Abstract
One of the common cardiac disorders is a cardiac attack called Myocardial infarction (MI), which occurs due to the blockage of one or more coronary arteries. Timely treatment of MI is important and slight delay results in severe consequences. Electrocardiogram (ECG) is the main diagnostic tool to monitor and reveal the MI signals. The complex nature of MI signals along with noise poses challenges to doctors for accurate and quick diagnosis. Manually studying large amounts of ECG data can be tedious and time-consuming. Therefore, there is a need for methods to automatically analyze the ECG data and make diagnosis. Number of studies has been presented to address MI detection, but most of these methods are computationally expensive and faces the problem of overfitting while dealing real data. In this paper, an effective computer-aided diagnosis (CAD) system is presented to detect MI signals using the convolution neural network (CNN) for urban healthcare in smart cities. Two types of transfer learning techniques are employed to retrain the pre-trained VGG-Net (Fine-tuning and VGG-Net as fixed feature extractor) and obtained two new networks VGG-MI1 and VGG-MI2. In the VGG-MI1 model, the last layer of the VGG-Net model is replaced with a specific layer according to our requirements and various functions are optimized to reduce overfitting. In the VGG-MI2 model, one layer of the VGG-Net model is selected as a feature descriptor of the ECG images to describe it with informative features. Considering the limited availability of dataset, ECG data is augmented which has increased the classification performance. A standard well-known database Physikalisch-Technische Bundesanstalt (PTB) Diagnostic ECG is used for the validation of the proposed framework. It is evident from experimental results that the proposed framework achieves a high accuracy surpasses the existing methods. In terms of accuracy, sensitivity, and specificity; VGG-MI1 achieved 99.02%, 98.76%, and 99.17%, respectively, while VGG-MI2 models achieved an accuracy of 99.22%, a sensitivity of 99.15%, and a specificity of 99.49%.
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References
Acharya UR, Fujita H, Sudarshan VK, Shu LO, Adam M, Koh JEW et al (2016) Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads. Knowl-Based Syst 99:146–156
Acharya UR, Fujita H, Lih OS, Hagiwara Y, Tan JH, Adam M (2017) Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf Sci 415-416:190–198
Al-Kindi SG, Ali F, Farghaly A, Nathani M, Tafreshi R (2011) Towards real-time detection of myocardial infarction by digital analysis of electrocardiograms. IEEE, 1st Middle East Conference on Biomedical Engineering, p.454–457
Amrani M, Hammad M, Jiang F, Wang K, Amrani A (2018) Very deep feature extraction and fusion for arrhythmias detection. Neural Comput Applic 30(7):2047–2057
Arif M, Malagore IA, Afsar FA (2012) Detection and localization of myocardial infarction using k-nearest neighbor classifier. J Med Syst 36(1):279–289
Bousseljot R, Kreiseler D, Schnabel, A (1995) Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet. Biomedizinische Technik, Band 40, Ergänzungsband 1 S 317
Bouvrie J (2007) Notes on convolutional neural network
Cardiovascular disease, “World Heart Day” (2019) [Online]. Available: http://www.who.int/cardiovascular_diseases/world-heart-day/en/. Accessed: 30-Jan-2019.
Chang PC, Lin JJ, Hsieh JC, Weng J (2012) Myocardial infarction classification with multi-lead ECG using hidden markov models and gaussian mixture models. Appl Soft Comput 12(10):3165–3175
Chatfield K, Simonyan K, Vedaldi A, Zisserman A (2014) Return of the devil in the details: Delving deep into convolutional nets. In Proc. British Mach Vis Conf (BMVC)
Dan C, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. IEEE Comput Vis Pattern Recognit 157:3642–3649
Dohare AK, Kumar V, Kumar R (2018) Detection of myocardial infarction in 12 lead ECG using support vector machine. Appl Soft Comput 64:138–147
Duda RO, Hart PE, Stork DG (2001) Pattern Classification 2nd Edition. Pattern classification. John Wiley and Sons, New York, pp 55–88
Girshick R, Donahue J, Darrell T, Malik J (2014) Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. 2014 IEEE conference on computer vision and pattern recognition (CVPR). IEEE Comput Soc
Hall J (2010) Guyton and Hall textbook of medical physiology: enhanced E-book: Elsevier Health Sciences
Hammad M, Wang K (2017) Fingerprint classification based on a Q-Gaussian multiclass support vector machine. In: Proceedings of the 2017 international conference on biometrics engineering and application. ACM
Hammad M, Wang K (2019) Parallel score fusion of ECG and fingerprint for human authentication based on convolution neural network. Comput Secur 81:107–122
Hammad M, Maher A, Wang K, Jiang F, Amrani M (2018) Detection of abnormal heart conditions based on characteristics of ECG signals. Measurement 125:634–644
Hammad M, Liu Y, Wang K (2018) Multimodal biometric authentication systems using convolution neural network based on different level fusion of ECG and fingerprint. IEEE Access 99:1–1. https://doi.org/10.1109/ACCESS.2018.2886573 Available from: https://ieeexplore.ieee.org/document/8575133/
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. Futur Gener Comput Syst
Han C, & Shi L (2019). ML–ResNet: a novel network to detect and locate myocardial infarction using 12 leads ECG. Comput Methods Prog Biomed 105138.
