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Kamińska D, Kamińska O, Sochacka M, Sokół-Szawłowska M. The Role of Selected Speech Signal Characteristics in Discriminating Unipolar and Bipolar Disorders. SENSORS (BASEL, SWITZERLAND) 2024; 24:4721. [PMID: 39066117 PMCID: PMC11281009 DOI: 10.3390/s24144721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 06/23/2024] [Accepted: 07/12/2024] [Indexed: 07/28/2024]
Abstract
OBJECTIVE The objective of this study is to explore and enhance the diagnostic process of unipolar and bipolar disorders. The primary focus is on leveraging automated processes to improve the accuracy and accessibility of diagnosis. The study aims to introduce an audio corpus collected from patients diagnosed with these disorders, annotated using the Clinical Global Impressions Scale (CGI) by psychiatrists. METHODS AND PROCEDURES Traditional diagnostic methods rely on the clinician's expertise and consideration of co-existing mental disorders. However, this study proposes the implementation of automated processes in the diagnosis, providing quantitative measures and enabling prolonged observation of patients. The paper introduces a speech signal pipeline for CGI state classification, with a specific focus on selecting the most discriminative features. Acoustic features such as prosodies, MFCC, and LPC coefficients are examined in the study. The classification process utilizes common machine learning methods. RESULTS The results of the study indicate promising outcomes for the automated diagnosis of bipolar and unipolar disorders using the proposed speech signal pipeline. The audio corpus annotated with CGI by psychiatrists achieved a classification accuracy of 95% for the two-class classification. For the four- and seven-class classifications, the results were 77.3% and 73%, respectively, demonstrating the potential of the developed method in distinguishing different states of the disorders.
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Affiliation(s)
- Dorota Kamińska
- Institute of Mechatronics and Information Systems, Lodz University of Technology, 116 Żeromskiego Street, 90-924 Lodz, Poland
| | - Olga Kamińska
- Systems Research Institute, Polish Academy of Sciences, 01-447 Warsaw, Poland;
| | | | - Marlena Sokół-Szawłowska
- Outpatient Psychiatric Clinic, Institute of Psychiatry and Neurology, 9 Jana III Sobieskiego Street, 02-957 Warsaw, Poland;
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Li C, Xiao Z, Li Y, Chen Z, Ji X, Liu Y, Feng S, Zhang Z, Zhang K, Feng J, Robbins TW, Xiong S, Chen Y, Xiao X. Deep learning-based activity recognition and fine motor identification using 2D skeletons of cynomolgus monkeys. Zool Res 2023; 44:967-980. [PMID: 37721106 PMCID: PMC10559098 DOI: 10.24272/j.issn.2095-8137.2022.449] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 09/14/2023] [Indexed: 09/19/2023] Open
Abstract
Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and prediction. However, action recognition currently used in non-human primate (NHP) research relies heavily on intense manual labor and lacks standardized assessment. In this work, we established two standard benchmark datasets of NHPs in the laboratory: MonkeyinLab (MiL), which includes 13 categories of actions and postures, and MiL2D, which includes sequences of two-dimensional (2D) skeleton features. Furthermore, based on recent methodological advances in deep learning and skeleton visualization, we introduced the MonkeyMonitorKit (MonKit) toolbox for automatic action recognition, posture estimation, and identification of fine motor activity in monkeys. Using the datasets and MonKit, we evaluated the daily behaviors of wild-type cynomolgus monkeys within their home cages and experimental environments and compared these observations with the behaviors exhibited by cynomolgus monkeys possessing mutations in the MECP2 gene as a disease model of Rett syndrome (RTT). MonKit was used to assess motor function, stereotyped behaviors, and depressive phenotypes, with the outcomes compared with human manual detection. MonKit established consistent criteria for identifying behavior in NHPs with high accuracy and efficiency, thus providing a novel and comprehensive tool for assessing phenotypic behavior in monkeys.
