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Schizophrenia detection using Multivariate Empirical Mode Decomposition and entropy measures from multichannel EEG signal

Krishnan Palani Thanaraj, Joseph Raj Alex Noel, Balasubramanian Parvathavarthini, Chen Yuanzhu

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

1124--1139

EN

Multivariate analysis of the EEG signal for the detection of Schizophrenia condition is proposed here. Multivariate Empirical Mode Decomposition (MEMD) is used to decompose the EEG signal into Intrinsic Mode Functions (IMF) signal. The randomness measure of the IMF signal is determined by computing the entropy of the signal. Five entropy measures such as Approximate entropy, Sample entropy, Permutation entropy, Spectral entropy, and Singular Value Decomposition entropy are measured from the IMF signal. These entropy measures showed a significant difference ( p < 0.01) between the healthy controls (HC) and Schizophrenia (SZ) subjects. Many state-of-the-art (SoA) machine learning classifiers are trained on the feature matrix obtained from entropy values of the IMF signal, amongst them Support Vector Machine based on Radial Basis Function (SVM-RBF) provided the highest accuracy and F1-score of 93% for the 95 features. The area under the curve (AUC) value of 0.9831 was obtained using this classifier. These performance metrics suggests that computation of randomness measure such as entropy in the multivariate IMF domain provided better discriminating power in detection of Schizophrenia condition from the multichannel EEG signal.

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Entropies for automated detection of coronary artery disease using ECG signals: A review

Acharya U. R., Hagiwara Y., Koh J. E. W., Oh S. L., Tan J. H., Adam M., Tan R. S.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 2

373--384

EN

Coronary artery disease (CAD) develops when coronary arteries are unable to supply oxygen-rich blood to the heart due to the accumulation of cholesterol plaque on the inner walls of the arteries. Chronic insufficient blood flow leads to the complications, including angina and heart failure. In addition, acute plaque rupture may lead to vessel occlusion, causing a heart attack. Thus, it is encouraged to have regular check-ups to diagnose CAD early and avert complications. The electrocardiogram (ECG) is a widely used diagnostic tool to study the electrical activity of the heart. However, ECG signals are highly chaotic, complex, and non-stationary in their behaviour. It is laborious, and requires expertise, to visually interpret these signals. Hence, the computer-aided detection system (CADS) is developed to assist clinicians to interpret the ECG signals fast and reliably. In this work, we have employed sixteen entropies to extract the various hidden signatures from ECG signals of normal healthy persons as well as patients with CAD. We observed that the majority of extracted entropy features showed lower values for CAD patients compared to normal subjects. We believe that there is one possible reason which could be the decreased in the variability of ECG signals is associated with reduced heart pump function.

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Automated diagnosis of atrial fibrillation ECG signals using entropy features extracted from flexible analytic wavelet transform

Kumar M., Pachori R. B., Rajendra Acharya U.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 3

564--573

EN

Atrial fibrillation (AF) is the most common type of sustained arrhythmia. The electrocardiogram (ECG) signals are widely used to diagnose the AF. Automated diagnosis of AF can aid the clinicians to make a more accurate diagnosis. Hence, in this work, we have proposed a decision support system for AF using a novel nonlinear approach based on flexible analytic wavelet transform (FAWT). First, we have extracted 1000 ECG samples from the long duration ECG signals. Then, log energy entropy (LEE), and permutation entropy (PEn) are computed from the sub-band signals obtained using FAWT. The LEE and PEn features are extracted from different frequency bands of FAWT.We have found that LEE features showed better classification results as compared to PEn. The LEE features obtained maximum accuracy, sensitivity, and specificity of 96.84%, 95.8%, and 97.6% respectively with random forest (RF) classifier. Our system can be deployed in hospitals to assist cardiac physicians in their diagnosis.