帕金森病动作震颤的监督学习模型检测。

Minglong Sun, Woosub Jung, Kenneth Koltermann, Gang Zhou, Amanda Watson, Ginamari Blackwell, Noah Helm, Leslie Cloud, Ingrid Pretzer-Aboff
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引用次数: 0

摘要

帕金森病(PD)患者在疾病的不同阶段有多种症状,如步态冻结、手抖、言语困难和平衡问题。在这些症状中,手抖出现在疾病的各个阶段。帕金森病手抖具有严重后果,并对帕金森病患者的日常生活质量产生负面影响。研究人员提出了各种可穿戴设备来减轻帕金森氏症的震颤。然而,这些设备需要精确的震颤检测技术才能在震颤发生时有效工作。本文介绍了一种从常规活动中识别局部放电震颤的局部放电动作震颤检测方法。我们使用了30名手腕上戴着加速度计和陀螺仪传感器的帕金森病患者的数据集。我们选择了时域和频域手工制作的功能。此外,我们将我们手工制作的特征与现有的CNN数据驱动特征进行了比较,并且在使用t-SNE工具的二维特征可视化中,我们的特征具有更具体的边界。我们将我们的特征输入到多个监督机器学习模型中,包括逻辑回归(LR)、K近邻(KNN)、支持向量机(SVM)和卷积神经网络(CNNs),用于检测PD动作震颤。这些模型用30名帕金森病患者的数据进行了评估。使用我们的功能的所有模型的性能在五倍交叉验证中具有90%以上的F1分数,在遗漏一项评估中具有88%的F1分数。具体而言,支持向量机(SVM)在五次交叉验证中表现最好,F1得分超过92%。SVM在排除一项评估中也表现出最好的表现,F1得分超过90%。
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Parkinson's Disease Action Tremor Detection with Supervised-Leaning Models.

People with Parkinson's Disease (PD) have multiple symptoms, such as freezing of gait (FoG), hand tremors, speech difficulties, and balance issues, in different stages of the disease. Among these symptoms, hand tremors are present across all stages of the disease. PD hand tremors have critical consequences and negatively impact the quality of PD patients' everyday lives. Researchers have proposed a variety of wearable devices to mitigate PD tremors. However, these devices require accurate tremor detection technology to work effectively while the tremor occurs. This paper introduces a PD action tremor detection method to recognize PD tremors from regular activities. We used a dataset from 30 PD patients wearing accelerometers and gyroscope sensors on their wrists. We selected time-domain and frequency-domain hand-crafted features. Also, we compared our hand-crafted features with existing CNN data-driven features, and our features have more specific boundaries in 2-D feature visualization using the t-SNE tool. We fed our features into multiple supervised machine learning models, including Logistic Regression (LR), K-Nearest Neighbours (KNNs), Support Vector Machines (SVMs), and Convolutional Neural Networks (CNNs), for detecting PD action tremors. These models were evaluated with 30 PD patients' data. The performance of all models using our features has more than 90% of F1 scores in five-fold cross-validations and 88% F1 scores in the leave-one-out evaluation. Specifically, Support Vector Machines (SVMs) perform the best in five-fold cross-validation with over 92% F1 scores. SVMs also show the best performance in the leave-one-out evaluation with over 90% F1 scores.

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ToPick: Time-of-Pickup Measurement for the Elderly using Wearables. Gait-Guard: Turn-aware Freezing of Gait Detection for Non-intrusive Intervention Systems. HealthGAT: Node Classifications in Electronic Health Records using Graph Attention Networks. FoG-Finder: Real-time Freezing of Gait Detection and Treatment. Parkinson's Disease Action Tremor Detection with Supervised-Leaning Models.
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