1
|
Lee T, Baek S, Lee J, Chung ES, Yun K, Kim TS, Oh J. A Deep Learning Driven Simulation Analysis of the Emotional Profiles of Depression Based on Facial Expression Dynamics. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2024; 22:87-94. [PMID: 38247415 PMCID: PMC10811404 DOI: 10.9758/cpn.23.1059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/31/2023] [Accepted: 04/10/2023] [Indexed: 01/23/2024]
Abstract
Objective : Diagnosis and assessment of depression rely on scoring systems based on questionnaires, either self-reported by patients or administered by clinicians, and observation of patient facial expressions during the interviews plays a crucial role in making impressions in clinical settings. Deep learning driven approaches can assist clinicians in the course of diagnosis of depression by recognizing subtle facial expressions and emotions in depression patients. Methods : Seventeen simulated patients who acted as depressed patients participated in this study. A trained psychiatrist structurally interviewed each participant with moderate depression in accordance with a prepared scenario and without depressive features. Interviews were video-recorded, and a facial emotion recognition algorithm was used to classify emotions of each frame. Results : Among seven emotions (anger, disgust, fear, happiness, neutral, sadness, and surprise), sadness was expressed in a higher proportion on average in the depression-simulated group compared to the normal group. Neutral and fear were expressed in higher proportions on average in the normal group compared to the normal group. The overall distribution of emotions between the two groups was significantly different (p < 0.001). Variance in emotion was significantly less in the depression-simulated group (p < 0.05). Conclusion : This study suggests a novel and practical approach to understand the emotional expression of depression patients based on deep learning techniques. Further research would allow us to obtain more perspectives on the emotional profiles of clinical patients, potentially providing helpful insights in making diagnosis of depression patients.
Collapse
Affiliation(s)
- Taekgyu Lee
- College of Medicine, The Catholic University of Korea, Seoul, Korea
| | | | - Jongseo Lee
- College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Eun Su Chung
- College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Kyongsik Yun
- Computation and Neural Systems, California Institute of Technology, Pasadena, CA USA
- Bio-Inspired Technologies and Systems, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Tae-Suk Kim
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jihoon Oh
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| |
Collapse
|
2
|
Oh J, Lee T, Chung ES, Kim H, Cho K, Kim H, Choi J, Sim HH, Lee J, Choi IY, Kim DJ. Development of depression detection algorithm using text scripts of routine psychiatric interview. Front Psychiatry 2024; 14:1256571. [PMID: 38239906 PMCID: PMC10794729 DOI: 10.3389/fpsyt.2023.1256571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 12/13/2023] [Indexed: 01/22/2024] Open
Abstract
Background A psychiatric interview is one of the important procedures in diagnosing psychiatric disorders. Through this interview, psychiatrists listen to the patient's medical history and major complaints, check their emotional state, and obtain clues for clinical diagnosis. Although there have been attempts to diagnose a specific mental disorder from a short doctor-patient conversation, there has been no attempt to classify the patient's emotional state based on the text scripts from a formal interview of more than 30 min and use it to diagnose depression. This study aimed to utilize the existing machine learning algorithm in diagnosing depression using the transcripts of one-on-one interviews between psychiatrists and depressed patients. Methods Seventy-seven clinical patients [with depression (n = 60); without depression (n = 17)] with a prior psychiatric diagnosis history participated in this study. The study was conducted with 24 male and 53 female subjects with the mean age of 33.8 (± 3.0). Psychiatrists conducted a conversational interview with each patient that lasted at least 30 min. All interviews with the subjects between August 2021 and November 2022 were recorded and transcribed into text scripts, and a text emotion recognition module was used to indicate the subject's representative emotions of each sentence. A machine learning algorithm discriminates patients with depression and those without depression based on text scripts. Results A machine learning model classified text scripts from depressive patients with non-depressive ones with an acceptable accuracy rate (AUC of 0.85). The distribution of emotions (surprise, fear, anger, love, sadness, disgust, neutral, and happiness) was significantly different between patients with depression and those without depression (p < 0.001), and the most contributing emotion in classifying the two groups was disgust (p < 0.001). Conclusion This is a qualitative and retrospective study to develop a tool to detect depression against patients without depression based on the text scripts of psychiatric interview, suggesting a novel and practical approach to understand the emotional characteristics of depression patients and to use them to detect the diagnosis of depression based on machine learning methods. This model could assist psychiatrists in clinical settings who conduct routine conversations with patients using text transcripts of the interviews.
