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Gozzi N, Preatoni G, Ciotti F, Hubli M, Schweinhardt P, Curt A, Raspopovic S. Unraveling the physiological and psychosocial signatures of pain by machine learning. MED 2024:S2666-6340(24)00298-8. [PMID: 39116869 DOI: 10.1016/j.medj.2024.07.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 04/12/2024] [Accepted: 07/15/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND Pain is a complex subjective experience, strongly impacting health and quality of life. Despite many attempts to find effective solutions, present treatments are generic, often unsuccessful, and present significant side effects. Designing individualized therapies requires understanding of multidimensional pain experience, considering physical and emotional aspects. Current clinical pain assessments, relying on subjective one-dimensional numeric self-reports, fail to capture this complexity. METHODS To this aim, we exploited machine learning to disentangle physiological and psychosocial components shaping the pain experience. Clinical, psychosocial, and physiological data were collected from 118 chronic pain and healthy participants undergoing 40 pain trials (4,697 trials). FINDINGS To understand the objective response to nociception, we classified pain from the physiological signals (accuracy >0.87), extracting the most important biomarkers. Then, using multilevel mixed-effects models, we predicted the reported pain, quantifying the mismatch between subjective level and measured physiological response. From these models, we introduced two metrics: TIP (subjective index of pain) and Φ (physiological index). These represent possible added value in the clinical process, capturing psychosocial and physiological pain dimensions, respectively. Patients with high TIP are characterized by frequent sick leave from work and increased clinical depression and anxiety, factors associated with long-term disability and poor recovery, and are indicated for alternative treatments, such as psychological ones. By contrast, patients with high Φ show strong nociceptive pain components and could benefit more from pharmacotherapy. CONCLUSIONS TIP and Φ, explaining the multidimensionality of pain, might provide a new tool potentially leading to targeted treatments, thereby reducing the costs of inefficient generic therapies. FUNDING RESC-PainSense, SNSF-MOVE-IT197271.
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Affiliation(s)
- Noemi Gozzi
- Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland
| | - Greta Preatoni
- Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland
| | - Federico Ciotti
- Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland
| | - Michèle Hubli
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, 8008 Zürich, Switzerland
| | - Petra Schweinhardt
- Department of Chiropractic Medicine, Balgrist University Hospital, University of Zürich, 8008 Zürich, Switzerland
| | - Armin Curt
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, 8008 Zürich, Switzerland
| | - Stanisa Raspopovic
- Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland; Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria.
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2
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Fu Z, Zhu H, Zhang Y, Huan R, Chen S, Pan Y. A Spatiotemporal Deep Learning Framework for Scalp EEG-Based Automated Pain Assessment in Children. IEEE Trans Biomed Eng 2024; 71:1889-1900. [PMID: 38231823 DOI: 10.1109/tbme.2024.3355215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
OBJECTIVE Common pain assessment approaches such as self-evaluation and observation scales are inappropriate for children as they require patients to have reasonable communication ability. Subjective, inconsistent, and discontinuous pain assessment in children may reduce therapeutic effectiveness and thus affect their later life. METHODS To address the need for suitable assessment measures, this paper proposes a spatiotemporal deep learning framework for scalp electroencephalogram (EEG)-based automated pain assessment in children. The dataset comprises scalp EEG data recorded from 33 pediatric patients with an arterial puncture as a pain stimulus. Two electrode reduction plans in line with clinical findings are proposed. Combining three-dimensional hand-crafted features and preprocessed raw signals, the proposed transformer-based pain assessment network (STPA-Net) integrates both spatial and temporal information. RESULTS STPA-Net achieves superior performance with a subject-independent accuracy of 87.83% for pain recognition, and outperforms other state-of-the-art approaches. The effectiveness of electrode combinations is explored to analyze pain-related cortical activities and correspondingly reduce cost. The two proposed electrode reduction plans both demonstrate competitive pain assessment performance qualitatively and quantitatively. CONCLUSION AND SIGNIFICANCE This study is the first to develop a scalp EEG-based automated pain assessment for children adopting a method that is objective, standardized, and consistent. The findings provide a potential reference for future clinical research.
