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Spectral entropy and deep convolutional neural network for ECG beat classification

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Asgharzadeh-Bonab A. , Amirani M. C. , Mehri A.

Biocybernetics and Biomedical Engineering

2020

tom Vol. 40, no. 2

691--700

Sudden cardiac death is the result of abnormal heart conditions. Therefore, early detection of such abnormal conditions is vital to identify heart problems. Hence, in this paper, we aim to present a new computer-aided diagnosis (CAD) method based on time-frequency analysis of electrocardiogram (ECG) signals and deep neural networks for arrhythmia detection. Time-frequency transforms have the capability of providing spectral information at different times, which is very useful for analyzing non-stationary signals. On the other side, entropy is an attractive measurement from ECG signals which can distinguish different types of them. In this paper, time-frequency spectral entropy is proposed to extract the efficient features from ECG signals. All computed entropies cannot provide separability among different classes, two-directional two-dimensional principal component analysis (2D2PCA) can be used to reduce the dimension of the extracted features. Finally, the convolutional neural network (CNN) classifies the time-frequency features to diagnose the ECG beat signals and detect arrhythmias. The results show that the spectral entropy can provide good separation between different among ECG beats and the proposed method outperforms the recently introduced method for analyzing ECG signals.

Automatic identification of respiratory diseases from stethoscopic lung sound signals using ensemble classifiers

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Fraiwan L. , Hassanin O. , Fraiwan M. , Khassawneh B. , Ibnian A. M. , Alkhodari M.

Biocybernetics and Biomedical Engineering

2021

tom Vol. 41, no. 1

1--14

This paper investigates the application of different homogeneous ensemble learning methods to perform multi-class classification of respiratory diseases. The case sample involved a total of 215 subjects and consisted of 308 clinically acquired lung sound recordings and 1176 recordings obtained from the ICBHI Challenge database. These recordings corresponded to a wide range of conditions including healthy, asthma, pneumonia, heart failure, bronchiectasis or bronchitis, and chronic obstructive pulmonary disease. Feature representation of the lung sound signals was based on Shannon entropy, logarithmic energy entropy, and spectrogram-based spectral entropy. Decision trees and discriminant classifiers were employed as base learners to build bootstrap aggregation and adaptive boosting ensembles. The optimal structure of the investigated ensemble models was identified through Bayesian hyperparameter optimization and was then compared to typical classifiers in literature. Experimental results showed that boosted decision trees provided the best overall accuracy, sensitivity, specificity, F1-score, and Cohen's kappa coefficient of 98.27%, 95.28%, 98.9%, 93.61%, and 92.28%, respectively. Among the baseline methods, SVM provided the best yet a slightly poorer performance, as demonstrated by its average accuracy (98.20%), sensitivity (91.5%), and specificity (98.55%). Despite their simplicity, the investigated ensemble classification methods exhibited a promising performance for detecting a wide range of respiratory disease conditions. The data fusion approach provides a promising insight into an alternative and more suitable solution to reduce the effect of imbalanced data for clinical applications in general and respiratory sound analysis studies in specific.