Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers
Diagnosis of most neuropsychiatric disorders relies on subjective measures, which makes the reliability of final clinical decisions questionable. The aim of this study was to propose a machine learning-based classification approach for objective diagnosis of three disorders of neuropsychiatric or ne...
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| Format: | Article |
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MDPI AG
2022-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/14/5407 |
| _version_ | 1797415943623671808 |
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| author | Sinem Burcu Erdoğan Gülnaz Yükselen |
| author_facet | Sinem Burcu Erdoğan Gülnaz Yükselen |
| author_sort | Sinem Burcu Erdoğan |
| collection | DOAJ |
| description | Diagnosis of most neuropsychiatric disorders relies on subjective measures, which makes the reliability of final clinical decisions questionable. The aim of this study was to propose a machine learning-based classification approach for objective diagnosis of three disorders of neuropsychiatric or neurological origin with functional near-infrared spectroscopy (fNIRS) derived biomarkers. Thirteen healthy adolescents and sixty-seven patients who were clinically diagnosed with migraine, obsessive compulsive disorder, or schizophrenia performed a Stroop task, while prefrontal cortex hemodynamics were monitored with fNIRS. Hemodynamic and cognitive features were extracted for training three supervised learning algorithms (naïve bayes (NB), linear discriminant analysis (LDA), and support vector machines (SVM)). The performance of each algorithm in correctly predicting the class of each participant across the four classes was tested with ten runs of a ten-fold cross-validation procedure. All algorithms achieved four-class classification performances with accuracies above 81% and specificities above 94%. SVM had the highest performance in terms of accuracy (85.1 ± 1.77%), sensitivity (84 ± 1.7%), specificity (95 ± 0.5%), precision (86 ± 1.6%), and F1-score (85 ± 1.7%). fNIRS-derived features have no subjective report bias when used for automated classification purposes. The presented methodology might have significant potential for assisting in the objective diagnosis of neuropsychiatric disorders associated with frontal lobe dysfunction. |
| first_indexed | 2024-03-09T05:56:43Z |
| format | Article |
| id | doaj.art-e4a43a18e0004e7fb5adb72b647039bd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T05:56:43Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e4a43a18e0004e7fb5adb72b647039bd2023-12-03T12:13:39ZengMDPI AGSensors1424-82202022-07-012214540710.3390/s22145407Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived BiomarkersSinem Burcu Erdoğan0Gülnaz Yükselen1Department of Biomedical Engineering, Acıbadem Mehmet Ali Aydınlar University, Istanbul 34684, TurkeyDepartment of Biomedical Engineering, Acıbadem Mehmet Ali Aydınlar University, Istanbul 34684, TurkeyDiagnosis of most neuropsychiatric disorders relies on subjective measures, which makes the reliability of final clinical decisions questionable. The aim of this study was to propose a machine learning-based classification approach for objective diagnosis of three disorders of neuropsychiatric or neurological origin with functional near-infrared spectroscopy (fNIRS) derived biomarkers. Thirteen healthy adolescents and sixty-seven patients who were clinically diagnosed with migraine, obsessive compulsive disorder, or schizophrenia performed a Stroop task, while prefrontal cortex hemodynamics were monitored with fNIRS. Hemodynamic and cognitive features were extracted for training three supervised learning algorithms (naïve bayes (NB), linear discriminant analysis (LDA), and support vector machines (SVM)). The performance of each algorithm in correctly predicting the class of each participant across the four classes was tested with ten runs of a ten-fold cross-validation procedure. All algorithms achieved four-class classification performances with accuracies above 81% and specificities above 94%. SVM had the highest performance in terms of accuracy (85.1 ± 1.77%), sensitivity (84 ± 1.7%), specificity (95 ± 0.5%), precision (86 ± 1.6%), and F1-score (85 ± 1.7%). fNIRS-derived features have no subjective report bias when used for automated classification purposes. The presented methodology might have significant potential for assisting in the objective diagnosis of neuropsychiatric disorders associated with frontal lobe dysfunction.https://www.mdpi.com/1424-8220/22/14/5407fNIRSBCIclassificationschizophreniaobsessive compulsive disordermigraine |
| spellingShingle | Sinem Burcu Erdoğan Gülnaz Yükselen Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers Sensors fNIRS BCI classification schizophrenia obsessive compulsive disorder migraine |
| title | Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers |
| title_full | Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers |
| title_fullStr | Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers |
| title_full_unstemmed | Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers |
| title_short | Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers |
| title_sort | four class classification of neuropsychiatric disorders by use of functional near infrared spectroscopy derived biomarkers |
| topic | fNIRS BCI classification schizophrenia obsessive compulsive disorder migraine |
| url | https://www.mdpi.com/1424-8220/22/14/5407 |
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