Decomposition of biomedical signals for enhancement of their time–frequency distributions

Sun, Mingui; Scheuer, Mark L.; Sclabassi, R.J.

doi:10.1016/s0016-0032(00)00030-2

Cited by 15 publications

(8 citation statements)

References 20 publications

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“…The average smallest amplitude distortion is obtained with RF=1.25, while the average smallest worst case aliasing distortion is achieved with RF=1. 35. The proposed method is also tested for different bands of CM filter banks.…”

Section: Design Examplesmentioning

confidence: 99%

“…Among all the filter banks, the cosine-modulated filter bank (CMFB) is one of the most frequently used filter banks in audio coding [28][29][30], adaptive signal processing [31,32], and ECG signal processing [33][34][35][36], because of its matching properties of the analysis filter bank to the characteristics of the input signal. In ECG signal processing, CMFB filter banks are used in several applications such as beat detection, signal enhancement, beat classification, and compression.…”

Section: Application To Sub-band Codingmentioning

confidence: 99%

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A simple design method for the cosine-modulated filter banks using weighted constrained least square technique

Kumar

Singh

Anand

2011

Journal of the Franklin Institute

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Section: Design Examplesmentioning

confidence: 99%

Section: Application To Sub-band Codingmentioning

confidence: 99%

A simple design method for the cosine-modulated filter banks using weighted constrained least square technique

Kumar

Singh

Anand

2011

Journal of the Franklin Institute

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“…The time-frequency (t-f ) representations has been also proposed, planned to determine the energy distribution along the frequency axis at each time instant, to investigate the time-variant properties of the spectral parameters during either transient physiological or pathological episodes [5]. In this line of analysis, the use of relevant and nonredundant dynamic features is presented in [6] Short Time Fourier Transform (STFT) and a linear variant of the Continuous Wavelet Transform (CWT); nevertheless, there are some normal t-f maps whose waveform are similar to the pathological ones, and vice versa; so, the spatial distribution of the energy in each sub-band is not clear.…”

Section: Introductionmentioning

confidence: 99%

Training using short-time features for OSA discrimination

Sepúlveda-Cano

Álvarez-Meza

Castellanos-Domínguez

2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Heart rate variability (HRV) is one of the promising directions for a simple and noninvasive way for obstructive sleep apnea syndrome detection (OSA). The interaction between the sympathetic and parasympathetic systems on the HRV recordings, gives rise to several non-stationary components added to the signal. Aiming to improve the classifier accuracy for obstructive sleep apnoea detection, the use of more appropriated techniques for leading with non-stationarity and mixed dynamics, are needed. This work aims at searching a convenient training strategy of combining the feature set to be further fed in to the classifier, which should take into consideration the different dynamics in the HRV signal. Therefore, a set of the short-time features, extracted from a given HRV time-varying decomposition, and selected by spectral splitting is considered. Additionally, three methods of projection are used: none, simple, and multivariate. Finally, the different approaches are tested and compared, using k-nn and support vector machines (SVM) classifiers. Attained results show that using continuous wavelet transform with short-time features and multivariate projection, followed by a SVM classifier, allow to obtain a suitable OSA detection.

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“…Specifically, the Wavelet Transform (WT) and he Short Time Fourier Transform (STFT) are commonly used. Though the former TFR is likely to avoid the t-f resolution compromise, the latter nonparametric approach is desirable for biosignals with a slow time varying spectrum [5], as it is the case for PPG recordings. However, the application of TFR to the analysis of short transient signals (like in case of PPG envelope) is a complex, and difficult task due to the inherent limitations of the TFR techniques for extracting the relevant, but not redundant characteristics.…”

Section: Introductionmentioning

confidence: 99%

Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children

Sepúlveda-Cano

Gil

Laguna

et al. 2011

EURASIP J. Adv. Signal Process.

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This paper discusses the methodology for selecting a set of relevant nonstationary features to increase the specificity of the obstructive sleep apnea detector. Dynamic features are extracted from time-evolving spectral representation of photoplethysmography envelope recordings. In this regard, a time-evolving version of the standard linear multivariate decomposition is discussed to perform stochastic dimensionality reduction. For training aim, this work analyzes the concrete set comprising filter banked dynamic features that include spectral centroids, the cepstral coefficients as well as their timevariant energies. Performance of classifier accuracy is provided for the collected polysomnography recordings of 21 children. Moreover, since the apnea diagnosing is based on analysis of set of fragments partitioned from the photoplethysmography envelope recordings, a new approach for their indirect labeling is described. As a result, performed outcomes of accuracy bring enough evidence that if using a subset of cepstral-based dynamic features, then patient classification accuracy can reach as much as 83.3% value, when using a k-nn classifier, as well. Therefore, photoplethysmography-based detection provides an adequate scheme for obstructive sleep apnea diagnosis.

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Decomposition of biomedical signals for enhancement of their time–frequency distributions

Cited by 15 publications

References 20 publications

A simple design method for the cosine-modulated filter banks using weighted constrained least square technique

A simple design method for the cosine-modulated filter banks using weighted constrained least square technique

Training using short-time features for OSA discrimination

Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children

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