ECG sensor signal analysis to represent cases in a case-based stress diagnosis system

Begum, Shahina; Ahmed, Mobyen Uddin; Funk, Peter

doi:10.1109/itab.2010.5687657

Cited by 6 publications

(1 citation statement)

References 22 publications

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“…Feature extraction and selection have been conducted using the traditional approaches discussed in [ 18 , 19 , 21 , 23 , 26 , 27 ]. Here, time and frequency domains features are extracted from the HR, RR, SpO 2 and CO 2 signals.…”

Section: Sensor Signals Classification Using Decision-level Fusionmentioning

confidence: 99%

Physiological Sensor Signals Classification for Healthcare Using Sensor Data Fusion and Case-Based Reasoning

Begum

Barua

Ahmed

2014

Sensors

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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.

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Section: Sensor Signals Classification Using Decision-level Fusionmentioning

confidence: 99%

Physiological Sensor Signals Classification for Healthcare Using Sensor Data Fusion and Case-Based Reasoning

Begum

Barua

Ahmed

2014

Sensors

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show abstract

Artificial Intelligence, Machine Learning and Reasoning in Health Informatics—Case Studies

Ahmed

Begum

2020

Intelligent Systems Reference Library

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Classification of physiological signals for wheel loader operators using Multi-scale Entropy analysis and case-based reasoning

Begum

Barua

Filla

et al. 2014

Expert Systems with Applications

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ECG sensor signal analysis to represent cases in a case-based stress diagnosis system

Cited by 6 publications

References 22 publications

Physiological Sensor Signals Classification for Healthcare Using Sensor Data Fusion and Case-Based Reasoning

Physiological Sensor Signals Classification for Healthcare Using Sensor Data Fusion and Case-Based Reasoning

Artificial Intelligence, Machine Learning and Reasoning in Health Informatics—Case Studies

Classification of physiological signals for wheel loader operators using Multi-scale Entropy analysis and case-based reasoning

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