The relationship between snoring sounds and EEG signals on polysomnography


KAYABEKİR M., YAĞANOĞLU M.

SLEEP AND BREATHING, cilt.26, sa.3, ss.1219-1226, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 26 Sayı: 3
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s11325-021-02516-8
  • Dergi Adı: SLEEP AND BREATHING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, EMBASE, MEDLINE
  • Sayfa Sayıları: ss.1219-1226
  • Anahtar Kelimeler: Sleep Physiology, PSG, EEG, Snoring, Feature extraction, Simple diagnostic method, OBSTRUCTIVE SLEEP-APNEA, SLOW-WAVE, CLASSIFICATION, RESPONSES, ENTROPY
  • Atatürk Üniversitesi Adresli: Evet

Özet

Purpose The aim of this study was to analyze the relationship of snoring sound signals obtained by polysomnography (PSG) in the sleep laboratory with cortical EEG (6 channel) signals to find answers to two important questions that have been covered to a limited extent in the literature: (1) Would the sounds generated by a snoring individual have an effect on the cerebral electrical waves occurring during sleep (specifically deep restorative sleep)? (2) Would the snoring sounds of an individual being examined by PSG have more of an effect on any one of the EEG electrodes? Methods PSG recordings were obtained from volunteers with primary snoring and those with obstructive sleep apnea syndrome (OSAS) on six different EEG channels (F4-M1, C4-M1, and O2-M1, F3-M2, C3-M2, and O1-M2). The relationship of each of these recordings and snoring sound signals was analyzed by using a computer-based electrophysiological signal analysis method. A three-tier approach was used in this relationship: "Feature extraction, Feature selection, and Classification". Results Data were obtained from a total of 40 volunteers (32 men, mean age (+/- SD) 47.5 +/- 3.2 years), 20 with primary snoring and 20 with OSAS. The discrete wavelet transform (DWT) feature extraction method was the most successful method, and by utilizing this method for analyzing EEG channels, snoring sound signals were found to affect the C3-M2 channel the most (Duncan test, p < 0.05). Delta wave frequency levels during snoring were decreased compared to both before snoring (p = 0.160) and after snoring (p = 0.04) periods (paired sample test). Discussion When snoring sounds and EEG signals were analyzed for frequency, time, and wave conversion with feature extraction methods, the C3-M2 channel was to be found the most affected channel. The sleep physiologist who made the PSG analyses reported that, among the 6 EEG channels analyzed for periods where there was no apnea or hypopnea events but only snoring, C3-M2 was the channel showing changes in delta wave activity. Conclusion Our study showed that the monotonous and repetitive snoring sounds of the snorer do not wake the individual, but do affect deep restorative sleep (N3). PSG signal analysis revealed that the most significant changes were in the C3-M2 channel (N3 delta wave amplitude increase and frequency decrease during snoring). Thus, clinicians may be able to monitor the characteristic changes occuring in large cortical delta waves in snoring individuals with innovative single-channel EEG devices without microphones.