Artifact Removal in Physiological Signals Using Singular Value Decomposition

Abstract

Sleep staging is a laborious and time consuming process performed by sleep specialists in sleep laboratories. Patient's sleeping process is analyzed after this process. These evidences give direction to the treatment process for many diseases that are described as sleep disorders in the literature. Automatic sleep staging systems are computer aided systems designed to make these operations more reliable and faster. The decision making mechanisms of these systems are based on signal processing and classification. For this reason, working with artefact-free signals is of great importance for increasing the classification accuracy. This study aims to eliminate the negative effects of Electrooculography (EOG) artifacts on sleep Electroencephalography (EEG) signals. The proposed artefact elimination process has been performed using the Singular Value Decomposition method and this method is very useful for online sleep staging systems.

Keywords

• Artefact
• EEG
• EOG
• Sleep EEG
• SVD

Tekil Değer Ayrıştırması Kullanarak Fizyolojik Sinyallerde Artefakt Giderme

Abstract

Uyku evreleme işlemi, uyku laboratuvarlarında uyku uzmanları tarafından yapılan zahmetli ve zaman alan bir işlemdir. Bu işlem sonucunda hastanın uyku süreci analiz edilir. Elde edilen bu bulgular literatürde uyku bozuklukları olarak tanımlanmış pek çok hastalık için tedavi sürecine yön verir. Otomatik uyku evreleme sistemleri, bu işlemlerin daha güvenilir ve daha hızlı olarak yapılması için tasarlanmış bilgisayar destekli sistemlerdir. Bu sistemlerin karar verme mekanizmaları sinyal işleme ve sınıflandırma temellidir. Bu sebeple artefaktsız sinyaller ile çalışmak sınıflama doğruluğunu artırmak için büyük önem arz etmektedir. Çalışma Elektrookülogram (EOG) artefaktlarının uyku Elektroensefalografi (EEG) sinyalleri üzerindeki negatif etkilerini elemine etmeyi amaçlamaktadır. Sunulan artefakt eleme işlemi Tekil Değer Ayrışımı yöntemi kullanılarak yapılmış olup online uyku evreleme sistemleri için oldukça kullanışlıdır.

Keywords

• Artefakt
• EEG
• EOG
• Uyku EEG’si
• TDA

References

1. [1] Penev P.D., Uyku yoksunluğu ve enerji metabolizması: uyumak, yemek için bir şans mı?, Current Opinion in Endocrinology, Diabetes And Obesity, 2007, 2(4), pp. 233-241.
2. [2] Altun Emirza M.A., Bican A., and Bora İ., Uyku Laboratuvarı'nda Kimler Uyuyor? Bir Retrospektif€ alışma, Turkish Journal of Neurology/Turk Noroloji Dergisi, 2012, 18(1).
3. [3] Rechtschaffen A., A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects, 睡眠脳波アトラス 標準用語 手技 判定法, 1968, pp. 1-55.
4. [4] Iber C., The AASM manual for the scoring of sleep and associated events: rules, terminology, and technical specification, (No Title), 2007.
5. [5] Berry R.B., Brooks R., Gamaldo C.E., Harding S.M., Marcus C., and Vaughn B.V., The AASM manual for the scoring of sleep and associated events, Rules, Terminology and Technical Specifications, Darien, Illinois, American Academy of Sleep Medicine, 2012, 176(2012), pp. 7.
6. [6] Berry R.B., Brooks R., Gamaldo C., Harding S.M., Lloyd R.M., Quan S.F., Troester M.T., and Vaughn B.V., AASM scoring manual updates for 2017 (version 2.4), 2017, American Academy of Sleep Medicine. pp. 665-666.
7. [7] Köktürk O., Scoring of Sleep Recordings, 2013.
8. [8] Yarğı V. and Postalcıoğlu S., EEG işareti kullanılarak bağımlılığa yatkınlığın makine öğrenmesi teknikleri ile analizi, El-Cezeri, 2021, 8(1), pp. 142-154.

