PsyneuroNet architecture for multi-class prediction of neurological disorders

Abstract

Early detection and diagnosis of psychiatric disorders are crucial for timely intervention, improved treatment outcomes, and preventing complications. The purpose of this study is to use artificial intelligence methods to diagnose symptoms of psychiatric conditions such as depression, schizophrenia, sleep disorder, and Alzheimer’s disease in epileptic patients using EEG data. The research uses a cross-validation procedure with ten iterations and looks at three data sets: the CHB-MIT dataset with EEG recordings from neonatal subjects, the DEAP dataset containing physiological signals and emotional ratings, and the OpenNeuro dataset containing multichannel EEG. Preprocessing involves dividing the EEG signals into five brain wave frequency components and applying filtering techniques for further analysis. We introduce a novel architecture called PsyneuroNet, which aims to improve feature extraction and prediction accuracy for real-time psychiatric disorder recognition and integrates three key components: cross-normalization across convolution, a bidirectional long-short-term memory network (Bi-LSTM), and an enhanced intuition-based convolutional neural network (ICNN). The proposed methodology is evaluated based on the 99.65% prediction accuracy achieved on the CHB-MIT dataset, the 97.27% prediction accuracy achieved on the DEAP dataset, and the 98.08% prediction accuracy achieved on the OpenNeuro dataset for prediction problems. The proposed method outperforms the state-of-the-art approaches in terms of accuracy by 3.64%, with a sensitivity of 99.65%, a specificity of 99.75%, and a very low false positive rate per hour (FPR/h) of 0.3%, all achieved by using ten folds cross-validation. The PsyneuroNet’s capability to classify a diverse range of neurological disorders within a single architecture is noteworthy. This versatility makes the model suitable for addressing a broad spectrum of clinical scenarios. The model’s ability to generalize effectively to unseen data, as demonstrated by testing its performance on the DEAP and OpenNeuro datasets after training on CHB-MIT data, is a significant aspect of novelty.