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A Fusion Based System for Physiological Sensor Signal Classification
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1212-7637
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-7305-7169
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-5562-1424
2014 (English)In: Medicinteknikdagarna 2014 MTD10, 2014Conference paper, Published paper (Refereed)
Abstract [en]

Today, usage of physiological sensor signals is essential in medical applications for diagnoses and classification of diseases. Clinicians often rely on information collected from several physiological sensor signals to diagnose a patient. However, sensor signals are mostly non-stationary and noisy, and single sensor signal could easily be contaminated by uncertain noises and interferences that could cause miscalculation of measurements and reduce clinical usefulness. Therefore, an apparent choice is to use multiple sensor signals that could provide more robust and reliable decision. Therefore, a physiological signal classification approach is presented based on sensor signal fusion and case-based reasoning. To classify Stressed and Relaxed individuals from physiological signals, data level and decision level fusion are performed and case-based reasoning is applied as classification algorithm. Five 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, data level fusion is performed using Multivariate Multiscale Entropy (MMSE) and extracted features are then used to build a case- library. Decision level fusion is performed on the features extracted using traditional time and frequency domain analysis. 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.

Place, publisher, year, edition, pages
2014.
Keyword [en]
Sensor FusionPhysiological DataMultivariate Multiscale Entropy AnalysisCase-Based Reasoning
National Category
Medical Engineering Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-28144OAI: oai:DiVA.org:mdh-28144DiVA: diva2:818328
Conference
Medicinteknikdagarna 2014 MTD10, 14-16 Oct 2014, Göteborg, Sweden
Projects
VDM - Vehicle Driver Monitoring
Available from: 2015-06-08 Created: 2015-06-08 Last updated: 2017-01-25Bibliographically approved

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Begum, ShahinaBarua, ShaibalAhmed, Mobyen UddinFunk, Peter

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