2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 13 , Issue 1 , PP: 99-110, 2024 | Cite this article as | XML | Html | PDF
Madhura K. 1 * , Asha KS 2 , Mary Christeena Thomas 3 , Anubhav Bhalla 4 , Rajat Saini 5 , Aws Zuhair Sameen 6
Doi: https://doi.org/10.54216/JISIoT.130108
Reducing the influence of significant noise signal components on the obtained raw ECG signal is essential for precise identification of cardiac arrhythmias (CA), which frequently present as irregularities in heart rate or rhythm. Preprocessing is used to remove noise signals and baseline drift from the ECG wave that is recorded using the internet of things (IoT). After that, the denoised signal is subjected to dimensionality reduction and feature extraction. In order to determine whether classification method is more effective in detecting cardiac arrhythmias, this study compares two methods: an adaptive neuro-fuzzy inference system and artificial feed-forward neural networks trained with the back-propagation learning algorithm. An Adaptive Neuro Fuzzy Inference System analyses ICA features obtained by non-parametric power spectral estimates, and an Artificial Neural Network (ANN) classifier uses the ECG signal's morphological and statistical aspects to identify patterns. The creation of artificial feed-forward neural networks provides a rich framework for studying the Back Propagation Algorithm. Sensitivity, specificity, accuracy, and positive predictiveivity are some of the performance characteristics that are thoroughly examined. An overall accuracy of 97.79%, sensitivity of 99.82%, specificity of 99.68%, and positive predictivity of 98.58% were seen in the results of the Artificial Neural Feed Forward Network (ANFFN). The Adaptive Neuro Fuzzy Inference System (ANFIS) outperforms these metrics with an astounding overall accuracy of 99.62%, specificity of 98.63%, and positive predictivity of 99.46%. With a classification accuracy of 99.82%, ANFIS demonstrates to be the most effective classifier for identifying cardiac arrhythmias.
Cardiac Arrhythmia , Fuzzy , ANFFN , ANFIS , ICA , CA , IoT
[1] Diale Melvin, Christiaan Van Der Walt, Turgay Celik & Abiodun Modupe 2016, “Feature Selection and Support Vector Machine hyper parameter optimization for spam detection”, IEEE- 2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech) held on 30 Nov- 2 Dec 2016 at Stellenbosch, South Africa, DOI:10.1109/RoboMech.2016.7813162.
[2] Dima, SM, Panagiotou, C, Mazomenos, EB, Rosengarten, JA, Maharatna, K, Gialelis JV, Curzen, N & Morgan, J 2013, “On the Detection of Myocardial Scar-Based on ECG/VCG Analysis”, IEEE Transactions On Biomedical Engineering, vol. 60, issue. 12, pp. 3399- 3409.
[3] Kavitha, R & Christopher, T 2014, “A Study on ECG Signal Classification Techniques”, International Journal of Computer Applications (0975–8887), vol. 86, issue. 14, pp. 9 -14.
[4] Kelwade, JP & Salankar, SS 2015, “Prediction of Cardiac Arrhythmia using ANN”, International Journal of Computer Applications, ISSN:0975-8887, vol. 115, issue. 20, pp. 30-35.
[5] Lee, WK, Yoon, H & Park, KS 2016, “Smart ECG Monitoring Patch with Built-in R peak detection for Long term HRV Analysis”, Annals of Biomedical Engineering, vol.44, DOI: 10.1007/s 10439-015-1502-5.
[6] Sai, Y.P, “A review on arrhythmia classification using ECG signals”, In Proceedings of the 2020 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), Bhopal, India, 22–23 February 2020; pp. 1–6.
[7] Mhamdi, L., Dammak, O., Cottin, F., Dhaou, I.B, “Artificial Intelligence for Cardiac Diseases Diagnosis and Prediction Using ECG Images on Embedded Systems”, Biomedicines 2022, 10, 2013.
[8] Yu, J.; Park, S.; Kwon, S.H.; Cho, K.H.; Lee, H. “AI-Based Stroke Disease Prediction System Using ECG and PPG Bio-Signals”, IEEE Access 2022, 10, 43623–43638.
[9] Ghosh, S.K.; Tripathy, R.K.; Paternina, M.R.; Arrieta, J.J.; Zamora-Mendez, A.; Naik, G.R, “Detection of atrial fibrillation from single lead ECG signal using multi-rate cosine filter bank and deep neural network”, J. Med. Syst. 2020, 44, 1–15.
[10] Attallah, O. ECG-BiCoNet: “An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration” Comput. Biol. Med. 2022, 142, 105210.
[11] Khan, A.H.; Hussain, M.; Malik, M.K, “ECG Images dataset of Cardiac and COVID-19 Patients”, Data Brief 2021, 34, 106762.
