Diagnosing Bipolar Disorders in a Wearable Device
Bipolar disorder is a common chronic recurrent psychosis and it mainly relies on doctors’ experience to determine the patient’s condition currently. We aimed to find a useful methodology to diagnose the mental state and guide medical treatment by using speech signal processing. Methods: Firstly, the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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European Alliance for Innovation (EAI)
2015-12-01
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| Series: | EAI Endorsed Transactions on Ambient Systems |
| Subjects: | |
| Online Access: | http://eudl.eu/doi/10.4108/eai.28-9-2015.2261428 |
| _version_ | 1818888071869890560 |
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| author | Chao Gui Jie Zhu |
| author_facet | Chao Gui Jie Zhu |
| author_sort | Chao Gui |
| collection | DOAJ |
| description | Bipolar disorder is a common chronic recurrent psychosis and it mainly relies on doctors’ experience to determine the patient’s condition currently. We aimed to find a useful methodology to diagnose the mental state and guide medical treatment by using speech signal processing. Methods: Firstly, the feature classes were extracted (e.g., Pitch, Formant, MFCC, GT). Secondly, class separability criterion based on distance (the Between-class Variance and Within-class Variance) was adopted as an evaluation criteria to get the features assessment, and then, we found LPC played a core role on the all features. According to the experiment, the SVM have a good performance for the single patient up to 90%, and the GMM classifier yields the best performance with a classification rate of 72% for multi patients. The newly proposed methodology provide a good method for helping diagnose bipolar disorder. |
| first_indexed | 2024-12-19T16:47:18Z |
| format | Article |
| id | doaj.art-2faae78159844d95bf38bd49da4a5d04 |
| institution | Directory Open Access Journal |
| issn | 2032-927X |
| language | English |
| last_indexed | 2024-12-19T16:47:18Z |
| publishDate | 2015-12-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Ambient Systems |
| spelling | doaj.art-2faae78159844d95bf38bd49da4a5d042022-12-21T20:13:37ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Ambient Systems2032-927X2015-12-013111110.4108/eai.28-9-2015.2261428Diagnosing Bipolar Disorders in a Wearable DeviceChao Gui0Jie Zhu1Shanghai Jiao Tong UniversityShanghai Jiao Tong University; zhujie@sjtu.edu.cnBipolar disorder is a common chronic recurrent psychosis and it mainly relies on doctors’ experience to determine the patient’s condition currently. We aimed to find a useful methodology to diagnose the mental state and guide medical treatment by using speech signal processing. Methods: Firstly, the feature classes were extracted (e.g., Pitch, Formant, MFCC, GT). Secondly, class separability criterion based on distance (the Between-class Variance and Within-class Variance) was adopted as an evaluation criteria to get the features assessment, and then, we found LPC played a core role on the all features. According to the experiment, the SVM have a good performance for the single patient up to 90%, and the GMM classifier yields the best performance with a classification rate of 72% for multi patients. The newly proposed methodology provide a good method for helping diagnose bipolar disorder.http://eudl.eu/doi/10.4108/eai.28-9-2015.2261428bipolar disordersupport vector machine (svm)gaussian mixture model (gmm)wearable device |
| spellingShingle | Chao Gui Jie Zhu Diagnosing Bipolar Disorders in a Wearable Device EAI Endorsed Transactions on Ambient Systems bipolar disorder support vector machine (svm) gaussian mixture model (gmm) wearable device |
| title | Diagnosing Bipolar Disorders in a Wearable Device |
| title_full | Diagnosing Bipolar Disorders in a Wearable Device |
| title_fullStr | Diagnosing Bipolar Disorders in a Wearable Device |
| title_full_unstemmed | Diagnosing Bipolar Disorders in a Wearable Device |
| title_short | Diagnosing Bipolar Disorders in a Wearable Device |
| title_sort | diagnosing bipolar disorders in a wearable device |
| topic | bipolar disorder support vector machine (svm) gaussian mixture model (gmm) wearable device |
| url | http://eudl.eu/doi/10.4108/eai.28-9-2015.2261428 |
| work_keys_str_mv | AT chaogui diagnosingbipolardisordersinawearabledevice AT jiezhu diagnosingbipolardisordersinawearabledevice |