Intelligent Information Extractor through Artificial Data Analyzer Mechanism in Electrocardiogramic Data
Dhayalan D and Nooray Salma S*
Department of Computer Application, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala College of Engineering, Chennai, India
- Corresponding Author:
- Nooray Salma S
Department of Computer Application
Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala College of Engineering
E-mail: [email protected]
Received date: Dec 21, 2015; Accepted date: Feb 01, 2016; Published date: Feb 09, 2016
Citation: Dhayalan D, Nooray SS (2016) Intelligent Information Extractor through Artificial Data Analyzer Mechanism in Electrocardiogramic Data. J Pat Care 2:107. doi:10.4172/2573-4598.1000107
Copyright: © 2016 Dhayalan D, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Electrocardiography is a test that checks for problems with the heart. It gives details of the state of the heart and any disturbance in the heart sound can be diagnosed. It is very useful for the medical field. The XML ontology integrates ECG waveform data, data descriptions, and cardiac diagnosis rules. It is used for providing an ability to both represent ECG waveform as well as do automated diagnosis of 37 cardiac abnormalities. It does not tune-up the image of the ECG before image processing as the noise percentage misleads to the diagnosis report. The histogram process is performed to rectify the noise from the input image and the image is tuned up. The RGB image is converted to the grayscale using the image blending technique for the segmentation process. The tuned up image with enhancement in quality is performed in perfectly. In the proposed system, an image validation of histogram process is formulated and it is to change the noise obtained in the input ECG Image. The Validated ECG image has been measured with its amplitude and height to measure the abnormalities using XML ontology. It overcomes in terms of time and accuracy has been visualized graphically.