Non-linear trend estimation of cardiac repolarization using wavelet thresholding for improved T-wave alternans analysis

Bakhshi, Asim Dilawer; Bashir, Sajid; Shah, Syed Muhammad Hassan; Maud, M.A.

doi:10.1016/j.dsp.2013.03.006

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…The system can be used to give clinical support to health centers with reduced cost and effort, undoubtedly leading to an increase in knowledge of actual HRT and HRV domains, in connection with basic data in the EHR. Another example of highly relevant ECG-derived risk index is the TWA, which consists of changes in a low-voltage scale for waveform characteristics (times, amplitudes, or morphology) of the ST–T complex occurring on a beat-by-beat basis [ 41 , 42 , 43 ]. This subdomain has not been addressed here, but it is undoubtedly the following one to include in the system.…”

Section: Discussionmentioning

confidence: 99%

An Interoperable System toward Cardiac Risk Stratification from ECG Monitoring

Soguero-Ruíz

Mora-Jiménez

Ramos-López

et al. 2018

IJERPH

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Many indices have been proposed for cardiovascular risk stratification from electrocardiogram signal processing, still with limited use in clinical practice. We created a system integrating the clinical definition of cardiac risk subdomains from ECGs and the use of diverse signal processing techniques. Three subdomains were defined from the joint analysis of the technical and clinical viewpoints. One subdomain was devoted to demographic and clinical data. The other two subdomains were intended to obtain widely defined risk indices from ECG monitoring: a simple-domain (heart rate turbulence (HRT)), and a complex-domain (heart rate variability (HRV)). Data provided by the three subdomains allowed for the generation of alerts with different intensity and nature, as well as for the grouping and scrutinization of patients according to the established processing and risk-thresholding criteria. The implemented system was tested by connecting data from real-world in-hospital electronic health records and ECG monitoring by considering standards for syntactic (HL7 messages) and semantic interoperability (archetypes based on CEN/ISO EN13606 and SNOMED-CT). The system was able to provide risk indices and to generate alerts in the health records to support decision-making. Overall, the system allows for the agile interaction of research and clinical practice in the Holter-ECG-based cardiac risk domain.

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Section: Discussionmentioning

confidence: 99%

An Interoperable System toward Cardiac Risk Stratification from ECG Monitoring

Soguero-Ruíz

Mora-Jiménez

Ramos-López

et al. 2018

IJERPH

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“…This phenomenon is generated at the myocites level and it may be caused by differences among action potential shapes. Thus, alternation can be given because different regions have distinct and non-natural action potential durations (modified spatial dispersion of repolarization), or by alternate duration of action potentials of a single cell (temporal dispersion) (Narayan, 2006 ; Bakhshi et al, 2013 ).…”

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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“…Also, wavelet power spectrum (WPS) have been widely used in numerous-related fields (Beltrán and Ponce de León 2010;Hafez et al 2010;Mandrikova et al 2010Mandrikova et al , 2011Mandrikova et al , 2012aMandrikova et al , 2012bMandrikova et al , 2012cMandrikova et al , 2013Hafez and Ghamry 2011;Liming et al 2011;Ghamry et al 2012;Gwal et al 2012;Bakhshi et al 2013;Castillo et al 2013;Han and Chang 2013). WPS present facts on the amplitude of phase signals during the time series and how its amplitude changes with time.…”

Section: Introductionmentioning

confidence: 99%

Study of nonlinear time series and wavelet power spectrum analysis using solar wind parameters and geomagnetic indices

Falayi

Adewole

Adelaja

et al. 2020

NRIAG Journal of Astronomy and Geophysics

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We investigate the time series of solar wind parameters (interplanetary magnetic field, Bz and solar wind speed, Vx) and geomagnetic indices (disturbance storm time, Dst and auroral electrojet, AE) using wavelet analysis and nonlinear dynamics time series techniques. The data were collected from the Flight Center Space Physics Data Facility (GSFC/SPDF) OMNIWEB interface between 2008 and 2017. Wavelet power spectrum (WPS) analysis assists in breaking down the time series of Bz, Vx, Dst and AE parameters into different scales. It was noted that there is a greater concentration of power between the 512 and 1024 months bands across the Bz, Vx, Dst and AE parameters. We also applied non-linear time series modelling methods to examine the Bz, Vx, Dst and AE parameters. We utilised both the time delay and embedded dimension in computing average mutual information (AMI) and false nearest neighbors (FNN), respectively. The Lyapunov exponent (LE) is used to express the complexity of the nonlinear dynamics based on embedding parameters. The Lyapunov exponents depict positive values which confirm that the complex solar wind parameters and the geomagnetic indices are deterministic chaotic systems. The results show noticeable chaotic characteristics in the Bz, Vx, Dst and AE parameters.

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Non-linear trend estimation of cardiac repolarization using wavelet thresholding for improved T-wave alternans analysis

Cited by 9 publications

References 28 publications

An Interoperable System toward Cardiac Risk Stratification from ECG Monitoring

An Interoperable System toward Cardiac Risk Stratification from ECG Monitoring

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

Study of nonlinear time series and wavelet power spectrum analysis using solar wind parameters and geomagnetic indices

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