Schizophrenia remains a severe neuropsychiatric disorder that drastically impacts an individual's daily functioning. While clinical assessments are traditional, modern schizophrenia detection via EEG offers a more objective diagnostic pathway. Researchers have identified that abnormal asymmetry in neural activity reflects significant cognitive impairment. Consequently, this study explores how complexity measures in brain dynamics can automate the diagnostic process.

High Accuracy in Schizophrenia Detection via EEG

The research team utilized a hybrid approach combining EEG-based asymmetric entropy analysis with a CNN-LSTM classification model. Specifically, they extracted feature maps from frequency bands using approximate, sample, and spectral entropies. Furthermore, they processed these maps through a pre-trained Inception-V3 model. The results showed that approximate and sample entropies provided superior discrimination between patients and healthy subjects. Because of this high sensitivity, the model achieved a 94.11% accuracy in the delta band. In addition, the precision reached 100%.

Moreover, the classification model demonstrated high effectiveness by identifying functional alterations. Consequently, the integration of Long Short-Term Memory (LSTM) units allowed the system to account for temporal dependencies. Therefore, this technology facilitates more accurate detection of pathological conditions. Specifically, it targets the irregularities found in channel pairs. Thus, clinicians can rely on these automated measures for better diagnostic clarity. Nevertheless, further validation remains important for diverse populations.

Clinical Significance of Entropy Asymmetry

In contrast to standard metrics, asymmetric entropy feature maps provide a detailed view of brain connectivity. For instance, these maps capture the specific functional alterations caused by schizophrenia. Because the model utilizes inter-channel asymmetries, it identifies subtle pathological signatures. Consequently, this AI-driven method reduces the reliance on subjective interpretation. Therefore, it paves the way for precision psychiatry in clinical practice.

FAQs about EEG-Based Schizophrenia Detection

How does EEG help in schizophrenia detection?

EEG captures electrical activity and identifies abnormal neural asymmetries and complexity measures that are characteristic of schizophrenia.

What is the accuracy of the CNN-LSTM model?

The study reported a high classification accuracy of 94.11% and a precision of 100%, particularly within the delta frequency band.

Disclaimer: This content is for informational and educational purposes only. It is not intended as medical advice or a substitute for professional clinical judgment, diagnosis, or treatment. Always seek the advice of a qualified healthcare provider with any questions regarding a medical condition. Refer to the latest local and national guidelines for clinical practice.

References

1. Selvarasu L et al. EEG based detection of schizophrenia using asymmetry of entropy and CNN-LSTM model. Proc Inst Mech Eng H. 2026 Mar 10. doi: 10.1177/09544119261422821. PMID: 41807278.

2. Bao X et al. A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning. PMC. 2023.