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Baghestani F, Kong Y, D'Angelo W, Chon KH. Analysis of sympathetic responses to cognitive stress and pain through skin sympathetic nerve activity and electrodermal activity. Comput Biol Med 2024; 170:108070. [PMID: 38330822 DOI: 10.1016/j.compbiomed.2024.108070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 12/28/2023] [Accepted: 01/27/2024] [Indexed: 02/10/2024]
Abstract
We explored the non-invasive evaluation of the sympathetic nervous system (SNS) by employing two distinct physiological signals: skin sympathetic nerve activity (SKNA), extracted from electrocardiogram (ECG) signals, and electrodermal activity (EDA), a well-studied marker in the context of the SNS assessment. Our investigation focused on cognitive stress and pain; two conditions closely associated with the SNS. We sought to determine if the information and dynamics of EDA could be derived from the novel SKNA signal. To this end, ECG and EDA signals were recorded simultaneously during three experiments aimed at sympathetic stimulation, Valsalva maneuver (VM), Stroop test, and thermal-grill pain test. We calculated the integral area under the rectified SKNA signal (iSKNA) and decomposed the EDA signal to its phasic component (EDAphasic). An average delay of more than 4.6 s was observed in the onset of EDAphasic bursts compared to their corresponding iSKNA bursts. After shifting the EDAphasic segments by the extent of this delay and smoothing the corresponding iSKNA bursts, our results revealed a strong average correlation coefficient of 0.85±0.14 between the iSKNA and EDAphasic bursts, indicating a noteworthy similarity between the two signals. We also reconstructed the EDA signals with time-varying sympathetic (TVSymp) and modified TVSymp (MTVSymp) methods. Then we extracted the following features from iSKNA, EDAphasic, TVSymp, and MTVSymp signals: peak amplitude, average amplitude (aSKNA), standard deviation (vSKNA), and the cumulative duration during which the signals had higher amplitudes than a specified threshold (HaSKNA). A strong average correlation of 0.89±0.18 was found between vSKNA and subjects' self-rated pain levels during the pain test. Our statistical analysis also included applying Linear Mixed-Effects Models to check if there were significant differences in features across baseline and different levels of SNS stimulation. We then assessed the discriminating power of the features using Area Under the Receiver Operating Characteristic Curve (AUROC) and Fisher's Ratio. Finally, using all the four EDA features, a multi-layer perceptron (MLP) classifier reached the classification accuracies 95.56%, 89.29%, and 67.88% for the VM, Stroop, and thermal-grill pain control and stimulation classes. On the other hand, the highest classification accuracies based on SKNA features were achieved using K-nearest neighbors (KNN) (98.89%), KNN (89.29%), and MLP (95.11%) classifiers for the same experiments. Our comparative analysis showed the feasibility of SKNA as a novel tool for assessing the SNS with accurate classification capability, with a faster onset of amplitude increase in response to SNS activity, compared to EDA.
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Affiliation(s)
- Farnoush Baghestani
- Biomedical Engineering Department, University of Connecticut, United States of America
| | - Youngsun Kong
- Biomedical Engineering Department, University of Connecticut, United States of America
| | - William D'Angelo
- Biomedical Systems Engineering and Evaluation Department, Naval Medical Research Unit Department, San Antonio, TX, United States of America
| | - Ki H Chon
- Biomedical Engineering Department, University of Connecticut, United States of America.
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4
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Kim Y, Han I, Jung J, Yang S, Lee S, Koo B, Ahn S, Nam Y, Song SH. Measurements of Electrodermal Activity, Tissue Oxygen Saturation, and Visual Analog Scale for Different Cuff Pressures. SENSORS (BASEL, SWITZERLAND) 2024; 24:917. [PMID: 38339639 PMCID: PMC10857413 DOI: 10.3390/s24030917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 01/29/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024]
Abstract
The quantification of comfort in binding parts, essential human-machine interfaces (HMI) for the functioning of rehabilitation robots, is necessary to reduce physical strain on the user despite great achievements in their structure and control. This study aims to investigate the physiological impacts of binding parts by measuring electrodermal activity (EDA) and tissue oxygen saturation (StO2). In Experiment 1, EDA was measured from 13 healthy subjects under three different pressure conditions (10, 20, and 30 kPa) for 1 min using a pneumatic cuff on the right thigh. In Experiment 2, EDA and StO2 were measured from 10 healthy subjects for 5 min. To analyze the correlation between EDA parameters and the decrease in StO2, a survey using the visual analog scale (VAS) was conducted to assess the level of discomfort at each pressure. The EDA signal was decomposed into phasic and tonic components, and the EDA parameters were extracted from these two components. RM ANOVA and a post hoc paired t-test were used to determine significant differences in parameters as the pressure increased. The results showed that EDA parameters and the decrease in StO2 significantly increased with the pressure increase. Among the extracted parameters, the decrease in StO2 and the mean SCL proved to be effective indicators. Such analysis outcomes would be highly beneficial for studies focusing on the comfort assessment of the binding parts of rehabilitation robots.
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Affiliation(s)
- Youngho Kim
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea; (I.H.); (J.J.); (S.Y.); (S.L.); (B.K.)
| | - Incheol Han
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea; (I.H.); (J.J.); (S.Y.); (S.L.); (B.K.)
| | - Jeyong Jung
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea; (I.H.); (J.J.); (S.Y.); (S.L.); (B.K.)
| | - Sumin Yang
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea; (I.H.); (J.J.); (S.Y.); (S.L.); (B.K.)
| | - Seunghee Lee
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea; (I.H.); (J.J.); (S.Y.); (S.L.); (B.K.)
| | - Bummo Koo
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea; (I.H.); (J.J.); (S.Y.); (S.L.); (B.K.)
