.1109/TBME.2017.2756869</p><p>Yaseen, Z. M., Naghshara, S., Salih, S. Q., Kim, S., Malik, A., & Ghorbani, M. A. (2020). Lake water level modeling using newly developed hybrid data intelligence model. Theoretical and Applied Climatology, 141(34), 12851300. https://doi.org/10.1007/s00704-020-03263-8</p><p>Yildirim, O., Tan, R. S., & Acharya, U. R. (2018). An efficient compression of ECG signals using deep convolutional autoencoders. Cognitive Systems Research, 52, 198211. https://doi.org/10.1016/j.cogsys.2018.07.004</p><p>Yin, W., Yang, X., Zhang, L., & Oki, E. (2016). ECG Monitoring System Integrated with IR-UWB Radar Based on CNN. IEEE Access, 4(c), 63446351. https://doi.org/10.1109/ACCESS.2016.2608777</p><p>Yildirim, ., Plawiak, P., Tan, R. S., & Acharya, U. R. (2018). Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Computers in Biology and Medicine, 102, 411420. https://doi.org/10.1016/j.compbiomed.2018.09.009</p><p>Yoo, H.-J. (2015). Deep convolution neural networks in computer vision: a review. IEIE Transactions on Smart Processing and Computing, 4(1), 3543.</p><p>Zaharie, D. (2002). Critical values for the control parameters of differential evolution algorithms. Proceedings of 8th International Mendel Conference on Soft Computing, 2, 6267.</p><p>Zhai, X., & Tin, C. (2018). Automated ECG Classification Using Dual Heartbeat Coupling Based on Convolutional Neural Network. IEEE Access, 6, 2746527472. https://doi.org/10.1109/ACCESS.2018.2833841</p><p>Zhang, F., & Lian, Y. (2009). QRS detection based on multiscale mathematical morphology for wearable ECG devices in body area networks. IEEE Transactions on Biomedical Circuits and Systems, 3(4), 220228. https://doi.org/10.1109/TBCAS.2009.2020093</p><p>Zhang, Q., Zhou, D., & Zeng, X. (2017). HeartID: A Multiresolution Convolutional Neural Network for ECG-Based Biometric Human Identification in Smart Health Applications. IEEE Access, 5(c), 1180511816. https://doi.org/10.1109/ACCESS.2017.2707460</p><p>Zhao, Z., Wang, X., Cai, Z., Li, J., & Liu, C. (2019). Noise Rejection for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks. IEEE Access, 2019-Septe(1), 200. https://doi.org/10.23919/CinC49843.2019.9005872</p><p>Zhao, Z., Zhang, Y., Deng, Y., & Zhang, X. (2018). ECG authentication system design incorporating a convolutional neural network and generalized S-Transformation. Computers in Biology and Medicine, 102(July), 168179. https://doi.org/10.1016/j.compbiomed.2018.09.027</p><p>Zipser, D., & Andersen, R. A. (1988). A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons. Nature, 331(6158), 679684.</p><p>Zubair M, Kim J, Y. C. (2016). An automated ECG beat classification system using convolutional neural networks. In IT Convergence and Security (ICITCS), 2016 6th International Conference on. IEEE.</p><p>Zubair, M., Kim, J., & Yoon, C. (2016). An automated ECG beat classification system using convolutional neural networks. 2016 6th International Conference on IT Convergence and Security (ICITCS), 15.</p><p></p><p></p><p></p><p></p> |