以生理訊號為基礎建構疼痛程度辨識系統

A Pain Ranking System Based on Physiological Parameters

葉青釗(Jing-Jhao Ye)

指導教授 ： 莊炯承

中原大學/工學院/生物醫學工程研究所/博士(2017年)

https://doi.org/10.6840/cycu201700651

摘要

疼痛是個非常普遍且日益重要的問題。2001 年，美國醫療機構評鑑聯合會(Joint Commission on Accreditation of Healthcare Organizations, JCAHO)已將「疼痛評估」列為新的評鑑項目，並強調「疼痛」在臨床上應被視為除了血壓、呼吸、脈搏與體溫之外的第五生命徵象，必須隨時隨地加以監測並記錄其變化；世界衛生組織(World Health Organization, WHO)也在1990年提出了一套三階梯式的疼痛治療法。近年來，疼痛的控制被認為是基本權利，進而使疼痛照顧意識逐漸在全球醫療上抬頭，疼痛評估及處置也愈來愈受醫院重視，甚至成為國內外醫院評鑑的項目之一。目前市面上已發展出一套傷害感受程度指標(nociception level index, NoL)系統，可連續監測患者在麻醉狀態下的傷害感受程度。然而，就目前資料顯示，此套系統仍只適用於麻醉狀態下，似乎仍無法適用在一般的疼痛患者身上。因此，針對一般疼痛患者而言，疼痛評估方式主要仍以主觀式的刻度量表或問卷為主。這些評估方法只能記錄患者當下的疼痛狀況，無法達到連續監測的效果，也可能因為患者無法理解問卷問題或醫護人員與患者的主觀意識不同而導致評估準確度不足。因此，如何準確的進行疼痛監測與評估仍是疼痛醫療的重要前提。
根據疼痛生理背景的相關訊息，可得知疼痛會影響我們的交感神經與副交感神經活性，進而反映在人體的生理表現上，例如：心跳、血壓、呼吸、汗腺分泌…等。先前研究也指出心率變異度(Heart rate variability, HRV)可用於評估交感與副交感神經活性並被認為與疼痛有關；光體積描記圖(Photoplethysmography, PPG)則可分析血液體積變化並能反映出傷害性刺激的產生，從PPG訊號中也可分析出脈率變異度(Pulse rate variability, PRV)。另，根據 R. Treister et al.研究指出結合多種生理參數可以更有效地應用於疼痛評估上。因此，本研究之目的是從上述那些生理參數中，找出能適當反映出疼痛產生與舒緩之相關參數，並針對這些參數來建構出一套即時分析系統，藉由參數的變化來反映出疼痛強度變化用於疼痛監測上。
本研究於2013年，先是自製出一套心電圖(electrocardiogram, ECG)與PPG訊號的量測裝置，並以LabVIEW作為參數演算工具。與台北市立萬芳醫院以及中壢天晟醫院合作，針對慢性疼痛患者來收集相關數據，比較不同疼痛程度下之HRV參數與PPG參數之差異；也在本實驗室建構出一套急性疼痛刺激系統，針對健康自願者進行試驗，藉此取得疼痛產生與舒緩時相關生理參數變化之數據，進而藉由這些數據找出可明顯反映出疼痛刺激與舒緩之參數。此外，本研究認為若能確定PRV可取代HRV應用於疼痛監測上，則能增加即時分析系統的便利性，因此我們也針對PRV與HRV間的相關性進行探討。結果顯示，在疼痛產生與舒緩時，HRV參數中的HR、RRI、HF以及LF有顯著且相對的變化，而PRV參數也與HRV參數呈現高度相關；然而，無論是在疼痛產生狀態或舒緩過程，PPG參數皆產生相同的變化趨勢。因此，本研究認為可針對PRV中的HR、PPI、HF以及LF來進行即時分析應用於疼痛監測上。
本研究以微控制系統結合微型光感測元件 (樹梅派3 (Raspberry Pi 3) 以及MAX 30101 micro LED sensor)做為系統，藉由內建的python 2.7程式語言來進行PRV參數(HR、PPI、HF以及LF)即時分析程式撰寫並與原型系統以及Matlab程式來進行驗證。經驗證結果可知本系統的PRV參數計算結果與原型系統以及Matlab內建程式相同，確認此系統可以每30秒為單位，正確的分析出PRV參數。如此便能達到連續生理參數監測，用以反映疼痛強度變化，也能提供一種疼痛評估的新視角來協助臨床醫師進行疼痛評估，提高評估之準確度。