| | - Soonjae Ahn
- Institute of Smart Rehabilitation Engineering and Assistive Technology, Dong-Eui University, Busan 47340, Republic of Korea;
| | - Yejin Nam
- Department of Clinical Development, Angel Robotics, Seoul 04798, Republic of Korea;
| | - Sung-Hyuk Song
- Department of Robotics & Mechatronics, Korea Institute of Machinery & Materials, Daejeon 34103, Republic of Korea;
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Pattyn E, Thammasan N, Lutin E, Tourolle D, Van Kraaij A, Kosunen I, De Raedt W, Van Hoof C. Simulation of ambulatory electrodermal activity and the handling of low-quality segments. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107859. [PMID: 37863009 DOI: 10.1016/j.cmpb.2023.107859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 10/05/2023] [Accepted: 10/09/2023] [Indexed: 10/22/2023]
Abstract
BACKGROUND AND OBJECTIVES Monitoring electrodermal activity (EDA) in daily life requires effective handling of low-quality segments, which are common in ambulatory EDA data. Although several low-quality handling methods have been implemented, systematic comparison of these methods, which requires a large annotated dataset, is lacking. METHODS Therefore, we proposed the simulation of realistic ambulatory EDA data starting from high-quality EDA signals, which were subsequently contaminated with varying concentrations of artifacts. Subsequently, three approaches for handling low-quality data were evaluated regarding the preservation of several EDA-derived features: removing all artifacts, interpolating over removed artifacts, and retaining all artifacts. Specifically, multiple EDA features were assessed, derived from response detection (evaluated using F1, precision, recall) as well as EDA, phasic, and tonic features (assessed using absolute error), by comparing the simulated EDA data with and without the inserted artifacts, using the latter as ground truth. RESULTS For response detection, retaining artifacts resulted in the highest F1-scores, while interpolating over removed artifacts achieved the highest F1-scores for the phasic signal. The approaches did significantly differ in the mean error for the phasic but not for the tonic component and raw EDA. CONCLUSION This work generated ambulatory EDA datasets of 200 h, containing 0.125 to 3 artifacts per minute, and showed that interpolation over removed artifacts was an effective approach to reconstruct phasic-derived features up to 2 artifacts per minute. The proposed simulation and evaluation methodology, which are easily customizable, offer opportunities for future research to develop and systematically compare signal quality indicators, decomposition methods, and response detectors for processing ambulatory EDA.
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Affiliation(s)
- E Pattyn
- Department of Electrical Engineering, Elektronische Circuits en Systemen (ECS), KU Leuven, Leuven (Arenberg), Kasteelpark Arenberg 10 - bus 2443, Heverlee, Leuven 3001, Belgium; OnePlanet Research Center, Wageningen, The Netherlands.
| | | | - E Lutin
- Department of Electrical Engineering, Elektronische Circuits en Systemen (ECS), KU Leuven, Leuven (Arenberg), Kasteelpark Arenberg 10 - bus 2443, Heverlee, Leuven 3001, Belgium; OnePlanet Research Center, Wageningen, The Netherlands
| | | | | | | | - W De Raedt
- OnePlanet Research Center, Wageningen, The Netherlands
| | - C Van Hoof
- Department of Electrical Engineering, Elektronische Circuits en Systemen (ECS), KU Leuven, Leuven (Arenberg), Kasteelpark Arenberg 10 - bus 2443, Heverlee, Leuven 3001, Belgium; Imec Leuven, Leuven, Belgium; OnePlanet Research Center, Wageningen, The Netherlands
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6
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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 (BASEL, SWITZERLAND) 2023; 23:8231. [PMID: 37837061 PMCID: PMC10575054 DOI: 10.3390/s23198231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023]
Abstract
Multiple attempts to quantify pain objectively using single measures of physiological body responses have been performed in the past, but the variability across participants reduces the usefulness of such methods. Therefore, this study aims to evaluate whether combining multiple autonomic parameters is more appropriate to quantify the perceived pain intensity of healthy subjects (HSs) and chronic back pain patients (CBPPs) during experimental heat pain stimulation. HS and CBPP received different heat pain stimuli adjusted for individual pain tolerance via a CE-certified thermode. Different sensors measured physiological responses. Machine learning models were trained to evaluate performance in distinguishing pain levels and identify key sensors and features for the classification task. The results show that distinguishing between no and severe pain is significantly easier than discriminating lower pain levels. Electrodermal activity is the best marker for distinguishing between low and high pain levels. However, recursive feature elimination showed that an optimal subset of features for all modalities includes characteristics retrieved from several modalities. Moreover, the study's findings indicate that differences in physiological responses to pain in HS and CBPP remain small.
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Affiliation(s)
- Luisa Luebke
- Institute of Health Sciences, Department of Physiotherapy, Pain and Exercise Research Luebeck (P.E.R.L.), Universität zu Lübeck, 23562 Lübeck, Germany; (L.L.); (T.M.S.); (K.L.)
- Center of Brain, Behavior and Metabolism (CBBM), University of Luebeck, 23562 Lübeck, Germany
| | - Philip Gouverneur
- Institute of Medical Informatics, University of Lübeck, 23562 Lübeck, Germany;
| | - Tibor M. Szikszay
- Institute of Health Sciences, Department of Physiotherapy, Pain and Exercise Research Luebeck (P.E.R.L.), Universität zu Lübeck, 23562 Lübeck, Germany; (L.L.); (T.M.S.); (K.L.)
- Center of Brain, Behavior and Metabolism (CBBM), University of Luebeck, 23562 Lübeck, Germany
| | - Wacław M. Adamczyk
- Laboratory of Pain Research, Institute of Physiotherapy and Health Sciences, The Jerzy Kukuczka Academy of Physical Education, 40-065 Katowice, Poland;
- Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229-3026, USA
| | - Kerstin Luedtke
- Institute of Health Sciences, Department of Physiotherapy, Pain and Exercise Research Luebeck (P.E.R.L.), Universität zu Lübeck, 23562 Lübeck, Germany; (L.L.); (T.M.S.); (K.L.)
