Decoding acute pain with combined EEG and physiological data

J. Lancaster, H. Mano, D. Callan, M. Kawato, B. Seymour
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引用次数: 10

Abstract

Across neuroscience research, clinical diagnostics, and engineering applications in pain evaluation and treatment, there is a need for an objective measure of pain experience and detection when it occurs. This detector should be reliable in real-world settings using easily accessible, non-invasive data sources. We present a simple yet robust paradigm for decoding pain using neural and physiological data including electroencephalography (EEG), pulse, and skin conductance (GSR) measurements. The present study uses multivariate classification to distinguish painful events from non-painful multimodal sensory stimuli. To classify the pain response and detect relevant data attributes, we employed a sparse logistic regression (SLR) machine learning protocol with automatic feature selection. EEG input consisted of time-frequency changes under trial conditions, and physiological data included fluctuations and spikes in pulse and skin conductance. Classification averaged 70% accuracy and selected between 5 and 15 features. In our experiment, pain was induced by cold stimulation which became noxious with prolonged exposure. Due to the long, ramp-and-hold nature of the stimulus, along with individual variability in sensitivity to pain, we did not observe specific rapid evoked responses or time-locked events common across participants. However, this format more closely resembles the experience of pain conditions requiring intervention which could be facilitated by a decoding system. The results illustrate the feasibility of developing a wireless pain detection system and give insight to important temporal, spectral, and spatial EEG events and physiological indicators of pain states. Success of the classifier protocol using these parameters could lead to the creation of a closed-loop system for decoding and intervention which can be applied in engineering and medical contexts.
结合脑电图和生理数据解码急性疼痛
在神经科学研究、临床诊断和疼痛评估和治疗的工程应用中,需要一种客观的疼痛体验测量和疼痛发生时的检测。该检测器在使用易于访问的非侵入性数据源的实际环境中应该是可靠的。我们提出了一个简单而稳健的范例来解码疼痛使用神经和生理数据,包括脑电图(EEG),脉搏和皮肤电导(GSR)测量。本研究使用多元分类来区分疼痛事件和非疼痛的多模态感觉刺激。为了对疼痛反应进行分类并检测相关数据属性,我们采用了具有自动特征选择的稀疏逻辑回归(SLR)机器学习协议。脑电图输入包括试验条件下的时频变化,生理数据包括脉冲和皮肤电导的波动和峰值。分类平均准确率为70%,并在5到15个特征之间进行选择。在我们的实验中,疼痛是由冷刺激引起的,随着时间的延长,这种刺激会变得有害。由于刺激的长时间、缓坡性和持续性,以及个体对疼痛敏感性的差异,我们没有观察到特定的快速诱发反应或参与者共同的时间锁定事件。然而,这种格式更接近于需要干预的疼痛状况的经验,这可以通过解码系统来促进。研究结果说明了开发无线疼痛检测系统的可行性,并提供了重要的时间、频谱和空间EEG事件和疼痛状态的生理指标。使用这些参数的分类器协议的成功可以导致解码和干预的闭环系统的创建,可以应用于工程和医学环境。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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