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Tesi etd-04082019-141024


Thesis type
Tesi di laurea magistrale
Author
WORKU, FELEKE FISEHA
URN
etd-04082019-141024
Title
Using contactless bed sensors for ballistocardiographic identification of sleep stages
Struttura
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
COMPUTER ENGINEERING
Commissione
relatore Prof. Cimino, Mario Giovanni Cosimo Antonio
relatore Vaglini, Gigliola
relatore Palumbo, Filippo
relatore Crivello, Antonino
Parole chiave
  • sleep stages
  • ballistocardiographic
Data inizio appello
03/05/2019;
Consultabilità
secretata d'ufficio
Data di rilascio
03/05/2022
Riassunto analitico
The aim of this thesis is to analyze and identify sleep stages using ballistocardiography(BCG) sleep monitoring method. Identifying sleep stages by studying the behaviour of heart rate respiration rate, and movement properties is one of the important indicator of sleep problems.

There is no doubt the consequence of Sleep problem has a huge impact on our mental , physical health, weight gain, and productivity. To analysis sleep problems golden standard method is Polysomnography(PCG). However, PCG method is expensive, intrusive ,complicated and measured in controlled environment. Due to this, for future use it is important to analysis non-intrusive long-term physiological monitoring ballistocardiography(BCG) method.

In this thesis, ballistocardiography monitoring method is used to analyze and identify sleep stages.In BCG-based monitoring system developed to measure heart rate, respiratory rate, heart rate variability and movements, and able to drive other important parameters like respiratory rate variability and respiratory depth which are important to analyze and identify sleep stages.

Nowadays, there are a different kind of BCG sensors are available in the markets.For this thesis, BCG sensor are selected based on the parameters they provide and accuracy. In this BCG-based monitoring system record of Murata sensor parameters used as to identify sleep stages and as a ground truth inferred Emfit qs sensor record.By studying behavior of parameters each stage and by performing k-means clustering able to identify each stage and ,furthermore experiments are performed to get better accuracy using classification on data by inferring as a ground truth Emfit qs sensor record. At the end all those experiment result are discussed.
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