Collapse
Affiliation(s)
- Jihoon Oh
- Department of Psychiatry, College of Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | - Taekgyu Lee
- College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Eun Su Chung
- College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | | | | | | | - Jihye Choi
- Department of Psychiatry, College of Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hyeon-Hee Sim
- Department of Psychiatry, College of Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jongseo Lee
- College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - In Young Choi
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Dai-Jin Kim
- Department of Psychiatry, College of Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, Seoul, Republic of Korea
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| |
Collapse
|
3
|
Hu B, Tao Y, Yang M. Detecting depression based on facial cues elicited by emotional stimuli in video. Comput Biol Med 2023; 165:107457. [PMID: 37708718 DOI: 10.1016/j.compbiomed.2023.107457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/11/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023]
Abstract
Recently, depression research has received considerable attention and there is an urgent need for objective and validated methods to detect depression. Depression detection based on facial expressions may be a promising adjunct to depression detection due to its non-contact nature. Stimulated facial expressions may contain more information that is useful in detecting depression than natural facial expressions. To explore facial cues in healthy controls and depressed patients in response to different emotional stimuli, facial expressions of 62 subjects were collected while watching video stimuli, and a local face reorganization method for depression detection is proposed. The method extracts the local phase pattern features, facial action unit (AU) features and head motion features of a local face reconstructed according to facial proportions, and then fed into the classifier for classification. The classification accuracy was 76.25%, with a recall of 80.44% and a specificity of 83.21%. The results demonstrated that the negative video stimuli in the single-attribute stimulus analysis were more effective in eliciting changes in facial expressions in both healthy controls and depressed patients. Fusion of facial features under both neutral and negative stimuli was found to be useful in discriminating between healthy controls and depressed individuals. The Pearson correlation coefficient (PCC) showed that changes in the emotional stimulus paradigm were more strongly correlated with changes in subjects' facial AU when exposed to negative stimuli compared to stimuli of other attributes. These results demonstrate the feasibility of our proposed method and provide a framework for future work in assisting diagnosis.
Collapse
Affiliation(s)
- Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computin, Lanzhou University, Lanzhou, 730000, Gansu, China.
| | - Yongfeng Tao
- Gansu Provincial Key Laboratory of Wearable Computin, Lanzhou University, Lanzhou, 730000, Gansu, China.
| | - Minqiang Yang
- Gansu Provincial Key Laboratory of Wearable Computin, Lanzhou University, Lanzhou, 730000, Gansu, China.
