A cloud based architecture for hosting ECG arrhythmia data classification service.

Healthcare industry is in demand to adopt the new technologies that are in the market to improve their service quality. The telecommunication system is integrated with computing, Inter-networking, mobility, data storage, data analytics IoT (Internet of Things) centred technology is the order of the day. In this research work, our objective is to develop software that integrates the cloud computing and mobile technology for health care monitoring system. In present research, the stable fractal values are extracted from the ECG signals using Higuchi's method, which was not attempted by any researcher in the field of developing a computer aided diagnosis system for arrhythmia. From the results, the maximum classification accuracy for fractal features has been registered by support vector machine. 92.08% and 90.36%. The comparative evaluation was performed on 1) cloud -two node cluster 2) cloud-Ten node cluster. We presented software that integrates mobile computing and cloud computing for analyzing the ECG arrhythmia for five different arrhythmia classes. a) Class N b) Class S c) Class V d) Class F e) Class Q. This work proposed to evaluate cloud based architecture 1) cloud -two node cluster 2) cloud-Ten node cluster. Where the multi nodes cluster computing reduces the execution to almost to 50 per cent and maintains the response time. Dynamic resource allocation frame work has ability to distribute the load and maintain the time response. [ABSTRACT FROM AUTHOR]