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Salsone M, Vescio B, Quattrone A, Marelli S, Castelnuovo A, Casoni F, Quattrone A, Ferini-Strambi L. Periodic Leg Movements during Sleep Associated with REM Sleep Behavior Disorder: A Machine Learning Study. Diagnostics (Basel) 2024; 14:363. [PMID: 38396401 PMCID: PMC10888394 DOI: 10.3390/diagnostics14040363] [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: 12/07/2023] [Revised: 01/20/2024] [Accepted: 02/01/2024] [Indexed: 02/25/2024] Open
Abstract
Most patients with idiopathic REM sleep behavior disorder (iRBD) present peculiar repetitive leg jerks during sleep in their clinical spectrum, called periodic leg movements (PLMS). The clinical differentiation of iRBD patients with and without PLMS is challenging, without polysomnographic confirmation. The aim of this study is to develop a new Machine Learning (ML) approach to distinguish between iRBD phenotypes. Heart rate variability (HRV) data were acquired from forty-two consecutive iRBD patients (23 with PLMS and 19 without PLMS). All participants underwent video-polysomnography to confirm the clinical diagnosis. ML models based on Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were trained on HRV data, and classification performances were assessed using Leave-One-Out cross-validation. No significant clinical differences emerged between the two groups. The RF model showed the best performance in differentiating between iRBD phenotypes with excellent accuracy (86%), sensitivity (96%), and specificity (74%); SVM and XGBoost had good accuracy (81% and 78%, respectively), sensitivity (83% for both), and specificity (79% and 72%, respectively). In contrast, LR had low performances (accuracy 71%). Our results demonstrate that ML algorithms accurately differentiate iRBD patients from those without PLMS, encouraging the use of Artificial Intelligence to support the diagnosis of clinically indistinguishable iRBD phenotypes.
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Affiliation(s)
- Maria Salsone
- Institute of Molecular Bioimaging and Physiology, National Research Council, 20054 Segrate, Italy
- Sleep Disorders Center, Division of Neuroscience, San Raffaele Scientific Institute, 20132 Milan, Italy; (S.M.); (F.C.); (L.F.-S.)
| | - Basilio Vescio
- Neuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), 88100 Catanzaro, Italy;
- Biotecnomed S.C.aR.L., c/o Magna Graecia University, G Building, lev.1, 88100 Catanzaro, Italy
| | - Andrea Quattrone
- Institute of Neurology, Magna Graecia University, 88100 Catanzaro, Italy;
| | - Sara Marelli
- Sleep Disorders Center, Division of Neuroscience, San Raffaele Scientific Institute, 20132 Milan, Italy; (S.M.); (F.C.); (L.F.-S.)
| | - Alessandra Castelnuovo
- Sleep Disorders Center, Division of Neuroscience, Vita-Salute San Raffaele University, 20132 Milan, Italy;
| | - Francesca Casoni
- Sleep Disorders Center, Division of Neuroscience, San Raffaele Scientific Institute, 20132 Milan, Italy; (S.M.); (F.C.); (L.F.-S.)
| | - Aldo Quattrone
- Neuroscience Research Center, Magna Graecia University, 88100 Catanzaro, Italy
| | - Luigi Ferini-Strambi
- Sleep Disorders Center, Division of Neuroscience, San Raffaele Scientific Institute, 20132 Milan, Italy; (S.M.); (F.C.); (L.F.-S.)
- Sleep Disorders Center, Division of Neuroscience, Vita-Salute San Raffaele University, 20132 Milan, Italy;
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