Deep Learning-Based Evaluation of Postural Control Impairments Caused by Stroke Under Altered Sensory Conditions.

IF 3.9 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Armin Najipour, Siamak Khorramymehr, Mehdi Razeghi, Kamran Hassani
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引用次数: 0

Abstract

Accurate and timely detection of postural control impairments in stroke patients is crucial for effective rehabilitation and fall prevention. Traditional clinical assessments often rely on qualitative observation and handcrafted features, which may fail to capture the nonlinear and uncertain nature of postural deficits. This study addresses these limitations by introducing a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) with Type-2 fuzzy logic activation to robustly classify sensory dysfunction under altered balance conditions. Using an EquiTest-derived dataset of 8316 labeled samples from 700 participants across six standardized sensory manipulation scenarios, the proposed method achieved 97% accuracy, 96% precision, 97% sensitivity, and 96% specificity, outperforming conventional CNN and other baseline classifiers. The approach demonstrated resilience to measurement noise down to 1 dB SNR, confirming its robustness in realistic clinical environments. These results suggest that the proposed system can serve as a practical, non-invasive tool for clinical diagnosis and personalized rehabilitation planning, supporting data-driven decision-making in stroke care.

Abstract Image

Abstract Image

Abstract Image

基于深度学习的感觉条件改变下中风引起的姿势控制障碍评估。
准确、及时地发现脑卒中患者的姿势控制障碍对于有效的康复和预防跌倒至关重要。传统的临床评估往往依赖于定性观察和手工特征,这可能无法捕捉到姿势缺陷的非线性和不确定性。本研究通过引入混合深度学习框架解决了这些限制,该框架集成了卷积神经网络(cnn)和2型模糊逻辑激活,以鲁棒分类改变平衡条件下的感觉功能障碍。使用equitest衍生的数据集,包括来自700名参与者的8316个标记样本,涵盖6种标准化感官操作场景,所提出的方法达到了97%的准确度、96%的精密度、97%的灵敏度和96%的特异性,优于传统的CNN和其他基线分类器。该方法显示了对低至1 dB信噪比的测量噪声的弹性,证实了其在现实临床环境中的稳健性。这些结果表明,所提出的系统可以作为临床诊断和个性化康复计划的实用、无创工具,支持数据驱动的脑卒中护理决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
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