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Received:August 10, 2013 Revised:September 12, 2013
Received:August 10, 2013 Revised:September 12, 2013
中文摘要: 本文介绍了一款新的SAS监测系统，系统在不添加任何设备的情况下，利用手机调用自行设计的手机软件采集用户的鼾声数据，并采用ftp协议，利用家庭无线网传输数据，最终将数据存储在PC机中，并将神经网络算法和语音识别技术加入到系统的核心算法中，用于识别语音和鼾声，结合SAS的病征实现对SAS病症的诊断. 实践证明，本系统相对于传统的SAS监测系统，具有更高的病症判别率.
Abstract:This paper introduces a new SAS monitoring system. The system utilizes a self-designed app invoked by mobile phones to collect the data of users' snore. Then it transmits the data via home wireless network by means of ftp and finally stores the data in a PC. Moreover, the neural net algorithm and voice-recognition technology have been inserted into core algorithm of the system to identify voice and snore, which can implement the diagnosis of the SAS by combining the analyses of symptoms. The system has a higher disease classification rate than a traditional SAS monitoring system.
keywords: SAS ftp Mel cepstrum k-means RBF neural network
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孟泽民,林生生.基于神经网络的家用型睡眠呼吸暂停综合症监测系统.计算机系统应用,2014,23(3):220-223,131
MENG Ze-Min,LIN Sheng-Sheng.Home Sleep Apnea Syndrome Observation System Based on Neural Network.COMPUTER SYSTEMS APPLICATIONS,2014,23(3):220-223,131
孟泽民,林生生.基于神经网络的家用型睡眠呼吸暂停综合症监测系统.计算机系统应用,2014,23(3):220-223,131
MENG Ze-Min,LIN Sheng-Sheng.Home Sleep Apnea Syndrome Observation System Based on Neural Network.COMPUTER SYSTEMS APPLICATIONS,2014,23(3):220-223,131
