Key result
Hilbert transform and harmonic search classifier detects CVD from PPG signals with ~98% accuracy.
Why the study?
PPG signals are noninvasive and inexpensive for CVD detection but occupy large amounts of memory, prompting the need for machine learning techniques to dimensionally reduce signals and improve classifier performance.
Can machine learning techniques applied to PPG signals accurately detect cardiovascular disease?
Can machine learning techniques applied to PPG signals accurately detect cardiovascular disease?
Machine learning techniques, specifically the Hilbert transform combined with a harmonic search classifier, can accurately detect cardiovascular disease from noninvasive PPG signals.
May support PPG-based detection feasibility in small cohorts; leaves open validation in larger prospective studies.
Photoplethysmography (PPG) signals are widely used in clinical practice as a diagnostic tool since PPG is noninvasive and inexpensive. In this article, machine learning techniques were used to improve the performance of classifiers for the detection of cardiovascular disease (CVD) from PPG signals. PPG signals occupy a large amount of memory and, hence, the signals were dimensionally reduced in the initial stage. A total of 41 subjects from the Capno database were analyzed in this study, including 20 CVD cases and 21 normal subjects. PPG signals are sampled at 200 samples per second. Therefore, 144,000 samples per patient are available. Now, a one-second-long PPG signal is considered a segment. There are 720 PPG segments per patient. For a total of 41 subjects, 29,520 segments of PPG signals are analyzed in this study. Five dimensionality reduction techniques, such as heuristic- (ABC-PSO, cuckoo clusters, and dragonfly clusters) and transformation-based techniques (Hilbert transform and nonlinear regression) were used in this research. Twelve different classifiers, such as PCA, EM, logistic regression, GMM, BLDC, firefly clusters, harmonic search, detrend fluctuation analysis, PAC Bayesian learning, KNN-PAC Bayesian, softmax discriminant classifier, and detrend with SDC were utilized to detect CVD from dimensionally reduced PPG signals. The performance of the classifiers was assessed based on their metrics, such as accuracy, performance index, error rate, and a good detection rate. The Hilbert transform techniques with the harmonic search classifier outperformed all other classifiers, with an accuracy of 98.31% and a good detection rate of 96.55%.
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Palanisamy et al. (2023) studied Cardiovascular disease (n=41). Hilbert transform techniques with harmonic search classifier applied to PPG signals vs. Other dimensionality reduction techniques and classifiers was evaluated on Accuracy and good detection rate for CVD detection. The Hilbert transform technique combined with a harmonic search classifier detected cardiovascular disease from PPG signals with 98.31% accuracy and a 96.55% good detection rate.
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