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Query: UMLS:C0018799 (heart disease)
 34,133 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The electrocardiogram (ECG) is a key diagnostic tool in heart disease and may serve to detect ischemia, arrhythmias, and other conditions. Automatic, low cost monitoring of the ECG signal could be used to provide instantaneous analysis in case of symptoms and may trigger the presentation to the emergency department. Currently, since mobile devices (smartphones, tablets) are an integral part of daily life, they could form an ideal basis for automatic and low cost monitoring solution of the ECG signal. In this work, we aim for a realtime classification system for arrhythmia detection that is able to run on Android-based mobile devices. Our analysis is based on 70% of the MIT-BIH Arrhythmia and on 70% of the MIT-BIH Supraventricular Arrhythmia databases. The remaining 30% are reserved for the final evaluation. We detected the R-peaks with a QRS detection algorithm and based on the detected R-peaks, we calculated 16 features (statistical, heartbeat, and template-based). With these features and four different feature subsets we trained 8 classifiers using the Embedded Classification Software Toolbox (ECST) and compared the computational costs for each classification decision and the memory demand for each classifier. We conclude that the C4.5 classifier is best for our two-class classification problem (distinction of normal and abnormal heartbeats) with an accuracy of 91.6%. This classifier still needs a detailed feature selection evaluation. Our next steps are implementing the C4.5 classifier for Android-based mobile devices and evaluating the final system using the remaining 30% of the two used databases.
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PMID:Comparison of real-time classification systems for arrhythmia detection on Android-based mobile devices. 2557 May 45

Heart Disease affects approximately 70 million people worldwide where most people do not even know the symptoms. This research examines the prototype of early warning system for heart disease by android application. It aims to facilitate users to early detect heart disease which can be used independently. To build the application in android phone, variable centered intelligence rule system (VCIRS) as decision makers and pulse sensor - Arduino as heart rate detector were applied in this study. Moreover, in Arduino, the heart rate will become an input for symptoms in Android Application. The output of this system is the conclusion statement of users diagnosed with either coronary heart disease, hypertension heart disease, rheumatic heart disease or do not get any kind of heart disease. The result of diagnosis followed by analysis of the value of usage variable rate (VUR) rule usage rate (RUR) and node usage rate (NUR) that shows the value of the rule that will increase when the symptoms frequently appear. This application was compared with the medical analysis from 35 cases of heart disease and it showed concordance between diagnosis from android application and expert diagnosis of the doctors.
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PMID:Prototype early warning system for heart disease detection using Android Application. 2557 Jul 37