Statistical features-based comparison of analysis and synthesis of normal   and epileptic electroencephalograms for various wavelets

Revati SHRIRAM; Mahalingam SUNDHARARAJAN; Supriya SHETE; Nivedita DAIMIWAL

Makale detayları

Statistical features-based comparison of analysis and synthesis of normal and epileptic electroencephalograms for various wavelets

Yazarlar:

Revati SHRIRAM | Mahalingam SUNDHARARAJAN | Supriya SHETE | Nivedita DAIMIWAL

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 3, pp.1795-1806

Belge Tipi:

Makale

Yayın Tarihi:

5/2017

Belge Dili:

Türkçe

Document ID (Org No.):

2000736359

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

An electroencephalogram (EEG) is an electrical signal in microvolts captured   noninvasively from the brain, which provides important and unique   information about the brain. The frequency of an EEG signal lies between 0   and 100 Hz. Decomposition of an EEG signal into various bands such as alpha,   beta, delta, theta, and gamma is essential in seizure-related studies. EEGs   play a key role in the diagnosis of epileptic seizures and neurological   disorders. In this paper, multiple wavelet families for decomposition and   reconstruction are explored and are compared based on risk functions and   reconstruction measures. While dealing with the wavelets it is a difficult   task to choose the correct/accurate wavelet for the given biosignal   analysis. Various statistical properties were studied by the authors to   check the suitability of various wavelets for normal and diseased EEG signal   decomposition and reconstruction. The methodology was applied to 3 groups   (63 subjects) consisting of both sexes and aged between 1 and 80 years: 1)   normal healthy subjects, 2) patients with focal seizures, and 3) patients   with generalized seizures. Our result shows that the Haar and Bior3.7   wavelets are more suitable for normal as well as diseased EEG signals, as   the mean square error, mean approximate error, and percent root mean square   difference of these wavelets are much smaller than in other wavelets. The   signal-to-error ratio for Haar and Bior3.7 was much higher than in any other   wavelet studied.

Makale detayları

Statistical features-based comparison of analysis and synthesis of normal and epileptic electroencephalograms for various wavelets

Yazarlar: Revati SHRIRAM | Mahalingam SUNDHARARAJAN | Supriya SHETE | Nivedita DAIMIWAL

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 3, pp.1795-1806

Belge Tipi: Makale

Yayın Tarihi: 5/2017

Belge Dili: Türkçe

Document ID (Org No.): 2000736359

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Revati SHRIRAM | Mahalingam SUNDHARARAJAN | Supriya SHETE | Nivedita DAIMIWAL

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 3, pp.1795-1806

Belge Tipi:

Makale

Yayın Tarihi:

5/2017

Belge Dili:

Türkçe

Document ID (Org No.):

2000736359

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632