Exploring Biosignals for Quantitative Pain Assessment in Cancer Patients: A Proof of Concept
Perception and expression of pain in cancer patients are influenced by distress levels, tumor type and progression, and the underlying pathophysiology of pain. Relying on traditional pain assessment tools can present limitations due to the highly subjective and multifaceted nature of the symptoms. I...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/17/3716 |
| _version_ | 1797582599899578368 |
|---|---|
| author | Marco Cascella Vincenzo Norman Vitale Michela D’Antò Arturo Cuomo Francesco Amato Maria Romano Alfonso Maria Ponsiglione |
| author_facet | Marco Cascella Vincenzo Norman Vitale Michela D’Antò Arturo Cuomo Francesco Amato Maria Romano Alfonso Maria Ponsiglione |
| author_sort | Marco Cascella |
| collection | DOAJ |
| description | Perception and expression of pain in cancer patients are influenced by distress levels, tumor type and progression, and the underlying pathophysiology of pain. Relying on traditional pain assessment tools can present limitations due to the highly subjective and multifaceted nature of the symptoms. In this scenario, objective pain assessment is an open research challenge. This work introduces a framework for automatic pain assessment. The proposed method is based on a wearable biosignal platform to extract quantitative indicators of the patient pain experience, evaluated through a self-assessment report. Two preliminary case studies focused on the simultaneous acquisition of electrocardiography (ECG), electrodermal activity (EDA), and accelerometer signals are illustrated and discussed. The results demonstrate the feasibility of the approach, highlighting the potential of EDA in capturing skin conductance responses (SCR) related to pain events in chronic cancer pain. A weak correlation (R = 0.2) is found between SCR parameters and the standard deviation of the interbeat interval series (SDRR), selected as the Heart Rate Variability index. A statistically significant (<i>p</i> < 0.001) increase in both EDA signal and SDRR is detected in movement with respect to rest conditions (assessed by means of the accelerometer signals) in the case of motion-associated cancer pain, thus reflecting the relationship between motor dynamics, which trigger painful responses, and the subsequent activation of the autonomous nervous system. With the objective of integrating parameters obtained from biosignals to establish pain signatures within different clinical scenarios, the proposed framework proves to be a promising research approach to define pain signatures in different clinical contexts. |
| first_indexed | 2024-03-10T23:24:39Z |
| format | Article |
| id | doaj.art-5ed8bc0cefec4e04a2edbc826e3a83cc |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T23:24:39Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-5ed8bc0cefec4e04a2edbc826e3a83cc2023-11-19T08:03:02ZengMDPI AGElectronics2079-92922023-09-011217371610.3390/electronics12173716Exploring Biosignals for Quantitative Pain Assessment in Cancer Patients: A Proof of ConceptMarco Cascella0Vincenzo Norman Vitale1Michela D’Antò2Arturo Cuomo3Francesco Amato4Maria Romano5Alfonso Maria Ponsiglione6Department of Anesthesia and Critical Care, Istituto Nazionale Tumori-IRCCS Fondazione Pascale, 80100 Naples, ItalyInterdepartmental Research Center URBAN/ECO, University of Naples “Federico II”, 80127 Naples, ItalyDepartment of Anesthesia and Critical Care, Istituto Nazionale Tumori-IRCCS Fondazione Pascale, 80100 Naples, ItalyDepartment of Anesthesia and Critical Care, Istituto Nazionale Tumori-IRCCS Fondazione Pascale, 80100 Naples, ItalyDepartment of Information Technology and Electrical Engineering, University of Naples “Federico II”, 80125 Naples, ItalyDepartment of Information Technology and Electrical Engineering, University of Naples “Federico II”, 80125 Naples, ItalyDepartment of Information Technology and Electrical Engineering, University of Naples “Federico II”, 80125 Naples, ItalyPerception and expression of pain in cancer patients are influenced by distress levels, tumor type and progression, and the underlying pathophysiology of pain. Relying on traditional pain assessment tools can present limitations due to the highly subjective and multifaceted nature of the symptoms. In this scenario, objective pain assessment is an open research challenge. This work introduces a framework for automatic pain assessment. The proposed method is based on a wearable biosignal platform to extract quantitative indicators of the patient pain experience, evaluated through a self-assessment report. Two preliminary case studies focused on the simultaneous acquisition of electrocardiography (ECG), electrodermal activity (EDA), and accelerometer signals are illustrated and discussed. The results demonstrate the feasibility of the approach, highlighting the potential of EDA in capturing skin conductance responses (SCR) related to pain events in chronic cancer pain. A weak correlation (R = 0.2) is found between SCR parameters and the standard deviation of the interbeat interval series (SDRR), selected as the Heart Rate Variability index. A statistically significant (<i>p</i> < 0.001) increase in both EDA signal and SDRR is detected in movement with respect to rest conditions (assessed by means of the accelerometer signals) in the case of motion-associated cancer pain, thus reflecting the relationship between motor dynamics, which trigger painful responses, and the subsequent activation of the autonomous nervous system. With the objective of integrating parameters obtained from biosignals to establish pain signatures within different clinical scenarios, the proposed framework proves to be a promising research approach to define pain signatures in different clinical contexts.https://www.mdpi.com/2079-9292/12/17/3716biosignalselectrodermal activityheart rate variabilitycancer painautomatic pain assessment |
| spellingShingle | Marco Cascella Vincenzo Norman Vitale Michela D’Antò Arturo Cuomo Francesco Amato Maria Romano Alfonso Maria Ponsiglione Exploring Biosignals for Quantitative Pain Assessment in Cancer Patients: A Proof of Concept Electronics biosignals electrodermal activity heart rate variability cancer pain automatic pain assessment |
| title | Exploring Biosignals for Quantitative Pain Assessment in Cancer Patients: A Proof of Concept |
| title_full | Exploring Biosignals for Quantitative Pain Assessment in Cancer Patients: A Proof of Concept |
| title_fullStr | Exploring Biosignals for Quantitative Pain Assessment in Cancer Patients: A Proof of Concept |
| title_full_unstemmed | Exploring Biosignals for Quantitative Pain Assessment in Cancer Patients: A Proof of Concept |
| title_short | Exploring Biosignals for Quantitative Pain Assessment in Cancer Patients: A Proof of Concept |
| title_sort | exploring biosignals for quantitative pain assessment in cancer patients a proof of concept |
| topic | biosignals electrodermal activity heart rate variability cancer pain automatic pain assessment |
| url | https://www.mdpi.com/2079-9292/12/17/3716 |
| work_keys_str_mv | AT marcocascella exploringbiosignalsforquantitativepainassessmentincancerpatientsaproofofconcept AT vincenzonormanvitale exploringbiosignalsforquantitativepainassessmentincancerpatientsaproofofconcept AT micheladanto exploringbiosignalsforquantitativepainassessmentincancerpatientsaproofofconcept AT arturocuomo exploringbiosignalsforquantitativepainassessmentincancerpatientsaproofofconcept AT francescoamato exploringbiosignalsforquantitativepainassessmentincancerpatientsaproofofconcept AT mariaromano exploringbiosignalsforquantitativepainassessmentincancerpatientsaproofofconcept AT alfonsomariaponsiglione exploringbiosignalsforquantitativepainassessmentincancerpatientsaproofofconcept |