| |
Collapse
|
4
|
Li Y, Liu Z, Zhou L, Yuan X, Shangguan Z, Hu X, Hu B. A facial depression recognition method based on hybrid multi-head cross attention network. Front Neurosci 2023; 17:1188434. [PMID: 37292164 PMCID: PMC10244529 DOI: 10.3389/fnins.2023.1188434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 05/02/2023] [Indexed: 06/10/2023] Open
Abstract
Introduction Deep-learn methods based on convolutional neural networks (CNNs) have demonstrated impressive performance in depression analysis. Nevertheless, some critical challenges need to be resolved in these methods: (1) It is still difficult for CNNs to learn long-range inductive biases in the low-level feature extraction of different facial regions because of the spatial locality. (2) It is difficult for a model with only a single attention head to concentrate on various parts of the face simultaneously, leading to less sensitivity to other important facial regions associated with depression. In the case of facial depression recognition, many of the clues come from a few areas of the face simultaneously, e.g., the mouth and eyes. Methods To address these issues, we present an end-to-end integrated framework called Hybrid Multi-head Cross Attention Network (HMHN), which includes two stages. The first stage consists of the Grid-Wise Attention block (GWA) and Deep Feature Fusion block (DFF) for the low-level visual depression feature learning. In the second stage, we obtain the global representation by encoding high-order interactions among local features with Multi-head Cross Attention block (MAB) and Attention Fusion block (AFB). Results We experimented on AVEC2013 and AVEC2014 depression datasets. The results of AVEC 2013 (RMSE = 7.38, MAE = 6.05) and AVEC 2014 (RMSE = 7.60, MAE = 6.01) demonstrated the efficacy of our method and outperformed most of the state-of-the-art video-based depression recognition approaches. Discussion We proposed a deep learning hybrid model for depression recognition by capturing the higher-order interactions between the depression features of multiple facial regions, which can effectively reduce the error in depression recognition and gives great potential for clinical experiments.
Collapse
|
5
|
Ettore E, Müller P, Hinze J, Benoit M, Giordana B, Postin D, Lecomte A, Lindsay H, Robert P, König A. Digital Phenotyping for Differential Diagnosis of Major Depressive Episode: Narrative Review. JMIR Ment Health 2023; 10:e37225. [PMID: 36689265 PMCID: PMC9903183 DOI: 10.2196/37225] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 09/02/2022] [Accepted: 09/30/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Major depressive episode (MDE) is a common clinical syndrome. It can be found in different pathologies such as major depressive disorder (MDD), bipolar disorder (BD), posttraumatic stress disorder (PTSD), or even occur in the context of psychological trauma. However, only 1 syndrome is described in international classifications (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5]/International Classification of Diseases 11th Revision [ICD-11]), which do not take into account the underlying pathology at the origin of the MDE. Clinical interviews are currently the best source of information to obtain the etiological diagnosis of MDE. Nevertheless, it does not allow an early diagnosis and there are no objective measures of extracted clinical information. To remedy this, the use of digital tools and their correlation with clinical symptomatology could be useful. OBJECTIVE We aimed to review the current application of digital tools for MDE diagnosis while highlighting shortcomings for further research. In addition, our work was focused on digital devices easy to use during clinical interview and mental health issues where depression is common. METHODS We conducted a narrative review of the use of digital tools during clinical interviews for MDE by searching papers published in PubMed/MEDLINE, Web of Science, and Google Scholar databases since February 2010. The search was conducted from June to September 2021. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) automated voice analysis, behavior analysis by (2) video and physiological measures, (3) heart rate variability (HRV), and (4) electrodermal activity (EDA). For this purpose, we were interested in 4 frequently found clinical conditions in which MDE can occur: (1) MDD, (2) BD, (3) PTSD, and (4) psychological trauma. RESULTS A total of 74 relevant papers on the subject were qualitatively analyzed and the information was synthesized. Thus, a digital phenotype of MDE seems to emerge consisting of modifications in speech features (namely, temporal, prosodic, spectral, source, and formants) and in speech content, modifications in nonverbal behavior (head, hand, body and eyes movement, facial expressivity, and gaze), and a decrease in physiological measurements (HRV and EDA). We not only found similarities but also differences when MDE occurs in MDD, BD, PTSD, or psychological trauma. However, comparative studies were rare in BD or PTSD conditions, which does not allow us to identify clear and distinct digital phenotypes. CONCLUSIONS Our search identified markers from several modalities that hold promise for helping with a more objective diagnosis of MDE. To validate their potential, further longitudinal and prospective studies are needed.
