Performance Improvement of a Phase Space Detection Algorithm for Electrocardiogram Wave Morphology Classification
Herreros, Alberto ; Baeyens Lázaro, Enrique ; Riverta, Pedro and Johansson, Rolf LU
(2011)
In Journal of Electrocardiology
44(2).
p.31-31
- Abstract
- An algorithm based on embedding phase space signal was developed by the authors in previous works. The algorithm detects the characteristic points of the waves of a multilead electroacardiogram (ECG). In the present work, the parameters of this algorithm are optimized to improve its performance. The algorithm uses 2 configurable parameters to obtain the phase spacethe dimension of the phase space and the delayand a threshold to select the points of the ECG. By a proper selection of these parameters, the algorithm obtains all the points in the ECG that are similar to a reference one that was selected by the analyst. Several strategies have been developed and incorporated in the phase space algorithm to obtain the optimal values of these... (More)
- An algorithm based on embedding phase space signal was developed by the authors in previous works. The algorithm detects the characteristic points of the waves of a multilead electroacardiogram (ECG). In the present work, the parameters of this algorithm are optimized to improve its performance. The algorithm uses 2 configurable parameters to obtain the phase spacethe dimension of the phase space and the delayand a threshold to select the points of the ECG. By a proper selection of these parameters, the algorithm obtains all the points in the ECG that are similar to a reference one that was selected by the analyst. Several strategies have been developed and incorporated in the phase space algorithm to obtain the optimal values of these parameters based on the sampling rate and the number of leads in the records. The professional only needs to mark the reference point and the associated wave to it, for example, the start and end of a P wave and its peak. The algorithm obtains every P wave of the ECG record and a classification of their morphology using clustering techniques. Moreover, a simple graphical interface has been developed to ease its use.
The algorithm was applied to detect the start, peak, and end of the P waves of a collection of ECG records of 6 minutes. Using this information, the algorithm extracts and classifies the P waves by applying clustering techniques to study their variability. The algorithm can also be used online to detect and classify different types of morphologies in any ECG wave. A future use of this algorithm will be the detection of several extracardiac pathologies in the ECG Holter, for example, sleep apnea. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2155064
- author
- Herreros, Alberto
; Baeyens Lázaro, Enrique
; Riverta, Pedro
and Johansson, Rolf
LU
- organization
- publishing date
- 2011
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Electrocardiology
- volume
- 44
- issue
- 2
- pages
- 31 - 31
- publisher
- Elsevier
- ISSN
- 1532-8430
- language
- English
- LU publication?
- yes
- additional info
- key=JElectrocardiology2011
- id
- 007aea97-8478-4635-9633-b0317fc622f2 (old id 2155064)
- date added to LUP
- 2016-04-04 11:10:59
- date last changed
- 2023-09-04 12:20:46
@article{007aea97-8478-4635-9633-b0317fc622f2,
abstract = {{An algorithm based on embedding phase space signal was developed by the authors in previous works. The algorithm detects the characteristic points of the waves of a multilead electroacardiogram (ECG). In the present work, the parameters of this algorithm are optimized to improve its performance. The algorithm uses 2 configurable parameters to obtain the phase spacethe dimension of the phase space and the delayand a threshold to select the points of the ECG. By a proper selection of these parameters, the algorithm obtains all the points in the ECG that are similar to a reference one that was selected by the analyst. Several strategies have been developed and incorporated in the phase space algorithm to obtain the optimal values of these parameters based on the sampling rate and the number of leads in the records. The professional only needs to mark the reference point and the associated wave to it, for example, the start and end of a P wave and its peak. The algorithm obtains every P wave of the ECG record and a classification of their morphology using clustering techniques. Moreover, a simple graphical interface has been developed to ease its use.<br/><br>
<br/><br>
The algorithm was applied to detect the start, peak, and end of the P waves of a collection of ECG records of 6 minutes. Using this information, the algorithm extracts and classifies the P waves by applying clustering techniques to study their variability. The algorithm can also be used online to detect and classify different types of morphologies in any ECG wave. A future use of this algorithm will be the detection of several extracardiac pathologies in the ECG Holter, for example, sleep apnea.}},
author = {{Herreros, Alberto and Baeyens Lázaro, Enrique and Riverta, Pedro and Johansson, Rolf}},
issn = {{1532-8430}},
language = {{eng}},
number = {{2}},
pages = {{31--31}},
publisher = {{Elsevier}},
series = {{Journal of Electrocardiology}},
title = {{Performance Improvement of a Phase Space Detection Algorithm for Electrocardiogram Wave Morphology Classification}},
volume = {{44}},
year = {{2011}},
}