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Affiliation(s)
- Chuxi Li
- School of Information Science and Technology Micro Nano System Center, Fudan University, Shanghai 200433, China
| | - Zifan Xiao
- Department of Anesthesiology, Huashan Hospital
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education
- Behavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Yerong Li
- School of Information Science and Technology Micro Nano System Center, Fudan University, Shanghai 200433, China
| | - Zhinan Chen
- School of Information Science and Technology Micro Nano System Center, Fudan University, Shanghai 200433, China
| | - Xun Ji
- Kuang Yaming Honors School, Nanjing University, Nanjing, Jiangsu 210023, China
| | - Yiqun Liu
- Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China
| | - Shufei Feng
- State Key Laboratory of Primate Biomedical Research
- Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
| | - Zhen Zhang
- State Key Laboratory of Primate Biomedical Research
- Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
| | - Kaiming Zhang
- New Vision World LLC., Aliso Viejo, California 92656, USA
| | - Jianfeng Feng
- Department of Anesthesiology, Huashan Hospital
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education
- Behavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Trevor W Robbins
- Department of Anesthesiology, Huashan Hospital
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education
- Behavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 1TN, UK
| | - Shisheng Xiong
- School of Information Science and Technology Micro Nano System Center, Fudan University, Shanghai 200433, China. E-mail:
| | - Yongchang Chen
- State Key Laboratory of Primate Biomedical Research
- Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
| | - Xiao Xiao
- Department of Anesthesiology, Huashan Hospital
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education
- Behavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China. E-mail:
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Liu XQ, Ji XY, Weng X, Zhang YF. Artificial intelligence ecosystem for computational psychiatry: Ideas to practice. World J Meta-Anal 2023; 11:79-91. [DOI: 10.13105/wjma.v11.i4.79] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/18/2023] [Accepted: 04/04/2023] [Indexed: 04/14/2023] Open
Abstract
Computational psychiatry is an emerging field that not only explores the biological basis of mental illness but also considers the diagnoses and identifies the underlying mechanisms. One of the key strengths of computational psychiatry is that it may identify patterns in large datasets that are not easily identifiable. This may help researchers develop more effective treatments and interventions for mental health problems. This paper is a narrative review that reviews the literature and produces an artificial intelligence ecosystem for computational psychiatry. The artificial intelligence ecosystem for computational psychiatry includes data acquisition, preparation, modeling, application, and evaluation. This approach allows researchers to integrate data from a variety of sources, such as brain imaging, genetics, and behavioral experiments, to obtain a more complete understanding of mental health conditions. Through the process of data preprocessing, training, and testing, the data that are required for model building can be prepared. By using machine learning, neural networks, artificial intelligence, and other methods, researchers have been able to develop diagnostic tools that can accurately identify mental health conditions based on a patient’s symptoms and other factors. Despite the continuous development and breakthrough of computational psychiatry, it has not yet influenced routine clinical practice and still faces many challenges, such as data availability and quality, biological risks, equity, and data protection. As we move progress in this field, it is vital to ensure that computational psychiatry remains accessible and inclusive so that all researchers may contribute to this significant and exciting field.
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Affiliation(s)
- Xin-Qiao Liu
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xin-Yu Ji
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xing Weng
- Huzhou Educational Science & Research Center, Huzhou 313000, Zhejiang Province, China
| | - Yi-Fan Zhang
- School of Education, Tianjin University, Tianjin 300350, China
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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.
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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
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Lee KS, Ham BJ. Machine Learning on Early Diagnosis of Depression. Psychiatry Investig 2022; 19:597-605. [PMID: 36059048 PMCID: PMC9441463 DOI: 10.30773/pi.2022.0075] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 05/23/2022] [Indexed: 11/27/2022] Open
Abstract
To review the recent progress of machine learning for the early diagnosis of depression (major depressive disorder). The source of data was 32 original studies in the Web of Science. The search terms were "depression" (title) and "random forest" (abstract). The eligibility criteria were the dependent variable of depression, the interventions of machine learning (the decision tree, the naïve Bayesian, the random forest, the support vector machine and/or the artificial neural network), the outcomes of accuracy and/or the area under the receiver operating characteristic curve (AUC) for the early diagnosis of depression, the publication year of 2000 or later, the publication language of English and the publication journal of SCIE/SSCI. Different machine learning methods would be appropriate for different types of data for the early diagnosis of depression, e.g., logistic regression, the random forest, the support vector machine and/or the artificial neural network in the case of numeric data, the random forest in the case of genomic data. Their performance measures reported varied within 60.1-100.0 for accuracy and 64.0-96.0 for the AUC. Machine learning provides an effective, non-invasive decision support system for early diagnosis of depression.