- Center of Brain, Behavior and Metabolism (CBBM), University of Luebeck, 23562 Lübeck, Germany
| | - Marcin Grzegorzek
- Institute of Medical Informatics, University of Lübeck, 23562 Lübeck, Germany;
- Department of Knowledge Engineering, University of Economics in Katowice, 40-287 Katowice, Poland
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7
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Pinzon-Arenas JO, Kong Y, Chon KH, Posada-Quintero HF. Design and Evaluation of Deep Learning Models for Continuous Acute Pain Detection Based on Phasic Electrodermal Activity. IEEE J Biomed Health Inform 2023; 27:4250-4260. [PMID: 37399159 DOI: 10.1109/jbhi.2023.3291955] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Abstract
The current method for assessing pain in clinical practice is subjective and relies on self-reported scales. An objective and accurate method of pain assessment is needed for physicians to prescribe the proper medication dosage, which could reduce addiction to opioids. Hence, many works have used electrodermal activity (EDA) as a suitable signal for detecting pain. Previous studies have used machine learning and deep learning to detect pain responses, but none have used a sequence-to-sequence deep learning approach to continuously detect acute pain from EDA signals, as well as accurate detection of pain onset. In this study, we evaluated deep learning models including 1-dimensional convolutional neural networks (1D-CNN), long short-term memory networks (LSTM), and three hybrid CNN-LSTM architectures for continuous pain detection using phasic EDA features. We used a database consisting of 36 healthy volunteers who underwent pain stimuli induced by a thermal grill. We extracted the phasic component, phasic drivers, and time-frequency spectrum of the phasic EDA (TFS-phEDA), which was found to be the most discerning physiomarker. The best model was a parallel hybrid architecture of a temporal convolutional neural network and a stacked bi-directional and uni-directional LSTM, which obtained a F1-score of 77.8% and was able to correctly detect pain in 15-second signals. The model was evaluated using 37 independent subjects from the BioVid Heat Pain Database and outperformed other approaches in recognizing higher pain levels compared to baseline with an accuracy of 91.5%. The results show the feasibility of continuous pain detection using deep learning and EDA.
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Aurucci GV, Preatoni G, Damiani A, Raspopovic S. Brain-Computer Interface to Deliver Individualized Multisensory Intervention for Neuropathic Pain. Neurotherapeutics 2023; 20:1316-1329. [PMID: 37407726 PMCID: PMC10480109 DOI: 10.1007/s13311-023-01396-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/25/2023] [Indexed: 07/07/2023] Open
Abstract
To unravel the complexity of the neuropathic pain experience, researchers have tried to identify reliable pain signatures (biomarkers) using electroencephalography (EEG) and skin conductance (SC). Nevertheless, their use as a clinical aid to design personalized therapies remains scarce and patients are prescribed with common and inefficient painkillers. To address this need, novel non-pharmacological interventions, such as transcutaneous electrical nerve stimulation (TENS) to activate peripheral pain relief via neuromodulation and virtual reality (VR) to modulate patients' attention, have emerged. However, all present treatments suffer from the inherent bias of the patient's self-reported pain intensity, depending on their predisposition and tolerance, together with unspecific, pre-defined scheduling of sessions which does not consider the timing of pain episodes onset. Here, we show a Brain-Computer Interface (BCI) detecting in real-time neurophysiological signatures of neuropathic pain from EEG combined with SC and accordingly triggering a multisensory intervention combining TENS and VR. After validating that the multisensory intervention effectively decreased experimentally induced pain, the BCI was tested with thirteen healthy subjects by electrically inducing pain and showed 82% recall in decoding pain in real time. Such constructed BCI was then validated with eight neuropathic patients reaching 75% online pain precision, and consequently releasing the intervention inducing a significant decrease (50% NPSI score) in neuropathic patients' pain perception. Our results demonstrate the feasibility of real-time pain detection from objective neurophysiological signals, and the effectiveness of a triggered combination of VR and TENS to decrease neuropathic pain. This paves the way towards personalized, data-driven pain therapies using fully portable technologies.
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Affiliation(s)
- Giuseppe Valerio Aurucci
- Laboratory for Neuroengineering, Department of Health Science and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092, Zurich, Switzerland
| | - Greta Preatoni
- Laboratory for Neuroengineering, Department of Health Science and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092, Zurich, Switzerland
| | - Arianna Damiani
- Laboratory for Neuroengineering, Department of Health Science and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092, Zurich, Switzerland
| | - Stanisa Raspopovic
- Laboratory for Neuroengineering, Department of Health Science and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092, Zurich, Switzerland.
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Othman E, Werner P, Saxen F, Al-Hamadi A, Gruss S, Walter S. Automated Electrodermal Activity and Facial Expression Analysis for Continuous Pain Intensity Monitoring on the X-ITE Pain Database. Life (Basel) 2023; 13:1828. [PMID: 37763232 PMCID: PMC10533107 DOI: 10.3390/life13091828] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 08/14/2023] [Accepted: 08/25/2023] [Indexed: 09/29/2023] Open
Abstract
This study focuses on improving healthcare quality by introducing an automated system that continuously monitors patient pain intensity. The system analyzes the Electrodermal Activity (EDA) sensor modality modality, compares the results obtained from both EDA and facial expressions modalities, and late fuses EDA and facial expressions modalities. This work extends our previous studies of pain intensity monitoring via an expanded analysis of the two informative methods. The EDA sensor modality and facial expression analysis play a prominent role in pain recognition; the extracted features reflect the patient's responses to different pain levels. Three different approaches were applied: Random Forest (RF) baseline methods, Long-Short Term Memory Network (LSTM), and LSTM with the sample-weighting method (LSTM-SW). Evaluation metrics included Micro average F1-score for classification and Mean Squared Error (MSE) and intraclass correlation coefficient (ICC [3, 1]) for both classification and regression. The results highlight the effectiveness of late fusion for EDA and facial expressions, particularly in almost balanced datasets (Micro average F1-score around 61%, ICC about 0.35). EDA regression models, particularly LSTM and LSTM-SW, showed superiority in imbalanced datasets and outperformed guessing (where the majority of votes indicate no pain) and baseline methods (RF indicates Random Forest classifier (RFc) and Random Forest regression (RFr)). In conclusion, by integrating both modalities or utilizing EDA, they can provide medical centers with reliable and valuable insights into patients' pain experiences and responses.
