Recognition and Evaluation of Heart Arrhythmias via a General Sparse Neural Network
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Abstract
In clinical use, an electrocardiogram (ECG) is an essential medical tool for assessing heart arrhythmias. Thousands of human beings worldwide are affected by different cardiac problems nowadays. As a consequence, studying the features of the ECG pattern is critical for detecting a wide range of cardiac diseases. The ECG is a test which assesses the intensity of the electrical impulses in the circulatory system. In the present investigation, detection and examination of arrhythmias in the heart on the system using GSNNs (General sparsed neural network classifier) can be carried out[1]. In this paper, the methodologies of support vector regression(SVR), neural mode decomposition(NMD), Artificial Neural Network (ANN), Support Vector Machine(SVM) and are examined. To assess the suggested structure, three distinct ECG waveform situations are chosen from the MIT-BIH arrhythmia collection. The main objective of this assignment is to create a simple, accurate, and simply adaptable approach for classifying the three distinct heart diseases chosen. The wavelet transform Db4 is used in the present paper to obtain several features from an ECG signal. The suggested setup was created using the MATLAB programme. The algorithms suggested are 98% accurate for forecasting cardiac arrhythmias, which is greater than prior techniques.
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References
https://ecgwaves.com/topic/ecg-normal-p-wave-qrs-complex-st-segment-t-wave-j-point/
Zubair M, Kim J, Yoon C (2016) An automated ECG beat classification system using convolutional neural networks. In: 2016 6th International conference on IT convergence and security (ICITCS), Prague, pp 1–5. https: //doi.org/10.1109/icitcs.2016.77403 10
Bhagyalakshmi V, Pujeriand RV, Devanagavi GD (2018) GB-SVNN: genetic BAT assisted support vector neural network for arrhythmia classification using ECG signals. J King Saud Univ Comput Inf Sci. https ://doi.org/10.1016/j.jksuc i.2018.02.005
Isin A, Ozdalili S Singh P, Pradhan G (2021) A new ECG denoising framework using generative adversarial network. IEEE/ACM Trans Comput Biol Bioinform. https://doi.org/10.1109/TCBB.2020.297 6981
Kovács P (2012) ECG signal generator based on geometrical features. Ann Univ Sci Budapestinensis de Rolando Eötvös Nominatae Sect Computatorica 37
Danandeh Hesar H, Mohebbi M (2021) An adaptive kalman filter bank for ECG denoising. IEEE J Biomed Health Inform. https://doi.org/10.1109/JBHI.2020.2982935
Maaroof Maaroof, M. A. ., & Naimi, S. . (2023). Developing Adaptive Project Construction Cost Control Using Multi-Nonlinear Regression Engineering Techniques. International Journal of Intelligent Systems and Applications in Engineering, 11(4s), 579 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2729
Wang Z, Zhu J, Yan T, Yang L (2019) A new modified wavelet-based ECG denoising. Comput Assist Surg 24(sup1):174–183. https://doi.org/10.1080/24699322.2018.1560088
Mourad N (2019) New two-stage approach to ECG denoising 13(6):596–605. https://doi.org/1049/iet-spr.2018.5458.Print ISSN 1751-9675, Online ISSN 1751-9683
Kim J, Shin HS, Shin K, Lee M (2009) Robust algorithm for arrhythmia classification in ECG using extreme learning machine. BioMed Eng 8:31
Singhal A, Singh P, Fatimah B, Pachori RB (2020) An efficient removal of power-line interference and baseline wander from ECG signals by employing Fourier decomposition technique. Biomed Signal Process Control 57:101741. https://doi.org/10.1016/j.bspc.2019.101741
Savalia S, Emamian V (2018) Cardiac arrhythmia classification by multi-layer perceptron and convolution neural networks. Bioengineering (Basel, Switzerland) 5(2):35. https ://doi.org/10.3390/bioen ginee ring5 02003 5
