2/5/2024 0 Comments Svm vs random forestPermutation entropy: A natural complexity measure for time series. American Journal of Physiology-Heart and Circulatory Physiology., 278, H2039–H2049.īandt, C., & Pompe, B. Physiological time-series analysis using approximate entropy and sample entropy. International Journal of Computing, Communications & Instrumentation Engg, 1, 88–91. Analysis of various filter configurations on noise reduction in ECG waveform. Physionet/computing in cardiology challenge 2011, July 2011. Swarm and Evolutionary Computation, 28, 144–160. Microarray medical data classification using kernel ridge regression and modified cat swarm optimization based gene selection system. Mohapatra, P., Chakravarty, S., & Dash, P. A comprehensive evaluation of random vector functional link networks. A survey of randomized algorithms for training neural networks. IEEE Transactions on Cybernetics, 45, 2165–2176. Oblique decision tree ensemble via multisurface proximal support vector machine. Random forests with ensemble of feature spaces. K., Van Gestel, T., De Brabanter, J., De Moor, B., & Vandewalle, J. In Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 12th International Conference on (pp. Liu, A signal quality assessment method for mobile ECG using multiple features and fuzzy support vector machine. Computer Methods and Programs in Biomedicine, 117, 435–447. A machine learning approach to multi-level ECG signal quality classification. In Computing in Cardiology, IEEE, Hangzhou (Vol. Data driven approach to ECG signal quality assessment using multistep SVM classification. Kužílek, J., Huptych, M., Chudáček, V., Spilka1, J., Lhotská, L. Self-organized neural network for the quality control of 12-lead ECG signals. Assessing the usability of ECG by ensemble decision trees. Assessment of ECG quality on an Android platform. An algorithm for assessment of quality of ECGs acquired via mobile telephones. Y., King, S., Duncan, D., Di Maria, C., Duan, W., Bojarnejad, M., Zheng, D., Allen, J., & Murray, A. IEEE Journal of Biomedical and Health Informatics, 21, 1216–1223. Quality assessment of ambulatory ECG using wavelet entropy of the HRV signal. Dynamic time warping and machine learning for signal quality assessment of pulsatile signals. Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms. Journal of Zhejiang University Science C, 15, 564–573.Ĭlifford, G. ECG quality assessment based on a kernel support vector machine and genetic algorithm with a feature matrix. Journal of Electrocardiology, 45, 596–603. Signal quality and data fusion for false alarm reduction in the intensive care unit. Compared with the other classifiers, the LS-SVM classifier also demonstrated the superior generalization ability. For this sixth scheme, the LS-SVM yielded the highest Acc of 92.20% on hidden test data, as well as a relatively high Acc of 93.60% on training data. Using all features except ApEn features obtained the best performances for each classifier. The experiment results indicated PE and ELZC can help to improve performance of the aforementioned four classifiers for assessing ECG quality. Three indices i.e., sensitivity ( Se), specificity ( Sp) and accuracy ( Acc), were used for evaluating performances of the classifiers on the seven feature schemes, respectively. Up to 1500 mobile ECG recordings from the Physionet/Computing in Cardiology Challenge 2011 were employed in this study. Seven feature schemes include the first scheme consisting of 7 waveform features, the second consisting of 15 waveform and frequency features, the third consisting of 19 waveform, frequency and approximate entropy (ApEn) features, the fourth consisting of 19 waveform, frequency and permutation entropy (PE) features, the fifth consisting of 19 waveform, frequency and ELZC features, the sixth consisting of 23 waveform, frequency, PE and ELZC features, and the last consisting of all 27 features. kernel support vector machine (KSVM), random forest (RaF), least squares SVM (LS-SVM) and multi-surface proximal SVM based oblique RaF (ORaF) for ECG quality assessment we compared the four algorithms on 7 feature schemes yielded from 27 linear and nonlinear features including four features derived from a new encoding Lempel–Ziv complexity (ELZC) and the other 26 features. For evaluating performance of nonlinear features and iterative and non-iterative classification algorithms (i.e.
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