Objective Measurement of Subjective Pain Perception with Autonomic Body Reactions in Healthy Subjects and Chronic Back Pain Patients: An Experimental Heat Pain Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design and General Information
2.2. Participants
2.3. Outcome Measures
2.4. Study Procedure
2.5. Machine Learning Analyses
2.5.1. Segmentation
2.5.2. Feature Extraction
2.5.3. Classification and Evaluation
3. Results
3.1. Characteristics of Participants
3.2. Machine Learning
3.3. Autonomic Responses and Heat Pain Intensities
4. Discussion
Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ANS | Autonomic Nervous System |
BVDB | BioVid Heat Pain Database |
BVP | Blood Volume Pulse |
CBPPs | Chronic Back Pain Patients |
CoVAS | Computerized Visual Analogue Scale |
CV | Cross-Validation |
DL | Deep Learning |
DT | Decision Tree |
E4 | Empatica E4 |
ECG | Electrocardiogram |
EDA | Electrodermal Activity |
EEG | Electroencephalography |
EMG | Electromyogram |
EOG | Electrooculogram |
GSR | Galvanic Skin Response |
HCF | Hand-Crafted Features |
HR | Heart Rate |
HRV | Heart Rate Variability |
HSs | Healthy Subjects |
IASP | International Association for the Study of Pain |
IBI | Inter-Beats Interval |
LOSO | Leave-One-Subject-Out |
LR | Linear Regression |
LSTM | Long Short-Term Memory |
ML | Machine Learning |
MLP | Multi-Layer Perceptron |
MSE | Mean Squared Error |
NCS | Nociception Coma Scale |
NN | Neural Network |
NRS | Numerical Rating Scale |
PMDB | PainMonit Database |
PCS | Pain Catastrophizing Scale |
PET | Position Emission Tomography |
PHQ-9 | Pain Health Questionnaire |
PPG | Photoplethysmography |
PT | Pain Tolerance |
PTT | Pain Tolerance Threshold |
PVAQ | Pain Vigilance Awareness Questionnaire |
RB | respiBAN Professional |
RF | Random Forest |
RFE | Recursive Feature Elimination |
RMS | Root Mean Square |
RMSE | Root Mean Square Error |
RMSSD | Root Mean Square of the Successive Differences |
SC | Skin Conductance |
SCL | Skin Conductance Level |
SCR | Skin Conductance Response |
SDNN | Standard deviations of the IBIs |
sEMG | surface Electromyogram |
SVM | Support Vector Machine |
VAR | Variance |
VAS | Visual Analogue Scale |
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Characteristics | HSs (n = 52) | CBPPs (n = 20) | p |
---|---|---|---|
Age (Years), Mean (SD) | 27.4 (6.6) | 40.9 (14.4) | <0.001 |
Female, n (%) | 35 (67.3) | 15 (75) | 0.534 |
BMI, Mean (SD) | 23.4 (3.28) | 24.7 (3.33) | 0.106 |
PCS, Median (IQR) | 14.0 (10.0) | 12.0 (17.3) | 0.015 |
PHQ-9, Median (IQR) | 4.0 (2.3) | 7.5 (7.0) | 0.005 |
PVAQ, Median (IQR) | 34.0 (10.0) | 41.5 (18.8) | 0.006 |
Sensor | B vs. NP | B vs. | B vs. | B vs. | B vs. |
---|---|---|---|---|---|
Bvp | 53.49 | 52.52 | 50.45 | 50.72 | 53.52 |
Ecg | 52.16 | 45.79 | 51.63 | 54.33 | 61.63 |
Eda_E4 | 55.65 | 56.49 | 60.63 | 66.71 | 72.28 |
Eda_RB | 50.84 | 61.78 | 68.11 | 78.12 | 91.70 |
Emg | 50.36 | 52.28 | 49.21 | 48.20 | 52.02 |
