Deep Belief Neural Network Based Automatic NSTEMI CVD Prediction Using Adaptive Sliding Window Technique

Sachin Sambhaji Patil; T. B. Mohite-Patil

doi:10.4108/eetsis.v10i3.2891

Authors

Sachin Sambhaji Patil Shivaji University
T. B. Mohite-Patil Dr D. Y. Patil College of Engineering and Technology

DOI:

https://doi.org/10.4108/eetsis.v10i3.2891

Keywords:

CVD prediction, ECG image, sliding window segmentation, QRS detection, elephant herd optimization, deep belief network

Abstract

INTRODUCTION: Cardiac Vascular Disease (CVD) is determined to be the most prevailing disease all over the globe specifically in the case of elderly persons. Among various cardiac disease, CVD account for major mortality all over the globe. Diagnosis of cardiac disease at an early stage is mandatory to reduce the rate of mortality. Still, there is no availability of skilled specialists even in case of developed countries for accurate diagnosis.

OBJECTIVES: Achieving automated and accurate diagnosis, computer vision based methods that functions with the help of AI techniques are focused on by researchers. In this current research automated CVD prediction model is designed using a deep learning approach.

METHODS: ECG image dataset is utilized in this proposed CVD prediction model. Initially, the Non-ST-elevation myocardial infarction (NSTEMI) ECG data collected from the healthcare centre is taken as input. This input ECG image is converted into a signal and further, it is segmented using the sliding window segmentation technique. Then, using segmented signal QRS peak detection is achieved using Elephant Herd Optimization (EHO) algorithm. From the peak, detected signal features are extracted using Heart Rate Variability (HRV) analysis. Following that the extracted features are sent as input into the Deep Belief Network (DBN) classifier to predict CVD patients.

RESULTS: The proposed CVD prediction model is implemented and some of the performance metrics are calculated. Accuracy, error, precision, sensitivity and specificity attained by the proposed model using the second dataset are 95%, 5%. 96%, 94% and 96%. Results showed that the functioning of proposed CVD prediction model is better when compared with other existing techniques.

CONCLUSION: Based on this analysis it can be revealed that accurate and timely CVD prediction can be achieved with a lessor error rate. Further, this proposed model can be used in real time healthcare application by collecting NSTEMI ECG signal from patients.

References

Chakraborty A, Chatterjee S, Majumder K, Shaw RN, Ghosh A. A comparative study of myocardial infarction detection from ECG data using machine learning. InAdvanced Computing and Intelligent Technologies 2022 (pp. 257-267). Springer, Singapore.

Sree V, Mapes J, Dua S, Lih OS, Koh JE, Ciaccio EJ, Acharya UR. A novel machine learning framework for automated detection of arrhythmias in ECG segments. Journal of Ambient Intelligence and Humanized Computing. 2021 Nov;12(11):10145-62.

Sheta A, Turabieh H, Thaher T, Too J, Mafarja M, Hossain MS, Surani SR. Diagnosis of obstructive sleep apnea from ecg signals using machine learning and deep learning classifiers. Applied Sciences. 2021 Jul 19;11(14):6622.

Peimankar A, Puthusserypady S. DENS-ECG: A deep learning approach for ECG signal delineation. Expert systems with applications. 2021 Mar 1;165:113911.

Mishra S, Khatwani G, Patil R, Sapariya D, Shah V, Parmar D, Dinesh S, Daphal P, Mehendale N. ECG paper record digitization and diagnosis using deep learning. Journal of medical and biological engineering. 2021 Aug;41(4):422-32.

Ullah A, Anwar SM, Bilal M, Mehmood RM. Classification of arrhythmia by using deep learning with 2-D ECG spectral image representation. Remote Sensing. 2020 May 25;12(10):1685.

Belo D, Bento N, Silva H, Fred A, Gamboa H. ECG biometrics using deep learning and relative score threshold classification. Sensors. 2020 Jul 22;20(15):4078.

Rath A, Mishra D, Panda G, Satapathy SC. Heart disease detection using deep learning methods from imbalanced ECG samples. Biomedical Signal Processing and Control. 2021 Jul 1;68:102820.

Mousavi S, Afghah F, Khadem F, Acharya UR. ECG Language processing (ELP): A new technique to analyze ECG signals. Computer Methods and Programs in Biomedicine. 2021 Apr 1;202:105959.

Essa E, Xie X. An ensemble of deep learning-based multi-model for ECG heartbeats arrhythmia classification. IEEE Access. 2021 Jul 21;9:103452-64.

Attallah O. ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration. Computers in biology and medicine. 2022 Mar 1;142:105210.

Naik MS, Pancholi TK, Achary R. Prediction of congestive heart failure (chf) ecg data using machine learning. InIntelligent Data Communication Technologies and Internet of Things 2021 (pp. 325-333). Springer, Singapore.

