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Related Experiment Video

Updated: May 11, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

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

Shalbbya Ali¹, Suraiya Parveen², Ihtiram Raza Khan²

¹Department of Computer Science and Technology, Jamia Hamdard University, Near Batra Hospital, New Delhi, 110062, India.

Computers in Biology and Medicine

|January 8, 2025

Summary

This summary is machine-generated.

A new model, DA-SA-BiLSTM, improves schizophrenia detection using Electroencephalogram (EEG) signals. It accurately identifies brain patterns, offering higher precision than existing methods for clinical diagnosis.

Keywords:

Bidirectional long short-term memory Deep Learning, DistributedActivation function Electroencephalogram Schizophrenia detection

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Area of Science:

Neuroscience
Biomedical Engineering
Artificial Intelligence

Background:

Schizophrenia detection relies on analyzing Electroencephalogram (EEG) signals to identify brain activity patterns.
Existing models struggle with the complexity and variability of EEG data, limiting their ability to capture temporal dependencies and relevant features.
Traditional methods lack adaptability, hindering accurate differentiation of schizophrenia patterns from other brain activities.

Purpose of the Study:

To introduce a novel Distributed Activation function-based statistical Attention Bi-LSTM (DA-SA-BiLSTM) model for enhanced schizophrenia detection.
To improve the precision and interpretability of EEG signal analysis in identifying schizophrenia.
To address the limitations of existing models in capturing temporal dependencies and adapting to EEG data variability.

Main Methods:

Developed a DA-SA-BiLSTM model incorporating past and future data context to manage temporal dependencies.
Implemented dynamic feature weighting to emphasize critical segments and reduce noise, enhancing predictive accuracy.
Utilized different activation functions across layers for adaptive pattern recognition and refined feature relationships for precise classification.

Main Results:

The DA-SA-BiLSTM model achieved 95.9% accuracy in schizophrenia detection.
Demonstrated superior performance with the lowest Mean Square Error (MSE) of 5.86.
Reported high sensitivity (95.84%) and specificity (95.97%), outperforming existing models.

Conclusions:

The DA-SA-BiLSTM model significantly enhances schizophrenia detection accuracy and interpretability using EEG signals.
The model's ability to manage temporal dependencies and adapt to data characteristics makes it a promising tool for clinical applications.
This approach offers a more robust and precise method for classifying schizophrenia based on brain activity patterns.