Pain Assessment based on fNIRS using Bi-LSTM RNNs

Raul Fernandez Rojas, Julio Romero, Jehu Lopez-Aparicio, Keng-Liang Ou

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

Abstract

Assessing pain in patients unable to speak (also called non-verbal patients) is extremely complicated and often is done by clinical judgement. However, this method is not reliable since patients' vital signs can fluctuate significantly due to other underlying medical conditions. No objective diagnosis test exists to date that can assist medical practitioners in the diagnosis of pain. In this study we propose the use of functional near-infrared spectroscopy (fNIRS) and deep learning for the assessment of human pain. The aim of this study is to explore the use deep learning to automatically learn features from fNIRS raw data to reduce the level of subjectivity and domain knowledge required in the design of hand-crafted features. Four deep learning models were evaluated, multilayer perceptron (MLP), forward and backward long short-term memory net-works (LSTM), and bidirectional LSTM. The results showed that the Bi-LSTM model achieved the highest accuracy (90.6%) and faster to train than the other three models. These results represent a step forward in the development of a physiologically-based diagnosis of human pain, that will assist clinicians in the assessment of populations who cannot self-report pain.
Original languageEnglish
Title of host publicationProceedings of the 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
EditorsSilvestro Micera, Thomas Stieglitz
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages399-402
Number of pages4
ISBN (Electronic)9781728143378
ISBN (Print)9781728143385
DOIs
Publication statusPublished - 2 Jun 2021
Event10th International IEEE/EMBS Conference on Neural Engineering, NER 2021 - Italy, Italy, Italy
Duration: 4 May 20216 May 2021
https://neuro.embs.org/2021/

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2021-May
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
Abbreviated titleNER 2021
Country/TerritoryItaly
CityItaly
Period4/05/216/05/21
Internet address

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