Advanced daytime polysomnographic preprocessing: A versatile approach for stream-wise estimation |
| |
| Affiliation: | 1. Spinal Surgery Service, Department of Neurosurgery, Sheba Medical Center Affiliated to Sackler Medical School, Tel-Aviv University, Ramat-Gan, Israel;2. Dr. Pinchas Bornstein Talpiot Medical Leadership Program, Sheba Medical Center Affiliated to Sackler Medical School, Tel-Aviv University, Ramat-Gan, Israel;3. Department of Diagnostic Imaging, Sheba Medical Center Affiliated to Sackler Medical School, Tel-Aviv University, Ramat-Gan, Israel;4. Department of Neurology, Sheba Medical Center Affiliated to Sackler Medical School, Tel-Aviv University, Ramat-Gan, Israel;5. Joseph Sagol Neuroscience Center, Sheba Medical Center Affiliated to Sackler Medical School, Tel-Aviv University, Ramat-Gan, Israel;1. Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China;2. Department of Urology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, China;3. Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China;1. Department of Anatomy and Surgical Anatomy, Medical School, Faculty of Health Sciences, Aristotle University of Thessaloniki, P.O. Box 300, 54124 Thessaloniki, Greece;2. Department of Anatomy, Medical School, Faculty of Health Sciences, National and Kapodistrian University of Athens, 75 M. Asias Street, 11527 Goudi, Athens, Greece;1. Division of Complex HealthCare, Nationwide Children''s Hospital, Columbus, OH, USA;2. Section of Urology, Nationwide Children''s Hospital, Columbus, OH, USA;3. Department of Urology, University of Louisville, Louisville, KY, USA;4. Center for Microbial Pathogenesis at The Research Institute at Nationwide Children''s Hospital, Nationwide Children''s Hospital, Columbus, OH, USA;5. Department of Urology, College of Medicine, The Ohio State University, Columbus, OH, USA;6. Department of Urology, Cincinnati Children''s Hospital, Cincinnati, OH, USA |
| |
| Abstract: | The enhancement of monitoring biosignals plays a crucial role to thrive successfully computer-assisted diagnosis, ergo the deployment of outstanding approaches is an ongoing field of research demand. In the present article, a computational prototype for preprocessing short daytime polysomnographic (sdPSG) recordings based on advanced estimation techniques is introduced. The postulated model is capable of performing data segmentation, baseline correction, whitening, embedding artefacts removal and noise cancellation upon multivariate sdPSG data sets. The methodological framework includes Karhunen–Loève Transformation (KLT), Blind Source Separation with Second Order Statistics (BSS-SOS) and Wavelet Packet Transform (WPT) to attain low-order, time-to-diagnosis efficiency and modular autonomy. The data collected from 10 voluntary subjects were preprocessed by the model, in order to evaluate the withdrawal of noisy and artefactual activity from electroencephalographic (EEG) and electrooculographic (EOG) channels. The performance metrics are distinguished in qualitative (visual inspection) and quantitative manner, such as: Signal-to-Interference Ratio (SIR), Root Mean Square Error (RMSE) and Signal-to-Noise Ratio (SNR). The computational model demonstrated a complete artefact rejection in 80% of the preprocessed epochs, 4 to 8 dB for residual error and 12 to 30 dB in signal-to-noise gain after denoising trial. In comparison to previous approaches, N-way ANOVA tests were conducted to attest the prowess of the system in the improvement of electrophysiological signals to forthcoming processing and classification stages. |
| |
| Keywords: | Artefacts rejection EEG Noise removal Preprocessing PSG sleep |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
