Estimation of low level alternans using periodicity transform - simulation and European ST/T database results

Srikanth, T.; Lin, Dongliang; Kanaan, Nabil; Gu, Hong

doi:10.1109/iembs.2002.1106453

Cited by 8 publications

(6 citation statements)

References 5 publications

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“…CD fits a sinusoidal signal to the 0.5 cpb frequency of the aligned T waves [25]. Periodicity transform method decomposes time series TWA signals into sum of periodic sequences by projecting onto a set of "periodic subspace" [26]. Poincare mapping uses the concept of the quasi periodic behaviour of the TWA signals to quantify and detect the alternations [27].…”

Section: Signal Analysis In Atrial Fibrillationmentioning

confidence: 99%

Empirical Mode Decomposition and Analysis of Non-Stationary Cardiac Signals

Rahnama¹

2021

Preprint

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Each year 400,000 North Americans die from sudden cardiac death (SCD). T- wave alternans (TWA) refers to an alternating pattern in the T-wave portion of the surface electrocardiogram (ECG) and has been shown as a risk stratifier for SCD. These subtle changes in the T-waves are in the micro-volt scale and ambulatory ECG recordings usually contain biological noise. Also, data non-stationarity owing to heart rate variability and the amplitude variability in TWA magnitude can limit the accuracy of the detection techniques. This necessitates the need for robust detection algorithms for processing such non-stationary data. In this thesis, we have proposed an Empirical Mode Decomposition (EMD) based scheme combined with the Instantaneous Frequency (IF). EMD decomposes the signal into several monocomponent signals called Intrinsic Mode Functions (IMFs). IF extracted from these IMFs provides an accurate estimate of time varying frequency components and hence can aid during characterization of TWAs. In order to validate the performance of the proposed detection technique, the feature vectors extracted from the IMFs were fed to a linear discriminant analysis (LDA) classifier. The performance assessment was carried out using two datasets: (a) Synthetic TWAs: 72 signals obtained from publicly accessible Physionet database and (b) TWAs from patients: 55 ambulatory ECG signals obtained from the Toronto General Hospital. Using an unbiased leave-one-out cross validation strategy, maximum overall classification accuracies of 86.1% and 81.8% were achieved for TWA detection from synthetic and ambulatory ECG recordings respectively. In addition, the usability of the proposed technique has been investigated to assess its suitability for addressing another cardiovascular problem stroke. Atrial Fibrillation (AF) has been identified as a risk factor to increase the chances of stroke. The most common method in studying the complex AF electrograms is to employ dominant frequency (DF) analysis; however, due to signal non-stationarity DF does not always provide the best estimate of the atrial activation rate. As a result, analyzing the electrograms via EMD and IF has been investigated as the second contribution of this work.

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Section: Signal Analysis In Atrial Fibrillationmentioning

confidence: 99%

Empirical Mode Decomposition and Analysis of Non-Stationary Cardiac Signals

Rahnama¹

2021

Preprint

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“…A similar method proposed by Nearing and Verrier [44] known as complex demodulation (CD) fits a sinusoidal signal to the 0.5 cpb frequency of the aligned T waves. Other examples of transformbased approaches are poincare mapping [45] and the periodicity transform method [46].…”

