Support vector machines (SVMs) favorably classified heartbeat time series compared to other neural network-based approaches, even in signals with very low signal-to-noise ratio.
Does a support vector machine (SVM) classifier improve the classification rate of heartbeat time series compared to other neural network-based approaches?
Support vector machines demonstrate favorable performance compared to other neural network approaches for classifying heartbeat time series from ECG recordings.
In this study, heartbeat time series are classified using support vector machines (SVMs). Statistical methods and signal analysis techniques are used to extract features from the signals. The SVM classifier is favorably compared to other neural network-based classification approaches by performing leave-one-out cross validation. The performance of the SVM with respect to other state-of-the-art classifiers is also confirmed by the classification of signals presenting very low signal-to-noise ratio. Finally, the influence of the number of features to the classification rate was also investigated for two real datasets. The first dataset consists of long-term ECG recordings of young and elderly healthy subjects. The second dataset consists of long-term ECG recordings of normal subjects and subjects suffering from coronary artery disease.
Kampouraki et al. (Mon,) conducted a other in Coronary artery disease. Support vector machines (SVMs) vs. Other neural network-based classification approaches was evaluated on Classification performance. Support vector machines (SVMs) favorably classified heartbeat time series compared to other neural network-based approaches, even in signals with very low signal-to-noise ratio.