9. [9] Islam M.K., Rastegarnia A., and Yang Z., Methods for artifact detection and removal from scalp EEG: A review, Neurophysiologie Clinique/Clinical Neurophysiology, 2016, 46(4-5), pp. 287-305.
10. [10] Manoilov P., EEG eye-blinking artefacts power spectrum analysis. in Proc. Int. Conf. Comput. Syst. Technol, 2006.
11. [11] Anderer P., Roberts S., Schlögl A., Gruber G., Klösch G., Herrmann W., Rappelsberger P., Filz O., Barbanoj M.J., and Dorffner G., Artifact processing in computerized analysis of sleep EEG–a review, Neuropsychobiology, 1999, 40(3), pp. 150-157.
12. [12] Hoffmann S. and Falkenstein M., The correction of eye blink artefacts in the EEG: a comparison of two prominent methods, PloS one, 2008, 3(8), pp. e3004.
13. [13] Fortgens C. and De Bruin M., Removal of eye movement and ECG artifacts from the non-cephalic reference EEG, Electroencephalography and clinical Neurophysiology, 1983, 56(1), pp. 90-96.
14. [14] Puthusserypady S. and Ratnarajah T., Robust adaptive techniques for minimization of EOG artefacts from EEG signals, Signal processing, 2006, 86(9), pp. 2351-2363.
15. [15] Mariani S., Tarokh L., Djonlagic I., Cade B.E., Morrical M.G., Yaffe K., Stone K.L., Loparo K.A., Purcell S.M., and Redline S., Evaluation of an automated pipeline for large-scale EEG spectral analysis: the National Sleep Research Resource, Sleep medicine, 2018, 47, pp. 126-136.
16. [16] D’Rozario A.L., Dungan G.C., Banks S., Liu P.Y., Wong K.K., Killick R., Grunstein R.R., and Kim J.W., An automated algorithm to identify and reject artefacts for quantitative EEG analysis during sleep in patients with sleep-disordered breathing, Sleep and Breathing, 2015, 19(2), pp. 607- 615.
17. [17] Somervail R., Cataldi J., Stephan A.M., Siclari F., and Iannetti G.D., Dusk2Dawn: an EEGLAB plugin for automatic cleaning of whole-night sleep electroencephalogram using Artifact Subspace Reconstruction, Sleep, 2023, 46(12), pp. zsad208.
18. [18] Erdoğan Y.E.N., Ali, Elektrokardiyografi Yardımıyla Hipertansiyonun Otomatik Belirlenmesinde Ampirik Kip Ayrışımının Gürültülü ve Gürültüsüz Sinyaller Üzerindeki Performansının Karşılaştırılması, El-Cezeri, 2022, 9.
19. [19] Davud M., Accelerating Convergence in LMS Adaptive Filters Using Particle Swarm Optimization: A Hybrid Approach for Real-Time Signal Processing, El-Cezeri, 2025, 12(3), pp. 311-328.
20. [20] Mannan M.M.N., Kamran M.A., and Jeong M.Y., Identification and removal of physiological artifacts from electroencephalogram signals: A review, Ieee Access, 2018, 6, pp. 30630-30652.
21. [21] Jiang X., Bian G.-B., and Tian Z., Removal of artifacts from EEG signals: a review, Sensors, 2019, 19(5), pp. 987.
22. [22] Cox R., Weber F.D., and Van Someren E.J., Customizable automated cleaning of multichannel sleep EEG in SleepTrip, Frontiers in Neuroinformatics, 2024, 18, pp. 1415512.
23. [23] Ujma P.P., Dresler M., and Bódizs R., Comparing Manual and Automatic Artifact Detection in Sleep EEG Recordings, Psychophysiology, 2025, 62(2), pp. e70016.
24. [24] Leach S., Sousouri G., and Huber R., ‘High-Density-SleepCleaner’: An open-source, semi-automatic artifact removal routine tailored to highdensity sleep EEG, Journal of Neuroscience Methods, 2023, 391, pp. 109849.
25. [25] Viola F.C., Thorne J., Edmonds B., Schneider T., Eichele T., and Debener S., Semi-automatic identification of independent components representing EEG artifact, Clinical Neurophysiology, 2009, 120(5), pp. 868-877.
26. [26] Chaumon M., Bishop D.V., and Busch N.A., A practical guide to the selection of independent components of the electroencephalogram for artifact correction, Journal of neuroscience methods, 2015, 250, pp. 47-63.
27. [27] Klema V. and Laub A., The singular value decomposition: Its computation and some applications, IEEE Transactions on automatic control, 1980, 25(2), pp. 164-176.
28. [28] Reddy K.A. and Kumar V.J., Motion artifact reduction in photoplethysmographic signals using singular value decomposition. in 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007, 2007, IEEE.