E-Health Alert System Using Breathing Gestures (Breath Print) Classifying

Health Alert system

Abstract

Background: Electronic Health is an emerging field in the intersection of medical informatics, public health and business, referring to health services and information delivered or enhanced through the Internet and related technologies.
Purpose: One of the major needs of the E-Health is how to provide alert feature in order to perform specific action based on this alert and using this alert to provide the required health care to the patient.
Method: In this paper proposed an alert system for the E-Health purposes is presented which is based the using of patient breathing signal. This breathing signal passed through three main processing phases which are the reading and plotting phase, feature extraction phase using Gammatore Frequency Cepstral Coefficients (GFCC), and Classifying phase using Gaussian Mixture Model (GMM). Then the classification result will be used to give alert to the E-Health system.
Results: E-Health field requires a lot of needs in order to work in correct way. One of these needs is the alert feature. In the proposed system the person breathing signal is used in order to provide this feature. The person breathing signal is processed in three phases which are: Reading and plotting phases using (MSR) and power, feature extraction phase using (GFCC), and classification phase uses (GMM). Then the classification result will be used to give alert to the doctors it is classified as sniff.
Conclusion: The proposed E- health alert system using breathing gestures classifying found effective and may be used in hospital settings, especially intensive care and respiratory units and emergency ambulance settings.