Convolutional neural network based detection of early stage Parkinson's disease using the six minute walk test

被引：3

作者：

Choi, Hyejin ^{[1
]}

Youm, Changhong ^{[1
]}

Park, Hwayoung ^{[2
]}

Kim, Bohyun ^{[1
]}

Hwang, Juseon ^{[1
]}

Cheon, Sang-Myung ^{[3
]}

Shin, Sungtae ^{[4
]}

机构：

[1] Dong A Univ, Grad Sch, Dept Hlth Sci, Busan, South Korea

[2] Dong A Univ, Biomech Lab, Busan, South Korea

[3] Dong A Univ, Sch Med, Dept Neurol, Busan, South Korea

[4] Dong A Univ, Coll Engn, Dept Mech Engn, Busan, South Korea

来源：

SCIENTIFIC REPORTS | 2024年 / 14卷 / 01期

基金：

新加坡国家研究基金会;

关键词：

Parkinson's disease; Detection; Artificial intelligence; Deep learning; Convolutional neural network; Six-minute walk test; CLASSIFICATION; DIAGNOSIS; MOVEMENT; PEOPLE;

D O I：

10.1038/s41598-024-72648-w

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The heterogeneity of Parkinson's disease (PD) presents considerable challenges for accurate diagnosis, particularly during early-stage disease, when the symptoms may be extremely subtle. This study aimed to assess the accuracy of a convolutional neural network (CNN) technique based on the 6-min walk test (6MWT) measured using wearable sensors to distinguish patients with early-stage PD (n = 78) from healthy controls (n = 50). The participants wore six sensors, and performed the 6MWT. The time-series data were converted into new images. The results revealed that the gyroscopic vertical component of the lumbar spine displayed the highest classification accuracy of 83.5%, followed by those of the thoracic spine (83.1%) and right thigh (79.5%) segment. These findings suggest that the 6MWT and CNN models may facilitate earlier diagnosis and monitoring of PD symptoms, enabling clinicians to provide timely treatment during the critical transition from normal to pathologic gait patterns.

引用

页数：15

共 70 条

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