Sleep medicine · 2018
Automatic detection of cataplexy.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Bartolini Ilaria, Pizza Fabio, Di Luzio Andrea, Neccia Giulia, Antelmi Elena, Vandi Stefano, Plazzi Giuseppe
Аннотация
OBJECTIVE: Although being the most specific symptom of narcolepsy type 1 (NT1), cataplexy is currently investigated by clinical interview only, with potential diagnostic pitfalls. Our study aimed at testing the accuracy of an automatic video detection of cataplexy in NT1 patients vs. non-cataplectic subjects undergoing a standardized test with emotional stimulation.
METHODS: Fifteen drug-naive NT1 patients and 15 age- and sex-balanced non-cataplectic subjects underwent a standardized video recording procedure including emotional stimulation causing laughter. Video recordings were visually inspected by human scorers to detect three typical cataplexy facial motor patterns (ptosis, mouth opening and head drop), and then analysed by SHIATSU (Semantic-based HIearchical Automatic Tagging of videos by Segmentation using cUts). Expert-based and automatic attack detection was compared in NT1 patients and non-cataplectic subjects.
RESULTS: All NT1 patients and none of the non-cataplectic subjects displayed cataplexy during emotional stimulation. Automatic detection correlated well with experts' assessments in NT1 with an overall accuracy of 81%. In non-cataplectic subjects, automatic detection falsely identified cataplexy in two out of 15 (13.3%) subjects who showed active eyes closure during intense laughter as a confounder with ptosis.
CONCLUSIONS: Automatic cataplexy detection by applying SHIATSU to a standardized test for video documentation of cataplexy is feasible, with an overall accuracy of 81% compared to human examiners. Further studies are warranted to enlarge the range of elementary motor patterns detected, analyse their temporal/spatial relations and quantify cataplexy for diagnostic purposes.
METHODS: Fifteen drug-naive NT1 patients and 15 age- and sex-balanced non-cataplectic subjects underwent a standardized video recording procedure including emotional stimulation causing laughter. Video recordings were visually inspected by human scorers to detect three typical cataplexy facial motor patterns (ptosis, mouth opening and head drop), and then analysed by SHIATSU (Semantic-based HIearchical Automatic Tagging of videos by Segmentation using cUts). Expert-based and automatic attack detection was compared in NT1 patients and non-cataplectic subjects.
RESULTS: All NT1 patients and none of the non-cataplectic subjects displayed cataplexy during emotional stimulation. Automatic detection correlated well with experts' assessments in NT1 with an overall accuracy of 81%. In non-cataplectic subjects, automatic detection falsely identified cataplexy in two out of 15 (13.3%) subjects who showed active eyes closure during intense laughter as a confounder with ptosis.
CONCLUSIONS: Automatic cataplexy detection by applying SHIATSU to a standardized test for video documentation of cataplexy is feasible, with an overall accuracy of 81% compared to human examiners. Further studies are warranted to enlarge the range of elementary motor patterns detected, analyse their temporal/spatial relations and quantify cataplexy for diagnostic purposes.