IntroductionPain assessment is extremely important in patients unable to communicate and it is often done by clinical judgement. However, assessing pain using observable indicators can be challenging for clinicians due to the subjective perceptions, individual differences in pain expression, and potential confounding factors. Therefore, the need for an objective pain assessment method that can assist medical practitioners. Functional near-infrared spectroscopy (fNIRS) has shown promising results to assess the neural function in response of nociception and pain. Previous studies have explored the use of machine learning with hand-crafted features in the assessment of pain.MethodsIn this study, we aim to expand previous studies by exploring the use of deep learning models Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and (CNN-LSTM) to automatically extract features from fNIRS data and by comparing these with classical machine learning models using hand-crafted features.ResultsThe results showed that the deep learning models exhibited favourable results in the identification of different types of pain in our experiment using only fNIRS input data. The combination of CNN and LSTM in a hybrid model (CNN-LSTM) exhibited the highest performance (accuracy = 91.2%) in our problem setting. Statistical analysis using one-way ANOVA with Tukey's (post-hoc) test performed on accuracies showed that the deep learning models significantly improved accuracy performance as compared to the baseline models.DiscussionOverall, deep learning models showed their potential to learn features automatically without relying on manually-extracted features and the CNN-LSTM model could be used as a possible method of assessment of pain in non-verbal patients. Future research is needed to evaluate the generalisation of this method of pain assessment on independent populations and in real-life scenarios.

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fNIRS 疼痛解码深度学习模型的实证比较

简介疼痛评估对于无法沟通的患者极其重要，通常通过临床判断来完成。然而，由于主观感知、疼痛表达的个体差异以及潜在的混杂因素，使用可观察指标评估疼痛对临床医生来说可能具有挑战性。因此，需要一种客观的疼痛评估方法来辅助医生。功能性近红外光谱（fNIRS）在评估伤害感受和疼痛反应的神经功能方面显示出有希望的结果。先前的研究探索了使用具有手工制作特征的机器学习来评估疼痛。方法在本研究中，我们旨在通过探索深度学习模型卷积神经网络（CNN）、长短期记忆的使用来扩展先前的研究（LSTM）和（CNN-LSTM）自动从 fNIRS 数据中提取特征，并与使用手工制作的特征的经典机器学习模型进行比较。结果结果表明，深度学习模型在识别不同类型的我们的实验仅使用 fNIRS 输入数据是很痛苦的。混合模型 (CNN-LSTM) 中 CNN 和 LSTM 的组合在我们的问题设置中表现出了最高的性能（准确率 = 91.2%）。使用单向方差分析和 Tukey 进行统计分析 (事后）对准确性进行的测试表明，与基线模型相比，深度学习模型显着提高了准确性性能。讨论总体而言，深度学习模型显示了其在不依赖手动提取特征的情况下自动学习特征的潜力，并且可以使用 CNN-LSTM 模型作为评估非语言患者疼痛的一种可能方法。未来的研究需要评估这种疼痛评估方法对独立人群和现实生活场景的普遍性。