Time–frequency representation using IEVDHM–HT with application to classification of epileptic EEG signals

Sharma, Ram Sharan; Pachori, Ram Bilas

doi:10.1049/iet-smt.2017.0058

Cited by 115 publications

(49 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On comparing the various PCA methods with our method, it is found that the detection performances of the methods based on PCA are equivalent with or are slightly inferior to our method. Meanwhile, the result of the method proposed by Sharma et al was better than our result pertaining to the classification between NF and S. However, it is difficult to precisely compare the performances, because the results of other datasets could not be represented. Although Sharma et al utilized different datasets extracted from BNDB, we verified that our method is robust and reliable in BNDB in terms of the average accuracy (ACC) through its performance results.…”

Section: Discussioncontrasting

confidence: 79%

“…BNDB includes seizure, nonseizure, and normal EEG signals, and we utilized this database for the validation of our method. Other studies that have validated their algorithms using BNDB are summarized in Table . Sharma et al proposed the time‐frequency flexible wavelet transform and fractal dimension, and the performance of their method was only approximately 0.03% higher than that of our method in the classification between ZO and S classes.…”

Section: Discussionmentioning

confidence: 79%

See 1 more Smart Citation

Automated epileptic seizure waveform detection method based on the feature of the mean slope of wavelet coefficient counts using a hidden Markov model and EEG signals

Lee

Ryu²,

Kim

2020

ETRI Journal

View full text Add to dashboard Cite

Long‐term electroencephalography (EEG) monitoring is time‐consuming, and requires experts to interpret EEG signals to detect seizures in patients. In this paper, we propose a novel automated method called adaptive slope of wavelet coefficient counts over various thresholds (ASCOT) to classify patient episodes as seizure waveforms. ASCOT involves extracting the feature matrix by calculating the mean slope of wavelet coefficient counts over various thresholds in each frequency subband. We validated our method using our own database and a public database to avoid overtuning. The experimental results show that the proposed method achieved a reliable and promising accuracy in both our own database (98.93%) and the public database (99.78%). Finally, we evaluated the performance of the method considering various window sizes. In conclusion, the proposed method achieved a reliable seizure detection performance with a short‐term window size. Therefore, our method can be utilized to interpret long‐term EEG results and detect momentary seizure waveforms in diagnostic systems.

show abstract

Section: Discussioncontrasting

confidence: 79%

Section: Discussionmentioning

confidence: 79%

Automated epileptic seizure waveform detection method based on the feature of the mean slope of wavelet coefficient counts using a hidden Markov model and EEG signals

Lee

Ryu²,

Kim

2020

ETRI Journal

View full text Add to dashboard Cite

show abstract

“…SVM (Murugavel and Ramakrishnan, 2016) is classified by time-domain features. TF (time frequency) (Sharma and Pachori, 2017) algorithm establishes the relationship between time and frequency for analysis. Andrzejak et al (2001) establishes a multi-scale classification framework.…”

Section: Performance Comparison Of Classification Algorithmsmentioning

confidence: 99%

“…Tibdewal et al (2017) carries out research on the basis of multichannel epileptic EEG signals. Sharma and Pachori (2017) establishes a model from the time and space dimensions for analysis. Sharma et al (2018) uses iterative filtering to recognize EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Epilepsy EEG Signal Classification Algorithm Based on Improved RBF

Zhou

2020

Front. Neurosci.

View full text Add to dashboard Cite

Epilepsy is a chronic recurrent transient brain dysfunction syndrome. It is characterized by recurrent epilepsy caused by abnormal discharge of brain neurons. Epilepsy is one of the common diseases in nervous system. The analysis of EEG signals is a hot topic in current research. In order to solve the problem of epileptic EEG signals classification accurately, we carry out in-depth research on epileptic EEG signals, analyze features from linear and non-linear perspectives, input them into the improved RBF model to dynamically extract effective features, and introduce one against one strategy classifier to reduce the probability of error classification. Experiments show that the proposed algorithm has strong robustness and high epileptic signal recognition rate.

show abstract

“…"Principal component analysis enhanced cosine radial basis function neural network" technique is more effective than the traditional method of visual imaging for detection of robust epilepsy and seizure [2]. Another technique named "Eigen Value Decomposition" (EVD) is used for the decomposition of EEG signals for extracting features and utilising them in time-frequency depiction method as explained by Sharma and Pachori [3].…”

Section: Introductionmentioning

confidence: 99%

EEG Classification Using TQWT and Classifiers

Padhy¹,

Suryateja²

2020

IJISRT

View full text Add to dashboard Cite

The purpose of this study is to detect the epileptic seizures, which can be indicated by the abnormal disturbances in intracranial neurons using the electroencephalogram (EEG) signals. The EEG signals are grouped into three categories viz., Normal EEG signals (Z and O subsets), Seizure-free EEG signals (N and F subsets), and Seizure EEG signals (S subset). Whereas, for classification in this study, EEG signals are divided into three groups namely NF-S, O-FS, and ZO-NF-S. The signal length is fixed to be 4096 samples. The EEG signals will be decomposed by using Tunable-Q Wavelet Transform (TQWT), which produces intrinsic mode functions (IMFs) in decreasing order of frequency. These IMFs are analysed to gather the features of these signals, which help to classify them into various categories, and these features are fed as inputs to three classifiers viz., Random Forest (RF), Decision Table (DT), and Logistic Regression (LR). Logistic Regression classifier has showed higher accuracy, specificity and sensitivity for NF-S and O-F-S groups in comparison to RF and DT classifiers, whereas, Random Forest classifier expressed higher accuracy, specificity and sensitivity for ZO-NF-S groups in comparison to other classifiers. By utilising LR classifier, the suitable parameters of TQWT in NF-S (seizure-free vs. Seizure) are Q=6, r=3, and J=9 and showed maximum accuracy of 98%; and in O-F-S (Normal vs. Seizure-free vs. Seizure), Q=1, r=3, and J=9 attained maximum accuracy of 94.7%. Whereas, in ZONF-S (Normal vs. Seizure-free vs. Seizure), Q=4, r=3, and J=9 expressed maximum accuracy of 99.8% utilising Random Forest classifier.

show abstract

Time–frequency representation using IEVDHM–HT with application to classification of epileptic EEG signals

Cited by 115 publications

References 64 publications

Automated epileptic seizure waveform detection method based on the feature of the mean slope of wavelet coefficient counts using a hidden Markov model and EEG signals

Automated epileptic seizure waveform detection method based on the feature of the mean slope of wavelet coefficient counts using a hidden Markov model and EEG signals

Epilepsy EEG Signal Classification Algorithm Based on Improved RBF

EEG Classification Using TQWT and Classifiers

Contact Info

Product

Resources

About