基于外侧腓肠肌sEMG的帕金森神经调控步态量化测评方法

PDF下载HTML

Title:

A quantitative gait assessment method based on lateral gastrocnemius sEMG for neuromodulation of Parkinson’s disease

作者:

孙玉波^1,2, 李海涛³, 舒智林^1,2, 于洋⁴, 韩建达^1,2, 梁思泉³, 于宁波^1,2

1. 南开大学 人工智能学院, 天津 300350;
2. 南开大学 天津市智能机器人技术重点实验室, 天津 300350;
3. 天津市环湖医院 神经外科, 天津 300350;
4. 天津市环湖医院 康复医学科, 天津 300350

Author(s):

SUN Yubo^1,2, LI Haitao³, SHU Zhilin^1,2, YU Yang⁴, HAN Jianda^1,2, LIANG Siquan³, YU Ningbo^1,2

1. College of Artificial Intelligence, Nankai University, Tianjin 300350, China;
2. Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China;
3. Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin 300350, China;
4. Department of Rehabilitation Medicine, Tianjin Huanhu Hospital, Tianjin 300350, China

关键词:

脑深部电刺激; 帕金森病; 量化测评; 步态变异性; 表面肌电信号; 神经激活度; 肌肉激活度; 足底压力

Keywords:

deep brain stimulation; Parkinson’s disease; quantitative assessment; gait variability; sEMG; neural activation; muscle activation; plantar-pressure

分类号:

TP249;TP274

DOI:

10.11992/tis.202103045

摘要:

帕金森(Parkinson’s disease, PD)患者脑深部电刺激(deep brain stimulation, DBS)神经调控治疗迫切需要客观、量化的测评手段和分析方法。针对这一临床需求，本文提出一种基于外侧腓肠肌(lateral gastrocnemius, LG)表面肌电(surface electromyogram, sEMG)信号的步态量化测评方法。采用平滑先验算法去除肌电信号中的低频趋势并保留帕金森临床症状相关信息，通过小波变换检测足跟击地时刻，识别步态周期并对肌电信号进行划分，进而计算神经激活度、肌肉激活度及其变异性指标。设计一个肌电和足底压力数据无线同步采集的步态量化测评系统，取得伦理许可，针对接受DBS治疗的PD患者开展临床研究。结果表明，本文方法和系统具有良好的临床应用可行性，所提指标与传统步态周期时间变异性指标具有较高的一致性、从而验证了其有效性，且所提指标具有更高的区分度。

Abstract:

Deep brain stimulation (DBS) is proven to be an effective neuromodulation treatment method for Parkinson’s disease (PD), and there is an urgent need for objective and quantitative assessment during neuromodulation treatment. In this work, a gait assessment method based on lateral gastrocnemius (LG) surface electromyogram (sEMG) signals is designed. The low-frequency trend in sEMG signals is removed by a smooth a priori algorithm, while important information related to PD motion abnormality is retained. The wavelet transform analysis is performed to detect heel strike and recognize gait cycle, and the sEMG data can be accordingly segmented. Then, the variability indices on neural activation and muscle activation are extracted. A gait quantitative assessment system is established, using wireless wearable equipments to synchronously acquire the sEMG and plantar pressure data. Ethical approval is granted and clinical study is conducted on PD patients treated with DBS. The proposed indices have high consistency with the traditional gait cycle time variability index, verifying effectiveness of the proposed method. What’s more, the proposed sEMG variability indices show higher discrimination capability.