What is the clinical evidence from this study?

Study design: Other. Population: Major Depressive Disorder (MDD) (n=64). Intervention: Convolutional Neural Network (CNN) using EEG spectrogram images vs. Healthy subjects. Primary outcome: Classification accuracy.

What does this research mean for the field?

A novel deep learning model utilizing Convolutional Neural Networks and Short-Time Fourier Transform spectrograms of EEG signals can detect Major Depressive Disorder with 99.58% accuracy and 99.70% sensitivity. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

July 13, 2021

Decision support system for major depression detection using spectrogram and convolution neural network with EEG signals

Key Result

A novel deep learning model based on Convolutional Neural Network and EEG spectrogram images detected Major Depressive Disorder with 99.58% accuracy and 99.70% sensitivity.

Structured PICO

Does a CNN model using EEG spectrogram images accurately detect Major Depressive Disorder?

Population

64 subjects (34 Major Depressive Disorder patients and 30 healthy subjects) from a public database

Intervention

Automated MDD detection system using Convolutional Neural Network (CNN) and spectrogram images obtained via Short-Time Fourier Transform (STFT) of EEG signals

Outcome

Classification accuracy, precision, sensitivity, specificity, and F1-scoresurrogate

A novel deep learning model using CNN and EEG spectrograms demonstrated high accuracy (>99%) in detecting Major Depressive Disorder, offering a potential automated diagnostic tool.

Limitations

Needs to be validated with a more diverse MDD database before it can be used in clinical settings

Abstract

Abstract The number of Major Depressive Disorder (MDD) patients is rising rapidly these days following the incidence of COVID‐19 pandemic. It is challenging to detect MDD through personal interviews and by observing electroencephalogram (EEG) signals. Hence, an automated MDD detection system developed using deep learning techniques can help reduce the workload of clinicians by diagnosing MDD accurately. In this study, we have proposed a novel deep learning model based on Convolutional Neural Network (CNN) and spectrogram images. In this work, Short‐Time Fourier Transform (STFT) is first applied to the EEG signals to obtain spectrogram images of MDD patients and healthy subjects. These spectrogram images are then fed to the CNN model for automated detection of MDD patients and healthy subjects. The EEG signals used in this study were obtained from public database with 34 MDD patients and 30 healthy subjects. The highest classification accuracy, precision, sensitivity, specificity, and F1‐score of 99.58%, 99.40%, 99.70%, 99.48%, and 99.55% respectively were obtained with hold‐out validation. Our MDD detection model is highly accurate and needs to be validated with more diverse MDD database before it can be used in clinical settings. Also, we plan to use our developed prototype to detect depression using other physiological signals like electrocardiogram (ECG) and speech signals for accurate and faster diagnosis.

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