Subventions et des contributions :

Titre :
Identifying and removing low quality seismocardiogram cycles
Numéro de l’entente :
EGP
Valeur d'entente :
24 954,00 $
Date d'entente :
14 juin 2017 -
Organisation :
Conseil de recherches en sciences naturelles et en génie du Canada
Location :
Colombie-Britannique, Autre, CA
Numéro de référence :
GC-2017-Q1-00362
Type d'entente :
subvention
Type de rapport :
Subventions et des contributions
Informations supplémentaires :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2018-2019)

Nom légal du bénéficiaire :
Hodgson, Antony (The University of British Columbia)
Programme :
Subventions d'engagement partenarial pour les universités
But du programme :

Cardiovascular diseases (CVDs) affect the lives of 1.6 million Canadians, and are the second leading cause ofx000D
death in Canada. CVDs cost the Canadian economy more than $20 Billion annually. Heart Force Medical Inc.x000D
(HFM) is a Vancouver-based company developing software and hardware technologies for the noninvasivex000D
cardiac monitoring to prevent and diagnose CVDs. In particular, HFM's proprietary technology measures heartx000D
vibration, which is referred as the seismocardiogram (SCG). SCGs can be captured by placing an accelerometerx000D
on the sternum, and provide information about mechanical aspects of the heart. Due to the inherent sensitivityx000D
of the SCG signal to motion artifacts caused by respiration or body movements, sophisticated algorithms havex000D
to be developed to remove or minimize the effect of these artifacts. Another source of inadequate reliability ofx000D
the SCG signal is the inappropriate placement of the accelerometer sensor on the sternum. In these situations,x000D
the SCG signals are of low quality (LQ) and the extraction of cardiac information is unreliable. Therefore, thex000D
purpose of this proposal is to develop an algorithm that (1) can accommodate motion artifacts, and (2) canx000D
automatically identify and remove LQ SCG cycles prior to analysis. Such an algorithm has to learn thex000D
morphology of recognizable and acceptable SCG cardiac cycles and discard those cycles when important datax000D
cannot be extracted. This step is crucial in the overall electromechanical assessment of cardiac function. Thex000D
developed algorithm will be deployed in relevant HFM devices to increase their accuracy, reliability, andx000D
usability to evaluate cardiac performance.