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Current Signal Transduction Therapy

Author(s): Prerak Mathur, Tanu Sharma, and Karan Veer*

DOI: 10.2174/1574362417666220328144453

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Analysis of CNN and Feed-Forward ANN Model for the Evaluation of ECG Signal

Article ID: e280322202704 Pages: 8

  • * (Excluding Mailing and Handling)

Abstract

Background: Heart disease is considered one of the complex diseases that has affected a large number of people around the world. It is important to detect and identify cardiac diseases at early stages.

Objective: A large number of methods are already present that detect various heart diseases; however, there are some limitations to these methods, that have degraded their overall performance.

Methods: In this paper, an effective and efficient method based on a convolutional neural network (CNN) and a feed-forward artificial neural network (FFANN) is proposed that can effectively detect cardiac diseases after analysing the electrocardiogram (ECG) signals. In this ongoing study, the transformed signals are used to extract the information from the processed data. The extracted features are then passed to the proposed CNN-FFANN classifiers for training and testing purposes.

Results: The performance of the proposed CNN-FFANN model is evaluated in the MATLAB software in terms of performance matrices.

Conclusion: The simulated outcomes have proved the proposed CNN-FFANN model to be more accurate and efficient in detecting heart diseases from ECG signals and can be adopted for future biomedical applications.

Keywords: ECG signal, artificial neural network, health monitoring system, biomedical applications, Artificial intelligence, Machine learning.

