Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Apr 14;23(8):3980.
doi: 10.3390/s23083980.

Experimental Exploration of Multilevel Human Pain Assessment Using Blood Volume Pulse (BVP) Signals

Muhammad Umar Khan  1 , Sumair Aziz  1 , Niraj Hirachan  1 , Calvin Joseph  1 , Jasper Li  1 , Raul Fernandez-Rojas  1
Affiliations

Experimental Exploration of Multilevel Human Pain Assessment Using Blood Volume Pulse (BVP) Signals

Muhammad Umar Khan et al. Sensors (Basel). .

Abstract

Critically ill patients often lack cognitive or communicative functions, making it challenging to assess their pain levels using self-reporting mechanisms. There is an urgent need for an accurate system that can assess pain levels without relying on patient-reported information. Blood volume pulse (BVP) is a relatively unexplored physiological measure with the potential to assess pain levels. This study aims to develop an accurate pain intensity classification system based on BVP signals through comprehensive experimental analysis. Twenty-two healthy subjects participated in the study, in which we analyzed the classification performance of BVP signals for various pain intensities using time, frequency, and morphological features through fourteen different machine learning classifiers. Three experiments were conducted using leave-one-subject-out cross-validation to better examine the hidden signatures of BVP signals for pain level classification. The results of the experiments showed that BVP signals combined with machine learning can provide an objective and quantitative evaluation of pain levels in clinical settings. Specifically, no pain and high pain BVP signals were classified with 96.6% accuracy, 100% sensitivity, and 91.6% specificity using a combination of time, frequency, and morphological features with artificial neural networks (ANNs). The classification of no pain and low pain BVP signals yielded 83.3% accuracy using a combination of time and morphological features with the AdaBoost classifier. Finally, the multi-class experiment, which classified no pain, low pain, and high pain, achieved 69% overall accuracy using a combination of time and morphological features with ANN. In conclusion, the experimental results suggest that BVP signals combined with machine learning can offer an objective and reliable assessment of pain levels in clinical settings.

Keywords: PPG; blood volume pulse (BVP); feature extraction; machine learning; pain classification; pain intensity classification.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1
Figure 1
Schematic representation of stimulation and perception of pain.
Figure 2
Figure 2
Raw BVP signals of No Pain (NP), High Hand Pain (HHP), High Forearm Pain (HFP), Low Hand Pain (LHP), and Low Forearm Pain (LFP).
Figure 3
Figure 3
Processed signals of No Pain (NP), High Hand Pain (HHP), High Forearm Pain (HFP), Low Hand Pain (LHP), and Low Forearm Pain (LFP).
Figure 4
Figure 4
Performance evaluation scheme using leave one subject out cross validation (LOSOCV).
Figure 5
Figure 5
Design of study for assessment of pain using BVP signatures. Performance results are reported using the five most consistent classifiers.
Figure 6
Figure 6
Performance evaluation with time, frequency (Frq), and morphological (Mrp) features using different classifiers for Experiment 1 (no pain vs. high pain). (QSVM: Quadratic-SVM, WKNN: Weighted-KNN, AB: AdaBoost, BLNN: bi-layered neural network, TLNN: tri-layered neural network).
Figure 7
Figure 7
The proposed methodology for experiment 1 (no pain vs. high pain).
Figure 8
Figure 8
Performance evaluation with time, frequency (Frq), and morphological (Mrp) features using different classifiers for Experiment 2 (No Pain vs. Low Pain). (QSVM: Quadratic-SVM, WKNN: Weighted-KNN, AB: AdaBoost, BLNN: bi-layered neural network, TLNN: tri-layered neural network).
Figure 9
Figure 9
The proposed methodology for Experiment 2 (no pain vs. low pain).
Figure 10
Figure 10
Performance evaluation with time, frequency (Frq), and morphological (Mrp) features using different classifiers for Experiment 3 (no pain vs. low pain vs. high pain). (QSVM: Quadratic-SVM, WKNN: Weighted-KNN, AB: AdaBoost, BLNN: bi-layered neural network, TLNN: tri-layered neural network).
Figure 11
Figure 11
The proposed methodology for Experiment 3 (no pain vs. low pain vs. high pain).
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Jain K.K., Jain K.K. The Handbook of Biomarkers. Springer; Berlin/Heidelberg, Germany: 2010.
    1. Marieb E.N., Hoehn K. Human Anatomy & Physiology. Pearson Education; Upper Saddle River, NJ, USA: 2007.
    1. Bornhövd K., Quante M., Glauche V., Bromm B., Weiller C., Büchel C. Painful stimuli evoke different stimulus–response functions in the amygdala, prefrontal, insula and somatosensory cortex: A single-trial fMRI study. Brain. 2002;125:1326–1336. doi: 10.1093/brain/awf137. - DOI - PubMed
    1. Fernandez Rojas R., Huang X., Ou K.L. A machine learning approach for the identification of a biomarker of human pain using fNIRS. Sci. Rep. 2019;9:5645. doi: 10.1038/s41598-019-42098-w. - DOI - PMC - PubMed
    1. Melzack R. The McGill pain questionnaire. Anesthesiology. 2005;103:199–202. doi: 10.1097/00000542-200507000-00028. - DOI - PubMed

LinkOut - more resources