Automated detection of trace alternant during sleep in healthy full-term neonates using discrete wavelet transform

Objective: To develop an automated procedure for scoring neonatal sleep sta tes using signal processing which are based on visual pattern recognition t echniques. Methods: We are developing an automated computer system to study relationsh ips among multiple non-cerebral physiologic measures and brain activity in newborn infants, and are evaluating the usefulness of a number of different time-frequency domain transforms as potential diagnostic tools. Results: Wavelet transforms yield excellent results in the detection of all twenty trace alternant quiet sleep segments for 6 full-term healthy infant s. Conclusions: We suggest that this method will be useful for the automated d etection of neonatal sleep states, and may help delineate when sleep cycle disturbances occur on either an environmental or disease basis. More accura te physiologic descriptions of neonatal state may improve the clinician's a bility to assess functional brain organization for a given post-conceptiona l age as well as document functional brain maturation at progressively olde r corrected ages. (C) 2001 Published by Elsevier Science Ireland Ltd.