A data mining technique using attribute selection and clustering successfully identified cardiac abnormalities directly from 18 compressed ECG segments with a 100% success rate.
Does a data mining technique applied directly to compressed ECGs accurately identify cardiac abnormalities?
A novel data mining technique allows for the direct diagnosis of cardiac abnormalities from compressed ECGs without decompression, potentially enabling faster telecardiology applications.
Usage of compressed Electrocardiography (ECG) for fast and efficient telecardiology application is crucial, as ECG signals are enormously large in size. However, conventional ECG diagnosis algorithms require the compressed ECG to be decompressed before diagnosis can be applied. This added step of decompression before performing diagnosis for every ECG packets introduces unnecessary delays, which is undesirable for cardiovascular patients. In this paper, we first used an attribute selection method that selects only a few features from the compressed ECG. Then we used clustering techniques to create normal and abnormal ECG clusters. 18 different segments (12 normal and 6 abnormal) of compressed ECG were tested with 100% success on our model. This innovative data mining technique on compressed ECGs, now enables faster identification of cardiac abnormality directly from the compressed ECG, resulting in an efficient telecardiology diagnosis system.
Sufi et al. (Sun,) conducted a other in Cardiovascular abnormalities (n=18). Data mining technique (attribute selection and clustering) on compressed ECG was evaluated on Identification of cardiac abnormality directly from compressed ECG. A data mining technique using attribute selection and clustering successfully identified cardiac abnormalities directly from 18 compressed ECG segments with a 100% success rate.