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Affiliation(s)
- Ehsan Othman
- Department of Neuro-Information Technology, Institute for Information Technology and Communications, Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany; (P.W.); (F.S.); (A.A.-H.)
| | - Philipp Werner
- Department of Neuro-Information Technology, Institute for Information Technology and Communications, Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany; (P.W.); (F.S.); (A.A.-H.)
| | - Frerk Saxen
- Department of Neuro-Information Technology, Institute for Information Technology and Communications, Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany; (P.W.); (F.S.); (A.A.-H.)
| | - Ayoub Al-Hamadi
- Department of Neuro-Information Technology, Institute for Information Technology and Communications, Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany; (P.W.); (F.S.); (A.A.-H.)
| | - Sascha Gruss
- Department of Medical Psychology, Ulm University, 89081 Ulm, Germany; (S.G.); (S.W.)
| | - Steffen Walter
- Department of Medical Psychology, Ulm University, 89081 Ulm, Germany; (S.G.); (S.W.)
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Fernandez Rojas R, Hirachan N, Brown N, Waddington G, Murtagh L, Seymour B, Goecke R. Multimodal physiological sensing for the assessment of acute pain. FRONTIERS IN PAIN RESEARCH 2023; 4:1150264. [PMID: 37415829 PMCID: PMC10321707 DOI: 10.3389/fpain.2023.1150264] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 05/29/2023] [Indexed: 07/08/2023] Open
Abstract
Pain assessment is a challenging task encountered by clinicians. In clinical settings, patients' self-report is considered the gold standard in pain assessment. However, patients who are unable to self-report pain are at a higher risk of undiagnosed pain. In the present study, we explore the use of multiple sensing technologies to monitor physiological changes that can be used as a proxy for objective measurement of acute pain. Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) signals were collected from 22 participants under two pain intensities (low and high) and on two different anatomical locations (forearm and hand). Three machine learning models were implemented, including support vector machines (SVM), decision trees (DT), and linear discriminant analysis (LDA) for the identification of pain. Various pain scenarios were investigated, identification of pain (no pain, pain), multiclass (no pain, low pain, high pain), and identification of pain location (forearm, hand). Reference classification results from individual sensors and from all sensors together were obtained. After feature selection, results showed that EDA was the most informative sensor in the three pain conditions, 93.2±8% in identification of pain, 68.9±10% in the multiclass problem, and 56.0±8% for the identification of pain location. These results identify EDA as the superior sensor in our experimental conditions. Future work is required to validate the obtained features to improve its feasibility in more realistic scenarios. Finally, this study proposes EDA as a candidate to design a tool that can assist clinicians in the assessment of acute pain of nonverbal patients.
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Affiliation(s)
- Raul Fernandez Rojas
- Human-Centred Technology Research Centre, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia
| | - Niraj Hirachan
- Human-Centred Technology Research Centre, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia
| | - Nicholas Brown
- Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Gordon Waddington
- Australian Institute of Sport, Canberra, ACT, Australia
- University of Canberra Research Institute for Sport and Exercise (UCRISE), University of Canberra, Canberra, ACT, Australia
| | - Luke Murtagh
- Department of Anaesthesia, Pain and Perioperative Medicine, The Canberra Hospital, Canberra, ACT, Australia
| | - Ben Seymour
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, John Radcliffe Hospital, Headington, UK
- Oxford Institute for Biomedical Engineering, University of Oxford, Headington, UK
| | - Roland Goecke
- Human-Centred Technology Research Centre, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia
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Fernandez Rojas R, Brown N, Waddington G, Goecke R. A systematic review of neurophysiological sensing for the assessment of acute pain. NPJ Digit Med 2023; 6:76. [PMID: 37100924 PMCID: PMC10133304 DOI: 10.1038/s41746-023-00810-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 03/30/2023] [Indexed: 04/28/2023] Open
Abstract
Pain is a complex and personal experience that presents diverse measurement challenges. Different sensing technologies can be used as a surrogate measure of pain to overcome these challenges. The objective of this review is to summarise and synthesise the published literature to: (a) identify relevant non-invasive physiological sensing technologies that can be used for the assessment of human pain, (b) describe the analytical tools used in artificial intelligence (AI) to decode pain data collected from sensing technologies, and (c) describe the main implications in the application of these technologies. A literature search was conducted in July 2022 to query PubMed, Web of Sciences, and Scopus. Papers published between January 2013 and July 2022 are considered. Forty-eight studies are included in this literature review. Two main sensing technologies (neurological and physiological) are identified in the literature. The sensing technologies and their modality (unimodal or multimodal) are presented. The literature provided numerous examples of how different analytical tools in AI have been applied to decode pain. This review identifies different non-invasive sensing technologies, their analytical tools, and the implications for their use. There are significant opportunities to leverage multimodal sensing and deep learning to improve accuracy of pain monitoring systems. This review also identifies the need for analyses and datasets that explore the inclusion of neural and physiological information together. Finally, challenges and opportunities for designing better systems for pain assessment are also presented.
