Evaluation of patient electrocardiogram datasets using signal quality indexing

Electrocardiogram (ECG) is widely used in the hospital emergency rooms for detecting vital signs, such as heart rate variability and respiratory rate. However, the quality of the ECGs is inconsistent. ECG signals lose information because of noise resulting from motion artifacts. To obtain an accurate information from ECG, signal quality indexing (SQI) is used where acceptable thresholds are set in order to select or eliminate the signals for the subsequent information extraction process. A good evaluation of SQI depends on the R-peak detection quality. Nevertheless, most R-peak detectors in the literature are prone to noise. This paper assessed and compared five peak detectors from different resources. The two best peak detectors were further tested using MIT-BIH arrhythmia database and then used for SQI evaluation. These peak detectors robustly detected the R-peak for signals that include noise. Finally, the overall SQI of three patient datasets, namely, Fantasia, CapnoBase, and MIMIC-II, was conducted by providing the interquartile range (IQR) and median SQI of the signals as the outputs. The evaluation results revealed that the R-peak detectors developed by Clifford and Behar showed accuracies of 98% and 97%, respectively. By introducing SQI and choosing only high-quality ECG signals, more accurate vital sign information will be achieved

Methods of Extracting Feature from Photoplethysmogram Waveform for Non-Invasive Diagnostic Applications

This paper presents a bibliographical survey of recently-published research on different techniques to extract feature from photoplethysmogram (PPG). These techniques and approaches have been implemented for better accuracy in detecting diseases. Moreover, several aspects in analyzing PPG waveform are discussed on the techniques in feature extraction, parameters involved and performance comparisons. This review will serve as a comparative study and reference for researches working on PPG waveform in health care applications

Methods of Extracting Feature from Photoplethysmogram Waveform for Non-Invasive Diagnostic Applications

This paper presents a bibliographical survey of recently-published research on different techniques to extract feature from photoplethysmogram (PPG). These techniques and approaches have been implemented for better accuracy in detecting diseases. Moreover, several aspects in analyzing PPG waveform are discussed on the techniques in feature extraction, parameters involved and performance comparisons. This review will serve as a comparative study and reference for researches working on PPG waveform in health care applications.</p

Cardiovascular Disease Prediction from Electrocardiogram by Using Machine Learning

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, k-nearest 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