Key result
EMD-CNN deep learning achieves ~86% accuracy detecting schizophrenia from EEG signals, outperforming K-NN models.
Why the study?
Traditional diagnosis of schizophrenia through psychiatric interviews is time-consuming and prone to errors, prompting the need for tools to assist physicians in diagnosing schizophrenia effectively.
Does an EMD-CNN based classifier improve the detection accuracy of schizophrenia from EEG signals compared to a K-NN model?
Does an EMD-CNN based classifier improve the detection accuracy of schizophrenia from EEG signals compared to a K-NN model?
Absolute Event Rate: 86% vs 83.33%
An EMD-CNN based deep learning model outperformed a K-NN machine learning model in detecting schizophrenia from EEG signals, achieving 86% accuracy.
May support EEG-based schizophrenia screening; leaves open prospective validation before clinical adoption.
The United Nations has designated schizophrenia (SZ) as a serious mental illness that affects 20 million people globally. Hallucinations, delusions, and incredibly chaotic thought and behavior are some of the symptoms. SZ has an impact on a person in all facets of his life and makes it challenging to go on. Traditionally, a skilled psychiatrist uses thorough and incisive patient interviews to make the diagnosis of SZ. This procedure takes a long time and couldlead to mistakes. Therefore, the purpose of our effort is to assist physicians in making diagnoses effectively. In this work two different methods for the detection of SZ is compared and the results are analyzed. In the first method Local binary pattern which is a computationally simple andproven technique in image processing is modified and madecapable to be applied to the 1D EEG signal and histogram based features were extracted from it. Using the histogram features a feature matrix is formulated. The obtained matrix is used to train various machine learning models using the classification learner toolbox in matlab and K- nearest neighbour with medium kernel obtained a comparatively better training accuracy. Further this model is tested and an overall accuracy of 83.3 % is obtained. In the second method the EEG signals were decomposed using empirical mode decomposition (EMD). EMD is one of best signal processing technique that handle the nonlinear, nonstationary and aperiodic signal like EEG. EMD is performed to decompose the signal into various Intrinsic mode functions (IMF). Then differential entropy, a stastistical feature used to determine the randomness measure of a signal were extracted from the EEG signals. Further a feature matrix is formulated and it is trained andtested on the renowned deep learning model, the convolutional neural network. CNN performed well on the EEG data and an overall accuracy of 86 % is obtained. From the results we can see that the deep learning model (CNN) outperformed the machine learning model (K-NN) and is good and robust in detecting SZ using EEG signals.
No takes yet. Share an insight, caveat, or question.
Padayatty et al. (2024) studied Schizophrenia (n=81). EMD-CNN based classifier vs. LBP with K-NN classifier was evaluated on Classification accuracy for detecting schizophrenia. The EMD-CNN based deep learning classifier achieved an overall accuracy of 86.0% in detecting schizophrenia from EEG signals, outperforming the LBP with K-NN machine learning model which achieved 83.33%.
Synapse has enriched 2 closely related papers on similar clinical questions. Consider them for comparative context: