A binary classification method using support vector machine classifiers with gyrocardiography training features achieved an overall accuracy of >94% for detecting cardiovascular abnormalities.
Cross-Sectional (n=24)
Does a digital signal processing framework using SCG and GCG signals accurately classify cardiovascular abnormalities?
A novel digital signal processing framework using seismocardiography and gyrocardiography signals demonstrated high accuracy (>94%) in classifying cardiovascular abnormalities.
This paper introduces a novel method of binary classification of cardiovascular abnormality using the time-frequency features of cardio-mechanical signals, namely seismocardiography (SCG) and gyrocardiography (GCG) signals. A digital signal processing framework is proposed which utilizes decision tree and support vector machine methods with features generated by continuous wavelet transform. Experimental measurements were collected from twelve patients with cardiovascular diseases as well as twelve healthy subjects to evaluate the proposed method. Results reveal an overall accuracy of more than 94% with the best performance achieved from SVM classifiers with GCG training features. This suggests that the proposed solution could be a promising method for classifying cardiovascular abnormalities.
Yang et al. (Sun,) conducted a cross-sectional in Cardiovascular abnormality (n=24). Binary classification using time-frequency features of SCG and GCG signals was evaluated on Overall classification accuracy. A binary classification method using support vector machine classifiers with gyrocardiography training features achieved an overall accuracy of >94% for detecting cardiovascular abnormalities.