Seizure Detection Based on EEG Signals Using Katz Fractal and SVM Classifiers

Wijayanto, Inung; Rizal, Achmad; Humairani, Annisa

doi:10.1109/icsitech46713.2019.8987487

Cited by 15 publications

(14 citation statements)

References 25 publications

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“…Meanwhile, fractal calculations for epileptic EEG detection were simulated in [18]- [20]. According to these studies, the complexity features combined with various classifiers were able to generate high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Seizure Detection in Epileptic EEG Using Short-Time Fourier Transform and Support Vector Machine

2021

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Epilepsy is the most common form of neurological disease. The electroencephalogram (EEG) is the main tool in the observation of epilepsy. The detection and prediction of seizures in EEG signals require multi-domain analysis, one of which is the time domain combined with other approaches for feature extraction. In this study, a method for detecting seizures in epileptic EEG is proposed using analysis of the distribution of the signal spectrum in the time range t. The EEG signal which includes normal, inter-ictal and ictal is transformed into the time-frequency domain using the Short-Time Fourier Transform (STFT). Simulations were carried out on varying window length, overlap and FFT points to find the highest detection accuracy. The frequency distribution and first-order statistics were then calculated as feature vectors for the classification process. A support vector machine was employed to evaluate the proposed method. The simulation results showed the highest accuracy of 92.3% using 25-20-512 STFT and quadratic SVM. The proposed method in this study is expected to be a basis for the detection and prediction of seizures in long-term EEG recordings or real-time EEG monitoring of epilepsy patients.

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Section: Introductionmentioning

confidence: 99%

Seizure Detection in Epileptic EEG Using Short-Time Fourier Transform and Support Vector Machine

2021

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“…People with epilepsy sometimes go through discrimination, misunderstanding, and depression. Meanwhile, this disease is endangering if a risky activity performed by a person living with epilepsy, epilepsy would lead to death [3,4]. Diagnosis of epilepsy is individualized, and so are the treatment decisions.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, a review study done by Acharya et al tackled the statement since spectral entropy (SpecEn) performed better than the other entropies [11]. In another study, Wijayanto et al adequated Katz's fractal dimensions to detect seizure since they were very beneficial in handling the irregularity property of EEG signals [4].…”

Section: Introductionmentioning

confidence: 99%

Signal Dynamics Analysis for Epileptic Seizure Classification on EEG Signals

Hadiyoso¹,

Wijayanto²,

Humairani³

2021

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Epilepsy is the most common form of neurological disease. Patients with epilepsy may experience seizures of a certain duration with or without provocation. Epilepsy analysis can be done with an electroencephalogram (EEG) examination. Observation of qualitative EEG signals generates high cost and often confuses due to the nature of the non-linear EEG signal and noise. In this study, we proposed an EEG signal processing system for EEG seizure detection. The signal dynamics approach to normal and seizure signals' characterization became the main focus of this study. Spectral Entropy (SpecEn) and fractal analysis are used to estimate the EEG signal dynamics and used as feature sets. The proposed method is validated using a public EEG dataset, which included preictal, ictal, and interictal stages using the Naïve Bayes classifier. The test results showed that the proposed method is able to generate an ictal detection accuracy of up to 100%. It is hoped that the proposed method can be considered in the detection of seizure signals on the long-term EEG recording. Thus it can simplify the diagnosis of epilepsy.

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“…One of the methods of biological signal analysis frequently used is the fractal method. Fractal analysis has been shown to provide promising results in the classification of various biological signals such as lung sounds [8], electrocardiogram signal (ECG) [9], and electroencephalogram signal (EEG) [10]. The fractal dimension can distinguish the signal complexity in the normal signal and the pathological signal.…”

Section: Introductionmentioning

confidence: 99%

“…In(9), m indicates the indicator start of signal (m=1, 2, …, k), and length of curve , is defined as(10).…”

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confidence: 99%

Classification of Normal and Murmur Hearts Sound using the Fractal Method

2020

IJATCSE

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Heart sounds contain important information related to heart health. Normal heart sounds produce sound patterns that are different from abnormal ones. Various digital signal processing methods have been used to differentiate these heart sound signals and the most usual methods are wavelet analysis, entropy analysis, or a combination of both. In this study, the fractal dimension was used to classify normal heart sounds and murmur sounds by using the box-counting fractal dimension (BCFD), Katz fractal dimension (KFD), Sevcik fractal dimension (SFD), and Higuchi fractal dimension (HFD) as the heart sound features. The highest accuracy reached 100% using SFD as a feature and KNN as a classifier. These results were tested on 50 normal heart sounds and 50 heart sounds murmurs with 3-fold cross-validation. The results showed that choosing the right fractal dimension can distinguish normal heart sounds and murmurs.

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Seizure Detection Based on EEG Signals Using Katz Fractal and SVM Classifiers

Cited by 15 publications

References 25 publications

Seizure Detection in Epileptic EEG Using Short-Time Fourier Transform and Support Vector Machine

Seizure Detection in Epileptic EEG Using Short-Time Fourier Transform and Support Vector Machine

Signal Dynamics Analysis for Epileptic Seizure Classification on EEG Signals

Classification of Normal and Murmur Hearts Sound using the Fractal Method

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