Collapse
Affiliation(s)
- Eric Ettore
- Department of Psychiatry and Memory Clinic, University Hospital of Nice, Nice, France
| | - Philipp Müller
- Research Department Cognitive Assistants, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, Saarbrücken, Germany
| | - Jonas Hinze
- Department of Psychiatry and Psychotherapy, Saarland University Medical Center, Hombourg, Germany
| | - Michel Benoit
- Department of Psychiatry, Hopital Pasteur, University Hospital of Nice, Nice, France
| | - Bruno Giordana
- Department of Psychiatry, Hopital Pasteur, University Hospital of Nice, Nice, France
| | - Danilo Postin
- Department of Psychiatry, School of Medicine and Health Sciences, Carl von Ossietzky University of Oldenburg, Bad Zwischenahn, Germany
| | - Amandine Lecomte
- Research Department Sémagramme Team, Institut national de recherche en informatique et en automatique, Nancy, France
| | - Hali Lindsay
- Research Department Cognitive Assistants, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, Saarbrücken, Germany
| | - Philippe Robert
- Research Department, Cognition-Behaviour-Technology Lab, University Côte d'Azur, Nice, France
| | - Alexandra König
- Research Department Stars Team, Institut national de recherche en informatique et en automatique, Sophia Antipolis - Valbonne, France
| |
Collapse
|
6
|
A self-supervised algorithm to detect signs of social isolation in the elderly from daily activity sequences. Artif Intell Med 2023; 135:102454. [PMID: 36628782 DOI: 10.1016/j.artmed.2022.102454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 11/10/2022] [Accepted: 11/15/2022] [Indexed: 11/18/2022]
Abstract
Considering the increasing aging of the population, multi-device monitoring of the activities of daily living (ADL) of older people becomes crucial to support independent living and early detection of symptoms of mental illnesses, such as depression and Alzheimer's disease. Anomalies can anticipate the diagnosis of these pathologies in the patient's normal behavior, such as reduced hygiene, changes in sleep habits, and fewer social interactions. These abnormalities are often subtle and hard to detect. Especially using non-intrusive monitoring devices might cause anomaly detectors to generate false alarms or ignore relevant clues. This limitation may hinder their usage by caregivers. Furthermore, the notion of abnormality here is context and patient-dependent, thus requiring untrained approaches. To reduce these problems, we propose a self-supervised model for multi-sensor time series signals based on Hyperbolic uncertainty for Anomaly Detection, which we dub HypAD. HypAD estimates uncertainty end-to-end, thanks to hyperbolic neural networks, and integrates it into the "classic" notion of reconstruction loss in anomaly detection. Based on hyperbolic uncertainty, HypAD introduces the principle of a detectable anomaly. HypAD assesses whether it is sure about the input signal and fails to reconstruct it because it is anomalous or whether the high reconstruction loss is due to the model uncertainty, e.g., a complex but regular signal (cf. this parallels the residual model error upon training). The proposed solution has been incorporated into an end-to-end ADL monitoring system for elderly patients in retirement homes, developed within a funded project leveraging an interdisciplinary consortium of computer scientists, engineers, and geriatricians. Healthcare professionals were involved in the design and verification process to foster trust in the system. In addition, the system has been equipped with explainability features.