Acharya UR, Fujita H, Lih OS, Adam M, Tan JH, Chua CK. Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network. Knowledge-Based Systems. 2017 Sep 15;132:62-71.

Hasan NI, Bhattacharjee A. Deep learning approach to cardiovascular disease classification employing modified ECG signal from empirical mode decomposition. Biomedical signal processing and control. 2019 Jul 1;52:128-40.

Dai H, Hwang HG, Tseng VS. Convolutional neural network based automatic screening tool for cardiovascular diseases using different intervals of ECG signals. Computer Methods and Programs in Biomedicine. 2021 May 1;203:106035.

Abdar M, Książek W, Acharya UR, Tan RS, Makarenkov V, Pławiak P. A new machine learning technique for an accurate diagnosis of coronary artery disease. Computer methods and programs in biomedicine. 2019 Oct 1;179:104992.

Suhail MM, Razak TA. Cardiac disease detection from ECG signal using discrete wavelet transform with machine learning method. Diabetes Research and Clinical Practice. 2022 May 1;187:109852.

Dolatabadi AD, Khadem SE, Asl BM. Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM. Computer methods and programs in biomedicine. 2017 Jan 1;138:117-26.

Tan JH, Hagiwara Y, Pang W, Lim I, Oh SL, Adam M, San Tan R, Chen M, Acharya UR. Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals. Computers in biology and medicine. 2018 Mar 1;94:19-26.

Raghavendra U, Fujita H, Gudigar A, Shetty R, Nayak K, Pai U, Samanth J, Acharya UR. Automated technique for coronary artery disease characterization and classification using DD-DTDWT in ultrasound images. Biomedical Signal Processing and Control. 2018 Feb 1;40:324-34.

Yıldırım Ö, Pławiak P, Tan RS, Acharya UR. Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Computers in biology and medicine. 2018 Nov 1;102:411-20.

Butun E, Yildirim O, Talo M, Tan RS, Acharya UR. 1D-CADCapsNet: One dimensional deep capsule networks for coronary artery disease detection using ECG signals. Physica Medica. 2020 Feb 1;70:39-48.

He, J., Rong, J., Sun, L., Wang, H., Zhang, Y., & Ma, J. (2020). A framework for cardiac arrhythmia detection from IoT-based ECGs. World Wide Web, 23, 2835-2850.

Sarki, R., Ahmed, K., Wang, H., Zhang, Y., & Wang, K. (2022). Convolutional neural network for multi-class classification of diabetic eye disease. EAI Endorsed Transactions on Scalable Information Systems, 9(4), e5-e5.

Alvi, A. M., Siuly, S., & Wang, H. (2022, January). Developing a deep learning based approach for anomalies detection from EEG data. In Web Information Systems Engineering–WISE 2021: 22nd International Conference on Web Information Systems Engineering, WISE 2021, Melbourne, VIC, Australia, October 26–29, 2021, Proceedings, Part I (pp. 591-602). Cham: Springer International Publishing.

Bae, T. W., & Kwon, K. K. (2019). Efficient real-time R and QRS detection method using a pair of derivative filters and max filter for portable ECG device. Applied Sciences, 9(19), 4128.

Ma C, Li W, Cao J, Du J, Li Q, Gravina R. Adaptive sliding window based activity recognition for assisted livings. Information Fusion. 2020 Jan 1;53:55-65.

Utomo TP, Nuryani N. QRS peak detection for heart rate monitoring on Android smartphone. InJournal of Physics: Conference Series 2017 Nov 1 (Vol. 909, No. 1, p. 012006). IOP Publishing.

Ismaeel AA, Elshaarawy IA, Houssein EH, Ismail FH, Hassanien AE. Enhanced elephant herding optimization for global optimization. IEEE Access. 2019 Mar 12;7:34738-52.

Jovic A, Bogunovic N. Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features. Artificial intelligence in medicine. 2011 Mar 1;51(3):175-86.

Taji B, Chan AD, Shirmohammadi S. False alarm reduction in atrial fibrillation detection using deep belief networks. IEEE Transactions on Instrumentation and Measurement. 2017 Nov 28;67(5):1124-31.

Kusuma, S., & Udayan, J. D. (2020). Analysis on deep learning methods for ECG based cardiovascular disease prediction. Scalable Computing: Practice and Experience, 21(1), 127-136.

Islam, M. S., Umran, H. M., Umran, S. M., & Karim, M. (2019, May). Intelligent healthcare platform: cardiovascular disease risk factors prediction using attention module based LSTM. In 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD) (pp. 167-175). IEEE.

Kumar, N. K., Sindhu, G. S., Prashanthi, D. K., & Sulthana, A. S. (2020, March). Analysis and prediction of cardio vascular disease using machine learning classifiers. In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) (pp. 15-21). IEEE.

Alqahtani, A., Alsubai, S., Sha, M., Vilcekova, L., & Javed, T. (2022). Cardiovascular Disease Detection using Ensemble Learning. Computational Intelligence and Neuroscience, 2022.