Automatic segmentation of long-term ECG signals corrupted with broadband noise based on sample entropy

Micó, Pau; Mora, M.; Cuesta–Frau, David; Aboy, Mateo

doi:10.1016/j.cmpb.2009.08.010

Cited by 30 publications

(21 citation statements)

References 22 publications

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“…To tackle this problem, fuzzy entropy (FuzEn) was proposed [3]. These two entropy methods have attracted a great deal of attention over the recent years [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Refined composite multivariate generalized multiscale fuzzy entropy: A tool for complexity analysis of multichannel signals

Azami

Escudero

2017

Physica A: Statistical Mechanics and its Applications

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Multiscale entropy (MSE) is an appealing tool to characterize the complexity of time series over multiple temporal scales. Recent developments in the field have tried to extend the MSE technique in different ways. Building on these trends, we propose the so-called refined composite multivariate multiscale fuzzy entropy (RCmvMFE) whose coarse-graining step uses variance (RCmvMFEσ 2 ) or mean (RCmvMFEµ). We investigate the behavior of these multivariate methods on multichannel white Gaussian and 1/f noise signals, and two publicly available biomedical recordings. Our simulations demonstrate that RCmvMFEσ 2 and RCmvMFEµ lead to more stable results and are less sensitive to the signals' length in comparison with the other existing multivariate multiscale entropy-based methods. The classification results also show that using both the variance and mean in the coarse-graining step offer complexity profiles with complementary information for biomedical signal analysis. We also made freely available all the Matlab codes used in this paper. 2

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“…To tackle this problem, fuzzy entropy (FuzEn) was proposed [3]. These two entropy methods have attracted a great deal of attention over the recent years [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Refined composite multivariate generalized multiscale fuzzy entropy: A tool for complexity analysis of multichannel signals

Azami

Escudero

2017

Physica A: Statistical Mechanics and its Applications

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“…In essence, we are interested in the segments of the likelihood timeseries which differ from the normal. Such signal segmentation techniques have been successfully applied in ECG (electrocardiogram) and EEG (electroencephalogram) applications [18], [19]. We are using the algorithm presented in [18] to detect changes in our constructed likelihood signal L t (θ), in both states.…”

Section: Detection Algorithmmentioning

confidence: 99%

Wireless Gas Leak Detection and Localization

Chraim

Erol

Pister

2016

IEEE Trans. Ind. Inf.

110

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Thousands of industrial gas leaks occur every year, with many leading to injuries, deaths, equipment damage, and a disastrous environmental effect. There have been many attempts at solving this problem, but with limited success. This paper proposes a wireless gas leak detection and localization solution.With a monitoring network of 20 wireless devices covering 200m 2 , 60 propane releases are performed. The detection and localization algorithms proposed here are applied to the collected concentration data, and the methodology is evaluated. A detection rate of 91% is achieved, with seven false alarms recorded over three days, and an average detection delay of 108 seconds. The localization results show an accuracy of 5 meters. Recommendations for future explosive gas sensor design are then presented.

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“…Other important information includes the peak area, called the QRS complex, the duration of the PR and QT intervals, and the deviation of the PR and ST segments. These characteristics can be contaminated by the physical parameters of electronic and mechanical devices, electrical activity of muscles, degradation of the electrode-skin contact, and other causes [7][8][9]. Noise corruption can generate similar morphologies to the ECG waveform, reducing the discriminating power of heartbeat patterns, and increasing the rate of false alarms for cardiac monitors [9].…”

Section: Introductionmentioning

confidence: 99%

Noise-tolerant modular neural network system for classifying ECG signal

Ochoa¹,

Mena

Felix³

et al. 2019

IJCAI

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Millions of electrocardiograms (ECG) are interpreted every year, requiring specialized training for accurate interpretation. Because automated and accurate classification ECG signals will improve early diagnosis of heart condition, several neural network (NN) approaches have been proposed for classifying ECG signals. Current strategies for a critical step, the preprocessing for noise removal, are still unsatisfactory. We propose a modular NN approach based on artificial noise injection, to improve the generalization capability of the resulting model. The NN classifier initially performed a fairly accurate recognition of four types of cardiac anomalies in simulated ECG signals with minor, moderate, severe, and extreme noise, with an average accuracy of 99.2%, 95.1%, 91.4%, and 85.2% respectively. Ultimately we discriminated normal and abnormal heartbeat patterns for single lead of raw ECG signals, obtained 95.7% of overall accuracy and 99.5% of Precision. Therefore, the propose approach is a useful tool for the detection and diagnosis of cardiac abnormalities.Povzetek: V članku je opisana metoda modularnih nevronskim mrež za prepoznavanje šumnih ECG signalov. Materials and methodsThe NN classification comprises five stages: (i) simulation of ECG signal, (ii) extraction of features that indicate cardiac abnormalities, (iii) computer generation of normal and abnormal heartbeat patterns, (iv) artificial noise injection, and (v) cardiac rhythm classification on simulated and real ECG signals.

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Automatic segmentation of long-term ECG signals corrupted with broadband noise based on sample entropy

Cited by 30 publications

References 22 publications

Refined composite multivariate generalized multiscale fuzzy entropy: A tool for complexity analysis of multichannel signals

Refined composite multivariate generalized multiscale fuzzy entropy: A tool for complexity analysis of multichannel signals

Wireless Gas Leak Detection and Localization

Noise-tolerant modular neural network system for classifying ECG signal

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