神经科医用传感器关键技术研究

医学的进步离不开检测技术的发展。为了辅助临床医生对患者进行早期筛查与诊断，实现精准评估、个体化治疗，就必须要把先进传感技术与临床实践结合起来。神经系统是一个高度耦合的系统，巨量的信息在神经系统中以电信号的形式迅速、精确、稳定地传递，控制着肌肉表现出各种动作与行为，也构建了各种初级、高级的认知功能。因此对于神经系统疾病的诊断与评估要坚持系统观念，既要关注电信号在大脑的起源，也要关注肌肉对电信号的的反应，还要关注外在的具体症状表现。

在对大脑和肌肉的评估中，生理电传感具有极高的时间分辨率；在对症状的评估中，毫米波雷达具有非接触、高精度的特点。本文基于ADS1299生理电采集芯片，设计并搭建了用于肌电信号检测与脑电信号检测的生理电传感器；基于A111毫米波雷达芯片，设计并搭建了一套用于检测与评估静止型震颤和姿势型震颤的多毫米波雷达传感系统。

随后，本文研究了这两种传感技术在神经科临床的实际应用。本文使用多毫米波雷达传感系统采集了帕金森震颤和原发性震颤数据，非病理性的身体抖动和呼吸引起的胸腔起伏会导致雷达信号基线漂移，本文使用经验模态分解法解析信号，通过频谱分析来筛选用于信号重构的分量，并在此基础上计算震颤频率和震颤幅度两个重要指标。该方法有望未来在神经科临床得到推广，用于患者震颤的定量评估。

传统的脑功能评估一般是对大脑的静态测量，经颅磁刺激同步脑电通过检测大脑对外界磁场刺激的动态响应来评估大脑功能。本文采集了认知障碍患者与正常对照的经颅磁刺激同步脑电数据并进行了预处理，在保留脑电时空特征的基础上，对经颅磁刺激诱发电位进行特征工程，然后使用机器学习实现了对认知障碍患者与健康对照的有效分类，最后从数据统计和神经病理学两个角度讨论了特征选择对于分类结果的影响。该方法提供了认知障碍的潜在神经生物标记物，未来有望应用于认知障碍患者的辅助诊断。

The progress of medicine is inseparable from the development of sensing technology. It is necessary to combine advanced sensing technology with clinical practice in order to assist clinicians in early screening and diagnosis, accurate assessment and individualized treatment. The nervous system is a highly coupled system, a huge amount of information is transmitted quickly, accurately and stably in this complex system, which controls the external behavior of the human body and also forms various primary and advanced cognitive functions. Therefore, the diagnosis of nervous system diseases must adhere to the systematic concept. It is necessary to pay attention not only to the origin of electrical signals in the brain, but also to the response of muscles, and to the specific external symptoms.

In the evaluation of the brain and muscles, physiological electricity has extremely high temporal resolution. In the evaluation of symptoms, millimeter-wave radar has the characteristics of non-contact and high precision. Based on the ADS1299 chip, this paper designed and built physiological electrical sensors for electromyography (EMG) detection and electroencephalography (EEG) detection. Based on the A111 millimeter-wave radar chip, this paper designed and built a multi millimeter-wave radar sensing system for detecting and evaluating resting tremor and postural tremor.

Subsequently, this paper investigated the practical application of these two sensing technologies in neurology. This paper used the multi millimeter-wave radar sensing system to collect Parkinson's tremor and essential tremor data. Non-pathological body shaking and thoracic undulation caused by breathing can lead to baseline drift of the radar signal, the empirical mode decomposition method was used to analyze the signal, the components used for signal reconstruction were selected by spectrum analysis. Finally, the tremor frequency and tremor amplitude were calculated on this basis. This method is expected to be promoted in neurology for quantitative assessment of tremor.

Traditional brain function assessments are generally static measurements of the brain, but transcranial magnetic stimulation (TMS) synchronized EEG evaluates brain by detecting the brain's dynamic response to external magnetic field. In this paper, the TMS-EEG of patients with cognitive impairment and healthy controls were collected and preprocessed. On the premise of preserving the temporal and spatial features of EEG, feature engineering was carried out on the transcranial magnetic stimulation evoked potentials. Then, effective classification of cognitive impairment and healthy controls achieved by machine learning. Finally, we discussed the impact of features selection on classification results from the perspectives of data statistics and neuropathology. This method provides potential neural biomarkers of cognitive impairment. It is expected to be used in the auxiliary diagnosis of cognitive impairment in the future.

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