Showing papers by "Choo Min Lim published in 2017"
TL;DR: This work proposes a novel algorithm using empirical mode decomposition (EMD) combined with wavelet packet decompose (WPD), for automated prediction of pregnant women going to have premature delivery by using uterine EMG signals, which can be used in gynecology departments of hospitals to predict the preterm or normal delivery of pregnantWomen.
90 citations
TL;DR: The proposed CWT combined with contourlet-based technique can be implemented in hospitals to speed up the diagnosis of three different cardiac abnormalities using a single ECG test, and minimizes the unnecessary diagnostic tests required to confirm the diagnosis.
Abstract: Undiagnosed coronary artery disease (CAD) progresses rapidly and leads to myocardial infarction (MI) by reducing the blood flow to the cardiac muscles. Timely diagnosis of MI and its location is significant, else, it expands and may impair the left ventricular (LV) function. Thus, if CAD and MI are not picked up by electrocardiogram (ECG) during diagnostic test, it can lead to congestive heart failure (CHF). Therefore, in this paper, the characterization of three cardiac abnormalities namely, CAD, MI and CHF are compared. Performance of novel algorithms is based on contourlet and shearlet transformations of the ECG signals. Continuous wavelet transform (CWT) is performed on normal, CAD, MI and CHF ECG beat to obtain scalograms. Subsequently, contourlet and shearlet transformations are applied on the scalograms to obtain the respective coefficients. Entropies, first and second order statistical features namely, mean ( M n i ), min ( M i n i ), max ( M x i ), standard deviation ( D s t i ), average power ( P a v g i ), inter-quartile range (IQRi), Shannon entropy ( E s h i ), mean Tsallis entropy ( E m t s i ), kurtosis ( K u r i ), mean absolute deviation ( M A D i ), and mean energy ( Ω m i ), are extracted from each contourlet and shearlet coefficients. Only significant features are selected using improved binary particle swarm optimization (IBPSO) feature selection method. Selected features are ranked using analysis of variance (ANOVA) and relieff techniques. The highly ranked features are subjected to decision tree (DT) and K-nearest neighbor (KNN) classifiers. Proposed method has achieved accuracy, sensitivity and specificity of (i) 99.55%, 99.93% and 99.24% using contourlet transform, and (ii) 99.01%, 99.82% and 98.75% using shearlet transform. Among the two proposed techniques, contourlet transform method performed marginally better than shearlet transform technique in classifying the four classes. The proposed CWT combined with contourlet-based technique can be implemented in hospitals to speed up the diagnosis of three different cardiac abnormalities using a single ECG test. This technique, minimizes the unnecessary diagnostic tests required to confirm the diagnosis.
80 citations
TL;DR: This study documents its efforts to provide computer support for the diagnosis of congestive heart failure (CHF) in the form of an index value which is based on a sophisticated algorithm chain which takes electrocardiogram signals as input.
Abstract: This study documents our efforts to provide computer support for the diagnosis of congestive heart failure (CHF). That computer support takes the form of an index value. A high index value indicates a low probability of CHF, and an index value below a threshold of 25.6 suggests a high probability of CHF. To create that index, we have designed a sophisticated algorithm chain which takes electrocardiogram signals as input. The signals are pre-processed before they are sent to a range of nonlinear feature extraction algorithms. The top 10 feature extraction methods were used to create the CHF index. By using objective feature extraction algorithms, we avoid the problem of inter- and intra-observer variability. We observed that the nonlinear feature extraction methods reflect the nature of the human heart very well. That observation is based on the fact that the nonlinear features achieved low p-values and high feature ranking criterion scores.
2 citations