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Affiliation(s)
- Kwang-Sig Lee
- AI Center, Korea University Anam Hospital, Seoul, Republic of Korea
| | - Byung-Joo Ham
- Department of Mental Health, Korea University Anam Hospital, Seoul, Republic of Korea
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Gupta S, Goel L, Singh A, Prasad A, Ullah MA. Psychological Analysis for Depression Detection from Social Networking Sites. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4395358. [PMID: 35432513 PMCID: PMC9007657 DOI: 10.1155/2022/4395358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 02/28/2022] [Accepted: 03/24/2022] [Indexed: 11/23/2022]
Abstract
Rapid technological advancements are altering people's communication styles. With the growth of the Internet, social networks (Twitter, Facebook, Telegram, and Instagram) have become popular forums for people to share their thoughts, psychological behavior, and emotions. Psychological analysis analyzes text and extracts facts, features, and important information from the opinions of users. Researchers working on psychological analysis rely on social networks for the detection of depression-related behavior and activity. Social networks provide innumerable data on mindsets of a person's onset of depression, such as low sociology and activities such as undergoing medical treatment, a primary emphasis on oneself, and a high rate of activity during the day and night. In this paper, we used five machine learning classifiers-decision trees, K-nearest neighbor, support vector machines, logistic regression, and LSTM-for depression detection in tweets. The dataset is collected in two forms-balanced and imbalanced-where the oversampling of techniques is studied technically. The results show that the LSTM classification model outperforms the other baseline models in the depression detection healthcare approach for both balanced and imbalanced data.
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Affiliation(s)
- Sonam Gupta
- Department of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, India
| | - Lipika Goel
- Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India
| | - Arjun Singh
- School of Computing and Information Technology, Manipal University Jaipur, Jaipur, India
| | - Ajay Prasad
- University of Petroleum and Energy Studies, Dehradun, India
| | - Mohammad Aman Ullah
- Department of Computer Science and Engineering, International Islamic University Chittagong, Chittagong, Bangladesh
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Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study. SENSORS 2021; 21:s21196459. [PMID: 34640779 PMCID: PMC8512098 DOI: 10.3390/s21196459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 11/17/2022]
Abstract
Early and self-identification of locomotive degradation facilitates us with awareness and motivation to prevent further deterioration. We propose the usage of nine squat and four one-leg standing exercise features as input parameters to Machine Learning (ML) classifiers in order to perform lower limb skill assessment. The significance of this approach is that it does not demand manpower and infrastructure, unlike traditional methods. We base the output layer of the classifiers on the Short Test Battery Locomotive Syndrome (STBLS) test used to detect Locomotive Syndrome (LS) approved by the Japanese Orthopedic Association (JOA). We obtained three assessment scores by using this test, namely sit-stand, 2-stride, and Geriatric Locomotive Function Scale (GLFS-25). We tested two ML methods, namely an Artificial Neural Network (ANN) comprised of two hidden layers with six nodes per layer configured with Rectified-Linear-Unit (ReLU) activation function and a Random Forest (RF) regressor with number of estimators varied from 5 to 100. We could predict the stand-up and 2-stride scores of the STBLS test with correlation of 0.59 and 0.76 between the real and predicted data, respectively, by using the ANN. The best accuracies (R-squared values) obtained through the RF regressor were 0.86, 0.79, and 0.73 for stand-up, 2-stride, and GLFS-25 scores, respectively.
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