Graphical Abstract

[1]
Galli A, Giorg G, Narduzzi C. Multi-user ECG monitoring system based on IEEE standard 802.15. 6. In: 2019 IEEE International Symposium on Measurements & Networking (M&N); 2019; pp. 1-6.
[2]
World health Organization. Cardiovascular Diseases (CVDs). Available from: https://www.who.int/news-room/fact-sheets/detail/cardioovascular-diseases-(cvds) (Accessed 11 June, 2021)
[3]
Khandait NV, Shirolkar AA. ECG signal processing using classifier to analyses cardiovascular disease. In: 2019 IEEE 3rd International Conference on Computing Methodologies and Communication (ICCMC); 2019; pp. 855-9.
[http://dx.doi.org/10.1109/ICCMC.2019.8819777]
[4]
Sangle PS, Goudar RM, Bhute AN. Methodologies and techniques for heart disease classification and prediction. In: 2020 IEEE 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT); 2020; pp. 1-6.
[http://dx.doi.org/10.1109/ICCCNT49239.2020.9225673]
[5]
Alim A, Islam MK. Application of machine learning on ECG signal classification using morphological features. In: 2020 IEEE Region 10 Symposium (TENSYMP). 2020; pp. 1632-5.
[http://dx.doi.org/10.1109/TENSYMP50017.2020.9230780]
[6]
Zebin L, Wei L, Lifu G, Jinsi Z. Research on ECG denoising method based on empirical mode decomposition and wavelet transform. 2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP). 675-9.
[http://dx.doi.org/10.1109/ICSIP49896.2020.9339369]
[7]
Subramanian K, Prakash NK. Machine learning based cardiac arrhythmia detection from ECG signal In: 2020 IEEE Third International Conference on Smart Systems and Inventive Technology (ICSSIT). 2020; pp. 1137-41.
[http://dx.doi.org/10.1109/ICSSIT48917.2020.9214077]
[8]
Kharshid A, Alhichri HS, Ouni R, Bazi Y. Classification of short-time single-lead ECG recordings using deep residual CNN. In: 2019 IEEE 2nd International Conference on New Trends in Computing Sciences (ICTCS); 2019; pp. 1-6.
[http://dx.doi.org/10.1109/ICTCS.2019.8923079]
[9]
Sharma P, Pahuja SK, Veer K. Recent approaches on classification and feature extraction of EEG signal: A review. Robotica 2021; 1-25.
[10]
Celin S, Vasanth K. Survey on the methods for detecting arrhythmias using heart rate signals. J Pharm Sci Res 2017; 9(2): 183.
[11]
Lin CH. Frequency-domain features for ECG beat discrimination using grey relational analysis-based classifier. Comput Math Appl 2008; 55(4): 680-90.
[http://dx.doi.org/10.1016/j.camwa.2007.04.035]
[12]
Melillo P, Bracale M, Pecchia L. Nonlinear heart rate variability features for real-life stress detection. Case study: Students under stress due to university examination. Biomed Eng Online 2011; 10(1): 96.
[http://dx.doi.org/10.1186/1475-925X-10-96] [PMID: 22059697]
[13]
Piotrowskia Z, Rozanowski K. Robust algorithm for Heart Rate (HR) detection and Heart Rate Variability (HRV) estimation. Acta Phys Pol A 2010; 118(1): 131-5.
[http://dx.doi.org/10.12693/APhysPolA.118.131]
[14]
Syama S, Sweta GS, Kavyasree PI, Reddy KJ. Classification of ECG signal using machine learning techniques.IEEE 2nd International Conference on Power and Embedded Drive Control (ICPEDC). Chennai, India 2019.
[http://dx.doi.org/10.1109/ICPEDC47771.2019.9036613]
[15]
Wu CH, Liu WX, Lin MS, Chen JJ. An ECG extraction and reconstruction system with dynamic EMG filtering implemented on an ARM chip IEEE International Conference of Intelligent Applied Systems on Engineering (ICIASE). Fuzhou, China. April 26-29, 2019.
[http://dx.doi.org/10.1109/ICIASE45644.2019.9074076]
[16]
Veer K. Spectral and mathematical evaluation of electromyography signals for clinical use. Int J Biomath 2016; 9(06)1650094
[http://dx.doi.org/10.1142/S1793524516500947]
[17]
Veer K. A flexible approach for segregating physiological signals. Measurement 2016; 87: 21-26, 87.
[http://dx.doi.org/10.1016/j.measurement.2016.03.017]
[18]
Yadav D, Yadav S, Veer K. A comprehensive assessment of Brain Computer Interfaces: Recent trends and challenges. J Neurosci Methods 2020; 346108918
[http://dx.doi.org/10.1016/j.jneumeth.2020.108918] [PMID: 32853592]
[19]
Duda N, Barthule A, Ripperger S, Mayer F, Weigel R, Koelpin A. Non-invasive low power ECG for heart beat detection of bats.IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet). Orlando, FL; USA. 2019.January 20-23;
[http://dx.doi.org/10.1109/WISNET.2019.8711816]
[20]
Rahman A, Rahman T, Ghani NH, Hossain S, Uddin J. IoT based patient monitoring system using ECG sensor. IEEE International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST). 2019 Jan 10-12;
[http://dx.doi.org/10.1109/ICREST.2019.8644065]
[21]
Veer K. Flexible approach for classifying EMG signals for rehabilitation applications. Neurophysiology 2020; 52(1): 60-6.
[http://dx.doi.org/10.1007/s11062-020-09851-8]
[22]
Sharma T, Sharma KP, Veer K. Decomposition and classification of SEMG for the control of myoelectric prostheses. Measurement 2021; 186110102
[http://dx.doi.org/10.1016/j.measurement.2021.110102]
[23]
Yadav D, Shilpee Y, Veer K. Trends and applications of brain computer interfaces. Curr Signal Transduct Ther 2020; 15(1): 1-13.
[24]
Veer K, Vig R. Comparison of soft computing techniques in analyzing surface electromyogram signals. Curr Signal Transduct Ther 2018; 13(2): 168-72.
[http://dx.doi.org/10.2174/1574362413666180202163305]
[25]
Veer K, Vig R. Identification and classification of upper limb motions using PCA.Biomed Engin Biomedizi Tech,(BMT) 2018; 63(2): 191-6.
[26]
Mouquan S, Park J, Fei S. Event-triggered nonfragile H∞ filteringof Markov jump systems with imperfect transmissions. Signal Processing 2018; 149: 204-2013.
[http://dx.doi.org/10.1016/j.sigpro.2018.03.015]
[27]
Andreão RV, Dorizzi B, Boudy J. ECG signal analysis through hidden Markov models. IEEE Trans Biomed Eng 2006; 53(8): 1541-9.
[http://dx.doi.org/10.1109/TBME.2006.877103] [PMID: 16916088]
[28]
Mouquan S, Shen Y, Ze T, Zhou G. Finite-time H ∞ filtering of Markov jump systems with incomplete transition probabilities: A probability approach. IET Signal Process 2015; 9(7): 572-8.
[http://dx.doi.org/10.1049/iet-spr.2014.0376]
[29]
Moody GB, Mark RG. The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag 2001; 20(3): 45-50.
[http://dx.doi.org/10.1109/51.932724] [PMID: 11446209]
[30]
Veer K. A technique for classification and decomposition of muscle signal for control of myoelectric prostheses based on wavelet statistical classifier. Measurement 2015; 60: 283-91.
[http://dx.doi.org/10.1016/j.measurement.2014.10.023]
[31]
Avanzato R, Beritelli F. Automatic ECG diagnosis using convolutional neural network. Electronics (Basel) 2020; 9(6): 951.
[http://dx.doi.org/10.3390/electronics9060951]