Tamil EM, Bashar NS, Idris MYI, Tamil AM. A Review on Feature Extraction & Classification Techniques for Biosignal Processing (Part III: Electromyogram). ifmbe proceedings. 2008: 107–12.
John RM, UB T, et al. KB Ventricular arrhythmias and sudden cardiac death. Lancet 380(9852):1520–9
M. H, M. M, J.-P. V, H. C, A. D. Coronary three-vessel disease with occlusion of the right coronary artery: What are the most important factors that determine the right territory perfusion? IRBM. 2014;35(3):149–57.
S. E, Lieblich. Ambulatory sudden cardiac death: Mechanisms of production of fatal arrythmia on the basis of data from 157 cases Journal of Oral and Maxillofacial Surgery. 1989;117(1):151–9.
Chugh SS, Kelly KL, Titus JL. Sudden cardiac death with apparently normal heart. Circulation. 2000;102(6):649-54.
Article CAS PubMed Google Scholar
Zhang F, Li P, Jiang F, Lai D. A shockable rhythm detection algorithm for automatic external defibrillators by combining a slope variability analyzer with a band-pass digital filter. IEEE Workshop on Electronics2014. p. 828–31.
Acharya UR, Fujita H, Sudarshan VK, Sree VS, Eugene LWJ, Ghista DN, et al. An integrated index for detection of Sudden Cardiac Death using Discrete Wavelet Transform and nonlinear features. Knowledge-Based Systems. 2015;83:149-58. https://doi.org/10.1016/j.knosys.2015.03.015.
Vargas-Lopez O, Amezquita-Sanchez JP, De-Santiago-Perez JJ, Rivera-Guillen JR, Valtierra-Rodriguez M, Toledano-Ayala M, et al. A New Methodology Based on EMD and Nonlinear Measurements for Sudden Cardiac Death Detection. Sensors. 2019;20(1):1-18. https://doi.org/10.3390/s20010009.
Velazquez-Gonzalez JR, Peregrina-Barreto H, Rangel-Magdaleno JJ, Ramirez-Cortes JM, Amezquita-Sanchez JP. ECG-Based Identification of Sudden Cardiac Death through Sparse Representations. Sensors. 2021;21(22). https://doi.org/10.3390/s21227666.
Shi M, Yu H, Wang H. Automated Detection of Sudden Cardiac Death by Discrete Wavelet Transform of Electrocardiogram Signal. Symmetry. 2022;14(3):571. https://doi.org/10.3390/sym14030571.
Shen TW, Shen HP, Lin C, Ou YL. Detection and Prediction of Sudden Cardiac Death (SCD) For Personal Healthcare. in: 29th Annual International Conference of the IEEE. 2007;21:2575–8.
Ebrahimzadeh E, Pooyan M. Early detection of sudden cardiac death by using classical linear techniques and time-frequency methods on electrocardiogram signals. Biomed Sci Eng 2011;11:699-706. https://doi.org/10.4236/jbise.2011.411087.
Ebrahimzadeh E, Pooyan M, Bijar A. A novel approach to predict sudden cardiac death (SCD) using nonlinear and time-frequency analyses from HRV signals. PLoS One. 2014;9(2):e81896. https://doi.org/10.1371/journal.pone.0081896.
Article CAS PubMed PubMed Central Google Scholar
Murukesan L, Murugappan M, Iqbal M, Saravanan K. Machine Learning Approach for Sudden Cardiac Arrest Prediction Based on Optimal Heart Rate Variability Features. Journal of Medical Imaging and Health Informatics. 2014;4(4):521-32. https://doi.org/10.1166/jmihi.2014.1287.
Mirhoseini SR, Jahedmotlagh MR, Pooyan M. Improve Accuracy of Early Detection Sudden Cardiac Deaths (SCD) Using Decision Forest and SVM. International Conference on Robotics and Artificial Intelligence 2016 2016.
Ebrahimzadeh E, Manuchehri MS, Amoozegar S, Araabi BN, Soltanian-Zadeh H. A time local subset feature selection for prediction of sudden cardiac death from ECG signal. Medical & biological engineering & computing. 2018;56(7):1253-70. https://doi.org/10.1007/s11517-017-1764-1.
