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.
Highlights
Biomedical signals if processed correctly and efficiently have potential to facilitate advanced monitoring, diagnosis and treatment planning
This paper presents a physiological sensor signal classification approach based on sensor fusion to determine mental state in terms of Stressed or Relaxed
This paper presents physiological sensor signal classification approach based on the data-level and decision-level fusion
Summary
Biomedical signals if processed correctly and efficiently have potential to facilitate advanced monitoring, diagnosis and treatment planning. Lee and Chung [1] have proposed a system for monitoring driver safety levels in smart phones based on data fusion In the system, they have fused several parameters i.e., heart rate variability, blood pressure, in-vehicle temperature, vehicle speed and percentage of eyelid closure. The authors have compared the results with other methods such as linear discriminant function, support vector machine, k-nearest neighbour (kNN), naïve Bayes and C4.5 They have suggested that the fusion based method is an efficient approach and it improves performances of the stress identification system. This paper presents a physiological sensor signal classification approach based on sensor fusion to determine mental state in terms of Stressed or Relaxed. It investigates decision-level and data-level fusion in a Case-base classification system.
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