Khan Muhammad Umar, Sousani Maryam, Hirachan Niraj, Joseph Calvin, Ghahramani Maryam, Chetty Girija, Goecke Roland, Fernandez-Rojas Raul
Human-Centred Technology Research Centre, Faculty of Science and Technology, University of Canberra, Canberra, ACT 2617, Australia.
Sensors (Basel). 2024 Jan 11;24(2):0. doi: 10.3390/s24020458.
Assessing pain in non-verbal patients is challenging, often depending on clinical judgment which can be unreliable due to fluctuations in vital signs caused by underlying medical conditions. To date, there is a notable absence of objective diagnostic tests to aid healthcare practitioners in pain assessment, especially affecting critically-ill or advanced dementia patients. Neurophysiological information, i.e., functional near-infrared spectroscopy (fNIRS) or electroencephalogram (EEG), unveils the brain's active regions and patterns, revealing the neural mechanisms behind the experience and processing of pain. This study focuses on assessing pain via the analysis of fNIRS signals combined with machine learning, utilising multiple fNIRS measures including oxygenated haemoglobin (ΔHBO2) and deoxygenated haemoglobin (ΔHHB). Initially, a channel selection process filters out highly contaminated channels with high-frequency and high-amplitude artifacts from the 24-channel fNIRS data. The remaining channels are then preprocessed by applying a low-pass filter and common average referencing to remove cardio-respiratory artifacts and common gain noise, respectively. Subsequently, the preprocessed channels are averaged to create a single time series vector for both ΔHBO2 and ΔHHB measures. From each measure, ten statistical features are extracted and fusion occurs at the feature level, resulting in a fused feature vector. The most relevant features, selected using the Minimum Redundancy Maximum Relevance method, are passed to a Support Vector Machines classifier. Using leave-one-subject-out cross validation, the system achieved an accuracy of 68.51%±9.02% in a multi-class task (No Pain, Low Pain, and High Pain) using a fusion of ΔHBO2 and ΔHHB. These two measures collectively demonstrated superior performance compared to when they were used independently. This study contributes to the pursuit of an objective pain assessment and proposes a potential biomarker for human pain using fNIRS.
评估无语言表达能力患者的疼痛具有挑战性，通常依赖于临床判断，而由于潜在疾病导致生命体征波动，这种判断可能不可靠。迄今为止，明显缺乏客观的诊断测试来帮助医疗从业者进行疼痛评估，这尤其影响到重症或晚期痴呆患者。神经生理信息，即功能近红外光谱（fNIRS）或脑电图（EEG），揭示了大脑的活跃区域和模式，揭示了疼痛体验和处理背后的神经机制。本研究聚焦于通过结合机器学习分析fNIRS信号来评估疼痛，利用包括氧合血红蛋白（ΔHBO2）和脱氧血红蛋白（ΔHHB）在内的多种fNIRS测量方法。首先，通道选择过程从24通道fNIRS数据中滤除具有高频和高振幅伪迹的高度污染通道。然后，对其余通道应用低通滤波器和公共平均参考进行预处理，分别去除心肺伪迹和公共增益噪声。随后，对预处理后的通道进行平均，为ΔHBO2和ΔHHB测量创建单个时间序列向量。从每个测量中提取十个统计特征，并在特征级别进行融合，得到融合特征向量。使用最小冗余最大相关性方法选择的最相关特征被传递给支持向量机分类器。使用留一法交叉验证，该系统在使用ΔHBO2和ΔHHB融合的多类任务（无疼痛、轻度疼痛和重度疼痛）中实现了68.51%±9.02%的准确率。与单独使用这两种测量方法相比，这两种测量方法共同表现出更好的性能。本研究有助于追求客观的疼痛评估，并提出了一种使用fNIRS的人类疼痛潜在生物标志物。
