Evaluation of Postural Sway in Post-stroke Patients by Dynamic Time Warping Clustering

Post-stroke complications are the second most frequent cause of death and the third leading cause of disability worldwide. The motor function of post-stroke patients is often assessed by measuring the postural sway in the patients during quiet standing, based on sway measures, such as sway area and...

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Autores principales:	Dongdong Li, Kohei Kaminishi, Ryosuke Chiba, Kaoru Takakusaki, Masahiko Mukaino, Jun Ota
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	clustering dynamic time-warping post-stroke postural sway standing posture Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/59dee14bcbab458e98ffd18ea4fc2af1
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id	oai:doaj.org-article:59dee14bcbab458e98ffd18ea4fc2af1
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spelling	oai:doaj.org-article:59dee14bcbab458e98ffd18ea4fc2af12021-12-03T07:19:04ZEvaluation of Postural Sway in Post-stroke Patients by Dynamic Time Warping Clustering1662-516110.3389/fnhum.2021.731677https://doaj.org/article/59dee14bcbab458e98ffd18ea4fc2af12021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnhum.2021.731677/fullhttps://doaj.org/toc/1662-5161Post-stroke complications are the second most frequent cause of death and the third leading cause of disability worldwide. The motor function of post-stroke patients is often assessed by measuring the postural sway in the patients during quiet standing, based on sway measures, such as sway area and velocity, which are obtained from temporal variations of the center of pressure. However, such approaches to establish a relationship between the sway measures and patients' demographic factors have hardly been successful (e.g., days after onset). This study instead evaluates the postural sway features of post-stroke patients using the clustering method of machine learning. First, we collected the stroke patients' multi-variable motion-capture standing-posture data and processed them into t s long data slots. Then, we clustered the t-s data slots into K cluster groups using the dynamic-time-warping partition-around-medoid (DTW-PAM) method. The DTW measures the similarity between two temporal sequences that may vary in speed, whereas PAM identifies the centroids for the DTW clustering method. Finally, we used a post-hoc test and found that the sway amplitudes of markers in the shoulder, hip, knee, and center-of-mass are more important than their sway frequencies. We separately plotted the marker amplitudes and frequencies in the medial-lateral direction during a 5-s data slot and found that the post-stroke patients' postural sway frequency lay within the bandwidth of 0.5–1.5 Hz. Additionally, with an increase in the onset days, the cluster index of cerebral hemorrhage patients gradually transits in a four-cluster solution. However, the cerebral infarction patients did not exhibit such pronounced transitions over time. Moreover, we found that the postural-sway amplitude increased in clusters 1, 3, and 4. However, the amplitude of cluster 2 did not follow this pattern, owing to age effects related to the postural sway changes with age. A rehabilitation doctor can utilize these findings as guidelines to direct the post-stroke patient training.Dongdong LiKohei KaminishiRyosuke ChibaKaoru TakakusakiMasahiko MukainoJun OtaFrontiers Media S.A.articleclusteringdynamic time-warpingpost-strokepostural swaystanding postureNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Human Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	clustering dynamic time-warping post-stroke postural sway standing posture Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	clustering dynamic time-warping post-stroke postural sway standing posture Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Dongdong Li Kohei Kaminishi Ryosuke Chiba Kaoru Takakusaki Masahiko Mukaino Jun Ota Evaluation of Postural Sway in Post-stroke Patients by Dynamic Time Warping Clustering
description	Post-stroke complications are the second most frequent cause of death and the third leading cause of disability worldwide. The motor function of post-stroke patients is often assessed by measuring the postural sway in the patients during quiet standing, based on sway measures, such as sway area and velocity, which are obtained from temporal variations of the center of pressure. However, such approaches to establish a relationship between the sway measures and patients' demographic factors have hardly been successful (e.g., days after onset). This study instead evaluates the postural sway features of post-stroke patients using the clustering method of machine learning. First, we collected the stroke patients' multi-variable motion-capture standing-posture data and processed them into t s long data slots. Then, we clustered the t-s data slots into K cluster groups using the dynamic-time-warping partition-around-medoid (DTW-PAM) method. The DTW measures the similarity between two temporal sequences that may vary in speed, whereas PAM identifies the centroids for the DTW clustering method. Finally, we used a post-hoc test and found that the sway amplitudes of markers in the shoulder, hip, knee, and center-of-mass are more important than their sway frequencies. We separately plotted the marker amplitudes and frequencies in the medial-lateral direction during a 5-s data slot and found that the post-stroke patients' postural sway frequency lay within the bandwidth of 0.5–1.5 Hz. Additionally, with an increase in the onset days, the cluster index of cerebral hemorrhage patients gradually transits in a four-cluster solution. However, the cerebral infarction patients did not exhibit such pronounced transitions over time. Moreover, we found that the postural-sway amplitude increased in clusters 1, 3, and 4. However, the amplitude of cluster 2 did not follow this pattern, owing to age effects related to the postural sway changes with age. A rehabilitation doctor can utilize these findings as guidelines to direct the post-stroke patient training.
format	article
author	Dongdong Li Kohei Kaminishi Ryosuke Chiba Kaoru Takakusaki Masahiko Mukaino Jun Ota
author_facet	Dongdong Li Kohei Kaminishi Ryosuke Chiba Kaoru Takakusaki Masahiko Mukaino Jun Ota
author_sort	Dongdong Li
title	Evaluation of Postural Sway in Post-stroke Patients by Dynamic Time Warping Clustering
title_short	Evaluation of Postural Sway in Post-stroke Patients by Dynamic Time Warping Clustering
title_full	Evaluation of Postural Sway in Post-stroke Patients by Dynamic Time Warping Clustering
title_fullStr	Evaluation of Postural Sway in Post-stroke Patients by Dynamic Time Warping Clustering
title_full_unstemmed	Evaluation of Postural Sway in Post-stroke Patients by Dynamic Time Warping Clustering
title_sort	evaluation of postural sway in post-stroke patients by dynamic time warping clustering
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/59dee14bcbab458e98ffd18ea4fc2af1
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Evaluation of Postural Sway in Post-stroke Patients by Dynamic Time Warping Clustering

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