标题: 慢性疼痛心电图生理讯号资料串流深勘技术:即时追踪与疼痛临床关联
Data Stream Mining Technology for ECG Signals of Chronic Pain: Real-Time Tracking and Clinical Correlation
作者: 连培中
林进灯
Hiah, Pier Juhng
Lin, Chin-Teng
电机资讯国际学程
关键字: 心率变异性;慢性疼痛;实时流式传输和分析;Heart Rate Variability (HRV);Chronic Pain;Real-time Streaming and Analyzing
公开日期: 2017
摘要: 疼痛是一种主观经验,只能通过自我报告来做衡量。因此,评估及追踪慢性疼痛治疗的进展是具有挑战性的。心电图(ECG)已被证明是个有潜力的慢性疼痛生理标志物。在过去,有研究证明心率变异性(HRV)与不同类型的疼痛以及疼痛感觉有相互关系。为了识别出HRV指数与慢性疼痛之间的关系,本研究收集了治疗前后慢性偏头痛和纤维肌痛患者在静息状态下的心电图数据及主观疼痛严重程度。此外,健康受试者的静息ECG数据也被收录以进行对照比较。根据时间,频率和非线性分析的结果显示,慢性疼痛患者的HRV通常低于健康对照受试者的HRV。此外,在治疗有效组中发现慢性疼痛患者的HRV在治疗后有显着地增加,表明了HRV在治疗效果上是个有效的生物标志物。在10个HRV指数中,非线性庞加莱图分析 (Poincaré plot analysis) 是监测疼痛严重性以及判断治疗效果表现最好的HRV指数。最后,本研究同时也开发了用于实时流式传输和分析多模态数据的数据流挖掘平台。未来,该平台可以用作于辅助慢性疼痛生物反馈的治疗。
Evaluating and tracking the progress of treatment for chronic pain is challenging because pain is a subjective experience and can be measured only by self-report. Electrocardiography (ECG) has been proven to be a promising source of physiological biomarkers for chronic pain. Previous studies had demonstrated that heart rate variability (HRV) could be associated with different types of pain and also pain perception. This study aims to identify the relationship between HRV indices and chronic pain through collecting resting ECG data and subjective pain severity from patients with chronic migraine and fibromyalgia before and after treatments. In addition, resting ECG data from healthy controls were also collected for comparison. The results derived from time, frequency, and non-linear analyses showed that the HRV of chronic patients were generally lower than that of healthy control subjects. Besides, the HRV of the chronic pain patients in the responder group significantly increased after the medical treatment, indicating that a useful biomarker of the treatment efficacy. Among 10 HRV indices, the non-linear Poincaré plot analysis is a promising HRV indices in monitoring pain severity as well as determining treatment efficacy. Finally, a data stream mining platform was developed for real-time streaming and analyzing of multimodal data. This platform is presented such that they can be used as an aid for biofeedback treatment of chronic pain in the future.
URI: http://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070360834
http://hdl.handle.net/11536/140345
显示于类别:Thesis