Key result
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.
Why the study?
Does a support vector machine (SVM) classifier improve the classification rate of heartbeat time series compared to other neural network-based approaches?
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.
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SVMs may aid noisy ECG classification; leaves open clinical validation in arrhythmia detection.
Kampouraki et al. (2009) studied 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.
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