Kelwade JP, Salankar SS (2015) Prediction of cardiac arrhythmia using artificial neural network. Int J Comput Appl 115(20):0975–8887
Ayushi D, Nikita B, Nitin S (2020) A survey of ECG classification for arrhythmia diagnoses using SVM.In: Balaji S, RochaÁ,ChungYN(eds) Intelligent communication technologies and virtual mobile networks. ICICV 2019. Lecture notes on data engineering and communications technologies, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-030-28364-3_59
Kevin Harris, Lee Green, Juan Garcia, Juan Castro, Juan González. Intelligent Personal Assistants in Education: Applications and Challenges. Kuwait Journal of Machine Learning, 2(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/185
Fariha MAZ, Ikeura R, Hayakawa S, Tsutsumi S (2019) Analysis of Pan-Tompkins algorithm performance with noisy ECG signals. J Phys Conf Ser 1532. In: 4th ?nternational conference on engineering technology (ICET 2019) 6–7 July 2019. Darul Iman Training Centre (DITC), Kemaman, Terengganu, MALAYSIA
Bazi Y, Alajlan N, Hichri H, Malek S (2013) Domain adaptation methods for ECG classification. In: International conference on computer medical applications. IEEE
Kiranyaz S, Ince T, Gabbouj M (2016) Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans Biomed, Eng
Mustaqeem A, Anwar SM, Majid M (2018) Multiclass classification of cardiac arrhythmia using improved feature selection and SVM invariants. Comput Mat Methods Med 2018:10. https: //doi.org/10.1155/2018/73104 96
Sanamdikar ST, Hamde ST, Asutkar VG (2020) Analysis and classification of cardiac arrhythmia based on general sparsed neural network of ECG signal. SN Appl Sci 2(7):1–9. https://doi.org/10.1007/s42452-020-3058-8
Degadwala, D. S. ., & Vyas, D. . (2021). Data Mining Approach for Amino Acid Sequence Classification . International Journal of New Practices in Management and Engineering, 10(04), 01–08. https://doi.org/10.17762/ijnpme.v10i04.124
Qin Q, Li J, Zhang L et al (2017) Combining low-dimensional wavelet features and support vector machine for arrhythmia beat classification. Sci Rep 7:6067. https://doi.org/10.1038/s41 598-017-06596-z
Huang J, Chen B, Yao B, He W (2019) ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network. IEEE Access 7:92871–92880. https://doi.org/ 10.1109/ACCESS.2019.2928017
Gualsaquí MMV, Vizcaíno EIP, Flores-Calero MJ, Carrera EV (2017) ECG signal denoising through kernel principal components. In: 2017 IEEE XXIV ?nternational conference on electronics,electrical engineering and computing (INTERCON). Cusco, pp 1–4. https://doi.org/ 10.1109/INTERCON.2017.8079670
Sanamdikar ST,Hamde ST,AsutkarVG(2021) Classification and analysis ofECGsignal based on ?ncremental support vector regression on IOT platform. Biomed Signal Process Control 1–9. https://doi.org/10.1016/j.bspc.2020.102324
Andrew Hernandez, Stephen Wright, Yosef Ben-David, Rodrigo Costa, David Botha. Predictive Analytics for Decision-Making: Leveraging Machine Learning Techniques. Kuwait Journal of Machine Learning, 2(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/193
Hanbay K (2019) Deep neural network based approach for ECG classification using hybrid differential features and active learning. IET Signal Process 13(2):165–175. https ://doi.org/10.1049/ iet-spr.2018.5103
Jambukia SH, Dabhi VK, Prajapati HB (2018) ECG beat classification using machine learning techniques. Int J Biomed Eng Technol 26(1):32–53
Chaouch H, Ouni K, Nabli L (2018) Statistical method for ECG analysis and diagnostic. Int J Biomed Eng Technol 26(1):1–12
MIT-BIH Arrhythmia database. http://www.physi onet.org
Mondéjar-Guerra VM, Novo J, Rouco J, Gonzalez M, Ortega M (2018) Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers. Biomed Signal Process Control 47:41–48. https ://doi.org/10.1016/j. bspc.2018.08.007
Shukla DS (2012) a survey of electrocardiogram data capturing system using digital image processing: a review. Int J Comput Sci Technol 3:698–701.