UNLABELLED: Abnormal skeleton muscle activity during REM sleep is characterized as REM Behaviour Disorder (RBD), and may be an early marker for different neurodegenerative diseases. Early detection of RBD is therefore highly important, and in this ongoing study a semi-automatic method for RBD detection is proposed by analyzing the motor activity during sleep. METHOD: A total number of twelve patients have been involved in this study, six normal controls and six patients diagnosed with Parkinsons Disease (PD) with RBD. All subjects underwent at least one ambulant polysomnographic (PSG) recording. The sleep recordings were scored, according to the new sleep-scoring standard from the American Academy of Sleep Medicine, by two independent sleep specialists. A follow-up analysis of the scoring consensus between the two specialists has been conducted. Based on the agreement of the two manual scorings, a computerized algorithm has been attempted implemented. By analysing the REM and non-REM EMG activity, using advanced signal processing tools combined with a statistical classifier, it is possible to discriminate normal and abnormal EMG activity. Due to the small number of patients, the overall performance of the algorithm was calculated using the leave-one-out approach and benchmarked against a previously published computerized/visual method. RESULTS: Based on the available data and using optimal settings, it was possible to correctly classify PD subjects with RBD with 100% sensitivity, 100% specificity, which is an improvement compared to previous published studies. CONCLUSION: The overall result indicates the usefulness of a computerized scoring algorithm and may be a feasible way of reducing scoring time. Further enhancement on additional data, i.e. subjects with idiopathic RBD (iRBD) and PD without RBD, is needed to validate its robustness and the overall result.
UNLABELLED: Abnormal skeleton muscle activity during REM sleep is characterized as REM Behaviour Disorder (RBD), and may be an early marker for different neurodegenerative diseases. Early detection of RBD is therefore highly important, and in this ongoing study a semi-automatic method for RBD detection is proposed by analyzing the motor activity during sleep. METHOD: A total number of twelve patients have been involved in this study, six normal controls and six patients diagnosed with Parkinsons Disease (PD) with RBD. All subjects underwent at least one ambulant polysomnographic (PSG) recording. The sleep recordings were scored, according to the new sleep-scoring standard from the American Academy of Sleep Medicine, by two independent sleep specialists. A follow-up analysis of the scoring consensus between the two specialists has been conducted. Based on the agreement of the two manual scorings, a computerized algorithm has been attempted implemented. By analysing the REM and non-REM EMG activity, using advanced signal processing tools combined with a statistical classifier, it is possible to discriminate normal and abnormal EMG activity. Due to the small number of patients, the overall performance of the algorithm was calculated using the leave-one-out approach and benchmarked against a previously published computerized/visual method. RESULTS: Based on the available data and using optimal settings, it was possible to correctly classify PD subjects with RBD with 100% sensitivity, 100% specificity, which is an improvement compared to previous published studies. CONCLUSION: The overall result indicates the usefulness of a computerized scoring algorithm and may be a feasible way of reducing scoring time. Further enhancement on additional data, i.e. subjects with idiopathic RBD (iRBD) and PD without RBD, is needed to validate its robustness and the overall result.
Authors: Iva Milerska; Vaclav Kremen; Vaclav Gerla; Erik K St Louis; Lenka Lhotska Journal: Biomed Signal Process Control Date: 2019-03-07 Impact factor: 3.880
Authors: Jacqueline A Fairley; George Georgoulas; Nishant A Mehta; Alexander G Gray; Donald L Bliwise Journal: Biomed Signal Process Control Date: 2012-03-28 Impact factor: 3.880
Authors: Yves Dauvilliers; Carlos H Schenck; Ronald B Postuma; Alex Iranzo; Pierre-Herve Luppi; Giuseppe Plazzi; Jacques Montplaisir; Bradley Boeve Journal: Nat Rev Dis Primers Date: 2018-08-30 Impact factor: 52.329
Authors: C H Schenck; J Y Montplaisir; B Frauscher; B Hogl; J-F Gagnon; R Postuma; K Sonka; P Jennum; M Partinen; I Arnulf; V Cochen de Cock; Y Dauvilliers; P-H Luppi; A Heidbreder; G Mayer; F Sixel-Döring; C Trenkwalder; M Unger; P Young; Y K Wing; L Ferini-Strambi; R Ferri; G Plazzi; M Zucconi; Y Inoue; A Iranzo; J Santamaria; C Bassetti; J C Möller; B F Boeve; Y Y Lai; M Pavlova; C Saper; P Schmidt; J M Siegel; C Singer; E St Louis; A Videnovic; W Oertel Journal: Sleep Med Date: 2013-07-22 Impact factor: 4.842