Journal ArticleDOI
R-peak detection based chaos analysis of ECG signal
TLDR
In this study, physioNet ECG records have been considered for analysis and the R-peaks are detected using principal component analysis (PCA) which outperforms the existing state-of-the-art techniques.Abstract:
Electrocardiography (ECG) is a non-invasive test that is used for recording contraction and relaxation activities of the heart by using an electrocardiogram. Early detection of abnormalities of the heart through ECG is essential for reducing the prevalence of casualties due to cardiac arrests worldwide. In this study, physioNet ECG records have been considered for analysis. During recording, ECG signal is also affected by various noises, where analog filters fail due to the effect of temperature and drift, and digital filters fail due to inappropriate selection of passband and gain parameters. For adequate and frequent usage in the medical field, it demands correct and precise R-peak (QRS-complex) detection; which requires an appropriate combination of pre-processing, feature extraction and detection techniques. Therefore, independent component analysis (ICA) is used in the pre-processing stage due to nonlinear nature of the ECG signals and chaos analysis is applied for feature extraction for different ECG databases. The ICA method separates an individual signal from mixed signals by assuming that the original underlying source signals are mutually independently distributed. Chaos analysis examines the irregular attitude of the system and fits it into deterministic equations of motion. Chaos analysis is implemented by plotting different attractors against various time delay dimensions. R-peak detection is well known to be useful in diagnosing cardiac diseases. The R-peaks are detected using principal component analysis (PCA) which outperforms the existing state-of-the-art techniques.read more
Citations
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R-Peak Detection Using Chaos Analysis in Standard and Real Time ECG Databases
TL;DR: There are strong merits in using chaos analysis as a feature extraction method to reduce the incidence of false diagnosis of electrocardiogram (ECG) signal in critical conditions.
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Chaos Theory: An Emerging Tool for Arrhythmia Detection
TL;DR: A computer aided diagnosis (CAD) system to extract non-linearity and trajectory patterns using the theory of chaos analysis to aid cardiologists diagnose arrhythmia accurately is presented.
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ECG signal analysis using CWT, spectrogram and autoregressive technique
TL;DR: In this article, the authors used continuous wavelet transform (CWT), Spectrogram and Autoregressive (AR) techniques for interpreting nonlinear and non-stationary features of the ECG signals.
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Advances of ECG sensors from hardware, software and format interoperability perspectives
TL;DR: A comprehensive survey on ECG sensor from hardware, software and data format interoperability perspectives is provided to help researchers towards the development of modern ECG sensors that are suitable and approved for adoption in real clinical settings.
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Resonance Y-shape solitons and mixed solutions for a (2+1)-dimensional generalized Caudrey–Dodd–Gibbon–Kotera–Sawada equation in fluid mechanics
TL;DR: In this paper , the specific expression for N-soliton solutions of (2+1)-dimensional generalized Caudrey-Dodd-Gibbon-Kotera-Sawada (gCDGKS) equation in fluid mechanics is given.
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TL;DR: The additional genes identified in this study, have an array of functions previously implicated in Alzheimer's disease, including aspects of energy metabolism, protein degradation and the immune system and add further weight to these pathways as potential therapeutic targets in Alzheimers disease.
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