PRISME Lab, University of Orléans, Chartres, France.
Department of Biomechanical Engineering, Vilnius Gediminas Technical University, Vilnius, Lithuania.
Technol Health Care. 2023;31(6):2447-2455. doi: 10.3233/THC-235010.
Parkinson's disease (PD) is a chronic neurodegenerative disorder characterized by motor impairments and various other symptoms. Early and accurate classification of PD patients is crucial for timely intervention and personalized treatment. Inertial measurement units (IMUs) have emerged as a promising tool for gathering movement data and aiding in PD classification.
This paper proposes a Convolutional Wavelet Neural Network (CWNN) approach for PD classification using IMU data. CWNNs have emerged as effective models for sensor data classification. The objective is to determine the optimal combination of wavelet transform and IMU data type that yields the highest classification accuracy for PD.
The proposed CWNN architecture integrates convolutional neural networks and wavelet neural networks to capture spatial and temporal dependencies in IMU data. Different wavelet functions, such as Morlet, Mexican Hat, and Gaussian, are employed in the continuous wavelet transform (CWT) step. The CWNN is trained and evaluated using various combinations of accelerometer data, gyroscope data, and fusion data.
Extensive experiments are conducted using a comprehensive dataset of IMU data collected from individuals with and without PD. The performance of the proposed CWNN is evaluated in terms of classification accuracy, precision, recall, and F1-score. The results demonstrate the impact of different wavelet functions and IMU data types on PD classification performance, revealing that the combination of Morlet wavelet function and IMU data fusion achieves the highest accuracy.
The findings highlight the significance of combining CWT with IMU data fusion for PD classification using CWNNs. The integration of CWT-based feature extraction and the fusion of IMU data from multiple sensors enhance the representation of PD-related patterns, leading to improved classification accuracy. This research provides valuable insights into the potential of CWT and IMU data fusion for advancing PD classification models, enabling more accurate and reliable diagnosis.
帕金森病（PD）是一种慢性神经退行性疾病，其特征为运动障碍和多种其他症状。对 PD 患者进行早期且准确的分类对于及时干预和个性化治疗至关重要。惯性测量单元（IMU）已成为一种很有前途的工具，可以收集运动数据并辅助 PD 分类。
本文提出了一种使用 IMU 数据的卷积小波神经网络（CWNN）方法来对 PD 进行分类。CWNN 已成为传感器数据分类的有效模型。其目的是确定在对 PD 进行分类时，能够产生最高分类准确性的最优小波变换和 IMU 数据类型组合。
所提出的 CWNN 架构集成了卷积神经网络和小波神经网络，以捕获 IMU 数据中的空间和时间依赖性。在连续小波变换（CWT）步骤中采用不同的小波函数，例如 Morlet、墨西哥帽和高斯。使用来自 PD 患者和非 PD 患者的综合 IMU 数据对 CWNN 进行训练和评估。
通过使用从 PD 患者和非 PD 患者收集的综合 IMU 数据集进行了广泛的实验。使用分类准确率、精度、召回率和 F1 分数来评估所提出的 CWNN 的性能。结果表明，不同的小波函数和 IMU 数据类型对 PD 分类性能有影响，表明 Morlet 小波函数和 IMU 数据融合的组合实现了最高的准确性。
研究结果强调了结合 CWT 和 IMU 数据融合来使用 CWNN 对 PD 进行分类的重要性。基于 CWT 的特征提取与来自多个传感器的 IMU 数据融合相结合，增强了与 PD 相关模式的表示，从而提高了分类准确性。这项研究为 CWT 和 IMU 数据融合在推进 PD 分类模型方面的潜力提供了有价值的见解，使更准确和可靠的诊断成为可能。
