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Faculté des Sciences appliquées
Faculté des Sciences appliquées
MASTER THESIS
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Development of an automatic drowsiness monitoring system using the electrocardiogram

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Bourghelle, Florent ULiège
Promotor(s) : Verly, Jacques ULiège
Date of defense : 27-Jun-2016/28-Jun-2016 • Permalink : http://hdl.handle.net/2268.2/1451
Details
Title : Development of an automatic drowsiness monitoring system using the electrocardiogram
Translated title : [fr] Développement d'un système de surveillance automatique de la somnolence à l'aide de l'électrocardiogramme
Author : Bourghelle, Florent ULiège
Date of defense  : 27-Jun-2016/28-Jun-2016
Advisor(s) : Verly, Jacques ULiège
Committee's member(s) : Geurts, Pierre ULiège
Phillips, Christophe ULiège
Embrechts, Jean-Jacques ULiège
Kolh, Philippe ULiège
Language : English
Number of pages : 81
Keywords : [en] Drowsiness, ECG
Discipline(s) : Engineering, computing & technology > Electrical & electronics engineering
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur civil électricien, à finalité approfondie
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[en] The aim of this thesis consists of the development of an automatic drowsiness monitoring system based on the electrocardiogram (ECG). Moreover, as the feasibility of this physiological signal to detect drowsiness is still not proved, this thesis also investigates its feasibility.

This thesis is based on an experiment were subjects were sleep deprived during 28 hours. At 3 specific moments of sleep deprivation, subjects performed psychomotor vigilance task (PVT). During these tasks, different physiological signals whose electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) were recorded. Based on the EEG and EOG signals, which are the references to assess drowsiness, the true state of each subject is known on the Karolinska Drowsiness Scale and can be defined as awake or drowsy given a defined threshold.

First, this thesis performs a review of the literature to find the possible parameters indicative of drowsiness computed from the ECG. Then, a complete processing chain of the ECG signal is implemented to be able to compute these parameters in the time and statistical domains, the non-linear domain, and finally in the frequency domain from the raw ECG of the subjects. As the respiratory signal can be derived from the ECG (ECG-Derived Respiration signal), this thesis also incorporates parameters from the respiratory domain in order to see if this domain can be use to detect drowsiness.

Once these parameters are computed, a machine learning phase is developed. During this phase, the issue of the variability of the features between the subjects was highlighted. Several techniques to compensate this variability have been tested but none improved the results obtained. This variability makes the system developed to be not reliable enough on all the subjects of the experiment to only use the ECG to predict drowsiness.


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Author

  • Bourghelle, Florent ULiège Université de Liège > Master ingé. civ. électr., fin. appr. (ex 2e master)

Promotor(s)

Committee's member(s)

  • Geurts, Pierre ULiège Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
    ORBi View his publications on ORBi
  • Phillips, Christophe ULiège Université de Liège - ULg > Centre de recherches du cyclotron
    ORBi View his publications on ORBi
  • Embrechts, Jean-Jacques ULiège Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Techniques du son et de l'image
    ORBi View his publications on ORBi
  • Kolh, Philippe ULiège Université de Liège - ULg > Département des sciences biomédicales et précliniques > Biochimie et physiologie générales, humaines et path.
    ORBi View his publications on ORBi
  • Total number of views 120
  • Total number of downloads 1277










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