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Recently, depression recognition has received considerable attention. Due to easy acquisition at a distance, gait-based depression recognition can be a useful tool for auxiliary diagnosis and self-help depression risk assessment. Most existing ...

The behavioral symptoms of patients with mild to moderate depression (MMD) are usually not obvious enough, which poses a challenge to MMD recognition research. A three-level feature construction strategy for facial expression was proposed to fully ...

Depression diagnosis is easily affected by subjective consciousness.It is of great significance to study objective and accurate identification methods. Electroencephalogram (EEG) can reflect brain activity and working state. Therefore, this paper ...

Recent studies focus on the utilization of deep learning approaches to recognize depression from facial videos. However, these approaches have been hindered by their limited performance, which can be attributed to the inadequate consideration of ...

Depression, as a severe mental disorder, has significant impacts on individuals, families, and society. Accurate depression detection is of great significance. To this end, we propose a deep depression detection based on feature fusion and result ...

Mental disorders are rapidly increasing each year and have become a major challenge affecting the social and financial well-being of individuals. There is a need for phenotypic characterization of psychiatric disorders with biomarkers ...

• A review of clinical/non-clinical methods for depression relapse detection.
• A ...

Depression, a common mental illness worldwide, needs to be diagnosed and cured at an early stage. To assist clinical diagnosis, an EEG-based deep learning frame, which is named the gated temporal-separable attention network (GTSAN), is proposed ...

• An alternative strategy for EEG-based depression recognition is proposed.
• An improved TCN model is designed by introducing the separable convolution.
• A novel EEG feature learning frame named GTSAN is proposed.
• TSCN feature ...

Abnormal gamma oscillations in major depressive disorder (MDD) have been repeatedly reported. Auditory steady-state response (ASSR), induced by the periodic auditory stimuli with the frequency and phase synchronized response, is the most common brain ...

The rapidly growing number of depressed people increases the burden of clinical diagnosis. Due to the abnormal speech signal of depressed patients, automatic audio-based depression recognition has the potential to become a complementary method for ...

• A portable three-electrode EEG acquisition instrument is introduced to realize fast and convenient pervasive EEG acquisition.

In view of the depression characteristics such as high prevalence, high disability rate, high fatality rate, and high recurrence rate, early identification and early intervention are the most ...

Depression is a serious neurological disorder that has become a major health problem worldwide. The detection of mild depression is important for the diagnosis of depression in early stages. This ...

• A sequential fusion approach is proposed for facial depression recognition.
• The ...

In mental health assessment, it is validated that nonverbal cues like facial expressions can be indicative of depressive disorders. Recently, the multimodal fusion of facial appearance and dynamics based on convolutional neural ...

The diagnosis and treatment of depression is very important since it brings a heavy burden to family and society. Because of the high sensitivity, relatively low cost, and convenient recording, electroencephalogram (EEG) has become an important tool for ...

Intelligent monitoring systems and affective computing applications have emerged in recent years to enhance healthcare. Examples of these applications include assessment of affective states such as Major Depressive Disorder (MDD). MDD describes ...

Depression has become a common mental disorder that plagues more and more people. This paper uses speech signals to study a method for predicting the degree of depression and help clinicians judge the degree of depression in patients. In this paper, a ...