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Affiliation(s)
- Raul Fernandez Rojas
- Human-Centred Technology Research Centre, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia.
| | - Nicholas Brown
- Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Gordon Waddington
- Australian Institute of Sport, Canberra, ACT, Australia
- University of Canberra Research Institute for Sport and Exercise (UCRISE), University of Canberra, Canberra, ACT, Australia
| | - Roland Goecke
- Human-Centred Technology Research Centre, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia
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Gouverneur P, Li F, Shirahama K, Luebke L, Adamczyk WM, Szikszay TM, Luedtke K, Grzegorzek M. Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of Electrodermal Activity for Automated Pain Recognition. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23041959. [PMID: 36850556 PMCID: PMC9960387 DOI: 10.3390/s23041959] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/28/2023] [Accepted: 02/07/2023] [Indexed: 05/07/2023]
Abstract
Artificial intelligence and especially deep learning methods have achieved outstanding results for various applications in the past few years. Pain recognition is one of them, as various models have been proposed to replace the previous gold standard with an automated and objective assessment. While the accuracy of such models could be increased incrementally, the understandability and transparency of these systems have not been the main focus of the research community thus far. Thus, in this work, several outcomes and insights of explainable artificial intelligence applied to the electrodermal activity sensor data of the PainMonit and BioVid Heat Pain Database are presented. For this purpose, the importance of hand-crafted features is evaluated using recursive feature elimination based on impurity scores in Random Forest (RF) models. Additionally, Gradient-weighted class activation mapping is applied to highlight the most impactful features learned by deep learning models. Our studies highlight the following insights: (1) Very simple hand-crafted features can yield comparative performances to deep learning models for pain recognition, especially when properly selected with recursive feature elimination. Thus, the use of complex neural networks should be questioned in pain recognition, especially considering their computational costs; and (2) both traditional feature engineering and deep feature learning approaches rely on simple characteristics of the input time-series data to make their decision in the context of automated pain recognition.
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Affiliation(s)
- Philip Gouverneur
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Correspondence: ; Tel.: +49-451-3101-5613
| | - Frédéric Li
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Kimiaki Shirahama
- Faculty of Informatics, Kindai University, Higashiosaka 577-8502, Osaka, Japan
| | - Luisa Luebke
- Department of Physiotherapy, Pain and Exercise Research Luebeck (P.E.R.L.), Institute of Health Sciences, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Wacław M. Adamczyk
- Department of Physiotherapy, Pain and Exercise Research Luebeck (P.E.R.L.), Institute of Health Sciences, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Laboratory of Pain Research, Institute of Physiotherapy and Health Sciences, The Jerzy Kukuczka Academy of Physical Education, 40-065 Katowice, Poland
| | - Tibor M. Szikszay
- Department of Physiotherapy, Pain and Exercise Research Luebeck (P.E.R.L.), Institute of Health Sciences, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Kerstin Luedtke
- Department of Physiotherapy, Pain and Exercise Research Luebeck (P.E.R.L.), Institute of Health Sciences, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Marcin Grzegorzek
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Department of Knowledge Engineering, University of Economics in Katowice, Bogucicka 3, 40-287 Katowice, Poland
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Golzari K, Kong Y, Reed SA, Posada-Quintero HF. Sympathetic Arousal Detection in Horses Using Electrodermal Activity. Animals (Basel) 2023; 13:ani13020229. [PMID: 36670768 PMCID: PMC9855141 DOI: 10.3390/ani13020229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 01/10/2023] Open
Abstract
The continuous monitoring of stress, pain, and discomfort is key to providing a good quality of life for horses. The available tools based on observation are subjective and do not allow continuous monitoring. Given the link between emotions and sympathetic autonomic arousal, heart rate and heart rate variability are widely used for the non-invasive assessment of stress and pain in humans and horses. However, recent advances in pain and stress monitoring are increasingly using electrodermal activity (EDA), as it is a more sensitive and specific measure of sympathetic arousal than heart rate variability. In this study, for the first time, we have collected EDA signals from horses and tested the feasibility of the technique for the assessment of sympathetic arousal. Fifteen horses (six geldings, nine mares, aged 13.11 ± 5.4 years) underwent a long-lasting stimulus (Feeding test) and a short-lasting stimulus (umbrella Startle test) to elicit sympathetic arousal. The protocol was approved by the University of Connecticut. We found that EDA was sensitive to both stimuli. Our results show that EDA can capture sympathetic activation in horses and is a promising tool for non-invasive continuous monitoring of stress, pain, and discomfort in horses.
