A Pain Ranking System Based on Physiological Parameters

• Main Authors: Jing-Jhao Ye, 葉青釗
• Other Authors: Chiung-Cheng Chuang
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/47831422752610821890

博士 === 中原大學 === 生物醫學工程研究所 === 105 === 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.