Title :
An artificial neural network based snore detector
Author :
Lopez, Francisco Javier ; Behbehani, Khosrow ; Kamangar, Farhad
Author_Institution :
Dept. of Biomed. Eng., Texas Univ., Arlington, TX
Abstract :
A method to detect snores is presented that is based on the use of an artificial neural network (ANN). A network with 15 inputs, one output and two hidden layers with two adaline nodes per layer was used. The detector was tested using prerecorded data obtained from 13 male and 2 female patients (age 51.2±6.5, weight 252±31.15 lbs and height 5\´9.4"±2.49"). An overall accuracy of 75.2% in correctly detected snores showed an improvement of 12% over a previous method. The average number of false detections per hundred breaths was a 35% improvement
Keywords :
pneumodynamics; accuracy; adaline nodes; artificial neural network; artificial neural network based snore detector; correctly detected snores; false detections; female patients; hidden layers; male patients; obstructive sleep apnea; prerecorded data; Acoustic signal detection; Artificial neural networks; Assembly; Counting circuits; Detectors; Frequency; Instruments; Laboratories; Sleep apnea; Testing;
Conference_Titel :
Engineering in Medicine and Biology Society, 1994. Engineering Advances: New Opportunities for Biomedical Engineers. Proceedings of the 16th Annual International Conference of the IEEE
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-2050-6
DOI :
10.1109/IEMBS.1994.415346
