Bonfring International Journal of Advances in Image Processing
Online ISSN: 2277-503X | Print ISSN: 2250-1053 | Frequency: 4 Issues/Year
Impact Factor: 0.245 | International Scientific Indexing(ISI) calculate based on International Citation Report(ICR)
CVD Detection and Diagnosis Using Mobile from Compressed ECG by K-Means Clustering
R. Deepa and K. Dhivya
Abstract:
Compressed ECG is used for fast and efficient telecardiology application, since ECG signals are enormously large in size. The diagnosis algorithms which are used conventionally have to decompress the compressed ECG packets. This decompression produces delays which even leads to death of the patient. In this paper, demonstrating an innovative technique with data mining that performs real-time classification of CVD from compressed ECG packets. By this real time application any cardiac abnormalities found can be informed to emergency personel by means of SMS/MMS/e-mail automatically. The proposed system uses data mining techniques, such as attribute selection from the compressed ECG packets and K-based clustering. A set of constraints are generated in the hospital server for each abnormalities. These constraints are received by patients mobile phone and abnormal beats are identified in real-time. This innovative data mining technique on compressed ECG packets enables faster identification of cardiac abnormality directly from the compressed ECG, helping to build an efficient telecardiology diagnosis system. In this paper the software implementation of the project have been demonstrated.
Keywords: Cardiac Abnormality Detection, Faster Cardiovascular Diagnosis, M-Health, Medical Data Mining, Mobile Telecardiology
Volume: 2 | Issue: Special Issue on Communication Technology Interventions for Rural and Social Development
Pages: 50-54
Issue Date: February , 2012
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