Why the study?
Do hybrid systems improve the positive prediction accuracy of automated ECG diagnosis compared to single-method approaches?
Do hybrid systems improve the positive prediction accuracy of automated ECG diagnosis compared to single-method approaches?
Hybrid systems combining artificial neural networks and fuzzy logic provide the highest accuracy for automated ECG diagnosis of ischemia and arrhythmias.
Hybrid systems may enhance automated ECG accuracy; hypothesis-generating and requires prospective trials before clinical adoption.
Myocardial ischemia & other cardiac disorder diagnosis using long duration electrocardiographic recordings is a simple and non-invasive method that needs further development in order be used in the everyday medical practice. Several techniques that automate ischemia& other cardiac detection have been proposed during the last decade which are under evaluation. They are based on different methodological approaches, which include digital signal analysis, rule-based techniques, fuzzy logic methods and artificial neural networks, with each one of them exhibiting its own advantages and disadvantages. Most recent systems employ hybrid system (combination of ANN & Fuzzy logy) to perform diagnosis since they have demonstrated great consistency in producing accurate results. The performance of the developed detection systems is very promising but they need further evaluation
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Upasani D.E. (2012) studied this question.
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