ECG heart beat classification method based on modified ABC algorithm
2015
An ECG heart beat classification method is proposed based on modified ABC algorithm.Total 38 feature set is calculated, then most distinctive feature subset is used.Classification accuracy is achieved as 99.30% on examined ECG data from MITBIH db.Sensitivity values are higher than 89% for all sub types of examined arrhythmias.The result of proposed method is compared with seventeen other classifiers' results.When balanced data set is used, MABC provided the best result among all classifiers Electrocardiogram is the most commonly used tool for the diagnosis of cardiologic diseases. In order to help cardiologists to diagnose the arrhythmias automatically, new methods for automated, computer aided ECG analysis are being developed. In this paper, a Modified Artificial Bee Colony (MABC) algorithm for ECG heart beat classification is introduced. It is applied to ECG data set which is obtained from MITBIH database and the result of MABC is compared with seventeen other classifier's accuracy.In classification problem, some features have higher distinctiveness than others. In this study, in order to find higher distinctive features, a detailed analysis has been done on time domain features. By using the right features in MABC algorithm, high classification success rate (99.30%) is obtained. Other methods generally have high classification accuracy on examined data set, but they have relatively low or even poor sensitivities for some beat types. Different data sets, unbalanced sample numbers in different classes have effect on classification result. When a balanced data set is used, MABC provided the best result as 97.96% among all classifiers.Not only part of the records from examined MITBIH database, but also all data from selected records are used to be able to use developed algorithm on a real time system in the future by using additional software modules and making adaptation on a specific hardware.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
55
References
27
Citations
NaN
KQI