In this paper, a novel technique on a multiscale energy and eigenspace (MEES) approach is proposed for the detection and localization of myocardial infarction (MI) from multilead electrocardiogram (ECG). Wavelet decomposition of multilead ECG signals grossly segments the clinical components at different subbands. In MI, pathological characteristics such as hypercute T-wave, inversion of T-wave, changes in ST elevation, or pathological Q-wave are seen in ECG signals. This pathological information alters the covariance structures of multiscale multivariate matrices at different scales and the corresponding eigenvalues. The clinically relevant components can be captured by eigenvalues. In this study, multiscale wavelet energies and eigenvalues of multiscale covariance matrices are used as diagnostic features. Support vector machines (SVMs) with both linear and radial basis function (RBF) kernel and K-nearest neighbor are used as classifiers. Datasets, which include healthy control, and various types of MI, such as anterior, anteriolateral, anterioseptal, inferior, inferiolateral, and inferioposterio-lateral, from the PTB diagnostic ECG database are used for evaluation. The results show that the proposed technique can successfully detect the MI pathologies. The MEES approach also helps localize different types of MIs. For MI detection, the accuracy, the sensitivity, and the specificity values are 96%, 93%, and 99% respectively. The localization accuracy is 99.58%, using a multiclass SVM classifier with RBF kernel.

This work proposes a novel multivariate-multiscale approach for computing the spectral and temporal entropies from the multichannel electroencephalogram (EEG) signal. This facilitates the recognition of three human emotions: positive, neutral, and negative. The proposed approach is based on the application of the Fourier-Bessel series expansion based empirical wavelet transform (FBSE-EWT). We have extended the existing FBSE-EWT method for multichannel signals and derived FBSE-EWT based multivariate Hilbert marginal spectrum (MHMS) for computing spectral Shannon and K-nearest neighbor (KNN) entropies. The multivariate FBSE-EWT decomposes the multichannel EEG signals into narrow band subband signals. The multiscaling operation adapted in the spectral domain is based on the selection of successive joint instantaneous amplitude and frequency functions of the subband signals. On the other hand, the time domain multiscale K-NN entropy is computed from the cumulatively added multidimensional subband signals. The extracted spectral and temporal entropy features are smoothed and fed to the sparse autoencoder based random forest (ARF) classifier architecture for emotion classification. The proposed approach is tested using multichannel EEG signals available in a public database (SJTU emotion EEG dataset (SEED)). The bivariate EEG signals from different channel pairs with distinct spatial locations over the scalp are considered as input to our proposed system. The obtained overall classification accuracy of 94.4% reveals that the proposed approach is useful in classifying human emotions. The method is also validated using DREAMER emotion EEG public database. The method outperforms the existing state-of-the-art methods evaluated in these databases.

The damage to the heart valves causes heart valve disorders (HVDs). The detection of HVDs is crucial in a clinical study as these diseases may cause congestive heart failure, hypertrophy, and stroke. The phonocardiogram (PCG) signal reveals information regarding the mechanical activity of the heart. The early detection of HVDs using PCG signal is vital to minimize the chances of cardiac arrest and other cardiac complications. This article proposes the time–frequency-domain deep learning (TFDDL) framework for automatic detection of HVDs using PCG signals. The time–frequency (TF)-domain representations of PCG signals are evaluated using both time-domain polynomial chirplet transform (TDPCT) and frequency-domain polynomial chirplet transform (FDPCT). The deep convolutional neural network (CNN) model is used to detect four types of HVDs using the TF images of PCG signals obtained using both the TDPCT and FDPCT methods. The proposed TFDDL approach is evaluated using PCG signals from public databases. For the detection of HVDs using TDPCT- and FDPCT-based TF images of PCG signals, the suggested approach has achieved overall accuracy values of 99% and 99.48%, respectively. For the classification of normal and abnormal heart sound classes, the proposed TFDDL approach has obtained an accuracy of 85.16% using PCG signals from the Physionet challenge 2016 database. The proposed TFDDL framework is compared with TF-domain transfer learning models such as residual network (ResNet-50) and visual geometry group (VGGNet-16). The overall accuracy values obtained using VGGNet-16 and ResNet-50 are less than the proposed deep CNN model for the detection of HVDs. The proposed TFDDL model can be validated in real-time using heart sound signals recorded from different subjects for automated identification of HVDs.

The elimination of ocular artifacts is critical in analyzing electroencephalography (EEG) data for various brain-computer interface (BCI) applications. Despite numerous promising solutions, electrooculography (EOG) recording or an eye-blink detection algorithm is required for the majority of artifact removal algorithms. This reliance can hinder the model’s implementation in real-world applications. This paper proposes EEGANet, a framework based on generative adversarial networks (GANs), to address this issue as a data-driven assistive tool for ocular artifacts removal (source code is available at https://github.com/IoBT-VISTEC/EEGANet ). After the model was trained, the removal of ocular artifacts could be applied calibration-free without relying on the EOG channels or the eye blink detection algorithms. First, we tested EEGANet’s ability to generate multi-channel EEG signals, artifacts removal performance, and robustness using the EEG eye artifact dataset, which contains a significant degree of data fluctuation. According to the results, EEGANet is comparable to state-of-the-art approaches that utilize EOG channels for artifact removal. Moreover, we demonstrated the effectiveness of EEGANet in BCI applications utilizing two distinct datasets under inter-day and subject-independent schemes. Despite the absence of EOG signals, the classification performance of the signals processed by EEGANet is equivalent to that of traditional baseline methods. This study demonstrates the potential for further use of GANs as a data-driven artifact removal technique for any multivariate time-series bio-signal, which might be a valuable step towards building next-generation healthcare technology.