id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_yrlqw2ryznbwzp3nexixu5zhv4	Sebastián A. Ríos	An automatic apnea screening algorithm for children	2016	13	.pdf	application/pdf	8944	1373	64	We also showed empirically that no signal alone is a good SDB screening in children. osen, Palermo, Larkin, & Redline, 2002). Jarvis and Mitra (2000) Apnea diagnosis based on ECG signal. To extract relevant features from an ECG signal, it is important to Fig. 2 shows a sample of an ECG signal corresponding to the Before extracting features, all signals had to be normalized to igore the variance associated with natural variations among different With all these transformations the resulting feature set to characterize the ECG signal is: meanHRV, varHRV, meanEDR, varEDR, RMSSD and Finally, features extracted from each EEG signal correspond to the Processed EEG signal corresponding to patient A0001945. Different Wavelet Decompositions (blue) for the Thoracic Effort Signal (red) corresponding to patient A0000678. 17 shows a time window of the Oxygen Saturation Signal colected by a PSG from patient A0001183. Detection of sleep apnea from surface ecg based on features extracted by	./cache/work_yrlqw2ryznbwzp3nexixu5zhv4.pdf	./txt/work_yrlqw2ryznbwzp3nexixu5zhv4.txt