2023
DOI: 10.1016/j.jocs.2023.101943
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Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning

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Cited by 46 publications
(13 citation statements)
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“…A hybrid model was presented by Varli et al [28] that makes use of the time sequence of EEG data in addition to time-frequency-EEG data transformations of timedependent EEG signals. To transform signals into EEG data, CWT and STFT techniques were used.…”
Section: Literature Surveymentioning
confidence: 99%
“…A hybrid model was presented by Varli et al [28] that makes use of the time sequence of EEG data in addition to time-frequency-EEG data transformations of timedependent EEG signals. To transform signals into EEG data, CWT and STFT techniques were used.…”
Section: Literature Surveymentioning
confidence: 99%
“…LSTM networks [15,16,17] have shown substantial promise in capturing temporal dependencies within EEG signals, making them well-suited for seizure detection and prediction tasks. Hezam et al [8] proposed a novel hybrid LSTM model that leverages both short-term and long-term EEG patterns to achieve enhanced detection accuracy.…”
Section: Related Workmentioning
confidence: 99%
“…In contrast to other efforts that typically apply RNNs to the entire sequence LSTM networks [11,15,17], we reconstruct the initial long sequence into numerous shorter ones, each of which contains just local information and is processed by an ARNN cell. Specifically, we create a local window of size m of the sequence that encompasses m consecutive locations and forms l local windows.…”
Section: Arnn Cellmentioning
confidence: 99%
“…EEG signals are nonstationary, and therefore, time–frequency (TF) and time-scale representations are commonly used for their analysis [ 6 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. A time–frequency distribution (TFD) enables us to describe signal energy simultaneously in time and frequency.…”
Section: Introductionmentioning
confidence: 99%