Movement-related pain as a learned response: an investigation of the possible mechanisms underlyng movement-related pain persistence and recovery in subjects with chronic musculoskeletal pain

Alaiti, Rafael Krasic

doi:10.11606/t.47.2021.tde-01102021-162436

Cited by 1 publication

(1 citation statement)

References 275 publications

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“…Despite this theoretical possibility, studies using HR -a proxy of ANS activityhave focused only on emotional and metabolic aspects of behavior. The possibility that HR may be associated with the cognitive process is mainly unexplored 12 . We hypothesize that HR is a good proxy for the brain's cognitive states.…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Variability predicts the Subject-Driven Cognitive States

Farfán

Guzman

Bruel

et al. 2022

Preprint

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Methods for controlling machines using physiological signals have been the focus of much research over the last decades. One natural approach is to use brain signals for decoding subject-driven cognitive states. However, brain-based decoding approaches often require costly, challenging to handle, and uncomfortable devices inadequate for daily use. We propose an alternative method to decode cognitive states based on heart rate (HR). Although HR is capable of classifying general physiological conditions (e.g., level of physical activities, stress, some diseases), it is less clear if HR signals can discriminate cognitive states. To answer this question, we submitted 25 subjects (mean age +- standard deviation, 30.2+- 4.9 y.o.) to four cognitive tasks: (i) resting quietly, (ii) remembering their day's events, (iii) singing lyrics, and (iv) subtracting numbers. We collected the electrocardiogram twice for each individual, separated by approximately one week. We used an inexpensive commercially available chest sensor band to collect the HR. We trained a support vector machine to classify the cognitive tasks using data collected on day one. Then we validated it in the dataset collected on day two. Our results show classification accuracy higher than expected at random (p < 0.001). Therefore, we conclude that we can use HR to help decode cognitive states. Because we can easily monitor the HR using a wearable sensor, HR is potentially helpful for human-machine interface applications in daily conditions.

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Section: Introductionmentioning

confidence: 99%