Evaluation of deep convolutional neural networks for detection of freezing of gait in Parkinson's disease patients. (September 2018)

• Record Type: Journal Article
• Authors: Xia, Yi; Zhang, Jun; Ye, Qiang; Cheng, Nan; Lu, Yixiang; Zhang, Dexiang
• Is Part Of: Biomedical signal processing and control. Volume 46(2018)
• Page Start: 221
• Page End: 230
• Publication Date: 2018-09
• Subjects: Freezing of gait (FOG) -- Parkinson's disease (PD) -- Gait classification -- Convolution neural network -- Acceleration signal; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.07.015
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A CNN-based end-to-end classifier for detection of freezing of gait is proposed. The discriminative features are learned directly from multiple 1D time series. Two different feature fusion schemes are designed and compared. More than 99% accuracy was obtained in the patient-dependent setting. An average of 80.70% accuracy was obtained in the patient -independent setting. Abstract: Background and objective: Freezing of gait (FOG) is a symptom that manifests as an episodic inability to move. It happens typically in patients with advanced Parkinson's disease (PD), and it is a common cause of falls in PD patients. The management of FOG is extremely difficult due to its sudden and transient property. Methods: In this study, we implemented a novel FOG detection system that was based on deep convolutional neural network (CNN). By taking data segments from 1-dimensional (1D) acceleration signals as its inputs, the proposed CNN-based approach can realize automatic feature learning and discrimination of FOG events from normal walking in a streamline manner. By this way, it can remove the need for extracting hand-crafted features and the time-consuming feature selection. The proposed method was tested on a dataset comprised of more than eight hours of recorded lab data from 10 PD patients that experience FOG in their daily life. Results: The final system achieved more than 99% classification accuracy in a patient-dependent setting, and an average of 80.70% accuracy in the patient -independent setting. The time for classification of a 4 s data segment is only 3.6 ms without the acceleration of graphics processing unit (GPU). Conclusions: These results indicate that the proposed CNN-based system can provide satisfactory effectivity and efficiency in detecting FOG gaits if used suitably and can be beneficial to realize an accurate monitoring and gait assistance during daily living and rehabilitation therapy.