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This paper presents a bibliographical survey of recently published research on different techniques for extracting features from photoplethysmograms (PPG). These techniques and approaches have been implemented to increase the accuracy of disease detection. Moreover, several aspects of PPG waveform analysis, including techniques for feature extraction, parameters involved, and performance comparisons, are discussed. This review will serve as a comparative study and reference for researches working on PPG waveform in healthcare applications. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. This study presents a case-control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R-R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, knearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population. [ABSTRACT FROM AUTHOR]