Ebrahimzadeh E, Foroutan A, Shams M, Baradaran R. An optimal strategy for prediction of sudden cardiac death through a pioneering feature-selection approach from HRV signal. Computer methods and programs in biomedicine. 2019;169:19-36. https://doi.org/10.1016/j.cmpb.2018.12.001.
Shi M, He H, Geng W, Wu R, Zhan C, Jin Y, et al. Early Detection of Sudden Cardiac Death by Using Ensemble Empirical Mode Decomposition-Based Entropy and Classical Linear Features From Heart Rate Variability Signals. Frontiers in physiology. 2020;11(118). https://doi.org/10.3389/fphys.2020.00118.
Gupta V, Kanungo A, Saxena NK, Kumar P, Kumar P. An Adaptive Optimized Schizophrenia Electroencephalogram Disease Prediction Framework. Wireless Personal Communications: An International Journal. 2023;130(2):1191-213.
Locati E, Schwartz PJ. Prognostic value of QT interval prolongation in post myocardial infarction patients. European heart journal. 1987;8(suppl_A):121–6.
Spargias KS, Lindsay SJ, G.I. Kawar ea. QT dispersion as a predictor of long-term mortality in patients with acute myocardial infarction and clinical evidence of heart failure,”. Eur Heart J. 1999;20(16):1158–65.
Lai D, Zhang Y, Zhang X, Su Y, Bin Heyat MB. An Automated Strategy for Early Risk Identification of Sudden Cardiac Death by Using Machine Learning Approach on Measurable Arrhythmic Risk Markers. IEEE Access. 2019;7:94701-16. https://doi.org/10.1109/access.2019.2925847.
Murugappan M, Murugesan L, Jerritta S, Adeli H. Sudden Cardiac Arrest (SCA) Prediction Using ECG Morphological Features. Arabian journal for science and engineering. 2021;46(2):947-61.
Gupta V. Application of chaos theory for arrhythmia detection in pathological databases. International Journal of Medical Engineering and Informatics. 2022(2):191-202.
Gupta V, Mittal M, Mittal V. A Novel FrWT Based Arrhythmia Detection in ECG Signal Using YWARA and PCA. Wireless Personal Communications: An International Journal. 2022;124(2):1229-46.
Gupta V, Mittal M, Mittal V, Saxena NK. Spectrogram as an Emerging Tool in ECG Signal Processing. Lecture Notes in Mechanical Engineering. 2022:407–14.
GuptaCA V, Mittal M, Mittal V, Diwania S, Saxena NK. ECG Signal Analysis based on the Spectrogram and Spider Monkey Optimisation Technique. Journal of The Institution of Engineers (India): Series B. 2023;104(1):153–64.
VanHoogenhuyze D, Martin G, Weiss J, Schaad J, Singer D. Spectrum of heart rate variability. Proc Comput Cardiol. 1989;65.
Eltrass AS, Tayel MB, Ammar A. Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures. Neural Computing & Applications. 2022;34(11):8755-75.
Karimulla S, Patra DA. An Optimal Methodology for Early Prediction of Sudden Cardiac Death Using Advanced Heart Rate Variability Features of ECG Signal. Arabian Journal for Science & Engineering (Springer Science & Business Media BV ). 2024;49(5):6725-41.
Gupta V, Mittal M, Mittal V. Chaos Theory: An Emerging Tool for Arrhythmia Detection. Sensing and Imaging. 2020;21(1):1-22.
Degirmenci M, Ozdemir MA, Izci1 E. Arrhythmic Heartbeat Classification Using 2D Convolutional Neural Networks. IRBM. 2021;43(5):422–33.
Holmstrom L, Chugh H, Nakamura K, Bhanji Z, Seifer M, Uy-Evanado A, et al. An ECG-based artificial intelligence model for assessment of sudden cardiac death risk. Communications medicine. 2024;4(1):17.
Article PubMed PubMed Central Google Scholar
Kaspal R, Alsadoon A, Prasad PWC, Al-Saiyd NA, Nguyen TQV, Pham DTH. A novel approach for early prediction of sudden cardiac death (SCD) using hybrid deep learning. Multimedia Tools and Applications. 2020;80(5):8063-90. https://doi.org/10.1007/s11042-020-10150-x.
