Computers in biology and medicine · 2025
Explainable machine learning for movement disorders - Classification of tremor and myoclonus.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
van den Brandhof Elina L, Tuitert Inge, Madelein van der Stouwe A M, Elting Jan W J, Dalenberg Jelle R, Svorenova Tatiana, Klamer Marrit R, Marapin Ramesh S, Biehl Michael, Tijssen Marina A J
Аннотация
BACKGROUND: Treatment for essential tremor (ET) and cortical myoclonus (CM) differs. As their clinical distinction can be difficult, with large inter- and intra-observer variability, there is a need for additional diagnostic tools.
OBJECTIVES: We aim to develop a machine learning based approach using accelerometry data to support clinicians in their diagnosis of ET versus CM.
METHODS: We recorded the upper body movements of 19 ET and 19 CM patients performing 21 tasks using eight accelerometry sensors. The phenotype was classified by applying an explainable machine learning method called generalized matrix learning vector quantization (GMLVQ) to the power spectrum derived from the accelerometry recordings. In addition to the general classification performance, GMLVQ provides context for the decisions made. This includes the relevance of frequencies and thus the identification of distinct patterns in the phenotypes.
RESULTS: For all postures and dynamic tasks, excellent classification results were reached with AUROC approaching 1,0. For the classification, GMLVQ considers the overall shape of the power spectrum, whereby the frequencies 5-7 Hz as well as 3-4 and 9-10 Hz are of particular importance. These frequencies correspond to the tremor peak and local minima that are also observed as characteristics in the literature.
CONCLUSION: The excellent classification results provide a proof of concept for the discrimination of ET and CM by applying a GMLVQ machine learning analysis to power spectra derived from accelerometry recordings. Such a system could support clinicians to improve the diagnostic accuracy.
OBJECTIVES: We aim to develop a machine learning based approach using accelerometry data to support clinicians in their diagnosis of ET versus CM.
METHODS: We recorded the upper body movements of 19 ET and 19 CM patients performing 21 tasks using eight accelerometry sensors. The phenotype was classified by applying an explainable machine learning method called generalized matrix learning vector quantization (GMLVQ) to the power spectrum derived from the accelerometry recordings. In addition to the general classification performance, GMLVQ provides context for the decisions made. This includes the relevance of frequencies and thus the identification of distinct patterns in the phenotypes.
RESULTS: For all postures and dynamic tasks, excellent classification results were reached with AUROC approaching 1,0. For the classification, GMLVQ considers the overall shape of the power spectrum, whereby the frequencies 5-7 Hz as well as 3-4 and 9-10 Hz are of particular importance. These frequencies correspond to the tremor peak and local minima that are also observed as characteristics in the literature.
CONCLUSION: The excellent classification results provide a proof of concept for the discrimination of ET and CM by applying a GMLVQ machine learning analysis to power spectra derived from accelerometry recordings. Such a system could support clinicians to improve the diagnostic accuracy.