Section: T Wave Alternansmentioning

confidence: 99%

Time-Frequency Feature Analysis

Ghoraani¹

2021

Preprint

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Most of the real-world signals in nature are non-stationary, i.e., their statistics are time variant. Extracting the time-varying frequency characteristics of a signal is very important in understanding the signal better, which could be of immense use in various applications such as pattern recognition and automated-decision making systems. In order to extract meaningful time-frequency (TF) features, a joint TF analysis is required. The proposed work is an attempt to develop a generalized TF analysis methodology that exploits the benefits of TF distribution (TFD) in pattern classification systems as related to discriminant feature detection and classification. Our objective is to introduce a unique and efficient way of performing non-stationary signal analysis using adaptive and discriminant TF techniques. To fulfill this objective, in the first point, we build a novel TF matrix (TFM) decomposition that increases the effectiveness of segmentation in real-world signals. Instantaneous and unique features are extracted from each segment such that they successfully represent joint TF structure of the signal. In the second point, based on the above technique, two unique and novel discriminant TF analysis methods are proposed to perform an improved and discriminant feature selection of any non-stationary signals. The first approach is a new machine learning method that identifies the clusters of the discriminant features to compute the presence of the discriminative pattern in any given signal, and classify them accordingly. The second approach is a discriminant TFM (DTFM) framework, which is a combination of TFM decomposition and the discriminant clustering techniques. The developed DTFM analysis automatically identifies the differences between different classes as the distinguishing structure, and uses the identified structure to accurately classify and locate the discriminant structure in the signal. The theoretical properties of the proposed approaches pertaining to pattern recognition and detection are examined in this dissertation. The extracted TF features provide strong and successful characterization and classification of real and synthetic non-stationary signals. The proposed TF techniques facilitate the adaptation of TF quantification to any feature detection technique in automating the identification process of discriminatory TF features, and can find applications in many different fields including biomedical and multimedia signal processing.

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“…TWA is a very well defined signal processing problem, and this is another encouraging reason to address this matter from different viewpoints. Among them, we can highlight the following methods, which are described within an unified framework in Martínez and Olmos ( 2005 ): the Complex Demodulation method (Nearing and Verrier, 1993 ); the Correlation Method (Burattini et al, 1999 ); a method based on the Karhunen–Love Transform (Laguna et al, 1999 ) and Capon filtering (Martínez et al, 2000 ); methods using the Poincar mapping (Strumillo and Ruta, 2002 ), the periodicity transform (Srikanth et al, 2002a ), statistical t –test (Srikanth et al, 2002b ), and the Laplacian Likelihood Ratio (Martínez et al, 2006 ). Recently, matched filtering (Bashir et al, 2014 ) and wavelet transform (Romero et al, 2008 ) have been used in this issue.…”

Section: Algorithmic and Computational Indicesmentioning

confidence: 99%

Sudden Cardiac Risk Stratification with Electrocardiographic Indices - A Review on Computational Processing, Technology Transfer, and Scientific Evidence

et al. 2016

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Great effort has been devoted in recent years to the development of sudden cardiac risk predictors as a function of electric cardiac signals, mainly obtained from the electrocardiogram (ECG) analysis. But these prediction techniques are still seldom used in clinical practice, partly due to its limited diagnostic accuracy and to the lack of consensus about the appropriate computational signal processing implementation. This paper addresses a three-fold approach, based on ECG indices, to structure this review on sudden cardiac risk stratification. First, throughout the computational techniques that had been widely proposed for obtaining these indices in technical literature. Second, over the scientific evidence, that although is supported by observational clinical studies, they are not always representative enough. And third, via the limited technology transfer of academy-accepted algorithms, requiring further meditation for future systems. We focus on three families of ECG derived indices which are tackled from the aforementioned viewpoints, namely, heart rate turbulence (HRT), heart rate variability (HRV), and T-wave alternans. In terms of computational algorithms, we still need clearer scientific evidence, standardizing, and benchmarking, siting on advanced algorithms applied over large and representative datasets. New scenarios like electronic health recordings, big data, long-term monitoring, and cloud databases, will eventually open new frameworks to foresee suitable new paradigms in the near future.

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Estimation of low level alternans using periodicity transform - simulation and European ST/T database results

Cited by 8 publications

References 5 publications

Empirical Mode Decomposition and Analysis of Non-Stationary Cardiac Signals

Empirical Mode Decomposition and Analysis of Non-Stationary Cardiac Signals

Time-Frequency Feature Analysis

Sudden Cardiac Risk Stratification with Electrocardiographic Indices - A Review on Computational Processing, Technology Transfer, and Scientific Evidence

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