Fujita H, Acharya UR, Sudarshan VK, Ghista DN, Sree SV. Sudden cardiac death (SCD) prediction based on nonlinear heart rate variability features and SCD index. Applied Soft Computing. 2016;43:510-9. https://doi.org/10.1016/j.asoc.2016.02.049.
Sudarshan VK, Acharya UR, Oh SL, Adam M, Tan JH, Chua CK, et al. Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2s of ECG signals. Computers in biology and medicine. 2017;83:48-58. https://doi.org/10.1016/j.compbiomed.2017.01.019.
Singh BN, Tiwari AK. Optimal selection of wavelet basis function applied to ECG signal denoising. Digital Signal Processing. 2006;16(3):275-87. https://doi.org/10.1016/j.dsp.2005.12.003.
Gupta V, Sharma AK, Pandey PK, Jaiswal RK, Gupta A. Pre-Processing Based ECG Signal Analysis Using Emerging Tools. IETE Journal of Research. 2022:1–12.
Rekik S, Ellouze N. Enhanced and Optimal Algorithm for QRS Detection(Article). IRBM. 2017;38(1):56-61.
Gupta V, Mittal M. QRS Complex Detection Using STFT, Chaos Analysis, and PCA in Standard and Real-Time ECG Databases(Article). Journal of The Institution of Engineers (India): Series B. 2019;100(5):489–97.
Gu X, Hu J, Zhang L, Ding J, Yan F. An Improved Method with High Anti-interference Ability for R Peak Detection in Wearable Devices. IRBM. 2020;41(3):172-83.
Gupta V, Mittal M, Mittal V. FrWT-PPCA-Based R-peak Detection for Improved Management of Healthcare System. IETE Journal of Research. 2021;69(8):1-15.
Gupta V, Mittal M, Mittal V, Chaturvedi Y. Detection of R-peaks using fractional Fourier transform and principal component analysis. Journal of Ambient Intelligence and Humanized Computing. 2022;13(2):961-72.
Gupta V, Saxena NK, Kanungo A, Kumar P, Diwania S. PCA as an effective tool for the detection of R-peaks in an ECG signal processing. International Journal of System Assurance Engineering and Management. 2022;13(5):2391-403.
Gupta V, Kanungo A, Kumar P, Kumar N, Choubey C. A design of bat-based optimized deep learning model for EEG signal analysis. Multimedia Tools and Applications. 2023;82(29):45367-87.
Zhao H, Sun M, Deng W, Yang X. A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing. Entropy. 2016;19(1):14-34. https://doi.org/10.3390/e19010014.
Acharya UR, Fujita H, Sudarshan VK, Lih OS, Muhammad A, Koh JEW, et al. Application of empirical mode decomposition (EMD) for automated identification of congestive heart failure using heart rate signals. Neural Computing and Applications. 2016;28(10):3073-94. https://doi.org/10.1007/s00521-016-2612-1.
Flandrin P, Rilling G, Gonalves P. Empirical mode decomposition as a filter bank. IEEE. 2004(2).
Chen W, Wang Z, Xie H, Yu W. Characterization of Surface EMG Signal Based on Fuzzy Entropy. IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society. 2007(2):15.
Chen W, Zhuang J, Yu W, Wang Z. Measuring complexity using FuzzyEn, ApEn, and SampEn. Medical Engineering & Physics. 2009;31(1):61-8.
Ruxton GD. The unequal variance t-test is an underused alternative to Student's t-test and the Mann–Whitney U test. Behavioral Ecology. 2010;17(4):688-90.
Larose DT. Discovering Knowledge in Data: An Introduction to Data Mining, Willey Interscience, KNN, . USA: New Jersey; 2004.
Jung W-H, Lee S-G. An Arrhythmia Classification Method in Utilizing the Weighted KNN and the Fitness Rule(Article). IRBM. 2017;38(3):138-48.
Breiman L, Friedman J, Olshen R, Stone C. Classification and Regression Trees. Boca Raton, FL: CRC Press; 1984.
Suykens JAK, Gestel TV, Brabanter JD, Moor BD, Vandewalle J. Least Squares Support Vector Machines. Singapore: World Scientific; 2002.
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