Collapse
|
7
|
Liu Z, Yu H, Li G, Chen Q, Ding Z, Feng L, Yao Z, Hu B. Ensemble learning with speaker embeddings in multiple speech task stimuli for depression detection. Front Neurosci 2023; 17:1141621. [PMID: 37034153 PMCID: PMC10076578 DOI: 10.3389/fnins.2023.1141621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 03/09/2023] [Indexed: 04/11/2023] Open
Abstract
Introduction As a biomarker of depression, speech signal has attracted the interest of many researchers due to its characteristics of easy collection and non-invasive. However, subjects' speech variation under different scenes and emotional stimuli, the insufficient amount of depression speech data for deep learning, and the variable length of speech frame-level features have an impact on the recognition performance. Methods The above problems, this study proposes a multi-task ensemble learning method based on speaker embeddings for depression classification. First, we extract the Mel Frequency Cepstral Coefficients (MFCC), the Perceptual Linear Predictive Coefficients (PLP), and the Filter Bank (FBANK) from the out-domain dataset (CN-Celeb) and train the Resnet x-vector extractor, Time delay neural network (TDNN) x-vector extractor, and i-vector extractor. Then, we extract the corresponding speaker embeddings of fixed length from the depression speech database of the Gansu Provincial Key Laboratory of Wearable Computing. Support Vector Machine (SVM) and Random Forest (RF) are used to obtain the classification results of speaker embeddings in nine speech tasks. To make full use of the information of speech tasks with different scenes and emotions, we aggregate the classification results of nine tasks into new features and then obtain the final classification results by using Multilayer Perceptron (MLP). In order to take advantage of the complementary effects of different features, Resnet x-vectors based on different acoustic features are fused in the ensemble learning method. Results Experimental results demonstrate that (1) MFCC-based Resnet x-vectors perform best among the nine speaker embeddings for depression detection; (2) interview speech is better than picture descriptions speech, and neutral stimulus is the best among the three emotional valences in the depression recognition task; (3) our multi-task ensemble learning method with MFCC-based Resnet x-vectors can effectively identify depressed patients; (4) in all cases, the combination of MFCC-based Resnet x-vectors and PLP-based Resnet x-vectors in our ensemble learning method achieves the best results, outperforming other literature studies using the depression speech database. Discussion Our multi-task ensemble learning method with MFCC-based Resnet x-vectors can fuse the depression related information of different stimuli effectively, which provides a new approach for depression detection. The limitation of this method is that speaker embeddings extractors were pre-trained on the out-domain dataset. We will consider using the augmented in-domain dataset for pre-training to improve the depression recognition performance further.
Collapse
Affiliation(s)
- Zhenyu Liu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Huimin Yu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Gang Li
- Tianshui Third People’s Hospital, Tianshui, China
| | - Qiongqiong Chen
- Second Provincial People’s Hospital of Gansu, Lanzhou, China
- Affiliated Hospital of Northwest Minzu University, Lanzhou, China
| | - Zhijie Ding
- Tianshui Third People’s Hospital, Tianshui, China
| | - Lei Feng
- Department of Psychiatry, Beijing Anding Hospital of Capital Medical University, Beijing, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
- *Correspondence: Bin Hu,
| |
Collapse
|
8
|
Liu D, Liu B, Lin T, Liu G, Yang G, Qi D, Qiu Y, Lu Y, Yuan Q, Shuai SC, Li X, Liu O, Tang X, Shuai J, Cao Y, Lin H. Measuring depression severity based on facial expression and body movement using deep convolutional neural network. Front Psychiatry 2022; 13:1017064. [PMID: 36620657 PMCID: PMC9810804 DOI: 10.3389/fpsyt.2022.1017064] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 12/02/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction Real-time evaluations of the severity of depressive symptoms are of great significance for the diagnosis and treatment of patients with major depressive disorder (MDD). In clinical practice, the evaluation approaches are mainly based on psychological scales and doctor-patient interviews, which are time-consuming and labor-intensive. Also, the accuracy of results mainly depends on the subjective judgment of the clinician. With the development of artificial intelligence (AI) technology, more and more machine learning methods are used to diagnose depression by appearance characteristics. Most of the previous research focused on the study of single-modal data; however, in recent years, many studies have shown that multi-modal data has better prediction performance than single-modal data. This study aimed to develop a measurement of depression severity from expression and action features and to assess its validity among the patients with MDD. Methods We proposed a multi-modal deep convolutional neural network (CNN) to evaluate the severity of depressive symptoms in real-time, which was based on the detection of patients' facial expression and body movement from videos captured by ordinary cameras. We established behavioral depression degree (BDD) metrics, which combines expression entropy and action entropy to measure the depression severity of MDD patients. Results We found that the information extracted from different modes, when integrated in appropriate proportions, can significantly improve the accuracy of the evaluation, which has not been reported in previous studies. This method presented an over 74% Pearson similarity between BDD and self-rating depression scale (SDS), self-rating anxiety scale (SAS), and Hamilton depression scale (HAMD). In addition, we tracked and evaluated the changes of BDD in patients at different stages of a course of treatment and the results obtained were in agreement with the evaluation from the scales. Discussion The BDD can effectively measure the current state of patients' depression and its changing trend according to the patient's expression and action features. Our model may provide an automatic auxiliary tool for the diagnosis and treatment of MDD.
