Key result
A deep framework combining high-density EEG and speech signals achieved 0.972 precision, 0.973 recall, and 0.973 F1 score for diagnosing mild stage clinical depression.
Why the study?
Electroencephalography and speech signals have been used for early depression diagnosis, but prior work focused on moderate or severe depression.
Does a deep learning framework combining EEG and speech signals improve diagnostic performance for clinical depression?
Does a deep learning framework combining EEG and speech signals improve diagnostic performance for clinical depression?
Effect estimate: Precision 0.972, recall 0.973, F1 score 0.973
A multimodal deep learning framework combining EEG and speech signals demonstrates high precision and recall for diagnosing mild clinical depression.
May aid multimodal depression screening research; leaves open prospective clinical validation before practice change.
Depression is a mental disorder characterized by persistent depressed mood or loss of interest in performing activities, causing significant impairment in daily routine. Possible causes include psychological, biological, and social sources of distress. Clinical depression is the more-severe form of depression, also known as major depression or major depressive disorder. Recently, electroencephalography and speech signals have been used for early diagnosis of depression; however, they focus on moderate or severe depression. We have combined audio spectrogram and multiple frequencies of EEG signals to improve diagnostic performance. To do so, we have fused different levels of speech and EEG features to generate descriptive features and applied vision transformers and various pre-trained networks on the speech and EEG spectrum. We have conducted extensive experiments on Multimodal Open Dataset for Mental-disorder Analysis (MODMA) dataset, which showed significant improvement in performance in depression diagnosis ( 0.972 , 0.973 and 0.973 precision, recall and F1 score respectively) for patients at the mild stage. Besides, we provided a web-based framework using Flask and provided the source code publicly. 1
No takes yet. Share an insight, caveat, or question.
Qayyum et al. (2023) studied Clinical depression. High-density EEG and speech signal based deep framework was evaluated on Depression diagnosis performance (Precision 0.972, recall 0.973, F1 score 0.973). A deep framework combining high-density EEG and speech signals achieved 0.972 precision, 0.973 recall, and 0.973 F1 score for diagnosing mild stage clinical depression.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: