Efficient Neural Network Classification of Parkinson's Disease and Schizophrenia Using Resting-State EEG Data.

Timely identification of Parkinson's disease and schizophrenia is crucial for the effective management and enhancement of patients' quality of life. The utilization of electroencephalogram (EEG) monitoring applications has proven instrumental in diagnosing various brain disorders. Prior research has predominantly relied on predefined knowledge of physiological alterations associated with different diseases, employing feature extraction to discern brain conditions. This study introduces SwiftBrainNet, a neural network designed for the classification of Parkinson's disease and schizophrenia using short resting-state EEG segments. SwiftBrainNet aims to minimize reliance on manual feature extraction, relying solely on short EEG segments. Functioning as a single-input, dual-output neural network, SwiftBrainNet incorporates a deep supervisory mechanism facilitated by an auxiliary decoder, which enhances its classification performance by guiding the network in extracting shallow features. Our study conducts a clinical application-oriented experiment that uses continuous multi-segment EEG voting classification. This experiment demonstrates a noticeable improvement in accuracy compared to leave-one-out cross-validation (LOOCV), especially when combined with our data augmentation techniques. These findings underscore the method's practical value in clinical settings, where continuous data frames and enhanced generalization across subjects can significantly improve diagnostic accuracy. Additionally, the high accuracy observed in subject-dependent classification with very short data segments suggests that SwiftBrainNet might capture subject-specific EEG patterns, which could be further explored to enhance disease-related feature learning. This paper provides new evidence supporting the use of short-term EEG data for neurodiagnostic applications, making SwiftBrainNet a promising tool for the early detection of neurological disorders.