Prakash Kodali

Deception is a well-known term that refers to acting in a way that causes another person to believe something that is not true. Deception is a rather common occurrence in everyday life and frequently becomes a major national security concern. When investigating crimes, it’s very crucial to be able to detect dishonesty accurately. As a remedy to this problem, deception detection has recently attracted a lot of attention. In this paper, we sought to construct a deception detection system using electroencephalography (EEG) data gathered from a Concealed Information Test (CIT). To improve the system’s performance, we proposed a hybrid Teaching Learning Based Optimization-Differential Evolution (TLBO-DE) with a novel fitness function to select the best channels. Further, the DE mutant and difference vectors are modified for faster convergence while balancing the exploration and exploitation. In addition, a Residual Temporal Convolution Network (RTCN) is used to reduce model complexity without compromising the performance. The proposed approach is experimented on CIT dataset. The TLBO-DE hybrid channel selection achieved better results in terms of number of channels and accuracy-related measures when compared with TLBO, DE-based channel selection methods. Further, the proposed channel selection model with RTCN is compared with other deep learning models like LSTM, RNN and GRUs. The results proved that it not only improves accuracy, sensitivity, F1-score, and specificity but also reduced model complexity. The proposed system’s performance resulted in 96.30% accuracy and is outperformed compared to State-of-The-Art (SOTA) models.

Accurate prediction and detection of epileptic seizures using electroencephalogram (EEG) signals are crucial for advancing clinical diagnostics and improving patient outcomes. This letter proposes a distinctive hybrid framework that combines a linear prediction filter with a long short-term memory network, designed to address challenges in noise reduction and temporal pattern recognition in EEG signals. The detection performance, particularly specificity, is enhanced by applying dynamic thresholding based on residual energy analysis. The proposed method, with key aspects of the validation framework, enhances cross-patient generalization by validating the model on the CHB-MIT Scalp EEG Database across four distinct age groups: infants, children, adolescents, and young adults. The hybrid approach achieved 98.4% accuracy, 97.8% sensitivity, 96.2% specificity, and 0.98 area under the curve, outperforming traditional approaches by 3%–5%.

Epileptic seizures represent a critical concern in mental health, arising from abnormal synchronization and rapid neuronal activity in the brain. The consequences of such seizures, including loss of consciousness and cognitive impairment, have significant psychological, social, and cognitive implications. Electroencephalography (EEG) and sensor data play a pivotal role in epilepsy detection, leveraging machine learning techniques to analyze extensive datasets. This paper presents a novel approach that combines Long Short-Term Memory (LSTM) networks and Wavelet Transform (WT) techniques to enhance seizure detection accuracy. The proposed multi-module approach, featuring residual neural networks and convolutional neural networks, coupled with k-fold validation, achieves accuracy of 98.5%, sensitivity of 99.0%, specificity of 98.0% and an AUC-ROC value of 0.95 for classifying interictal epileptiform discharges. This underscores the model’s efficacy in addressing the complexities of EEG signal analysis.

Epileptic seizures present major challenges in neurological health, requiring accurate and efficient detection methods for timely diagnosis. This paper presents a advanced seizure detection framework using a Stacked Convolutional Restricted Boltzmann Machine (SCRBM) to analyze EEG signals. The proposed method integrates Convolutional Neural Networks (CNNs) with Restricted Boltzmann Machines (RBMs) to effectively capture both spatial patterns and temporal dependencies present in EEG data. Using the Bonn EEG dataset, the model performs remarkably well, achieving 98.7% accuracy, 98.5% sensitivity, and 98.6% precision. A comparison study highlights the benefits of the suggested framework over current techniques, highlighting its applicability, resilience, and effectiveness for real-time epileptic seizure detection. Based on the performance metrics obtained, the application of the Stacked CRBM model in clinical settings shows strong potential and effectiveness for real-time epileptic seizure detection.