關鍵字

疼痛監測 ； 心率變異度 ； 光體積描記圖 ； 脈率變異度 ； 即時分析系統

並列摘要

Pain is a prominent problem and has proved to be increasingly important. In 2001, the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) declared “pain assessment” to be a new assessment program, and emphasized that “pain” should be considered as one of the five vital signs along with blood pressure, respiration, pulse, and body temperature, and that it must be monitored and recorded. The World Health Organization (WHO) advanced a three-step ladder for pain therapy in 1990. In recent years, pain control is being considered as a basic right, and the awareness of pain care has gradually risen among the global medical community, and has even become an evaluation project. A nociception level index (NoL) system has been developed to continuously monitor the nociceptive response in patients under anesthesia. However, as NoL system is only applicable to patients under anesthesia, it cannot be used to evaluate pain in conscious patients. In the clinical setting, subjective scale or questionnaires are the major methods of pain assessment for general pain patients. These assessment methods are only suitable for recording the status of the patient’s pain at a particular moment. They cannot monitor the pain continuously, and may lack accuracy if the patient cannot understand the questionnaire or if the physician and patient have different subjective awareness. Therefore, the accurate assessment and monitor of pain is an important prerequisite for pain care.
According to pain physiology, pain affects the sympathetic (SNS) and parasympathetic (PNS) nervous activities, and is reflected in the body’s physiological performance, such as heartbeat, blood pressure, breathing, sweat gland secretion, etc. Moreover, previous studies have pointed out that the heart rate variability (HRV) may be used to assess the neurological activity of SNS and PNS, which could be related with pain. Photoplethysmography (PPG) can detect nociceptive stimuli and analyze the pulse rate variability (PRV) which may be related to HRV. In addition, R. Treister et al. indicated that a combination of multiple physiological parameters could be more effective in assessing pain. Therefore, the purpose of this study was to establish a real-time physiological system to reflect variability in pain intensity that could be used as a pain monitoring. We selected suitable parameters that could accurately reflect fluctuations in pain form the HRV and PPG parameters, and used them to create a real-time pain-monitoring system.
In 2013, a prototype system based on electrocardiogram (ECG) and PPG signal measurement was developed, and analyzed with LabVIEW. We collaborated with the Taipei Municipal Wanfang Hospital and the Ten-Chen Medical Group to collect data on patients with chronic pain, and compared these parameters between different pain intensities (between pre- and post-pain therapy). Additionally, an acute pain stimulation system was developed for testing healthy volunteers to find parameters that could indicate pain production and relief. We also suggested that the real-time analysis system may be more convenient if HRV parameters were replaced by PRV for pain monitoring. Therefore, we also explored the correlation between PRV and HRV. The results showed that the heart rate (HR), R-R interval (RRI), high-frequency spectrum (HF), and low-frequency spectrum (LF) of HRV had significant and opposite changes when pain was induced and relieved. The PRV parameters showed a high correlation with the HRV parameters; however, in both the pain-producing and relief states, the change of PPG parameters had the same trend.
Based on above results, a real-time analysis system was established using a micro-control system (Raspberry Pi 3) along with a micro-LED sensing element (MAX 30101) to calculate PRV parameters (HR, PPI, HF, and LF) using the Python 2.7. We verified the programing of our real-time analysis system using the time domain of HRV calculation in our prototype system and the built-in FFT in MATLAB. Verification results confirmed that the system could accurately calculate the PRV parameters every of 30 sec. Therefore, continuous PRV parameter analysis can be used to reflect changes in pain intensity of patients with consciousness. We expect that the production of this system will enable real-time pain monitoring in clinical settings, thus helping physicians to assess pain more effectively.

並列關鍵字

Pain monitoring ； Heart rate variability ； Photoplethysmography ； Pulse rate variability ； Real-time analysis system

參考文獻

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