Schizophrenia detection using distributed activation function-based statistical attentional bidirectional-long short-term memory

Abstract

Schizophrenia detection involves identifying the schizophrenia by analyzing specific patterns in Electroencephalogram (EEG) signals, which reflect brain activity associated with symptoms, like hallucinations and cognitive impairments. Existing models face challenges due to the complex and variable nature of EEG data, which may struggle to accurately capture critical temporal dependencies and relevant features. Traditional approaches often lack adaptability, limiting their ability to differentiate schizophrenia patterns from other brain activities. Hence, a Distributed Activation function-based statistical Attention Bi-LSTM (DA-SA-BiLSTM) is proposed for schizophrenia detection, which enhances the precision and interpretability of EEG signal analysis. This model effectively manages the temporal dependencies for the detection as it incorporates past and future data context to improve decision-making. By dynamically weighting features based on their relevance, the model emphasizes critical segments and reduces noise, increasing predictive accuracy. Using different activation functions in various layers, the DA-AB-LSTM is allowed to adapt to specific characteristics of the EEG data, strengthening its flexibility and pattern recognition abilities. Furthermore, this model refines relationships between features, facilitating precise class probability distribution for schizophrenia classification. In particular, the DA-SA-BiLSTM model outperforms the existing models with 95.9 % accuracy, the lowest mean square error (MSE) of 5.86, 95.84 % sensitivity, and 95.97 % specificity.

Keywords: Bidirectional long short-term memory; Deep Learning, DistributedActivation function; Electroencephalogram; Schizophrenia detection.