Collapse
Affiliation(s)
- Dongdong Liu
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
| | - Bowen Liu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Department of Psychiatry, Baoan Mental Health Center, Shenzhen Baoan Center for Chronic Disease Control, Shenzhen, China
| | - Tao Lin
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
| | - Guangya Liu
- Integrated Chinese and Western Therapy of Depression Ward, Hunan Brain Hospital, Changsha, China
| | - Guoyu Yang
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
| | - Dezhen Qi
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
| | - Ye Qiu
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
| | - Yuer Lu
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
| | - Qinmei Yuan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Stella C. Shuai
- Department of Biological Sciences, Northwestern University, Evanston, IL, United States
| | - Xiang Li
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
| | - Ou Liu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
| | - Xiangdong Tang
- Sleep Medicine Center, Mental Health Center, Department of Respiratory and Critical Care Medicine, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Jianwei Shuai
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Yuping Cao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Hai Lin
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
| |
Collapse
|
9
|
Barua PD, Vicnesh J, Lih OS, Palmer EE, Yamakawa T, Kobayashi M, Acharya UR. Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review. Cogn Neurodyn 2022:1-22. [PMID: 36467993 PMCID: PMC9684805 DOI: 10.1007/s11571-022-09904-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 09/26/2022] [Accepted: 10/17/2022] [Indexed: 11/24/2022] Open
Abstract
Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are increasing in some countries, such as the US. Suicide prevention is a challenging global public health problem. Currently, there aren't any validated clinical biomarkers for suicidal diagnosis, and traditional methods exhibit limitations. Artificial intelligence (AI) is budding in many fields, including in the diagnosis of medical conditions. This review paper summarizes recent studies (past 8 years) that employed AI tools for the automated detection of depression and/or anxiety disorder and discusses the limitations and effects of some modalities. The studies assert that AI tools produce promising results and could overcome the limitations of traditional diagnostic methods. Although using AI tools for suicidal ideation exhibits limitations, these are outweighed by the advantages. Thus, this review article also proposes extracting a fusion of features such as facial images, speech signals, and visual and clinical history features from deep models for the automated detection of depression and/or anxiety disorder in individuals, for future work. This may pave the way for the identification of individuals with suicidal thoughts.
Collapse
Affiliation(s)
- Prabal Datta Barua
- School of Management and Enterprise, University of Southern Queensland, Springfield, Australia
| | - Jahmunah Vicnesh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - Oh Shu Lih
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - Elizabeth Emma Palmer
- Discipline of Pediatric and Child Health, School of Clinical Medicine, University of New South Wales, Kensington, Australia
- Sydney Children’s Hospitals Network, Sydney, Australia
| | - Toshitaka Yamakawa
- Department of Computer Science and Electrical Engineering, Kumamoto University, Kumamoto, Japan
| | - Makiko Kobayashi
- Department of Computer Science and Electrical Engineering, Kumamoto University, Kumamoto, Japan
| | - Udyavara Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
- School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taizhong, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan
| |
Collapse
|
10
|
Aspect opinion routing network with interactive attention for aspect-based sentiment classification. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.09.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
11
|
Huang J, Zhao Y, Qu W, Tian Z, Tan Y, Wang Z, Tan S. Automatic recognition of schizophrenia from facial videos using 3D convolutional neural network. Asian J Psychiatr 2022; 77:103263. [PMID: 36152565 DOI: 10.1016/j.ajp.2022.103263] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/22/2022] [Accepted: 09/14/2022] [Indexed: 11/17/2022]
