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Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines.

Gruss S, Treister R, Werner P, Traue HC, Crawcour S, Andrade A, Walter S - PLoS ONE (2015)

Bottom Line: The clinically used methods of pain diagnosis do not allow for objective and robust measurement, and physicians must rely on the patient's report on the pain sensation.We achieved classification rates of 90.94% for baseline vs. pain tolerance threshold and 79.29% for baseline vs. pain threshold.The machine learning measurement of pain in patients could provide valuable information for a clinical team and thus support the treatment assessment.

View Article: PubMed Central - PubMed

Affiliation: University of Ulm, Medical Psychology, Department of Psychosomatic Medicine and Psychotherapy, Ulm, Germany.

ABSTRACT

Background: The clinically used methods of pain diagnosis do not allow for objective and robust measurement, and physicians must rely on the patient's report on the pain sensation. Verbal scales, visual analog scales (VAS) or numeric rating scales (NRS) count among the most common tools, which are restricted to patients with normal mental abilities. There also exist instruments for pain assessment in people with verbal and / or cognitive impairments and instruments for pain assessment in people who are sedated and automated ventilated. However, all these diagnostic methods either have limited reliability and validity or are very time-consuming. In contrast, biopotentials can be automatically analyzed with machine learning algorithms to provide a surrogate measure of pain intensity.

Methods: In this context, we created a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. Eighty-five participants were subjected to painful heat stimuli (baseline, pain threshold, two intermediate thresholds, and pain tolerance threshold) under controlled conditions and the signals of electromyography, skin conductance level, and electrocardiography were collected. A total of 159 features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, variability, and similarity.

Results: We achieved classification rates of 90.94% for baseline vs. pain tolerance threshold and 79.29% for baseline vs. pain threshold. The most selected pain features stemmed from the amplitude and similarity group and were derived from facial electromyography.

Conclusion: The machine learning measurement of pain in patients could provide valuable information for a clinical team and thus support the treatment assessment.

No MeSH data available.


Related in: MedlinePlus

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Mentions: We systematically extracted features [23], [24], [25], [26] from the mathematical groups of 1. amplitude (∑ = 40), 2. frequency (∑ = 24), 3. stationarity (∑ = 24), 4. entropy (∑ = 20), 5. linearity (∑ = 8), 6. variability (∑ = 19) and 7. similarity (Fig 3) (∑ = 24) (in total: ∑ = 159). Table 1 provides a detailed information overview of all features. The similarity features of a sample are calculated with regard to the associated mean baseline signal of the person. All features were normalized (z transformed) per person. The dataset of the study, including the raw and preprocessed signals, as well as the extracted features, is available at: https://www-e.uni-magdeburg.de/biovid/.


Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines.

Gruss S, Treister R, Werner P, Traue HC, Crawcour S, Andrade A, Walter S - PLoS ONE (2015)

Similarity feature.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4608770&req=5

pone.0140330.g003: Similarity feature.
Mentions: We systematically extracted features [23], [24], [25], [26] from the mathematical groups of 1. amplitude (∑ = 40), 2. frequency (∑ = 24), 3. stationarity (∑ = 24), 4. entropy (∑ = 20), 5. linearity (∑ = 8), 6. variability (∑ = 19) and 7. similarity (Fig 3) (∑ = 24) (in total: ∑ = 159). Table 1 provides a detailed information overview of all features. The similarity features of a sample are calculated with regard to the associated mean baseline signal of the person. All features were normalized (z transformed) per person. The dataset of the study, including the raw and preprocessed signals, as well as the extracted features, is available at: https://www-e.uni-magdeburg.de/biovid/.

Bottom Line: The clinically used methods of pain diagnosis do not allow for objective and robust measurement, and physicians must rely on the patient's report on the pain sensation.We achieved classification rates of 90.94% for baseline vs. pain tolerance threshold and 79.29% for baseline vs. pain threshold.The machine learning measurement of pain in patients could provide valuable information for a clinical team and thus support the treatment assessment.

View Article: PubMed Central - PubMed

Affiliation: University of Ulm, Medical Psychology, Department of Psychosomatic Medicine and Psychotherapy, Ulm, Germany.

ABSTRACT

Background: The clinically used methods of pain diagnosis do not allow for objective and robust measurement, and physicians must rely on the patient's report on the pain sensation. Verbal scales, visual analog scales (VAS) or numeric rating scales (NRS) count among the most common tools, which are restricted to patients with normal mental abilities. There also exist instruments for pain assessment in people with verbal and / or cognitive impairments and instruments for pain assessment in people who are sedated and automated ventilated. However, all these diagnostic methods either have limited reliability and validity or are very time-consuming. In contrast, biopotentials can be automatically analyzed with machine learning algorithms to provide a surrogate measure of pain intensity.

Methods: In this context, we created a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. Eighty-five participants were subjected to painful heat stimuli (baseline, pain threshold, two intermediate thresholds, and pain tolerance threshold) under controlled conditions and the signals of electromyography, skin conductance level, and electrocardiography were collected. A total of 159 features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, variability, and similarity.

Results: We achieved classification rates of 90.94% for baseline vs. pain tolerance threshold and 79.29% for baseline vs. pain threshold. The most selected pain features stemmed from the amplitude and similarity group and were derived from facial electromyography.

Conclusion: The machine learning measurement of pain in patients could provide valuable information for a clinical team and thus support the treatment assessment.

No MeSH data available.


Related in: MedlinePlus