Download ecg-presentation

Extracting the atrial signal from
an electrocardiogram (ECG)
Semester 2 MPhys Project
Sarah Medley
Jerry Orme
The ECG signal
atria
ventricles
Voltage
(mV)
Time (s)
• ECG = a recording of the
electrical activity of the heart
• 10 electrodes = 12 leads
Images from Wikimedia Commons and University of Maryland Medical Centre
Atrial Fibrillation (AF)
Normal
Sinus
Rhythm
Atrial
Fibrillation
• Difficult to accurately diagnose AF
− GPs: 79.8% of cases correctly diagnosed [Mant et. al]
− Interpretative software: 83.3%
• Long-term goal: accurate automated diagnostic systems
Mant et. al, Accuracy of diagnosing AF on ECG by primary care practitioners and interpretative diagnostic software,
BMJ (2007). Images from Wikimedia Commons and New Zealand Guidelines Group
Aims & Implementation
• Extraction of the atrial signal
• Develop quantitative methods to distinguish
between ECGs of NSR/AF patients
Implementation:
• PCA, Fourier analysis and wavelet transform
methods – all in C++
• 5s of real ECG data from 6 patients (PhysioNET)
• Assume the body is a homogeneous isotropic
sphere and the heart is a dipole
Image from A. Reisner et. al, The Physiological Basis of the Electrocardiogram
Principal Component Analysis
• Express a dataset in terms of a set of
uncorrelated and orthogonal variables:
principal components
5 seconds of a 12-lead ECG = 5001 x 12 matrix
Covariance matrix of mean-centred data
Eigenvectors of covariance matrix
ordered by eigenvalue = principal components
Express original data in terms of PCs
Select atrial activity components
Reconstruct original leads using atrial components
PCA results – healthy patients
PCA results – patients with AF
PCA – Grouping by Eigenvalue
• Previous research shows inconsistent
methods of selecting atrial components
• Developed the eigenvalue grouping method
Advantages:
• Based on a clear mathematical rule
• Suits the fact that PCs differ between
patients
• Our results: group containing PC3 provides
the best atrial reconstruction
Disadvantages:
• Can result in loss of distinction between
atrial and ventricular activity
• Inconsistent for comparison of “subtracted”
and reconstructed leads
Fourier Transform
• Discrete-Time Fourier Transform (DTFT)
• Periodicity: assumption about the behaviour
of the signal outside the known range
• Signal truncation
• Zero-padding
• Limiting sampling frequency:
• Aliasing  upper limit:
Nyquist frequency = 2×(highest
frequency in signal)
• In practice, (highest frequency in
sample) = 1/(sampling rate) = 1 kHz
• 24 spectra to analyse per patient...
http://www.control.isy.liu.se/student/tsrt78/zeropadding.pdf
https://en.wikipedia.org/wiki/File:AliasingSines.svg
Spectral Spread
Noise
• Sources of noise: respiratory, muscular, powerline hum, shot noise
• Choice of threshold frequencies?
• Baseline wander poses a problem
• Average respiratory rate is 12-18 breaths per minute
• Bradypnoea at < 12 or 13 breaths per minute → five seconds of data may not show a
full breathing cycle
http://www.cs.umd.edu/~djacobs/CMSC828seg/SmoothingConvolution.pdf
Heart Rate Variability Index
• Goal: single, quantitative index to distinguish NSR from AF
• Combination of WT and PCA [Sree et. al]
• DWT used to preprocess leads
• PCA performed on convoluted leads
• Largest three eigenvalues
HRVI  123
Sree’s results (using a Morlet wavelet):
NSR: 32.429 ± 15.9
AF: 17.340 ± 12.9
Sree et al.; Cardiac arrhythmia diagnosis by HRV signal processing using PCA; Mechanics in Medicine and Biology (2012)
Heart Rate Variability Index
• Our results using
• Lorentzian wavelet
• Haar wavelet
• Scope for optimisation
commons.wikimedia.org
mathworld.wolfram.com
Vectorcardiography
Erratum: mV
Vectorcardiographic plots for Patient 113. The amplitude of the cardiac vector
is visualised in the V3-V4 plane (left) and in the aVF-V1 plane (right). All units
are in millivolts.
Summary
PCA:
• Developed eigenvalue grouping method
• Identified that the group containing PC3 provides
optimal representation of atrial activity
Fourier analysis and wavelet transform:
• Defined a measure of spectral spread and
demonstrated expected increase across PCs 1-12
• Showed how wavelet transform can be used to
suppress/magnify ECG features
Summary
Combined wavelet transform & PCA:
• Tested the HRVI proposed by Sree et. al
• Demonstrated that the Lorentzian wavelet marginally
outperformed the Haar wavelet, due to a smaller standard
deviation on the HRVI
Potential further developments:
• Use of a reference atrial signal for comparison
• Spectral spread & HRVI – potential indices to quantify AF
• Lead normalisation using vectorcardiography
Thank you for listening
Any questions?