Resp | 51.08 | 48.32 | 52.80 | 53.12 | 54.82 |
All | 52.64 | 62.26 | 67.24 | 77.76 | 89.90 |
Sensor | B vs. NP | B vs. | B vs. | B vs. | B vs. |
---|---|---|---|---|---|
Bvp | 47.32 | 49.11 | 55.95 | 50.89 | 57.74 |
Ecg | 55.65 | 49.40 | 52.98 | 54.76 | 54.17 |
Eda_E4 | 49.70 | 50.60 | 56.25 | 58.93 | 74.40 |
Eda_RB | 52.68 | 54.17 | 69.05 | 76.19 | 89.58 |
Emg | 44.64 | 48.51 | 47.92 | 51.19 | 54.76 |
Resp | 47.02 | 52.98 | 54.17 | 48.21 | 51.19 |
All | 51.19 | 55.95 | 67.56 | 74.70 | 88.39 |
Sensor | B vs. NP | B vs. | B vs. | B vs. | B vs. |
---|---|---|---|---|---|
Bvp | 52.68 | 49.40 | 47.02 | 53.27 | 57.14 |
Ecg | 50.30 | 53.27 | 48.81 | 43.75 | 53.27 |
Eda_E4 | 52.68 | 54.17 | 59.82 | 68.15 | 75.00 |
Eda_RB | 53.87 | 56.25 | 66.96 | 77.98 | 87.80 |
Emg | 51.49 | 51.19 | 47.62 | 48.81 | 46.73 |
Resp | 49.11 | 53.57 | 55.65 | 48.21 | 56.25 |
All | 51.79 | 58.33 | 65.48 | 76.19 | 88.10 |
Dataset | # Features | Accuracy | Feature Set |
---|---|---|---|
CBPPs | 31 | 91.67 | ’Bvp_Rate_Max_nk’, ’Bvp_Rate_Min_nk’, ’Bvp_Rate_SD_nk’, ’Eda_E4_diff_start_end’, ’Eda_E4_range_tonic’, ’Eda_E4_mean_rise_times’, ’Eda_E4_mean_offsets’, ’Eda_E4_norm_mean’, ’Eda_E4_dPhEDA_3’, ’Eda_E4_dPhEDA_10’, ’Eda_E4_TVSymp_6’, ’Eda_E4_TVSymp_7’, ’Eda_E4_SCR_RecoveryTime_nk’, ’Eda_RB_range’, ’Eda_RB_mean_abs_2_diff’, ’Eda_RB_argmax’, ’Eda_RB_argmin’, ’Eda_RB_diff_start_end’, ’Eda_RB_range_tonic’, ’Eda_RB_dPhEDA_3’, ’Eda_RB_dPhEDA_8’, ’Eda_RB_dPhEDA_9’, ’Eda_RB_dPhEDA_13’, ’Eda_RB_dPhEDA_14’, ’Eda_RB_dPhEDA_15’, ’Eda_RB_TVSymp_1’, ’Eda_RB_MTVSymp_1’, ’Eda_RB_SCR_RecoveryTime_nk’, ’Ecg_Rate_Baseline_nk’, ’Emg_mean_abs_1_diff’, ’Emg_SM3’ |
HSs | 15 | 93.62 | ’Eda_E4_range_tonic’, ’Resp_min’, ’Resp_mean_in’, ’Eda_RB_max’, ’Eda_RB_min’, ’Eda_RB_iqr’, ’Eda_RB_argmax’, ’Eda_RB_argmin’, ’Eda_RB_diff_start_end’, ’Eda_RB_std_tonic’, ’Eda_RB_dPhEDA_3’, ’Eda_RB_dPhEDA_4’, ’Eda_RB_dPhEDA_6’, ’Eda_RB_dPhEDA_16’, ’Eda_RB_TVSymp_5’ |
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Luebke, L.; Gouverneur, P.; Szikszay, T.M.; Adamczyk, W.M.; Luedtke, K.; Grzegorzek, M. Objective Measurement of Subjective Pain Perception with Autonomic Body Reactions in Healthy Subjects and Chronic Back Pain Patients: An Experimental Heat Pain Study. Sensors 2023, 23, 8231. https://doi.org/10.3390/s23198231
Luebke L, Gouverneur P, Szikszay TM, Adamczyk WM, Luedtke K, Grzegorzek M. Objective Measurement of Subjective Pain Perception with Autonomic Body Reactions in Healthy Subjects and Chronic Back Pain Patients: An Experimental Heat Pain Study. Sensors. 2023; 23(19):8231. https://doi.org/10.3390/s23198231
Chicago/Turabian StyleLuebke, Luisa, Philip Gouverneur, Tibor M. Szikszay, Wacław M. Adamczyk, Kerstin Luedtke, and Marcin Grzegorzek. 2023. "Objective Measurement of Subjective Pain Perception with Autonomic Body Reactions in Healthy Subjects and Chronic Back Pain Patients: An Experimental Heat Pain Study" Sensors 23, no. 19: 8231. https://doi.org/10.3390/s23198231