Search

Hyperuricemia is an alarming issue that contributes to cardiovascular disease. Uric acid (UA) level was proven to be related to pulse wave velocity, a marker of arterial stiffness. A hyperuricemia prediction method utilizing photoplethysmogram (PPG) and arteriograph by using machine learning (ML) is proposed. From the literature search, there is no available papers found that relates PPG with UA level even though PPG is highly associated with vessel condition. The five phases in this research are data collection, signal preprocessing including denoising and signal quality indexes, features extraction for PPG and SDPPG waveform, statistical analysis for feature selection and classification of UA levels using ML. Adding PPG to the current arteriograph able to reduce cost and increase the prediction performance. PPG and arteriograph data were measured from 113 subjects, and 226 sets of data were collected from the left and right hands of the subjects. The performance of four types of ML, namely, artificial neural network (ANN), linear discriminant analysis (LDA), k-nearest neighbor (kNN), and support vector machine (SVM) in predicting hyperuricemia was compared. From the total of 98 features extracted, 16 features of which showed statistical significance for hyper and normouricemia. ANN gives the best performance compared to the other three ML techniques with 91.67%, 95.45%, and 94.12% for sensitivity, specificity, and accuracy, respectively. Features from PPG and arteriograph able to be used to predict hyperuricemia accurately and noninvasively. This study is the first to find the relationship of PPG with hyperuricemia. It shows a significant relation between PPG signals and arteriograph data toward the UA level. The proposed method of UA prediction shows its potential for noninvasive preliminary assessment. [ABSTRACT FROM AUTHOR]

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]