Physiological Signal-Based Method for Measurement of Pain Intensity

Yaqi Chu · Xingang Zhao · Jianda Han · Yang Su

91 citations0/0 files storedFrontiers in Neuroscience

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Abstract

The standard method for prediction of the absence and presence of pain has long been self-report. However, for patients with major cognitive or communicative impairments, it would be better if clinicians could quantify pain without having to rely on the patient’s self-description. Here, we present a newly pain intensity measurement method based on multiple physiological signals, including blood volume pulse (BVP), electrocardiogram (ECG) and skin conductance (SC), all of which are induced by external electrical stimulation. The proposed pain prediction system consists of signal acquisition and preprocessing, feature extraction, feature selection and feature reduction, and three types of pattern classifiers. Feature extraction phase is devised to extract pain-related characteristics from short-segment signals. A hybrid procedure of genetic algorithm-based feature selection and principal component analysis-based feature reduction was established to obtain high-quality features combination with significant discriminatory information. Three types of classification algorithms—linear discriminant analysis, k-nearest neighbor algorithm, and support vector machine—are adopted during various scenarios, including multi-signal scenario, multi-subject and between-subject scenario, and multi-day scenario. The classifiers gave correct classification ratios much higher than chance probability, with the overall average accuracy of 75% above for four pain intensity. Our experimental results demonstrate that the proposed method can provide an objective and quantitative evaluation of pain intensity. The method might be used to develop a wearable device that is suitable for daily use in clinical settings.

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References

2012Activity-Aware Mental Stress Detection Using Physiological Sensors 2010Automatically Detecting Pain in Video Through Facial Action Units 2012Continuous Pain Intensity Estimation from Facial Expressions 2014Automatic Pain Recognition from Video and Biomedical Signals 2016Methods for Person-Centered Continuous Pain Intensity Assessment From Bio-Physiological Channels

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