Han C, Shi L (2019) Automated interpretable detection of myocardial infarction fusing energy entropy and morphological features. Comput Methods Prog Biomed 175:9–23
Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31
Janwe NJ, Bhoyar KK (2017) Multi-label semantic concept detection in videos using fusion of asymmetrically trained deep convolutional neural networks and foreground driven concept co-occurrence matrix. Appl Intell 48:2047
Jayachandran ES, Joseph KP, Acharya UR (2010) Analysis of myocardial infarction using discrete wavelet transform. J Med Syst 34(6):985–992
Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: Convolutional architecture for fast feature embedding. arXiv:1408.5093v1 [preprint]. [cited 2014 Jun 20]: [4 p.]. Available from: https://arxiv.org/abs/1408.5093
Jun TJ, Nguyen HM, Kang D, Kim D, Kim D, Kim YH (2018) ECG arrhythmia classification using a 2-d convolutional neural network. arXiv:1804.06812v1 [Preprint]. [cited 2018 Apr 18]: [22 p.]. Available from: https://arxiv.org/abs/1804.06812.
Kligfield P (2018) Goldberger's clinical electrocardiography: a simplified approach, Ary L. Goldberger, Zachary D. Goldberger, Alexei Shvilkin. J Electrocardiol 51(4):620
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Int Conf Neural Inf Proc Syst 60:1097–1105 Curran Associates Inc
Kumar M, Pachori RB, Acharya UR (2017) Automated diagnosis of myocardial infarction ECG signals using sample entropy in flexible analytic wavelet transform framework. Entropy 19(9):488. https://doi.org/10.3390/e19090488
Liu B, Liu J, Wang G, Huang K, Li F, Zheng Y, Luo Y, Zhou F (2015) A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection. Comput Biol Med 61:178–184
Liu W, Huang Q, Chang S, Wang H, He J (2018) Multiple-feature-branch convolutional neural network for myocardial infarction diagnosis using electrocardiogram. Biomed Signal Proc Control 45:22–32
MathWorks, “Get Started with Transfer Learning”, (2019) [Online]. Available: https://www.mathworks.com/help/deeplearning/examples/get-started-with-transferlearning.html. Accessed: 30 Jan 2019
Naser S, Dabanloo N, Attarodi G (2014) A new pattern recognition method for detection and localization of myocardial infarction using T-wave integral and Total integral as extracted features from one cycle of ECG signal. J Biomed Sci Eng 5(7):818–824
Nogueira RF, Lotufo RDA, Machado RC (2017) Fingerprint liveness detection using convolutional neural networks. IEEE Trans Inf Forensics Secur 11(6):1206–1213
Oh SL, Vicnesh J, Ciaccio EJ, Yuvaraj R, Acharya UR (2019) Deep convolutional neural network model for automated diagnosis of schizophrenia using EEG signals. Appl Sci 9(14):2870
Pan J, Tompkins WJ (1985) A real-time QRS detection algorithm. Biomed Eng IEEE Trans 32(3):230–236
Paul JK, Iype T, Dileep R, Hagiwara Y, Koh JW, Acharya UR (2019). Characterization of fibromyalgia using sleep EEG signals with nonlinear dynamical features. Comput Biol Med 103331
Pławiak P (2018) Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals. Swarm Evol Comput 39:192–208
Pławiak P (2018) Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system. Expert Syst Appl 92:334–349
Pławiak P, Acharya UR (2019) Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals. Neural Comput & Applic:1–25
Protopapadakis E, Voulodimos A, Doulamis A, Doulamis N, Stathaki T (2019) Automatic crack detection for tunnel inspection using deep learning and heuristic image post-processing. Appl Intell 49:2793–2806. https://doi.org/10.1007/s10489-018-01396-y
Raghavendra U, Fujita H, Bhandary SV, Gudigar A, Tan JH, Acharya UR (2018) Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Inf Sci 441:41–49
