Title :
R&D for Home Sleep Apnea Syndrome Observation System
Author :
Fang Lu-Ping ; Meng Ze-Min ; Lin Sheng-Sheng
Author_Institution :
Coll. of Inf. Eng., Zhejiang Univ. of Technol., Hangzhou, China
Abstract :
Without any additive equipments, this system uses mobile phone to collect data, which will be transmitted by a home wireless network, and stored by a PC. Meanwhile, it takes neural network algorithm and voice reorganization into the identification of voices and snores, to recognize and detect the SAS. Compared with the traditional detection system of SAS, it owns a higher accuracy of recognizing disease.
Keywords :
diseases; mobile computing; neural nets; patient monitoring; speech recognition; telemedicine; PC; R&D; SAS; disease recognition; home sleep apnea syndrome observation system; home wireless network; mobile phone; neural network algorithm; voice reorganization; Mel frequency cepstral coefficient; Mobile handsets; Neural networks; Sleep apnea; Speech recognition; Synthetic aperture sonar; Training; K-means; Mel Cepstrum; RBF neural networks; SAS;
Conference_Titel :
Computer Sciences and Applications (CSA), 2013 International Conference on
Conference_Location :
Wuhan
DOI :
10.1109/CSA.2013.116
