Over the past several decades, numerous methods have been applied by research and professionals to detect and measure mental stress, varying from subjective methods such questionnaires and face-to-face interviews up t...Over the past several decades, numerous methods have been applied by research and professionals to detect and measure mental stress, varying from subjective methods such questionnaires and face-to-face interviews up to the objective methods using physiological signals and neuroimaging equipment such as salivary cortisol and functional magnetic resonance imaging (fMRI), respectively. Among those methods, an Electroencephalograph (EEG) is one of the utmost chosen non-invasive methods by professionals and researchers in recording real time brain signals. This paper highlights the state of art for each of the studies, by comparing and analyzing the method and protocol of EEG data collection, including the selection of electrodes and brain regions involving two major categories of mental stress: acute and chronic. Selection of EEG features, with the necessary signal pre-processing and processing techniques, and the classification models used in these studies have been summarized and discussed.展开更多
A comprehensive study was conducted to differentiate cardiovascular disease (CVD) subjects from non-CVD subjects using short recording electrocardiogram (ECG) of 244 Malaysian adults in The MalaysianCohort project. An...A comprehensive study was conducted to differentiate cardiovascular disease (CVD) subjects from non-CVD subjects using short recording electrocardiogram (ECG) of 244 Malaysian adults in The MalaysianCohort project. An automated peak detection algorithm to detect nine fiducialpoints of electrocardiogram (ECG) was developed. Forty-eight features wereextracted in both time and frequency domains, including statistical featuresobtained from heart rate variability and Poincare plot analysis. These includefive new features derived from spectrum counts of five different frequencyranges. Feature selection was then made based on p-value and correlationmatrix. Selected features were used as input for five classifiers of artificialneural network (ANN), k-nearest neighbors (kNN), support vector machine(SVM), discriminant analysis (DA), and decision tree (DT). Results showedthat six features related to T wave were statistically significant in distinguishingCVD and non-CVD groups. ANN had performed the best with 94.44% specificity and 86.3% accuracy, followed by kNN with 80.56% specificity, 86.49%sensitivity and 83.56% accuracy. The novelties of this study were in providingalternative solutions to detect P-onset, P-offset, T-offset as well as QRS-onsetpoints using discrete wavelet transform method. Additionally, two out of thefive newly proposed spectral features were significant in differentiating bothgroups, at frequency ranges of 1–10 Hz and 5–10 Hz. The prediction outcomeswere also comparable to previous related studies and significantly importantin using ECG to predict cardiac-related events among CVD and non-CVDsubjects in the Malaysian population.展开更多
文摘Over the past several decades, numerous methods have been applied by research and professionals to detect and measure mental stress, varying from subjective methods such questionnaires and face-to-face interviews up to the objective methods using physiological signals and neuroimaging equipment such as salivary cortisol and functional magnetic resonance imaging (fMRI), respectively. Among those methods, an Electroencephalograph (EEG) is one of the utmost chosen non-invasive methods by professionals and researchers in recording real time brain signals. This paper highlights the state of art for each of the studies, by comparing and analyzing the method and protocol of EEG data collection, including the selection of electrodes and brain regions involving two major categories of mental stress: acute and chronic. Selection of EEG features, with the necessary signal pre-processing and processing techniques, and the classification models used in these studies have been summarized and discussed.
基金This study was supported by the Ministry of Education Malaysia’s Fundamental Research Grant Scheme FRGS/1/2019/TK04/UKM/02/4TMC research was funded by a top-down grant from the Ministry of Education Malaysia(Grant Number PDE48).
文摘A comprehensive study was conducted to differentiate cardiovascular disease (CVD) subjects from non-CVD subjects using short recording electrocardiogram (ECG) of 244 Malaysian adults in The MalaysianCohort project. An automated peak detection algorithm to detect nine fiducialpoints of electrocardiogram (ECG) was developed. Forty-eight features wereextracted in both time and frequency domains, including statistical featuresobtained from heart rate variability and Poincare plot analysis. These includefive new features derived from spectrum counts of five different frequencyranges. Feature selection was then made based on p-value and correlationmatrix. Selected features were used as input for five classifiers of artificialneural network (ANN), k-nearest neighbors (kNN), support vector machine(SVM), discriminant analysis (DA), and decision tree (DT). Results showedthat six features related to T wave were statistically significant in distinguishingCVD and non-CVD groups. ANN had performed the best with 94.44% specificity and 86.3% accuracy, followed by kNN with 80.56% specificity, 86.49%sensitivity and 83.56% accuracy. The novelties of this study were in providingalternative solutions to detect P-onset, P-offset, T-offset as well as QRS-onsetpoints using discrete wavelet transform method. Additionally, two out of thefive newly proposed spectral features were significant in differentiating bothgroups, at frequency ranges of 1–10 Hz and 5–10 Hz. The prediction outcomeswere also comparable to previous related studies and significantly importantin using ECG to predict cardiac-related events among CVD and non-CVDsubjects in the Malaysian population.