Rajput JS, Sharma M, Acharya UR (2019) Hypertension diagnosis index for discrimination of high-risk hypertension ECG signals using optimal orthogonal wavelet filter Bank. Int J Environ Res Public Health 16(21):4068
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252
Sadhukhan D, Pal S, Mitra M (2018) Automated identification of myocardial infarction using harmonic phase distribution pattern of ECG data. IEEE Trans Instrum Meas 99:1–11
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117
Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y (2014) OverFeat: integrated recognition, localization and detection using convolutional networks. In: Proceedings of ICLR
Sharma LN, Tripathy RK, Dandapat S (2015) Multiscale energy and eigenspace approach to detection and localization of myocardial infarction. IEEE Trans Biomed Eng 62(7):1827–1837
Sharma M, Tan RS, Acharya UR (2018) A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank. Comput Biol Med 102:341–356
Sharma M, Singh S, Kumar A, San Tan R, Acharya UR (2019) Automated detection of shockable and non-shockable arrhythmia using novel wavelet-based ECG features. Comput Biol Med 115:103446
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958
Strom JB, Tanguturi VK, Nagueh SF, Klein AL, Manning WJ (2019) Demonstrating the value of outcomes in echocardiography: imaging-based registries in improving patient care. J Am Soc Echocardiogr
Sun L, Lu Y, Yang K, Li S (2012) ECG analysis using multiple instance learning for myocardial infarction detection. IEEE Trans Biomed Eng 59(12):3348–3356
Sun J, Cai X, Sun F, Zhang J (2016) Scene image classification method based on Alex-Net model. IEEE, 2016 3rd International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)
Thygesen K, Alpert JS, Jaffe AS, Simoons ML, Chaitman BR, White HD (2012) Third universal definition of myocardial infarction. Circulation. 126(16):2020–2035
Tsai DY, Kojima K (2005) Measurements of texture features of medical images and its application to computer-aided diagnosis in cardiomyopathy. Measurement. 37(3):284–292
Tuncer T, Dogan S, Pławiak P, Acharya UR (2019) Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals. Knowl.-Based Syst 104923
Velasco JM, Garnica O, Contador S, Lanchares J, Maqueda E, Botella M, et al. (2017) Data augmentation and evolutionary algorithms to improve the prediction of blood glucose levels in scarcity of training data. IEEE Congress on Evolutionary Computation (CEC)
Wang Z, Qian L, Han C, Shi L (2020) Application of multi-feature fusion and random forests to the automated detection of myocardial infarction. Cogn Syst Res 59:15–26
Wu JF, Bao YL, Chan SC, Wu HC, Zhang L, Wei XG (2017) Myocardial infarction detection and classification — A new multi-scale deep feature learning approach. IEEE Int Conf Digit Signal Proc 309–313
Yıldırım Ö, Pławiak P, Tan RS, Acharya UR (2018) Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput Biol Med 102:411–420
Zeng K, Ding S, Jia W (2019) Single image super-resolution using a polymorphic parallel CNN. Appl Intell 49:292–300. https://doi.org/10.1007/s10489-018-1270-7
Acknowledgments
This project was funded by University of Jeddah, Jeddah, Saudi Arabia (Project number: UJ-02-018-ICGR). The authors, therefore, gratefully acknowledge DSR technical and financial support. Also, Ahmed A. Abd El-Latif acknowledges support from TYSP-Talented Young Scientist Program (China) and Menoufia University (Egypt). Additionally, the authors warmly thank their families for their unconditional support.
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Alghamdi, A., Hammad, M., Ugail, H. et al. Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities. Multimed Tools Appl 83, 14913–14934 (2024). https://doi.org/10.1007/s11042-020-08769-x
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DOI: https://doi.org/10.1007/s11042-020-08769-x