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Affiliation(s)
- Kia Golzari
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
| | - Youngsun Kong
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
| | - Sarah A. Reed
- Department of Animal Science, University of Connecticut, Storrs, CT 06269, USA
| | - Hugo F. Posada-Quintero
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
- Correspondence: ; Tel.: +1-(860)-486-1556
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Kong Y, Posada-Quintero HF, Chon KH. Multi-level Pain Quantification using a Smartphone and Electrodermal Activity. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2475-2478. [PMID: 36085748 DOI: 10.1109/embc48229.2022.9871228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Appropriate prescription of pain medication is challenging because pain is difficult to quantify due to the subjectiveness of pain assessment. Currently, clinicians must entirely rely on pain scales based on patients' assessments. This has been alleged to be one of the causes of drug overdose and addiction, and a contributor to the opioid crisis. Therefore, there is an urgent unmet need for objective pain assessment. Furthermore, as pain can occur anytime and anywhere, ambulatory pain monitoring would be welcomed in practice. In our previous study, we developed electrodermal activity (EDA)-derived indices and implemented them in a smartphone application that can communicate via Bluetooth to an EDA wearable device. While we previously showed high accuracy for high-level pain detection, multi-level pain detection has not been demonstrated. In this paper, we tested our smartphone application with a multi-level pain-induced dataset. The dataset was collected from fifteen subjects who underwent four levels of pain-inducing electrical pulse (EP) stimuli. We then performed statistical analyses and machine-learning techniques to classify multiple pain levels. Significant differences were observed in our EDA-derived indices among no-pain, low-pain, and high-pain segments. A random forest classifier showed 62.6% for the balanced accuracy, and a random forest regressor exhibited 0.441 for the coefficient of determination. Clinical Relevance - This is one of the first studies to present a smartphone application for detecting multiple levels of pain in real time using an EDA wearable device. This work shows the feasibility of ambulatory pain monitoring which can potentially be useful for chronic pain management.
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Hossain MB, Posada-Quintero HF, Kong Y, McNaboe R, Chon KH. Automatic motion artifact detection in electrodermal activity data using machine learning. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103483] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Bhatkar V, Picard R, Staahl C. Combining Electrodermal Activity With the Peak-Pain Time to Quantify Three Temporal Regions of Pain Experience. FRONTIERS IN PAIN RESEARCH 2022; 3:764128. [PMID: 35399152 PMCID: PMC8983966 DOI: 10.3389/fpain.2022.764128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 02/15/2022] [Indexed: 11/13/2022] Open
Abstract
Background Self-reported pain levels, while easily measured, are often not reliable for quantifying pain. More objective methods are needed that supplement self-report without adding undue burden or cost to a study. Methods that integrate multiple measures, such as combining self-report with physiology in a structured and specific-to-pain protocol may improve measures. Method We propose and study a novel measure that combines the timing of the peak pain measured by an electronic visual-analog-scale (eVAS) with continuously-measured changes in electrodermal activity (EDA), a physiological measure quantifying sympathetic nervous system activity that is easily recorded with a skin-surface sensor. The new pain measure isolates and specifically quantifies three temporal regions of dynamic pain experience: I. Anticipation preceding the onset of a pain stimulus, II. Response rising to the level of peak pain, and III. Recovery from the peak pain level. We evaluate the measure across two pain models (cold pressor, capsaicin), and four types of treatments (none, A=pregabalin, B=oxycodone, C=placebo). Each of 24 patients made four visits within 8 weeks, for 96 visits total: A training visit (TV), followed by three visits double-blind presenting A, B, or C (randomized order). Within each visit, a participant experienced the cold pressor, followed by an hour of rest during which one of the four treatments was provided, followed by a repeat of the cold pressor, followed by capsaicin. Results The novel method successfully discriminates the pain reduction effects of the four treatments across both pain models, confirming maximal pain for no-treatment, mild pain reduction for placebo, and the most pain reduction with analgesics. The new measure maintains significant discrimination across the test conditions both within a single-day's visit (for relative pain relief within a visit) and across repeated visits spanning weeks, reducing different-day-physiology affects, and providing better discriminability than using self-reported eVAS. Conclusion The new method combines the subjectively-identified time of peak pain with capturing continuous physiological data to quantify the sympathetic nervous system response during a dynamic pain experience. The method accurately discriminates, for both pain models, the reduction of pain with clinically effective analgesics.
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Affiliation(s)
- Viprali Bhatkar
- Digital Health Independent Consultant, Arlington, MA, United States
- *Correspondence: Viprali Bhatkar
| | | | - Camilla Staahl
- Novo Nordisk A/S, R&D Business Development, Copenhagen, Denmark
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Kong Y, Posada-Quintero HF, Chon KH. Female-male Differences Should be Considered in Physical Pain Quantification based on Electrodermal Activity: Preliminary Study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6941-6944. [PMID: 34892700 DOI: 10.1109/embc46164.2021.9630637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective pain quantification is an important but difficult goal. Electrodermal activity (EDA) has been widely explored for this purpose, given its reported sensitivity to pain. However, cognitive stress can hinder successful estimation of physical pain when using EDA signals. We collected EDA signals from ten subjects (5 male and 5 female) undergoing pain stimulation, and calculated phasic, tonic, and frequency-domain features. Each subject experienced pain with and without stress. Three low and three high pain sessions were induced using two thermal grills (low-level for visual analog scale [VAS] 4 or 5 and high-level for VAS 7 or more). The Stroop test was performed for inducing cognitive stress. Significant differences between EDA features of painless and pain segments were observed. Significant differences between no pain and stress were also observed. Furthermore, we compared differences in EDA features between females and males under pain and cognitive stress. Frequency-domain EDA features of pain increased with stress for both females and males. Frequency-domain features derived from females also showed higher standard deviation than did those derived from males. We performed machine learning analysis and evaluated the models using leave-one-subject-out cross-validation. We obtained balanced accuracies of 63.5%, 72.4%, and 53.2% (combined, male, and female) when using training data of the same sex and 47.6%, 57.4%, and 42.7% (combined, male, and female) when using different sex for training.Clinical Relevance-Our preliminary results suggest that sex of patients should be considered to increase the accuracy of pain quantification based on EDA in the presence of cognitive stress.
