Seizure detection approach using S-transform and singular value decomposition

Xia, Yudan; Zhou, Weidong; Li, Chengcheng; Yuan, Qi; Geng, Shuqin

doi:10.1016/j.yebeh.2015.07.043

Cited by 28 publications

(8 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Section 4, the performances of several schemes of frequency band partitioning are compared. Our experiments indicate that the following frequency division into 11 subbands achieves the highest accuracy: (0.4-4), (4-8), (8-10), (10-13), (13)(14)(15)(16)(17)(18), (18)(19)(20)(21)(22)(23)(24)(25), (25)(26)(27)(28)(29)(30), (30)(31)(32)(33)(34)(35)(36), (36)(37)(38)(39)(40)(41), (41)(42)(43)(44)(45)(46), and (46-50) Hz. Therefore, 11 matrices (denoted by TF) are obtained.…”

Section: Frequency Partitioningmentioning

confidence: 89%

See 1 more Smart Citation

Sleep stages classification from EEG signal based on Stockwell transform

Ghasemzadeh

Kalbkhani

Shayesteh

2019

IET signal process.

View full text Add to dashboard Cite

Sleep has great effect on physical health and quality of life. Electroencephalogram (EEG) signal is used in studying sleep process and recently, time-frequency transforms are increasingly utilised in EEG signal analysis. This study proposes an efficient method for sleep stages classification based on a time-frequency transform, namely Stockwell transform. In the introduced method, at first, the Stockwell transform is used to map each 30 s epoch of EEG signal into the time-frequency domains, which results in a complex-valued matrix. Then, the frequency domain is divided into different non-overlapping segments, leading to several matrices. After that, entropy features are extracted from the obtained matrices. In order to determine the sleep stage of each epoch, the computed features are applied to classifier. Support vector machine, weighted Knearest neighbour, and ensemble bagged tree classifiers are considered. The Pz-Oz and Fpz-Cz channels of EEG signal from Sleep-EDF data set and C3-A2 channel from ISRUC-Sleep data set are used in this research. The results indicate that the proposed method outperforms the recently introduced methods.

show abstract

Section: Frequency Partitioningmentioning

confidence: 89%

“…Short-time Fourier transform (STFT) and WT were conventionally used for analysis of EEG signals. The Stockwell transform, which is the development of continuous WT (CWT) and STFT, is a technique to analyse non-stationary signals [36,37]. Gabor transform and Wigner transform are two known timefrequency transforms.…”

Section: Stockwell Transformmentioning

confidence: 99%

Sleep stages classification from EEG signal based on Stockwell transform

Ghasemzadeh

Kalbkhani

Shayesteh

2019

IET signal process.

View full text Add to dashboard Cite

show abstract

“…It has been reported that iEEG is useful for surgical decision-making regarding patients with magnetic resonance imaging (MRI)-negative temporal lobe epilepsy (TLE) 1 . Some investigators have attempted to develop systems for the automatic detection of epileptic seizures with iEEG, which will be beneficial for patients with epilepsy 2 3 4 5 . Previous studies revealed several characteristic features of iEEG during a seizure attack: rhythmic waveforms in the δ to low-γ frequency range 6 , a high frequency component (higher than the γ band) 7 8 9 , and a very low-frequency component (e.g., DC wave) 10 .…”

mentioning

confidence: 99%

Detection of Epileptic Seizures Using Phase–Amplitude Coupling in Intracranial Electroencephalography

Edakawa

Yanagisawa

Kishima

et al. 2016

Sci Rep

View full text Add to dashboard Cite

Seizure detection using intracranial electroencephalography (iEEG) contributes to improved treatment of patients with refractory epilepsy. For that purpose, a feature of iEEG to characterize the ictal state with high specificity and sensitivity is necessary. We evaluated the use of phase–amplitude coupling (PAC) of iEEG signals over a period of 24 h to detect the ictal and interictal states. PAC was estimated by using a synchronisation index (SI) for iEEG signals from seven patients with refractory temporal lobe epilepsy. iEEG signals of the ictal state was characterised by a strong PAC between the phase of β and the amplitude of high γ. Furthermore, using SI values, the ictal state was successfully detected with significantly higher accuracy than by using the amplitude of high γ alone. In conclusion, PAC accurately distinguished the ictal state from the interictal state.

show abstract

“…In previous studies carried out by the authors [12], 92% was the highest classification accuracy when training and testing samples were obtained from the same conditions and 65% accuracy was achieved when classifiers were trained and tested with different measurement conditions. In addition, the maximum accuracy was 72.9% when noisy PD signals were classified.…”

Section: Introductionmentioning

confidence: 83%

Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification

et al. 2021

View full text Add to dashboard Cite

Different types of classifiers for acoustic partial discharge (PD) pattern classification have been widely discussed in the literature. The classifier performance mainly depends on the measurement conditions (location and type of the PD, acoustic sensor position and frequency response) as well as extracted features. Recent research posits that features extracted by singular value decomposition (SVD) can exhibit the natural characteristics and energy contained in the signal. Though the technique by itself is not novel, in this paper, SVD is employed for PD classification in a revised way starting from data arrangement in Hankel form, to embedding the hypergraph-based features and finally to extracting the required set of optimal features. The algorithm is tested for various measurement conditions that include the influences of various PD locations and oil temperatures. The robustness of the algorithm is also tested using noisy PD signals. Experimental results show the proposed feature extraction method supremacy.

show abstract

Seizure detection approach using S-transform and singular value decomposition

Cited by 28 publications

References 34 publications

Sleep stages classification from EEG signal based on Stockwell transform

Sleep stages classification from EEG signal based on Stockwell transform

Detection of Epileptic Seizures Using Phase–Amplitude Coupling in Intracranial Electroencephalography

Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification

Contact Info

Product

Resources

About