Recurrence quantification analysis of sleep electoencephalogram in sleep apnea syndrome in humans

Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning

“…Gao, Cao, Gu, Harris, and Principe (2003) used recurrence time statistics to detect the weak transitions in signal dynamics. Song, Lee, and Kim (2004) suggested the information obtained from RQA of sleep EEGs was valuable for analyzing sleep stages in subjects with sleep apnea syndrome.…”

Effects of Speech Phonological Features during Passive Perception on Cortical Auditory Evoked Potential in Sensorineural Hearing Loss

“…Besides that, Song et al (2004) used recurrence quantification analysis (RQA) method to scrutinize cortical functional at different sleep stages including people suffering from sleep apnea syndrome.…”

Recurrence quantification analysis across sleep stages

“…We employ recurrence quantification analysis (RQA), which is known to be a powerful tool to study complex (physiological) systems. RQA has already successfully been applied to EEG data [44] to distinguish between sleep stages. Smietanoswki et al determined several RQA features using recurrence plots on heart rate (HR) signals to derive dynamics of the heart rate variability (HRV) in healthy subjects and patients with obstructive sleep apnea (OSA) [43].…”