Imperial College London
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ProfessorDaniloMandic

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Machine Intelligence
 
 
 
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Contact

 

+44 (0)20 7594 6271d.mandic Website

 
 
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Assistant

 

Miss Vanessa Rodriguez-Gonzalez +44 (0)20 7594 6267

 
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Location

 

813Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Adjei:2017:10.1109/JTEHM.2017.2734647,
author = {Adjei, T and von, Rosenberg W and Goverdovsky, V and Powezka, K and Jaffer, U and Mandic, D},
doi = {10.1109/JTEHM.2017.2734647},
journal = {IEEE Journal of Translational Engineering in Health and Medicine},
title = {Pain prediction from ECG in vascular surgery},
url = {http://dx.doi.org/10.1109/JTEHM.2017.2734647},
volume = {5},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Varicose vein surgeries are routine outpatient procedures, which are often performed under local anaesthesia. The use of local anaesthesia both minimises the risk to patients and is cost effective, however, a number of patients still experience pain during surgery. Surgical teams must therefore decide to administer either a general or local anaesthetic based on their subjective qualitative assessment of patient anxiety and sensitivity to pain, without any means to objectively validate their decision. To this end, we develop a 3-D polynomial surface fit, of physiological metrics and numerical pain ratings from patients, in order to model the link between the modulation of cardiovascular responses and pain in varicose vein surgeries. Spectral and structural complexity features found in heart rate variability signals, recorded immediately prior to 17 varicose vein surgeries, are used as pain metrics. The so obtained pain prediction model is validated through a leave-one-out validation, and achieved a Kappa coefficient of 0.72 (substantial agreement) and an area below a receiver operating characteristic curve of 0.97 (almost perfect accuracy). This proof-of-concept study conclusively demonstrates the feasibility of the accurate classification of pain sensitivity, and introduces a mathematical model to aid clinicians in the objective administration of the safest and most cost-effective anaesthetic to individual patients.
AU  - Adjei,T
AU  - von,Rosenberg W
AU  - Goverdovsky,V
AU  - Powezka,K
AU  - Jaffer,U
AU  - Mandic,D
DO  - 10.1109/JTEHM.2017.2734647
PY  - 2017///
SN  - 2168-2372
TI  - Pain prediction from ECG in vascular surgery
T2  - IEEE Journal of Translational Engineering in Health and Medicine
UR  - http://dx.doi.org/10.1109/JTEHM.2017.2734647
UR  - http://hdl.handle.net/10044/1/49886
VL  - 5
ER  -
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