An improved spectral method of detecting and quantifying T-wave alternans for SCD risk evaluation

Shen, Tsu-Wang; Tsao, Ya-Ting

doi:10.1109/cic.2008.4749115

Cited by 4 publications

(5 citation statements)

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“…SCD is a leading cause of cardiovascular mortality [1], [2] producing nine million deaths worldwide approximately in 2019 according to the World Health Organization [16]. SCD causes an estimated 400,000 deaths per year in the United States [9], [17]. Only 1-2 % of patients may survive when SCD occurs outside of a hospital [9].…”

Section: A Sudden Cardiac Deathmentioning

confidence: 99%

“…SCD causes an estimated 400,000 deaths per year in the United States [9], [17]. Only 1-2 % of patients may survive when SCD occurs outside of a hospital [9]. Most patients are identified only after they have experienced severe hearth diseases such as ischemia, infarction, ventricular conduction abnormalities, or potassium abnormalities [17], [18], [19].…”

Section: A Sudden Cardiac Deathmentioning

confidence: 99%

“…Let mTWA = w q be, the matrix that contains the alternans values of the trace. The rest of the equation ( 11) is the NC according (9). Thus, mTWA have been used for classification.…”

Section: ) Energy Reconstruction Of Tracementioning

confidence: 99%

“…The magnitude of the alternans is on the order of micro-volts so it is difficult to detect it. TWA is a heart rate dependent magnitude that has proven to be a noninvasive indicator to stratify cardiac risks [9], [10]. Different approaches have been proposed for automatic TWA analysis, like: Spectral Methods (SM), Modified Moving Average methods (MMA), Complex Demodulation methods (CD) and Statistical Test methods (ST) [8].…”

Section: Introductionmentioning

confidence: 99%

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Sudden Cardiac Death detection by using an hybrid method based on TWA and dictionary learning: A data experimentation

Flores-Calero

Almeida

2023

IEEE Access

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Sudden Cardiac Death (SCD) is considered one of the main causes of mortality worldwide. Understanding the origin of this heart disease continues to be a challenge for the scientific community. T-wave alternans (TWA) is the term used to describe changes in the T wave's amplitude or shape that are seen. According to the literature review, T wave alternans has been considered an important, non-invasive indicator to detect and stratify the risk of sudden cardiac death. On the other hand, dictionary learning is a digital signal processing technique that allows identify the main characteristics of a signal using a sparse representation. In this context, a new non-invasive method is proposed by mixing TWA spectral methods and dictionary learning. The method identifies the main characteristics of ECG signal by obtaining a sparse representation that adapts a matrix (dictionary) in order to use it for highlighting the TWA characteristics and then use these characteristics for detecting SCD. Experimental results show an improvement of 32% compared to the Physionet TWAnalyser program by using synthetic data set and an improvement of 20% over public databases.

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Section: A Sudden Cardiac Deathmentioning

confidence: 99%

Section: A Sudden Cardiac Deathmentioning

confidence: 99%

“…Let mTWA = w q be, the matrix that contains the alternans values of the trace. The rest of the equation ( 11) is the NC according (9). Thus, mTWA have been used for classification.…”

Section: ) Energy Reconstruction Of Tracementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sudden Cardiac Death detection by using an hybrid method based on TWA and dictionary learning: A data experimentation

Flores-Calero

Almeida

2023

IEEE Access

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“…Las técnicas mayormente utilizadas han sido desarrolladas en el dominio temporal, entre ellas está el método de la media móvil (MMA), el cual calcula el valor de TWA utilizando la diferencia del valor medio de los latidos pares e impares [9]; otro método es el de correlación (CM), que detecta alternancias utilizando la correlación cruzada [10]. Entre los métodos desarrollados en el dominio de la frecuencia se encuentran el método espectral (SM), el cual emplea la transformada discreta de Fourier (DFT) para analizar la componente de frecuencia a 0,5 ciclos por latido [11]. Un método similar es el de la demodulación compleja (CD), el cual ajusta una señal sinusoidal a la frecuencia de 0,5 ciclos por latido de las ondas T alineadas [12].…”

Section: Introductionunclassified

Detección de la muerte súbita cardíaca usando el método espectral adaptativo sobre la onda T: Estudio experimental sobre bases de datos públicas

Flores-Calero

Pilla-Barroso

2020

Ingenius

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El análisis de la alternancia de la onda T (TWA, T-wave alternans) constituye una de las principales técnicas para determinar la presencia del síndrome de muerte súbita cardíaca (MSC). Entre los métodos existentes para determinar TWA se encuentra el método espectral adaptativo (SM-Adaptativo), el cual utiliza distribuciones en tiempo-frecuencia (TFD, time-frecuency distribution) para el análisis. El objetivo del estudio es aplicar este método sobre las principales bases de datos públicas con el fin de detectar la presencia o ausencia de alternancias y obtener parámetros de calidad del método en mención. El método fue probado en señales sintéticas, 90 señales sin TWA y 450 con TWA; por otro lado, se utilizaron 10 señales de la base de datos TWADB de Physionet pertenecientes a pacientes sanos y 26 señales de pacientes con factores de riesgo asociados a la MSC. En las pruebas con señales sintéticas se obtuvo una sensibilidad de 94,89 %, especificidad de 92,22 % y exactitud de 94,44 %. En cuanto a las pruebas en la base de datos el método presenta una exactitud del 80,56 %, lo que indica que el método SM-Adaptativo permite detectar TWA con una exactitud aceptable, además, que presenta mayor robustez frente a ruido y a la estacionariedad de datos.

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Evaluation of Machine Learning Techniques for ECG T-Wave Alternans

Karnaukh

Karplyuk

Nataliia

2018

2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO)

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An improved spectral method of detecting and quantifying T-wave alternans for SCD risk evaluation

Cited by 4 publications

References 9 publications

Sudden Cardiac Death detection by using an hybrid method based on TWA and dictionary learning: A data experimentation

Sudden Cardiac Death detection by using an hybrid method based on TWA and dictionary learning: A data experimentation

Detección de la muerte súbita cardíaca usando el método espectral adaptativo sobre la onda T: Estudio experimental sobre bases de datos públicas

Evaluation of Machine Learning Techniques for ECG T-Wave Alternans

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