Physiological Signal-Based Method for Measurement of Pain Intensity

• Published in: Frontiers in neuroscience Vol. 11; p. 279
• Main Authors: Chu, Yaqi, Zhao, Xingang, Han, Jianda, Su, Yang
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 26.05.2017; Frontiers Media S.A
• Subjects: Algorithms; Classification; Cognitive ability; Conductance; Consciousness; EKG; Electrical stimuli; Electrocardiography; Electroencephalography; Electromyography; feature extraction; feature selection and reduction; International conferences; Neuroscience; Pain; pain intensity quantification; Patients; pattern classification; physiological signals; Physiology; Principal components analysis; Researchers; Sensors; Skin; China; feature selection and reduction; pattern classification; pain intensity quantification; physiological signals; feature extraction
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2017.00279

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 level (SCL), 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, -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.