Abstract
Schizophrenia affects patients and their families and society because of chronic impairments in cognition, behavior, and emotion. However, its clinical diagnosis mainly depends on the clinicians' knowledge of the patients' symptoms. Other auxiliary diagnostic methods such as MRI and EEG are cumbersome and time-consuming. Recently, the convolutional neural network (CNN) has been applied to the auxiliary diagnosis of psychiatry. Hence, in this study, a method based on deep learning and facial videos is proposed for the rapid detection of schizophrenia. Herein, 125 videos from 125 schizophrenic patients and 75 videos from 75 healthy controls based on emotional stimulation tasks were obtained. The video preprocessing included the experiment clips extraction, face detection, facial region cropping, resizing to 500 × 500 pixel size, and uniform sampling of 100 frames. The preprocessed facial videos were used to train the Resnet18_3D. We utilized ten-fold cross-validation, and held-out testing set to evaluate the model with the accuracy, the precision, the sensitivity, the specificity, the balanced accuracy, and the AUC. The Resnet18_3D trained on Film_order achieved the best performance with accuracy, sensitivity, specificity, balanced accuracy, and AUC of 89.00%, 96.80%, 76.00%, 86.40% and 0.9397. The neural network model indeed recognizes healthy controls and schizophrenic patients through the changes in the area of the face. The results show that facial video under emotional stimulation can be used to classify schizophrenic patients and help clinicians with diagnosis in the clinical environment. Among the different types of stimuli, the video stimuli with fixed emotional order showed the best classification performance.
Collapse
Affiliation(s)
- Jie Huang
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Yanli Zhao
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Wei Qu
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Zhanxiao Tian
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Yunlong Tan
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Zhiren Wang
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Shuping Tan
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China.
| |
Collapse
|
12
|
Kshirsagar PR, Manoharan H, Selvarajan S, Alterazi HA, Singh D, Lee HN. Perception Exploration on Robustness Syndromes With Pre-processing Entities Using Machine Learning Algorithm. Front Public Health 2022; 10:893989. [PMID: 35784247 PMCID: PMC9243559 DOI: 10.3389/fpubh.2022.893989] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 04/27/2022] [Indexed: 11/13/2022] Open
Abstract
The majority of the current-generation individuals all around the world are dealing with a variety of health-related issues. The most common cause of health problems has been found as depression, which is caused by intellectual difficulties. However, most people are unable to recognize such occurrences in them, and no procedures for discriminating them from normal people have been created so far. Even some advanced technologies do not support distinct classes of individuals as language writing skills vary greatly across numerous places, making the central operations cumbersome. As a result, the primary goal of the proposed research is to create a unique model that can detect a variety of diseases in humans, thereby averting a high level of depression. A machine learning method known as the Convolutional Neural Network (CNN) model has been included into this evolutionary process for extracting numerous features in three distinct units. The CNN also detects early-stage problems since it accepts input in the form of writing and sketching, both of which are turned to images. Furthermore, with this sort of image emotion analysis, ordinary reactions may be easily differentiated, resulting in more accurate prediction results. The characteristics such as reference line, tilt, length, edge, constraint, alignment, separation, and sectors are analyzed to test the usefulness of CNN for recognizing abnormalities, and the extracted features provide an enhanced value of around 74%higher than the conventional models.
