Time-varying spectrum estimation of heart rate variability signals with Kalman smoother algorithm

Tarvainen, Mika P.; Georgiadis, Stefanos; Lipponen, Jukka A.; Hakkarainen, Marko; Karjalainen, Pasi A.

doi:10.1109/iembs.2009.5332678

Cited by 13 publications

(20 citation statements)

References 6 publications

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“…A wide variety of such sensors have been developed to measure biosignals that reflect various underlying physiological phenomena. For example, gyroscope and accelerometers are employed for pathological and physiological tremor signal measurement [1], accelerometers are employed for cardiac mechanical vibrations monitoring [2], infrared sensors are employed for respiration motion monitoring [3], and common electrodes are employed for brain and heart electrical activity measurement [4,5]. In order to adequately interpret the signals and make useful observations, a proper understanding of the involved phenomena and their influence on the signals is necessary.…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner

Wang

Veluvolu

2017

Sensors

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It is often difficult to analyze biological signals because of their nonlinear and non-stationary characteristics. This necessitates the usage of time-frequency decomposition methods for analyzing the subtle changes in these signals that are often connected to an underlying phenomena. This paper presents a new approach to analyze the time-varying characteristics of such signals by employing a simple truncated Fourier series model, namely the band-limited multiple Fourier linear combiner (BMFLC). In contrast to the earlier designs, we first identified the sparsity imposed on the signal model in order to reformulate the model to a sparse linear regression model. The coefficients of the proposed model are then estimated by a convex optimization algorithm. The performance of the proposed method was analyzed with benchmark test signals. An energy ratio metric is employed to quantify the spectral performance and results show that the proposed method Sparse-BMFLC has high mean energy (0.9976) ratio and outperforms existing methods such as short-time Fourier transfrom (STFT), continuous Wavelet transform (CWT) and BMFLC Kalman Smoother. Furthermore, the proposed method provides an overall 6.22% in reconstruction error.

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Section: Introductionmentioning

confidence: 99%

Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner

Wang

Veluvolu

2017

Sensors

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“…Con el fin de analizar tales cambios, representaciones tiempo-frecuencia son requeridas (Tarvainen et al, 2009). Debido a esto, durante las últimas décadas representaciones tiempo-frecuencia han prevalecido en el análisis de señales no estacionarias, en particular FCG (Tarvainen et al, 2009;Quiceno et al, 2009;Seidic et al, 2009;Avendaño et al, 2010).…”

Section: Introductionunclassified

Análisis de la tolerancia al ruido de características basadas en dinámica no lineal sobre señales fonocardiográficas

Ospina-Aguirre

García

Delgado-Trejos

et al. 2010

TecnoL.

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ResumenUna propiedad muy deseable en sistemas de detección automática de patologías es la robustez ante el ruido, tal que su presencia no afecte notablemente la habilidad de detectar patologías. El presente trabajo estudia la capacidad discriminatoria que características de la dinámica no lineal, en particular, el Exponente de Hurst, el Máximo Exponente de Lyapunov y la Dimensión de Correlación, puedan proveer en la detección de soplos cardíacos usando señales fonocardiográficas contaminadas con diferentes niveles de ruido. A su vez se hacen comparaciones con características obtenidas a partir de representaciones tiempo frecuencia. Los resultados muestran la fortaleza de las características basadas en dinámica no lineal para tareas de clasificación de estados funcionales, aún sobre señales con altos niveles de ruido.

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“…Specially, changes in physiological conditions and pathologies may produce significant variations. It has been found that non-stationary conditions give rise to changes in the spectral content of the biosignal (Hassanpour et al, 2004;Quiceno-Manrique et al, 2010;Sepúlveda-Cano et al, 2011;Subasi, 2007;Tarvainen et al, 2009;Tzallas et al, 2008). Therefore, time-frequency (t-f) features have been previously proposed for examining the dynamic properties of the spectral parameters during transient physiological or pathological episodes.…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency Based Feature Extraction for Non-Stationary Signal Classification

Avendaño-Valencia¹,

Acosta-Medina²,

Castellanos-Domínguez³

2011

Applied Biomedical Engineering

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Time-varying spectrum estimation of heart rate variability signals with Kalman smoother algorithm

Cited by 13 publications

References 6 publications

Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner

Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner

Análisis de la tolerancia al ruido de características basadas en dinámica no lineal sobre señales fonocardiográficas

Time-Frequency Based Feature Extraction for Non-Stationary Signal Classification

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