[12] Wang, Y.; Chen, L.; Wang, J.; He, X.; Huang, F.; Chen, J.; Yang, X. “Electrocardiogram analysis of patients with different types of COVID-19” Ann. Noninvasive Electrocardiol. 2020, 25, e12806.
[13] Sun, W.; Kalmady, S.V.; Sepehrvan, N.; Chu, L.M.; Wang, Z.; Salimi, A.; Hindle, A.; Greiner, R.; Kaul, P. “Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale”, arXiv 2022, arXiv:2211.10431.
[14] Zhu, H.; Cheng, C.; Yin, H.; Li, X.; Zuo, P.; Ding, J.; Lin, F.; Wang, J.; Zhou, B.; Li, Y.; et al. “Automatic multi-label electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: A cohort study”, Lancet Digit. Health 2020, 2, e348–e357.
[15] Latif, G.; al Anezi, F.Y.; Zikria, M.; Alghazo, J. “EEG-ECG Signals Classification for Arrhythmia Detection using Decision Trees”, In Proceedings of the 4th International Conference on Inventive Systems and Control, ICISC 2020, TamilNadu, India, 8–10 January 2020; pp. 192–196
[16] Chou, Y.H.; Hong, S.; Zhou, Y.; Shang, J.; Song, M.; Li, H. Knowledge-shot learning: “An interpretable deep model for classifying imbalanced electrocardiography data”, Neurocomputing 2020, 417, 64–73.
[17] Abdullah, T.A.A.; Zahid, M.S.M.; Ali, W. “A review of interpretable ml in healthcare: Taxonomy, applications, challenges, and future directions” Symmetry 2021, 13, 2439.
[18] Abdullah, T.A.A.; Ali, W.; Abdulghafor, R. “Empirical study on intelligent android malware detection based on supervised machine learning”, Int. J. Adv. Comput. Sci. Appl. 2020, 11, 215–224.
[19] Abdullah, T.A.A.; Ali, W.; Malebary, S.; Ahmed, A.A. “A Review of Cyber Security Challenges, Attacks and Solutions for Internet of Things Based Smart Home”, IJCSNS Int. J. Comput. Sci. Netw. Secur. 2019, 19, 139–146.
[20] Al-Hiyali, M.I.; Yahya, N.; Faye, I.; Hussein, A.F. “Identification of autism subtypes based on wavelet coherence of BOLD FMRI signals using convolutional neural network”, Sensors 2021, 21, 5256.
[21] Alizadehsani, R.; Roshanzamir, M.; Abdar, M.; Beykikhoshk, A.; Khosravi, A.; Panahiazar, M.; Koohestani, A.; Khozeimeh, F.; Nahavandi, S.; Sarrafzadegan, N. “A database for using machine learning and data mining techniques for coronary artery disease diagnosis”, Sci. Data 2019, 6, 227.
[22] Acharya, U.R.; Oh, S.L.; Hagiwara, Y.; Tan, J.H.; Adeli, H. “Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals”, Comput. Biol. Med. 2018, 100, 270–278.
[23] Rai, H.M.; Chatterjee, K. “Hybrid CNN-LSTM deep learning model and ensemble technique for automatic detection of myocardial infarction using big ECG data”, Appl. Intell. 2022, 52, 5366–5384.
[24] Al-Zaiti, S.; Besomi, L.; Bouzid, Z.; Faramand, Z.; Frisch, S.; Martin-Gill, C.; Gregg, R.; Saba, S.; Callaway, C.; Sejdić, E. “Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram”, Nat. Commun. 2020, 11, 3966.
[25] Yildirim, Ö. “A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification”, Comput. Biol. Med. 2018, 96, 189–202.
[26] Saadatnejad, S.; Oveisi, M.; Hashemi, M, “LSTM-based ECG classification for continuous monitoring on personal wearable devices”, IEEE J. Biomed. Health Inform. 2019, 24, 515–523.
[27] Jain, P.; Gajbhiye, P.; Tripathy, R.; Acharya, U.R. “A two-stage Deep CNN Architecture for the Classification of Low-risk and High-risk Hypertension Classes using Multi-lead ECG Signals”, Inform. Med. Unlocked 2020, 21, 100479.
[28] Jun, T.J.; Nguyen, H.M.; Kang, D.; Kim, D.; Kim, D.; Kim, Y.H. “ECG arrhythmia classification using a 2-D convolutional neural network” arXiv 2018, arXiv:1804.06812.
[29] Anwar, S.M.; Gul, M.; Majid, M.; Alnowami, M. “Arrhythmia classification of ECG signals using hybrid features” Comput. Math. Methods Med. 2018, 2018, 1380348.
[30] Murat, F.; Yildirim, O.; Talo, M.; Baloglu, U.B.; Demir, Y.; Acharya, U.R. “Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review” Comput. Biol. Med. 2020, 120, 103726.