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Gouverneur P, Li F, Adamczyk WM, Szikszay TM, Luedtke K, Grzegorzek M. Comparison of Feature Extraction Methods for Physiological Signals for Heat-Based Pain Recognition. SENSORS 2021; 21:s21144838. [PMID: 34300578 PMCID: PMC8309734 DOI: 10.3390/s21144838] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/10/2021] [Accepted: 07/13/2021] [Indexed: 01/08/2023]
Abstract
While even the most common definition of pain is under debate, pain assessment has remained the same for decades. But the paramount importance of precise pain management for successful healthcare has encouraged initiatives to improve the way pain is assessed. Recent approaches have proposed automatic pain evaluation systems using machine learning models trained with data coming from behavioural or physiological sensors. Although yielding promising results, machine learning studies for sensor-based pain recognition remain scattered and not necessarily easy to compare to each other. In particular, the important process of extracting features is usually optimised towards specific datasets. We thus introduce a comparison of feature extraction methods for pain recognition based on physiological sensors in this paper. In addition, the PainMonit Database (PMDB), a new dataset including both objective and subjective annotations for heat-induced pain in 52 subjects, is introduced. In total, five different approaches including techniques based on feature engineering and feature learning with deep learning are evaluated on the BioVid and PMDB datasets. Our studies highlight the following insights: (1) Simple feature engineering approaches can still compete with deep learning approaches in terms of performance. (2) More complex deep learning architectures do not yield better performance compared to simpler ones. (3) Subjective self-reports by subjects can be used instead of objective temperature-based annotations to build a robust pain recognition system.
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Affiliation(s)
- Philip Gouverneur
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany; (F.L.); (M.G.)
- Correspondence: ; Tel.: +49-451-3101-5613
| | - Frédéric Li
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany; (F.L.); (M.G.)
| | - Wacław M. Adamczyk
- Institute of Health Sciences, Department of Physiotherapy, Pain and Exercise Research Lübeck (P.E.R.L.), University of Lübeck, 23562 Lübeck, Germany; (W.M.A.); (T.M.S.); (K.L.)
- Laboratory of Pain Research, Institute of Physiotherapy and Health Sciences, The Jerzy Kukuczka Academy of Physical Education, 40-959 Katowice, Poland
| | - Tibor M. Szikszay
- Institute of Health Sciences, Department of Physiotherapy, Pain and Exercise Research Lübeck (P.E.R.L.), University of Lübeck, 23562 Lübeck, Germany; (W.M.A.); (T.M.S.); (K.L.)
- Laboratory of Pain Research, Institute of Physiotherapy and Health Sciences, The Jerzy Kukuczka Academy of Physical Education, 40-959 Katowice, Poland
| | - Kerstin Luedtke
- Institute of Health Sciences, Department of Physiotherapy, Pain and Exercise Research Lübeck (P.E.R.L.), University of Lübeck, 23562 Lübeck, Germany; (W.M.A.); (T.M.S.); (K.L.)
- Laboratory of Pain Research, Institute of Physiotherapy and Health Sciences, The Jerzy Kukuczka Academy of Physical Education, 40-959 Katowice, Poland
| | - Marcin Grzegorzek
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany; (F.L.); (M.G.)
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Kong Y, Posada-Quintero HF, Chon KH. Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor. SENSORS (BASEL, SWITZERLAND) 2021; 21:3956. [PMID: 34201268 PMCID: PMC8227650 DOI: 10.3390/s21123956] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 06/02/2021] [Accepted: 06/03/2021] [Indexed: 01/02/2023]
Abstract
The subjectiveness of pain can lead to inaccurate prescribing of pain medication, which can exacerbate drug addiction and overdose. Given that pain is often experienced in patients' homes, there is an urgent need for ambulatory devices that can quantify pain in real-time. We implemented three time- and frequency-domain electrodermal activity (EDA) indices in our smartphone application that collects EDA signals using a wrist-worn device. We then evaluated our computational algorithms using thermal grill data from ten subjects. The thermal grill delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). Furthermore, we simulated the real-time processing of the smartphone application using a dataset pre-collected from another group of fifteen subjects who underwent pain stimulation using electrical pulses, which elicited a VAS pain score level 7 out of 10. All EDA features showed significant difference between painless and pain segments, termed for the 5-s segments before and after each pain stimulus. Random forest showed the highest accuracy in detecting pain, 81.5%, with 78.9% sensitivity and 84.2% specificity with leave-one-subject-out cross-validation approach. Our results show the potential of a smartphone application to provide near real-time objective pain detection.
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Affiliation(s)
| | | | - Ki H. Chon
- Biomedical Engineering Department, University of Connecticut, Storrs, CT 06269, USA; (Y.K.); (H.F.P.-Q.)
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