title: Physiological sensor signals classification for healthcare using sensor data fusion and case-based reasoning.
abstract: Today, clinicians often do diagnosis and classification of diseases based on information collected from several physiological sensor signals. However, sensor signal could easily be vulnerable to uncertain noises or interferences and due to large individual variations sensitivity to different physiological sensors could also vary. Therefore, multiple sensor signal fusion is valuable to provide more robust and reliable decision. This paper demonstrates a physiological sensor signal classification approach using sensor signal fusion and case-based reasoning. The proposed approach has been evaluated to classify Stressed or Relaxed individuals using sensor data fusion. Physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, sensor fusion has been done in two different ways: (i) decision-level fusion using features extracted through traditional approaches; and (ii) data-level fusion using features extracted by means of Multivariate Multiscale Entropy (MMSE). Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems.
authors.affiliation: School of Innovation, Design and Engineering, Mälardalen University, SE-72123 Västerås, Sweden. shahina.begum@mdh.se.
authors.forename: Shahina
authors.initials: S
authors.lastname: Begum
authors.affiliation: School of Innovation, Design and Engineering, Mälardalen University, SE-72123 Västerås, Sweden. shaibal.barua@mdh.se.
authors.forename: Shaibal
authors.initials: S
authors.lastname: Barua
authors.affiliation: School of Innovation, Design and Engineering, Mälardalen University, SE-72123 Västerås, Sweden. mobyen.ahmed@mdh.se.
authors.forename: Mobyen Uddin
authors.initials: MU
authors.lastname: Ahmed
mesh_terms: D000328:Adult; D001185:Artificial Intelligence; D001831:Body Temperature; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D005260:Female; D006339:Heart Rate; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011659:Pulmonary Gas Exchange; D056152:Respiratory Rate; D013315:Stress, Psychological / Q000175:diagnosis* / Q000503:physiopathology*