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Sensors (Basel). 2019 Jan 18;19(2). pii: E384. doi: 10.3390/s19020384.

A Deep Neural Network-Based Pain Classifier Using a Photoplethysmography Signal.

Author information

1
Department of Medical Engineering, Yonsei University College of Medicine, Seoul 03722, Korea. hhyunjjun@naver.com.
2
Department of Medical Engineering, Yonsei University College of Medicine, Seoul 03722, Korea. bnkim007@gmail.com.
3
Department of Anaesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea. nohgj@amc.seoul.kr.
4
Department of Clinical Pharmacology and Therapeutics, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea. nohgj@amc.seoul.kr.
5
Department of Medical Engineering, Yonsei University College of Medicine, Seoul 03722, Korea. sunkyoo@yuhs.ac.

Abstract

Side effects occur when excessive or low doses of analgesics are administered compared to the required amount to mediate the pain induced during surgery. It is important to accurately assess the pain level of the patient during surgery. We proposed a pain classifier based on a deep belief network (DBN) using photoplethysmography (PPG). Our DBN learned about a complex nonlinear relationship between extracted PPG features and pain status based on the numeric rating scale (NRS). A bagging ensemble model was used to improve classification performance. The DBN classifier showed better classification results than multilayer perceptron neural network (MLPNN) and support vector machine (SVM) models. In addition, the classification performance was improved when the selective bagging model was applied compared with the use of each single model classifier. The pain classifier based on DBN using a selective bagging model can be helpful in developing a pain classification system.

KEYWORDS:

bagging; deep belief network; pain; photoplethysmography

PMID:
30669327
PMCID:
PMC6358962
DOI:
10.3390/s19020384
[Indexed for MEDLINE]
Free PMC Article

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