基于多模态原型学习框架的帕金森病检测中步态冻结分析。

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Madhuri Thimmapuram;Ananda Rao Akepogu;P. Radhika Raju
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引用次数: 0

摘要

严重帕金森病(PD)患者经常有步态冻结(FOG),一种步态残疾。通过在FOG发生之前预测到它,先发制人的提示可以防止FOG或减轻其严重程度和持续时间。为了提高FOG检测的准确性,人们正在越来越多地探索脑电图(EEG)数据和其他补充模式,如基于步态的数据。多模式数据的使用尤其重要,因为它通过结合疾病的不同视角,提高了检测模型的稳健性和准确性。近年来,深度学习算法在自动FOG识别方面得到了很多关注;然而,由于缺乏数据样本,特别是诸如脑电图等医疗数据,它们的用途受到限制。数据的稀缺性可能导致深度学习模型的过拟合,因此研究人员开发能够在有限数量的样本下有效运行的鲁棒分类模型至关重要。少量的学习方法,如原型学习,已经被引入,通过使模型能够有效地从少量的标记样本中学习来缓解这些挑战。因此,在本研究中,我们提出了一个名为CSE-ProtoNet的原型学习框架,该框架利用consenet和SEBlock对PD患者进行FOG检测。重要的是,我们的研究不仅将利用EEG数据,还将利用多模态输入,这可以提高PD检测的鲁棒性和准确性。该方法在准确率、F-score、召回率、特异性、精密度和AUC方面优于CSE-ProtoNet-ED、ProtoNet-CS和ProtoNet-ED等基线模型。CSE-ProtoNet模型也能区分FOG和Non-FOG患者,准确率为98.75%。进行了交叉数据验证,以确保所提出方法的鲁棒性和泛化性,确保在不同折叠间的性能一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Freezing of Gait in Parkinson’s Disease Detection Using a Multimodal Prototype Learning Framework
Patients with severe Parkinson’s disease (PD) frequently have freezing of gait (FOG), a gait disability. By anticipating FOG before it occurs, pre-emptive cueing can either prevent FOG or lessen its severity and duration. To improve the accuracy of FOG detection, both electroencephalography (EEG) data and other complementary modalities, such as gait-based data, are increasingly being explored. The use of multimodal data is particularly important, as it enhances the robustness and accuracy of the detection models by combining different perspectives of the disease. Deep learning algorithms got a lot of attention in recent years for automated FOG identification; however their usefulness has been restricted due to a shortage of data samples, particularly medical data such as EEG. The scarcity of data can lead to overfitting in deep learning models, making it crucial for researchers to develop robust classification models that can operate effectively with a limited number of samples. Few-shot learning methods, such as prototype learning, have been introduced to mitigate these challenges by enabling models to effectively learn from a modest number of labeled samples. Thus in this research, we propose a prototype learning framework called CSE-ProtoNet, which utilizes CondenseNet with SEBlock for FOG detection in PD patients. Importantly, our study will leverage not only EEG data but also multimodal inputs, which can enhance the robustness and accuracy of PD detection. The method outperforms baseline models such as CSE-ProtoNet-ED, ProtoNet-CS, and ProtoNet-ED in terms of accuracy, F-score, recall, specificity, precision and AUC. The CSE-ProtoNet model also differentiated patients with FOG and Non-FOG with an accuracy of 98.75%. Cross-data validation was conducted to ensure the robustness and generalizability of the proposed method, confirming consistent performance across different folds.
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来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
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