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The neuronal activity has a unique genetic signature that can be used for personal identification and authentication. Continuous authentication of individuals is required in the field involving high security, such as military services, intelligence organizations and secret agencies. Electroencephalogram (EEG) based authentication method is favorable due to its uniqueness and the fact that it can be used even when the person is unconscious. In this study, we investigated the number of samples per subject required to reliably develop a biometric trait authentication system. Initially, we extracted the background EEG signals from the publicly available Temple University Hospital (TUH) database. A total of 46 statistical and frequency domain features were extracted from each EEG signal per subject. The classification was performed using the extreme Gradient Boosting (XGBoost) classifier. We varied the number of EEG signal segments per subject from 5 to 127 with an increment of 5 segments per trial. Finally, the performance parameters such as accuracy, sensitivity, specificity, precision and F-measure were obtained in each case for the biometric authentication system using the test data. Our model achieved the highest accuracy of 100% when more than 75 EEG signals per subject were considered for the analysis. We also identified alpha band power as the most efficient feature for authentication. Our results show that EEG signals can be effectively used for personal identification and authentication.

Emotions are mental states that result from neurophysiological changes associated with thoughts, feelings, and behavioral responses. Emotions lead to modifications in heart rate variability, which can be identified through electrocardiogram (ECG) signals. In this study, we attempted to analyze the ECG signals to detect categorical emotions using time-domain features and machine-learning algorithms. Initially, the ECG signals of 30 subjects were obtained from the publicly available continuously annotated signals of emotion dataset. Further, the signals were preprocessed and extracted 32-time domain features from ECG signals which were recorded during different emotional states such as amusing, boring, relaxing, and scary. The extracted features were fed to a random forest (RF) classifier to rank the features and to build the three machine learning models such as logistic regression (LR), support vector machine, and RF. We achieved the highest average classification accuracy, sensitivity, specificity, precision, and f1-score of 71.04%, 42.08%, 80.69%, 43.03%, and 42.32%, respectively, with the top 4 features using the LR classifier. We found that the mean of peaks, slope sign change, dynamic range, and mean of first derivative were ranked top and played a significant role in the classification model. Our study shows the effectiveness of utilizing ECG signals for emotion detection in wearable devices.

Differentially Expressed Genes (DEGs) are treated as candidate biomarkers, and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches like machine learning and statistical analysis. In this study, we used a combination of the machine learning algorithm and statistical tests to identify the top 300 genes that are differentially expressed in ASD compared to Typically Developed (TD). Initially, we extracted microarray gene expression data of 15 ASD and 15 TD from NCBI GEO database and used a standard pipeline to preprocess the data. Further, Random Forest (RF) was used to discriminate genes between ASD and TD. We, then analyzed the upregulated and downregulated genes using the logFC value to gain insights into their potential roles in the development of ASD. We further used drug-gene interaction analysis from ConnectivityMap to identify drugs that can inhibit the expression of these genes. Our results show that the proposed RF model yields average 5-fold cross-validation accuracy, sensitivity, and specificity of 96.67%. Further, we obtained precision and F-measure scores of 97.5% and 96.57%, respectively. Our analysis identified several novel genes that are dysregulated in ASD, including genes (such as proliferation-inducing protein 38 and germinal centre expressed transcript 32) involved in synaptic transmission, neural development, and immune function. We also identified several drugs (such as ATPase_Inhibitor, kinase inhibitors, and histone deacetylase inhibitors) that can potentially be used to treat ASD. Our findings provide new insights into the molecular mechanisms of ASD and suggest potential targets for drug development. These findings may lead to new therapeutic approaches for the treatment of ASD.

In clinical and scientific research on emotion recognition using physiological signals, selecting the appropriate segment is of utmost importance for enhanced results. In our study, we optimized the electrodermal activity (EDA) segment for an emotion recognition system. Initially, we obtained EDA signals from two publicly available datasets: the Continuously annotated signals of emotion (CASE) and Wearable stress and affect detection (WESAD) for 4-class dimensional and three-class categorical emotional classification, respectively. These signals were pre-processed, and decomposed into phasic signals using the 'convex optimization to EDA' method. Further, the phasic signals were segmented into two equal parts, each subsequently segmented into five nonoverlapping windows. Spectrograms were then generated using short-time Fourier transform and Mel-frequency cepstrum for each window, from which we extracted 85 features. We built four machine learning models for the first part, second part, and whole phasic signals to investigate their performance in emotion recognition. In the CASE dataset, we achieved the highest multi-class accuracy of 62.54% using the whole phasic and 61.75% with the second part phasic signals. Conversely, the WESAD dataset demonstrated superior performance in three-class emotions classification, attaining an accuracy of 96.44% for both whole phasic and second part phasic segments. As a result, the second part of EDA is strongly recommended for optimal outcomes. [ABSTRACT FROM AUTHOR]