Energy Distribution of EEG Signal Components by Wavelet Transform

Omerhodžić, Ibrahim; Avdaković, Samir; Nuhanovic, Amir; Dizdarevic, Kemal; Rotim, Krešimir

doi:10.5772/37914

Cited by 39 publications

(18 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Described work is a continuation of the research presented in the previous papers [14] where power spectrum values of the EEG components were explored using the Discrete WT, and the results confirm conclusions published in 2012 [14]. It is important to note that this study does not include the classification of EEG signals or improvements in so far suggested classifiers available in the literature, which represents an important chapter in the automatic classification of EEG signals in the clinical practice.…”

Section: Introductionsupporting

confidence: 61%

“…Epilepsy is a chronic disorder of cortex with varying symptoms and causes [1][2]. Electroencephalographic (EEG) brain observation represents one of the most significant approaches in studying epilepsy, since EEG signals contains huge amount of useful information.…”

Section: Introductionmentioning

confidence: 99%

“…However, EEG interpreting is not a simple task, and visual EEG analysis often do not provide for quality conclusions. In the last several years large number of multidisciplinary research teams suggested different methods and approaches addressing: disease development identification [3], fast and accurate EEG signal classification in the context of precise diagnosis [2], [4][5][6][7][8][9], etc. Number of different algorithms development were facilitated with innovation in mathematical techniques for signal processing as: wavelet transform (WT) [2], [4][5][6][7][8][9] and Hilbert Huang transform [10][11], and some applications in the context of EEG signal processing can be found in the work of Shayan et al [12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Diagnosis of Epilepsy from EEG Signals Using Global Wavelet Power Spectrum

et al. 2015

Self Cite

View full text Add to dashboard Cite

Abstract--Epilepsy diagnosis using EEG signals represents important segment in general clinical practice. However, EEG signal features such as amplitude, are not helpful in order to visually distinguish between healthy and epileptic patients; and therefore additional signal processing and results analysis is needed. In this paper, the analyses and the results of the properties of EEG signals of healthy subjects and patients with an epileptic syndrome without seizure, using global wavelet power spectrum (GWS) are presented. The results of the analysis of the 200 EEG signals confirm that this approach can enable a simple recognition of epileptic EEG signals in a standard clinical practice. The results indicate that the magnitudes of the EEG signal components for the patients with an epileptic syndrome are considerably different to the EEG signal components of the healthy subjects. Also, the GWS dominant values for selected signals of patients with an epileptic syndrome are found in the delta and theta frequency bands.

show abstract

Section: Introductionsupporting

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Diagnosis of Epilepsy from EEG Signals Using Global Wavelet Power Spectrum

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…Wavelet coefficients in different subbands reflect the partition of the signal energy according to Parseval's theorem (68). They can be interpreted in diverse degrees, which lead to different feature extraction strategies.…”

Section: Wavelet Featuresmentioning

confidence: 99%

A retrospective glance at automatic detection of epileptic spike in electroencephalogram

Zhou¹

2015

Eur J Bio Med Res

View full text Add to dashboard Cite

Automatic detection of epileptic spikes is an important clinical application. It has been developed nearly 40 years. Yet the current automatic detection results are still not as reliable as experienced human interpreters, mainly due to the complex morphology of spikes and the similarity between paroxysmal events in brain activities. By reviewing the previous work, it is noticeable that the implementation of wavelet, ANN and spatiotemporal analysis show promising prospect.By reasonable combination of detection strategies, the detection rate can reach 90% with acceptable false detection rate. Current researches also reveal the reality of lacking of uniform dataset and rules for algorithm comparison. This review aims to compare the pros and cons of current researches and discuss the trend of development in the future.

show abstract

“…where Ѱ (t) is called the 'mother wavelet', the asterisk denotes complex conjugate, whereas a and b are scaling parameters [8]. The corresponding normalized wavelet power is defined by: , (6) and σ is the standard deviation of the EEG segment used.…”

Section: Wavelet Transform (Wt)mentioning

confidence: 99%

ARMA Modelling for Sleep Disorders Diagnose

Costa¹,

Ortigueira²,

Batista³

et al. 2013

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Abstract. Differences in EEG sleep spindles constitute a promising indicator of sleep disorders. In this paper Sleep Spindles are extracted from real EEG data using a triple (Short Time Fourier Transform-STFT; Wavelet Transform-WT; Wave Morphology for Spindle Detection-WMSD) algorithm. After the detection, an Autoregressive-moving-average (ARMA) model is applied to each Spindle and finally the ARMA's coefficients' mean is computed in order to find a model for each patient. Regarding only the position of real poles and zeros, it is possible to distinguish normal from Parasomnia REM subjects.

show abstract

Energy Distribution of EEG Signal Components by Wavelet Transform

Cited by 39 publications

References 36 publications

Diagnosis of Epilepsy from EEG Signals Using Global Wavelet Power Spectrum

Diagnosis of Epilepsy from EEG Signals Using Global Wavelet Power Spectrum

A retrospective glance at automatic detection of epileptic spike in electroencephalogram

ARMA Modelling for Sleep Disorders Diagnose

Contact Info

Product

Resources

About