Key result
A k-nearest neighbors classifier using chaotic and statistical features from photoplethysmography signals achieved 95% accuracy and 90.4% specificity for detecting premature ventricular contractions.
Why the study?
Can machine learning algorithms accurately detect premature ventricular contractions using photoplethysmography signals?
Can machine learning algorithms accurately detect premature ventricular contractions using photoplethysmography signals?
A k-nearest neighbors classifier using chaotic and statistical features from photoplethysmography signals can detect premature ventricular contractions with 95% accuracy.
May support PPG-based PVC screening in wearables; leaves open clinical adoption pending larger validation.
This paper deals with the analysis of photoplethysmography (PPG) signals for the recognition of premature ventricular contractions (PVC). PGG is an optical method used to measure blood volume changes in a non-invasive manner. As a diagnostic tool, PPG has recently been considered to evaluate the functioning of the cardiovascular system and identify its related disorders. PPG signals from 22 healthy and unhealthy subjects were used in this work. A number of chaotic and statistical features including Lyapunov exponent, skewness, kurtosis, fuzzy entropy and spectral entropy were extracted from the signals and selective features were identified by principle component analysis (PCA) to be used during data classification. Feature reduction method. k-nearest neighbors (kNN), support vector machine (SVM) and neural network were examined as classification algorithms. Results showed that the highest recognition accuracy of 95% and specificity of 90.4% are obtained by the KNN classifier.
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Yousefi et al. (2018) studied Premature ventricular contractions (n=22). k-nearest neighbors (kNN) classifier vs. Support vector machine (SVM) and neural network was evaluated on Recognition accuracy. A k-nearest neighbors classifier using chaotic and statistical features from photoplethysmography signals achieved 95% accuracy and 90.4% specificity for detecting premature ventricular contractions.
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