A Machine Learning Approach for Detecting Idiopathic REM Sleep Behavior Disorder
<b>Background and purpose:</b> Growing evidence suggests that Machine Learning (ML) models can assist the diagnosis of neurological disorders. However, little is known about the potential application of ML in diagnosing idiopathic REM sleep behavior disorder (iRBD), a parasomnia characte...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/11/2689 |
| _version_ | 1827646825464594432 |
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| author | Maria Salsone Andrea Quattrone Basilio Vescio Luigi Ferini-Strambi Aldo Quattrone |
| author_facet | Maria Salsone Andrea Quattrone Basilio Vescio Luigi Ferini-Strambi Aldo Quattrone |
| author_sort | Maria Salsone |
| collection | DOAJ |
| description | <b>Background and purpose:</b> Growing evidence suggests that Machine Learning (ML) models can assist the diagnosis of neurological disorders. However, little is known about the potential application of ML in diagnosing idiopathic REM sleep behavior disorder (iRBD), a parasomnia characterized by a high risk of phenoconversion to synucleinopathies. This study aimed to develop a model using ML algorithms to identify iRBD patients and test its accuracy. <b>Methods:</b> Data were acquired from 32 participants (20 iRBD patients and 12 controls). All subjects underwent a video-polysomnography. In all subjects, we measured the components of heart rate variability (HRV) during 24 h recordings and calculated night-to-day ratios (cardiac autonomic indices). Discriminating performances of single HRV features were assessed. ML models based on Logistic Regression (LR), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were trained on HRV data. The utility of HRV features and ML models for detecting iRBD was evaluated by area under the ROC curve (AUC), sensitivity, specificity and accuracy corresponding to optimal models. <b>Results:</b> Cardiac autonomic indices had low performances (accuracy 63–69%) in distinguishing iRBD from control subjects. By contrast, the RF model performed the best, with excellent accuracy (94%), sensitivity (95%) and specificity (92%), while XGBoost showed accuracy (91%), specificity (83%) and sensitivity (95%). The mean triangular index during wake (TIw) was the best discriminating feature between iRBD and HC, with 81% accuracy, reaching 84% accuracy when combined with VLF power during sleep using an LR model. <b>Conclusions:</b> Our findings demonstrated that ML algorithms can accurately identify iRBD patients. Our model could be used in clinical practice to facilitate the early detection of this form of RBD. |
| first_indexed | 2024-03-09T19:09:49Z |
| format | Article |
| id | doaj.art-e4411cfcc52e4597be4fa9ce09282759 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T19:09:49Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-e4411cfcc52e4597be4fa9ce092827592023-11-24T04:19:22ZengMDPI AGDiagnostics2075-44182022-11-011211268910.3390/diagnostics12112689A Machine Learning Approach for Detecting Idiopathic REM Sleep Behavior DisorderMaria Salsone0Andrea Quattrone1Basilio Vescio2Luigi Ferini-Strambi3Aldo Quattrone4Institute of Molecular Bioimaging and Physiology, National Research Council, 20054 Segrate, ItalyInstitute of Neurology, Magna Graecia University, 88100 Catanzaro, ItalyNeuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), 88100 Catanzaro, ItalySleep Disorders Center, Division of Neuroscience, San Raffaele Scientific Institute, 20127 Milan, ItalyNeuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), 88100 Catanzaro, Italy<b>Background and purpose:</b> Growing evidence suggests that Machine Learning (ML) models can assist the diagnosis of neurological disorders. However, little is known about the potential application of ML in diagnosing idiopathic REM sleep behavior disorder (iRBD), a parasomnia characterized by a high risk of phenoconversion to synucleinopathies. This study aimed to develop a model using ML algorithms to identify iRBD patients and test its accuracy. <b>Methods:</b> Data were acquired from 32 participants (20 iRBD patients and 12 controls). All subjects underwent a video-polysomnography. In all subjects, we measured the components of heart rate variability (HRV) during 24 h recordings and calculated night-to-day ratios (cardiac autonomic indices). Discriminating performances of single HRV features were assessed. ML models based on Logistic Regression (LR), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were trained on HRV data. The utility of HRV features and ML models for detecting iRBD was evaluated by area under the ROC curve (AUC), sensitivity, specificity and accuracy corresponding to optimal models. <b>Results:</b> Cardiac autonomic indices had low performances (accuracy 63–69%) in distinguishing iRBD from control subjects. By contrast, the RF model performed the best, with excellent accuracy (94%), sensitivity (95%) and specificity (92%), while XGBoost showed accuracy (91%), specificity (83%) and sensitivity (95%). The mean triangular index during wake (TIw) was the best discriminating feature between iRBD and HC, with 81% accuracy, reaching 84% accuracy when combined with VLF power during sleep using an LR model. <b>Conclusions:</b> Our findings demonstrated that ML algorithms can accurately identify iRBD patients. Our model could be used in clinical practice to facilitate the early detection of this form of RBD.https://www.mdpi.com/2075-4418/12/11/2689heart rate variabilityidiopathic REM sleep behavior disordermachine learningclassification |
| spellingShingle | Maria Salsone Andrea Quattrone Basilio Vescio Luigi Ferini-Strambi Aldo Quattrone A Machine Learning Approach for Detecting Idiopathic REM Sleep Behavior Disorder Diagnostics heart rate variability idiopathic REM sleep behavior disorder machine learning classification |
| title | A Machine Learning Approach for Detecting Idiopathic REM Sleep Behavior Disorder |
| title_full | A Machine Learning Approach for Detecting Idiopathic REM Sleep Behavior Disorder |
| title_fullStr | A Machine Learning Approach for Detecting Idiopathic REM Sleep Behavior Disorder |
| title_full_unstemmed | A Machine Learning Approach for Detecting Idiopathic REM Sleep Behavior Disorder |
| title_short | A Machine Learning Approach for Detecting Idiopathic REM Sleep Behavior Disorder |
| title_sort | machine learning approach for detecting idiopathic rem sleep behavior disorder |
| topic | heart rate variability idiopathic REM sleep behavior disorder machine learning classification |
| url | https://www.mdpi.com/2075-4418/12/11/2689 |
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