慢性疼痛心電圖生理訊號資料串流深勘技術：即時追踪與疼痛臨床關聯

Abstract

疼痛是一種主觀經驗，只能通過自我報告來做衡量。因此，評估及追踪慢性疼痛治療的進展是具有挑戰性的。心電圖（ECG）已被證明是個有潛力的慢性疼痛生理標誌物。在過去，有研究證明心率變異性（HRV）與不同類型的疼痛以及疼痛感覺有相互關係。為了識別出HRV指數與慢性疼痛之間的關係，本研究收集了治療前後慢性偏頭痛和纖維肌痛患者在靜息狀態下的心電圖數據及主觀疼痛嚴重程度。此外，健康受試者的靜息ECG數據也被收錄以進行對照比較。根據時間，頻率和非線性分析的結果顯示，慢性疼痛患者的HRV通常低於健康對照受試者的HRV。此外，在治療有效組中發現慢性疼痛患者的HRV在治療後有顯著地增加，表明了HRV在治療效果上是個有效的生物標誌物。在10個HRV指數中，非線性龐加萊圖分析 (Poincaré plot analysis) 是監測疼痛嚴重性以及判斷治療效果表現最好的HRV指數。最後，本研究同時也開發了用於實時流式傳輸和分析多模態數據的數據流挖掘平台。未來，該平台可以用作於輔助慢性疼痛生物反饋的治療。

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.