2015
DOI: 10.1016/j.eswa.2015.06.010
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Fast and accurate PLS-based classification of EEG sleep using single channel data

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Cited by 43 publications
(24 citation statements)
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“…Berdasarkan frekuensi, amplitudo tegangan, dan kondisi objek, sinyal EEG dapat dibagi menjadi 4 gelombang, yaitu gelombang delta (kurang dari 4 Hz), theta (4 -7 Hz), alpha (8)(9)(10)(11)(12), dan beta (13 -49 Hz) [8].…”
Section: A Electroencephalographyunclassified
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“…Berdasarkan frekuensi, amplitudo tegangan, dan kondisi objek, sinyal EEG dapat dibagi menjadi 4 gelombang, yaitu gelombang delta (kurang dari 4 Hz), theta (4 -7 Hz), alpha (8)(9)(10)(11)(12), dan beta (13 -49 Hz) [8].…”
Section: A Electroencephalographyunclassified
“…Order 8 dan batas frekuensi 4 -7 Hz untuk theta, order 10 dan batas frekuensi 8 -12 Hz untuk alpha, order 11 dan batas frekuensi 13 -49 Hz untuk beta. Hasil dari proses filtering akan menjadi data masukan pada tahap ekstraksi fitur [8].…”
Section: B Preprocessingunclassified
“…In biomedical signal processing, it is crucial to determine the noise, artifacts and any trends present in the raw signals so that their influence in the feature extraction stage can be minimized [29,30,46,49,50]. EEG recordings have a wide variety of artifacts, some having a technical origin and others having a physiological origin mixed together with the brain signal [41,45,57,108,129].…”
Section: Signal Pre-processingmentioning
confidence: 99%
“…The achieved accuracies were 98.32% and 94.49%, respectively. Kayikcioglu et al [49] extracted Auto-Regressive (AR) coefficient features from a single EEG signal to classify both sleep and wake stages with an accuracy of 91% using a Partial Least Squares Regression (PLSR) classifier. Spectral analysis, Wavelet Transform (WT) and fuzzy clustering based on the FCM algorithm were used by [60] in an automatic sleep stage detector, which was able to distinguish the wake stage, as well as stages 1-4 and REM sleep stage, using single-channel EEG signals.…”
Section: State Of the Artmentioning
confidence: 99%
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