Prediction of Quality Estimation by Supervised Learning for Electrocardiogram Noise Detection
Keywords:
Electro Cardiogram (ECG), Baseline Wandering (BA), powerline interference (PLI), Weiner Filter (WF), Fourier-transform (FT)Abstract
The specificity and sensitivity of arrhythmia detection from electrocardiograms is a crucial objective in detecting pervasive computing methods. The noise is pervasive to electrocardiograms, which is since the electrocardiogram signals often transmit through distributed computing environments such as the medical internet of things (medical IOT). The noisy electrocardiograms are ubiquitous to false alarming in these distributed and pervasive computing-aided methods. The detection of noise scope in electrocardiograms is the primary objective in machine learning-based arrhythmia detection methods addressed in this manuscript. The proposed method classifies the given electrocardiograms are noisy or not. Concerning this, the method uses the electrocardiograms’ temporal and spectral features. The proposed method’s performance has been assessed using multifold cross-validation and scaled by comparing it with the contemporary contribution give better specifications as specificity 93.9% sensitivity 95.3%, and accuracy 94.4%.
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Kumar, A. (2010). ECG-simplified. LifeHugger. (2010) Nov. http://pn.lifehugger.com/doc/120/ecg-100-steps
Lilly, L. S. (2012). Braunwald’s heart disease: a textbook of cardiovascular medicine. Elsevier Health Sciences, vol. 2. Medico-legal Update, https://www.ijop.net/index.php/mlu/article/download/2535/2231
Wu, J. M.-T. (2020). A deep neural network electrocardiogram analysis framework for left ventricular hypertrophy prediction. Journal of Ambient Intelligence and Humanized Computing, 1-17, https://doi.org/10.1007/s12652-020-01826-1.
P, P., & J , K. (2023). Effective Predictor Model for Parkinson’s Disease Using Machine Learning . International Journal of Computer Engineering in Research Trends, 10(4), 204–209. https://doi.org/10.22362/ijcert.v10i4.27
Care, I. E. (2005). Part 8: Stabilization of the patient with acute coronary syndromes. Circulation, IV-90-IV-110. https://doi.org/10.1161/CIRCULATIONAHA.105.166561
Hongqiang Li, Danyang Yuan,Youxi Wang, Dianyin Cui, Lu Cao (2016). Arrhythmia classification based on multi-domain feature extraction for an ECG recognition system. Sensors, 16(10), 1744. DOI: 10.3390/s16101744
G, P., S, R., & Bedhapuri, M. (2023). Next-Generation Eye Care: IoT-Driven Disease Detection and Emergency Alerts using DL. International Journal of Computer Engineering in Research Trends, 9(3), 66–76. https://doi.org/10.22362/ijcert.v9i3.30
Rehman, U. a. (2014). Noise Removal From ECG Using Modified CSLMS Algorithm. International Journal of Electronics, Communication & Instrumentation Engineering Research & Development, 4(3), 53-60. www.tjprc.org
M. Rudra Kumar, V. K. (2020). Review of Machine Learning models for Credit Scoring Analysis. Revista Ingeniería Solidaria,, 16(1). https://doi.org/10.16925/2357-6014.2020.01.11
Boge, A. V. (2012). Clearing Artifacts using a Constrained Stability Least Mean Square Algorithm from Cardiac Signals. International Journal of Scientific & Engineering Research, 3(11), 1-6. http://www.ijser.org
Kim, J.-C. a. (2020). Neural-network based adaptive context prediction model for ambient intelligence. Journal of Ambient Intelligence and Humanized Computing, 11(4), 1451-1458. https://doi.org/10.1007/s12652-018-0972-3
Gayatri Khanvilkar, Deepali Vora (2018). Activation Functions and Training Algorithms for Deep Neural Network. International Journal of Computer Engineering in Research Trends, 5(4),98-104.
Joao Rodrigues, David Belo, Hugo Gamboa (2017). Noise detection on ECG based on agglomerative clustering of morphological features. Computers in biology and medicine, 87, 322-334. DOI: 10.1016/j.compbiomed.2017.06.009
Haemwaan Sivaraks, Chotirat Ann Ratanamahatana (2015). Robust and accurate anomaly detection in ECG artifacts using time series motif discovery. Computational and mathematical methods in medicine, vol. 2015, pp. 1-20, https://doi.org/10.1155/2015/453214.
Udit Satija, Barathram Ramkumar, M Sabarimalai Manikandan (2017). Automated ECG noise detection and classification system for unsupervised healthcare monitoring. IEEE Journal of biomedical and health informatics, 22(3), 722-732. DOI: 10.1109/JBHI.2017.2686436
Kumar.S, Panigrahy.D, Sahu.P.K. (2018). Denoising of Electrocardiogram (ECG) signal by using empirical mode decomposition (EMD) with non-local mean (NLM) technique. Biocybernetics and Biomedical Engineering, 38(2), 297-312. http://www.journals.elsevier.com/biocybernetics-and-biomedical-engineering/
Gummadi, A. ., & Rao, K. R. . (2023). EECLA: A Novel Clustering Model for Improvement of Localization and Energy Efficient Routing Protocols in Vehicle Tracking Using Wireless Sensor Networks. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 188–197. https://doi.org/10.17762/ijritcc.v11i2s.6044
Neha Agrawal, Shashikala Tapaswi (2018). Low rate cloud DDoS attack defense method based on power spectral density analysis. Information Processing Letters, 138, 44-50. https://doi.org/10.1016/j.ipl.2018.06.001
Gautier Marti, Frank Nielsen, Mikołaj Bińkowski, Philippe Donnat (2017). A review of two decades of correlations, hierarchies, networks and clustering in financial markets. arXiv preprint arXiv:1703.00485. https://doi.org/10.48550/arXiv.1703.00485
Asghar Ghasemi, Saleh Zahediasl1 (2012). Normality tests for statistical analysis: a guide for non-statisticians. International journal of endocrinology and metabolism, 10(2), 486. doi: 10.5812/ijem.3505
Tae-Ki An, Moon-Hyun Kim (2010). A new diverse AdaBoost classifier. 2010 International Conference on Artificial Intelligence and Computational Intelligence, 1, 359-363. DOI:10.1109/AICI.2010.82
Benedetto Barabino, Nicola Aldo Cabras, Claudio Conversano & Alessandro Olivo (2020). An integrated approach to select key quality indicators in transit services. Social Indicators Research, 1-36. DOI: 10.1007/s11205-020-02284-0
S Celin & K. Vasanth (2018). ECG signal classification using various machine learning techniques. Journal of medical systems, 42(12), 1-11. https://doi.org/10.1007/s10916-018-1083-6
Sherje, D. N. . (2021). Content Based Image Retrieval Based on Feature Extraction and Classification Using Deep Learning Techniques. Research Journal of Computer Systems and Engineering, 2(1), 16:22. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/14
Moody GB, M. R. (2001). The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20(3), 45-50.
S. Sandhya kumari , K.Sandhya Rani (2022). Selection of MSER region based Ultrasound Doppler scan Image Big data classification using a faster RCNN network. International Journal of Computer Engineering in Research Trends.9(10),184-192.
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