T-Wave Alternans: A comparison of different measurement techniques

D. Zheng; S. Stevens; P. Langley; K. Wang; A.J. Haigh; S. King; A. Murray

doi:10.1109/CIC.2008.4749112

T-Wave Alternans: A comparison of different measurement techniques

D. Zheng, S. Stevens, P. Langley, K. Wang, A.J. Haigh, S. King, A. Murray

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding

8 Citations (Scopus)

Abstract

T-wave alternans (TWA) is characterized by a pattern of beat-to-beat alternations in the amplitude of the Twave of the electrocardiogram (ECG). However, TWA is usually very small and hard to detect or measure. Our aim was to develop and compare three automated TWA measurement algorithms.
100 ECG recordings provided by the 2008 PhysioNet/Computers in Cardiology Challenge were analysed. After ECG pre-processing, moving four-beat window technique (FBW), Fast Fourier Transform (FFT) spectral analysis and principal component analysis (PCA) were used for TWA determination and quantification. These estimates were then ranked from 1 to 100, separately for each method, and the correlation between the results of each method analysed.
The correlation of the ranking of TWA estimates between FBW and FFT was 0.65, between FFT and PCA 0.33, and between FBW and PCA 0.24. With TWA criteria applied, the number of ECGs with TWA was for FBW 29, FFT 29, and PCA 38. Only 7 of the ECG
records had TWA detected by all three techniques.
In conclusion, we have shown that different analysis techniques produced substantially different results.

Original language	English
Title of host publication	Computers in Cardiology
Publisher	IEEE
Pages	597−600
Number of pages	4
Volume	35
ISBN (Print)	978-1-4244-3706-1
DOIs	https://doi.org/10.1109/CIC.2008.4749112
Publication status	Published - 2008
Externally published	Yes
Event	Computers in Cardiology 2008 - Bologna, Italy Duration: 14 Sep 2008 → 17 Sep 2008

Publication series

Name
ISSN (Print)	0276−6574

Name
ISSN (Electronic)	2325-8853

Conference

Conference	Computers in Cardiology 2008
Country	Italy
City	Bologna
Period	14/09/08 → 17/09/08

Bibliographical note

Open access under the Creative Commons Attribution License 3.0 (CCAL).

Since volume 33 (2006), CinC has been an open-access publication, in which copyright in each article is held by its authors, who grant permission to copy and redistribute their work with attribution, under the terms of the Creative Commons Attribution License.

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10.1109/CIC.2008.4749112

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Cite this

Zheng, D., Stevens, S., Langley, P., Wang, K., Haigh, A. J., King, S., & Murray, A. (2008). T-Wave Alternans: A comparison of different measurement techniques. In Computers in Cardiology (Vol. 35, pp. 597−600). IEEE. https://doi.org/10.1109/CIC.2008.4749112

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abstract = "T-wave alternans (TWA) is characterized by a pattern of beat-to-beat alternations in the amplitude of the Twave of the electrocardiogram (ECG). However, TWA is usually very small and hard to detect or measure. Our aim was to develop and compare three automated TWA measurement algorithms.100 ECG recordings provided by the 2008 PhysioNet/Computers in Cardiology Challenge were analysed. After ECG pre-processing, moving four-beat window technique (FBW), Fast Fourier Transform (FFT) spectral analysis and principal component analysis (PCA) were used for TWA determination and quantification. These estimates were then ranked from 1 to 100, separately for each method, and the correlation between the results of each method analysed.The correlation of the ranking of TWA estimates between FBW and FFT was 0.65, between FFT and PCA 0.33, and between FBW and PCA 0.24. With TWA criteria applied, the number of ECGs with TWA was for FBW 29, FFT 29, and PCA 38. Only 7 of the ECGrecords had TWA detected by all three techniques.In conclusion, we have shown that different analysis techniques produced substantially different results.",

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AU - Murray, A.

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N2 - T-wave alternans (TWA) is characterized by a pattern of beat-to-beat alternations in the amplitude of the Twave of the electrocardiogram (ECG). However, TWA is usually very small and hard to detect or measure. Our aim was to develop and compare three automated TWA measurement algorithms.100 ECG recordings provided by the 2008 PhysioNet/Computers in Cardiology Challenge were analysed. After ECG pre-processing, moving four-beat window technique (FBW), Fast Fourier Transform (FFT) spectral analysis and principal component analysis (PCA) were used for TWA determination and quantification. These estimates were then ranked from 1 to 100, separately for each method, and the correlation between the results of each method analysed.The correlation of the ranking of TWA estimates between FBW and FFT was 0.65, between FFT and PCA 0.33, and between FBW and PCA 0.24. With TWA criteria applied, the number of ECGs with TWA was for FBW 29, FFT 29, and PCA 38. Only 7 of the ECGrecords had TWA detected by all three techniques.In conclusion, we have shown that different analysis techniques produced substantially different results.

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