Toward a functional near-infrared spectroscopy-based monitoring of pain assessment for nonverbal patients

Raul Fernandez Rojas, Xu Huang, Keng Liang Ou

研究成果: 雜誌貢獻文章同行評審

26 引文 斯高帕斯(Scopus)


Pain diagnosis for nonverbal patients represents a challenge in clinical settings. Neuroimaging methods, such as functional magnetic resonance imaging and functional near-infrared spectroscopy (fNIRS), have shown promising results to assess neuronal function in response to nociception and pain. Recent studies suggest that neuroimaging in conjunction with machine learning models can be used to predict different cognitive tasks. The aim of this study is to expand previous studies by exploring the classification of fNIRS signals (oxyhaemoglobin) according to temperature level (cold and hot) and corresponding pain intensity (low and high) using machine learning models. Toward this aim, we used the quantitative sensory testing to determine pain threshold and pain tolerance to cold and heat in 18 healthy subjects (three females), mean age±standard deviation (31.9±5.5). The classification model is based on the bag-of-words approach, a histogram representation used in document classification based on the frequencies of extracted words and adapted for time series; two learning algorithms were used separately, K-nearest neighbor (K-NN) and support vector machines (SVM). A comparison between two sets of fNIRS channels was also made in the classification task, all 24 channels and 8 channels from the somatosensory region defined as our region of interest (RoI). The results showed that K-NN obtained slightly better results (92.08%) than SVM (91.25%) using the 24 channels; however, the performance slightly dropped using only channels from the RoI with K-NN (91.53%) and SVM (90.83%). These results indicate potential applications of fNIRS in the development of a physiologically based diagnosis of human pain that would benefit vulnerable patients who cannot self-report pain.

期刊Journal of Biomedical Optics
出版狀態已發佈 - 10月 1 2017

ASJC Scopus subject areas

  • 電子、光磁材料
  • 生物材料
  • 原子與分子物理與光學
  • 生物醫學工程


深入研究「Toward a functional near-infrared spectroscopy-based monitoring of pain assessment for nonverbal patients」主題。共同形成了獨特的指紋。