Automated Assessment of Pain Intensity based on EEG Signal Analysis

被引：10

作者：

Bonotis, Panagiotis A. ^{[1
]}

Tsouros, Dimosthenis C. ^{[1
]}

Smyrlis, Panagiotis N. ^{[1
]}

Tzallas, Alexandros T. ^{[2
]}

Giannakeas, Nikolaos ^{[2
]}

Glavas, Evripidis ^{[2
]}

Tsipouras, Markos G. ^{[3
,4
]}

机构：

[1] Univ Western Macedonia, Dept Elect & Comp Engn, Kozani, Greece

[2] Univ Ioannina, Dept Informat & Telecommun, Arta, Greece

[3] UNIVEYE IKE, Sci & Technol Pk Epirus, Ioannina, Greece

[4] Univ Western Macedonia, Dept Elect & Comp Engn, Kozani, Greece

来源：

2019 IEEE 19TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE) | 2019年

关键词：

Electroencephalogram (EEG); Portable EEG; Pain Intensity; Automated Assessment; Stochastic Forest Classifier; PERCEPTION; COLD;

D O I：

10.1109/BIBE.2019.00111

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Objective characterization of pain intensity is necessary under certain clinical conditions. The portable electroencephalogram (EEG) is a cost-effective assessment tool and lately, new methods using efficient analysis of related dynamic changes in brain activity in the EEG recordings proved that these can reflect the dynamic changes of pain intensity. In this paper, a novel method for automated assessment of pain intensity using EEG data is presented. EEG recordings from twenty-two (22) healthy volunteers are recorded with the Emotiv EPOC+ using the Cold Pressor Test (CPT) protocol. The relative power of each brain band's energy for each channel is extracted and the stochastic forest algorithm is employed for discrimination across five classes, depicting the pain intensity. Obtained results in terms of classification accuracy reached high levels (72.7%), which renders the proposed method suitable for automated pain detection and quantification of its intensity.

引用

页码：583 / 588

页数：6

共 25 条

[11]

Holmes G., 1994, Proceedings of the 1994 Second Australian and New Zealand Conference on Intelligent Information Systems (Cat. No.94TH8019), P357, DOI 10.1109/ANZIIS.1994.396988

[12] A novel approach to predict subjective pain perception from single-trial laser-evoked potentials [J].

Huang, G. ;

Xiao, P. ;

Hung, Y. S. ;

Iannetti, G. D. ;

Zhang, Z. G. ;

Hu, L. .

NEUROIMAGE, 2013, 81 :283-293

[13]

Kohavi R., 1995, A study of cross-validation and bootstrap for accuracy estimation and model selection, V14, P1137

[14] The Kyoto protocol of IASP Basic Pain Terminology [J].

Loeser, John D. ;

Treede, Rolf-Detlef .

PAIN, 2008, 137 (03) :473-477

[15] Pain perception and EEG dynamics: Does hypnotizability account for the efficacy of the suggestions of analgesia? [J].

Madeo, Dario ;

Castellani, Eleonora ;

Mocenni, Chiara ;

Santarcangelo, Enrica Laura .

PHYSIOLOGY & BEHAVIOR, 2015, 145 :57-63

[16]

Maki H, 2015, INT CONF ACOUST SPEE, P2639, DOI 10.1109/ICASSP.2015.7178449

[17] A Low-Complexity ECG Feature Extraction Algorithm for Mobile Healthcare Applications [J].

Mazomenos, Evangelos B. ;

Biswas, Dwaipayan ;

Acharyya, Amit ;

Chen, Taihai ;

Maharatna, Koushik ;

Rosengarten, James ;

Morgan, John ;

Curzen, Nick .

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2013, 17 (02) :459-469

[18]

Nezam T., 2018, IEEE T AFFECTIVE COM

[19] Subjective pain perception mediated by alpha rhythms [J].

Peng, Weiwei ;

Babiloni, Claudio ;

Mao, Yanhui ;

Hu, Yong .

BIOLOGICAL PSYCHOLOGY, 2015, 109 :141-150

[20] EMD-Based Temporal and Spectral Features for the Classification of EEG Signals Using Supervised Learning [J].

Riaz, Farhan ;

Hassan, Ali ;

Rehman, Saad ;

Niazi, Imran Khan ;

Dremstrup, Kim .

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2016, 24 (01) :28-35

← 1 2 3 →