A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
An adaptation of Relief for attribute estimation in regression
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Feature Selection as a Preprocessing Step for Hierarchical Clustering
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Dependency-based feature selection for clustering symbolic data
Intelligent Data Analysis
Remote health-care monitoring using personal care connect
IBM Systems Journal
An Efficient Clustering Scheme to Exploit Hierarchical Data in Network Traffic Analysis
IEEE Transactions on Knowledge and Data Engineering
Novel methods of faster cardiovascular diagnosis in wireless telecardiology
IEEE Journal on Selected Areas in Communications - Special issue on wireless and pervasive communications for healthcare
Optimal zonal wavelet-based ECG data compression for a mobile telecardiology system
IEEE Transactions on Information Technology in Biomedicine
Implementation of a WAP-based telemedicine system for patient monitoring
IEEE Transactions on Information Technology in Biomedicine
Wavelet-based low-delay ECG compression algorithm for continuous ECG transmission
IEEE Transactions on Information Technology in Biomedicine
A Mobile Care System With Alert Mechanism
IEEE Transactions on Information Technology in Biomedicine
| Hi-index | 12.05 |
Compressed Electrocardiography (ECG) is being used in modern telecardiology applications for faster and efficient transmission. However, existing ECG diagnosis algorithms require the compressed ECG packets to be decompressed before diagnosis can be applied. This additional process of decompression before performing diagnosis for every ECG packet introduces undesirable delays, which can have severe impact on the longevity of the patient. In this paper, we first used an attribute selection method that selects only a few features from the compressed ECG. Then we used Expected Maximization (EM) clustering technique to create normal and abnormal ECG clusters. Twenty different segments (13 normal and 7 abnormal) of compressed ECG from a MIT-BIH subject were tested with 100% success using our model. Apart from automatic clustering of normal and abnormal compressed ECG segments, this paper presents an algorithm to identify initiation of abnormality. Therefore, emergency personnel can be contacted for rescue mission, within the earliest possible time. This innovative technique based on data mining of compressed ECGs attributes, enables faster identification of cardiac abnormalities resulting in an efficient telecardiology diagnosis system.