Collapse
Affiliation(s)
- Pravin R. Kshirsagar
- Department of Artificial Intelligence, G.H. Raisoni College of Engineering, Nagpur, India
| | - Hariprasath Manoharan
- Department of Electronics and Communication Engineering, Panimalar Institute of Technology, Chennai, India
- Hariprasath Manoharan
| | - Shitharth Selvarajan
- Department of Computer Science and Engineering, Kebri Dehar University, Kebri Dehar, Ethiopia
| | - Hassan A. Alterazi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Dilbag Singh
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Heung-No Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea
- *Correspondence: Heung-No Lee
| |
Collapse
|
13
|
Guo Y, Liu X, Wang X, Zhu T, Zhan W. Automatic Decision-Making Style Recognition Method Using Kinect Technology. Front Psychol 2022; 13:751914. [PMID: 35310212 PMCID: PMC8931824 DOI: 10.3389/fpsyg.2022.751914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 01/25/2022] [Indexed: 11/13/2022] Open
Abstract
In recent years, somatosensory interaction technology, represented by Microsoft's Kinect hardware platform, has been widely used in various fields, such as entertainment, education, and medicine. Kinect technology can easily capture and record behavioral data, which provides new opportunities for behavioral and psychological correlation analysis research. In this paper, an automatic decision-style recognition method is proposed. Experiments involving 240 subjects were conducted to obtain face data and individual decision-making style score. The face data was obtained using the Kinect camera, and the decision-style score were obtained via a questionnaire. To realize automatic recognition of an individual decision-making style, machine learning was employed to establish the mapping relationship between the face data and a scaled evaluation of the decision-making style score. This study adopts a variety of classical machine learning algorithms, including Linear regression, Support vector machine regression, Ridge regression, and Bayesian ridge regression. The experimental results show that the linear regression model returns the best results. The correlation coefficient between the linear regression model evaluation results and the scale evaluation results was 0.6, which represents a medium and higher correlation. The results verify the feasibility of automatic decision-making style recognition method based on facial analysis.
Collapse
Affiliation(s)
- Yu Guo
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoqian Liu
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoyang Wang
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Tingshao Zhu
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Wei Zhan
- Information Science Research Institute, China Electronics Technology Group Corporation, Beijing, China
| |
Collapse
|
14
|
Ekundayo O, Viriri S. Multilabel convolution neural network for facial expression recognition and ordinal intensity estimation. PeerJ Comput Sci 2021; 7:e736. [PMID: 34909462 PMCID: PMC8641570 DOI: 10.7717/peerj-cs.736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 09/13/2021] [Indexed: 06/14/2023]
Abstract
Facial Expression Recognition (FER) has gained considerable attention in affective computing due to its vast area of applications. Diverse approaches and methods have been considered for a robust FER in the field, but only a few works considered the intensity of emotion embedded in the expression. Even the available studies on expression intensity estimation successfully assigned a nominal/regression value or classified emotion in a range of intervals. Most of the available works on facial expression intensity estimation successfully present only the emotion intensity estimation. At the same time, others proposed methods that predict emotion and its intensity in different channels. These multiclass approaches and extensions do not conform to man heuristic manner of recognising emotion and its intensity estimation. This work presents a Multilabel Convolution Neural Network (ML-CNN)-based model, which could simultaneously recognise emotion and provide ordinal metrics as the intensity estimation of the emotion. The proposed ML-CNN is enhanced with the aggregation of Binary Cross-Entropy (BCE) loss and Island Loss (IL) functions to minimise intraclass and interclass variations. Also, ML-CNN model is pre-trained with Visual Geometric Group (VGG-16) to control overfitting. In the experiments conducted on Binghampton University 3D Facial Expression (BU-3DFE) and Cohn Kanade extension (CK+) datasets, we evaluate ML-CNN's performance based on accuracy and loss. We also carried out a comparative study of our model with some popularly used multilabel algorithms using standard multilabel metrics. ML-CNN model simultaneously predicts emotion and intensity estimation using ordinal metrics. The model also shows appreciable and superior performance over four standard multilabel algorithms: Chain Classifier (CC), distinct Random K label set (RAKEL), Multilabel K Nearest Neighbour (MLKNN) and Multilabel ARAM (MLARAM).
Collapse
Affiliation(s)
- Olufisayo Ekundayo
- Computer Science Discipline, University of KwaZulu-Natal, Durban, South Africa
| | - Serestina Viriri
- Computer Science Discipline, University of KwaZulu-Natal, Durban, South Africa
| |
Collapse
|