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Identification of full-night sleep parameters using morphological features of ECG signals: A practical alternative to EEG and EOG signals.

, , , , and . Biomed. Signal Process. Control., 88 (Part B): 105633 (February 2024)

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Identification of full-night sleep parameters using morphological features of ECG signals: A practical alternative to EEG and EOG signals., , , , and . Biomed. Signal Process. Control., 88 (Part B): 105633 (February 2024)EKG parametrelerinin uyku evreleri ve OSAS ile ilişkisinin incelenmesi (Analyzing the relationship of ECG parameters with the sleep stages and OSAS). Selçuk University, Turkey, (2017)Pre-determination of OSA degree using morphological features of the ECG signal., , , , , and . Expert Syst. Appl., (2017)A new approach to eliminating EOG artifacts from the sleep EEG signals for the automatic sleep stage classification., , , , , , and . Neural Comput. Appl., 28 (10): 3095-3112 (2017)Elimination of EMG artifacts from EEG signal in sleep staging., , , , , and . SIU, page 1345-1348. IEEE, (2016)Analyzing the effect of data preprocessing techniques using machine learning algorithms on the diagnosis of COVID-19., , , and . Concurr. Comput. Pract. Exp., (2022)A novel system for automatic detection of K-complexes in sleep EEG., , , , , and . Neural Comput. Appl., 29 (8): 137-157 (2018)Effect of the Hilbert-Huang transform method on sleep staging., , , , and . SIU, page 1-4. IEEE, (2017)Automatic detection of sleep spindles with the use of STFT, EMD and DWT methods., , , , , and . Neural Comput. Appl., 29 (8): 17-33 (2018)Automatic sleep staging based on SVD, VMD, HHT and morphological features of single-lead ECG signal., , , , and . Expert Syst. Appl., (2018)