Wavelet Entropy Analysis of the High Resolution ECG

Natwong, B.; Sooraksa, Pitikhate; Pintavirooj, C.; Bunluechokchai, S.; Ussawawongaraya, Weerasak

doi:10.1109/iciea.2006.257276

Cited by 15 publications

(13 citation statements)

References 9 publications

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“…However, this current report focuses on obtaining wavelet entropy by continuous wavelet transform coefficients instead discrete wavelet based decomposition. In the literature, although the continuous wavelet transform was also used for determining wavelet entropy in [18,19], these studies are related to cardiac data which are very different to the complex EEG data analysis. Additionally in papers [15,17] the presented data also show decreased entropy in the post-stimulus section, confirming our findings of entropy change indicating an ordered state.…”

Section: Discussionmentioning

confidence: 99%

“…The RWE was obtained by continuous wavelet transform coefficients, with an approach differing from the previous analysis given in [15][16][17]. Wavelet entropy has been evaluated by using continuous wavelet transform coefficients as in the previous works [18,19]. Since EEG signals of length N are sampled at t n ¼ nT s ; n ¼ 1; 2; .…”

Section: Relative Wavelet Energymentioning

confidence: 99%

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Continuous time wavelet entropy of auditory evoked potentials

Cek

Özgören

Savacı

2010

Computers in Biology and Medicine

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a b s t r a c tIn this paper, the continuous time wavelet entropy (CTWE) of auditory evoked potentials (AEP) has been characterized by evaluating the relative wavelet energies (RWE) in specified EEG frequency bands. Thus, the rapid variations of CTWE due to the auditory stimulation could be detected in post-stimulus time interval. This approach removes the probability of missing the information hidden in short time intervals. The discrete time and continuous time wavelet based wavelet entropy variations were compared on non-target and target AEP data. It was observed that CTWE can also be an alternative method to analyze entropy as a function of time.

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Section: Discussionmentioning

confidence: 99%

Section: Relative Wavelet Energymentioning

confidence: 99%

Continuous time wavelet entropy of auditory evoked potentials

Cek

Özgören

Savacı

2010

Computers in Biology and Medicine

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“…[12] The rounded whiter patch represents micro-potential energy The colour intensity of the scalogram is proportional to the degree of correlation between the filtered signal and the detection wavelet at play. This means that the lighter zone in the scalogram represents the area where the signal is highly matched the detection wavelet [13]. It is known by the authors' experience that the detection wavelet cmor1-1.5 (Complex Morlet Wavelet) produces the best results regarding the detection of late potentials in the HR-ECG data, so it is set by default in the MicroECG software.…”

Section: A Continuous Wavelet Transformmentioning

confidence: 99%

MicroECG: An Integrated Platform for the Cardiac Arrythmia Detection and Characterization

Nascimento

Batista

Alves

et al. 2010

IFIP Advances in Information and Communication Technology

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Abstract.A software tool for the analysis of the High-Resolution Electrocardiogram (HR-ECG) for Arrhythmia detection is introduced. New algorithms based on Wavelet analysis are presented and compared with the classic Simson protocol over the P and QRS segments of the Electrocardiogram (EEG). A novel procedure based on a two step wavelet analysis and synthesis is performed in order to obtain a frequency description of the P, T or QRS segments. This frequency "signature" is useful for the detection of otherwise asymptomatic Arrhythmia patients. The tool has been developed in Matlab, and deployed for a standalone C application.

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“…The time-frequency characteristic of the EEGs and its non-linear nature is well exploited by making use of wavelet packet based on Haar mother wavelet with 5th level decomposition and entropy measure respectively. It has been reported that the physiological signals with disorders (abnormality) tend to yield high entropy values (Natwong et al 2006;Wang et al 2011;Das and Bhuiyan 2016). The features considered in our work were based on energy present in the signal which relatively increases the magnitude of the entropy value and shows a distinguishable difference between normal, pre-ictal and pure epileptic EEG.…”

Section: Introductionmentioning

confidence: 99%

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

Raghu¹,

Kumar

2016

Cogn Neurodyn

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Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50 Hz from raw EEG recordings. Raw EEGs were segmented into 1 s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The nonlinear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70 % for normal-pre-ictal, 99.70 % for normal-epileptic and 99.85 % for pre-ictalepileptic.

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Wavelet Entropy Analysis of the High Resolution ECG

Cited by 15 publications

References 9 publications

Continuous time wavelet entropy of auditory evoked potentials

Continuous time wavelet entropy of auditory evoked potentials

MicroECG: An Integrated Platform for the Cardiac Arrythmia Detection and Characterization

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

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