This paper is in the following e-collection/theme issue:

Wearable Devices and Sensors (818) Digital Pain Assessment and Management (150) Registered Report (518) Postoperative Pain Management (26) Machine Learning (1384)

Published on in Vol 9, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25258, first published .
Pain Assessment Tool With Electrodermal Activity for Postoperative Patients: Method Validation Study

Pain Assessment Tool With Electrodermal Activity for Postoperative Patients: Method Validation Study

Pain Assessment Tool With Electrodermal Activity for Postoperative Patients: Method Validation Study

Authors of this article:

Seyed Amir Hossein Aqajari1 Author Orcid Image ;   Rui Cao1 Author Orcid Image ;   Emad Kasaeyan Naeini2 Author Orcid Image ;   Michael-David Calderon3 Author Orcid Image ;   Kai Zheng4 Author Orcid Image ;   Nikil Dutt2 Author Orcid Image ;   Pasi Liljeberg5 Author Orcid Image ;   Sanna Salanterä6, 7 Author Orcid Image ;   Ariana M Nelson3, 8 Author Orcid Image ;   Amir M Rahmani1, 2, 9, 10 Author Orcid Image
Article Authors Cited by (31) Tweetations (5) Metrics

Journals

  1. Kong Y, Posada-Quintero H, Chon K. Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor. Sensors 2021;21(12):3956 View
  2. Wells C, Xu W, Penfold J, Keane C, Gharibans A, Bissett I, O’Grady G. Wearable devices to monitor recovery after abdominal surgery: scoping review. BJS Open 2022;6(2) View
  3. Afandizadeh Zargari A, Aqajari S, Khodabandeh H, Rahmani A, Kurdahi F. An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN. ACM Transactions on Computing for Healthcare 2023;4(1):1 View
  4. Tronstad C, Amini M, Bach D, Martinsen Ø. Current trends and opportunities in the methodology of electrodermal activity measurement. Physiological Measurement 2022;43(2):02TR01 View
  5. 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 View
  6. Somani S, Yu K, Chiu A, Sykes K, Villwock J. Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review. Otolaryngology–Head and Neck Surgery 2022;167(4):620 View
  7. Montero Quispe K, Utyiama D, dos Santos E, Oliveira H, Souto E. Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals. Sensors 2022;22(23):9102 View
  8. Kong Y, Posada-Quintero H, Tran H, Talati A, Acquista T, Chen I, Chon K. Differentiating between stress- and EPT-induced electrodermal activity during dental examination. Computers in Biology and Medicine 2023;155:106695 View
  9. Liikkanen S, Mäkinen M, Huttunen T, Sarapohja T, Stenfors C, Eccleston C. Body movement as a biomarker for use in chronic pain rehabilitation: An embedded analysis of an RCT of a virtual reality solution for adults with chronic pain. Frontiers in Pain Research 2022;3 View
  10. Klimek A, Mannheim I, Schouten G, Wouters E, Peeters M. Wearables measuring electrodermal activity to assess perceived stress in care: a scoping review. Acta Neuropsychiatrica 2023:1 View
  11. Gröhn T, Liikkanen S, Huttunen T, Mäkinen M, Liljeberg P, Marttinen P. Quantifying Movement Behavior of Chronic Low Back Pain Patients in Virtual Reality. ACM Transactions on Computing for Healthcare 2023;4(2):1 View
  12. Fernandez Rojas R, Brown N, Waddington G, Goecke R. A systematic review of neurophysiological sensing for the assessment of acute pain. npj Digital Medicine 2023;6(1) View
  13. Johansen A, Mølgaard J, Rasmussen S, Gu Y, Grønbæk K, Sørensen H, Aasvang E, Meyhoff C. Deviations in continuously monitored electrodermal activity before severe clinical complications: a clinical prospective observational explorative cohort study. Journal of Clinical Monitoring and Computing 2023;37(6):1573 View
  14. 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 View
  15. Pinzon-Arenas J, Kong Y, Chon K, Posada-Quintero H. Design and Evaluation of Deep Learning Models for Continuous Acute Pain Detection Based on Phasic Electrodermal Activity. IEEE Journal of Biomedical and Health Informatics 2023;27(9):4250 View
  16. Sen K, Maji U, Pal S. GSR-Based Auto-Monitoring of Pain Initiation/Elimination Time Using Nonlinear Dynamic Model. IEEE Sensors Journal 2023;23(22):28120 View
  17. Zargari A, AshrafiAmiri M, Seo M, Pudukotai Dinakarrao S, Fouda M, Kurdahi F. CAPTIVE: Constrained Adversarial Perturbations to Thwart IC Reverse Engineering. Information 2023;14(12):656 View
  18. Gkikas S, Tachos N, Andreadis S, Pezoulas V, Zaridis D, Gkois G, Matonaki A, Stavropoulos T, Fotiadis D. Multimodal automatic assessment of acute pain through facial videos and heart rate signals utilizing transformer-based architectures. Frontiers in Pain Research 2024;5 View
  19. Wang H, Wang Q, He Q, Li S, Zhao Y, Zuo Y. Current perioperative nociception monitoring and potential directions. Asian Journal of Surgery 2024;47(6):2558 View
  20. Albahdal D, Aljebreen W, Ibrahim D. PainMeter: Automatic Assessment of Pain Intensity Levels From Multiple Physiological Signals Using Machine Learning. IEEE Access 2024;12:48349 View
  21. Janssen Daalen J, van den Bergh R, Prins E, Moghadam M, van den Heuvel R, Veen J, Mathur S, Meijerink H, Mirelman A, Darweesh S, Evers L, Bloem B. Digital biomarkers for non-motor symptoms in Parkinson’s disease: the state of the art. npj Digital Medicine 2024;7(1) View

Books/Policy Documents

  1. Bieńkowska M, Badura A, Myśliwiec A, Pietka E. Information Technology in Biomedicine. View
  2. Kanduri A, Shahhosseini S, Naeini E, Alikhani H, Liljeberg P, Dutt N, Rahmani A. Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing. View