Grant: Collaborative Health Research Projects
Ryerson University
$207,573.00
Signal analysis of T-wave alternans for predicting sudden cardiac deaths
FY 2012-13
Summary
T-wave alternans (TWA) refers to the changes in T wave shape or amplitude appearing every second heart beat
on a surface electrocardiogram (ECG). The magnitude of TWA signal is very small, in the microvolt range,
but some clinical trials have recently suggested that its presence can predict sudden death in patients with heart
disease. From a signal analysis perspective, the accurate measurement of the TWA signal is challenging due to
very low signal to noise ratio (SNR), and signal non-stationarity. Several noise sources can potentially
confound TWA signal detection, including periodic noise from movement or respiration, and
non-periodic/spurious noise.
The primary objective of this proposal will be to develop a more accurate TWA detection algorithm that
tackles the signal non-stationarity and provides robust measurement of TWA signal parameters even in low
SNR conditions.In the proposed techniques the non-stationarity of the TWA signal is handled by transforming
the signal to a joint time-frequency representation (TFR) which provides the time-varying spectral
representation. The marginals computed as first-order and second-order moments along time or frequency
direction in a proper TFR will provide features such as instantaneous frequency (IF) and instantaneous
bandwidth (IBW). IF and IBW could be used to characterize the non-stationary TWA and thereby lead to better
detection and estimation of TWA. The secondary objective will be to evaluate the accuracy of the proposed
techniques in risk stratifying patients with heart disease. This will be achieved by measuring TWA every 6
months from a 24-hour ambulatory ECG recording in 450 patients with heart disease, and then following these
patients prospectively for 2 years to determine whether TWA predicts subsequent death or nonfatal ventricular
arrhythmias. Developing accurate risk assessment algorithms is essential to identify patients at highest risk of
sudden death who will derive the greatest survival benefit from costly medical device therapy, such as
implantable defibrillators.
Program
Collaborative Health Research Projects
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
397473-2011
Name nom
Krishnan, Sridhar
Department département
Electrical and Computer Engineering
Organization id
43
Institution établissement
Ryerson University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2012
Competition year année de concours
2011
Award amount
207573
Program id
CHRPJ
Program name en
Collaborative Health Research Projects
Program name fr
Projets de recherche concertée sur la santé
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
360
Committee name en
Collaborative Health Research Projects Selection Panel
Committee name fr
Comité de sélection de projets de recherche concertée sur la santé
Area of application code
1102
Area of application group en
Health, education and social services
Area of application group fr
Santé publique, éducation et services sociaux
Area of application en
Biomedical engineering
Area of application fr
Génie biomédical
Research subject code
1900
Research subject group en
Biomedical engineering
Research subject group fr
Génie biomédical
Research subject en
Biomedical engineering
Research subject fr
Génie biomédical
Partie
2
Nb partie
3
Application title
Signal analysis of T-wave alternans for predicting sudden cardiac deaths
Keyword
NSERC
Application summary
T-wave alternans (TWA) refers to the changes in T wave shape or amplitude appearing every second heart beat
on a surface electrocardiogram (ECG). The magnitude of TWA signal is very small, in the microvolt range,
but some clinical trials have recently suggested that its presence can predict sudden death in patients with heart
disease. From a signal analysis perspective, the accurate measurement of the TWA signal is challenging due to
very low signal to noise ratio (SNR), and signal non-stationarity. Several noise sources can potentially
confound TWA signal detection, including periodic noise from movement or respiration, and
non-periodic/spurious noise.
The primary objective of this proposal will be to develop a more accurate TWA detection algorithm that
tackles the signal non-stationarity and provides robust measurement of TWA signal parameters even in low
SNR conditions.In the proposed techniques the non-stationarity of the TWA signal is handled by transforming
the signal to a joint time-frequency representation (TFR) which provides the time-varying spectral
representation. The marginals computed as first-order and second-order moments along time or frequency
direction in a proper TFR will provide features such as instantaneous frequency (IF) and instantaneous
bandwidth (IBW). IF and IBW could be used to characterize the non-stationary TWA and thereby lead to better
detection and estimation of TWA. The secondary objective will be to evaluate the accuracy of the proposed
techniques in risk stratifying patients with heart disease. This will be achieved by measuring TWA every 6
months from a 24-hour ambulatory ECG recording in 450 patients with heart disease, and then following these
patients prospectively for 2 years to determine whether TWA predicts subsequent death or nonfatal ventricular
arrhythmias. Developing accurate risk assessment algorithms is essential to identify patients at highest risk of
sudden death who will derive the greatest survival benefit from costly medical device therapy, such as
implantable defibrillators.