Segregation of Cardiac Arrhythmias in Electrocardiography employing Fuzzy- Genetic classifier
Manbir Kaur,  Prof. Karamjeet Singh
One of the important reason for the increase in mortality rate is Cardiac Arrhythmias. These arrhythmias disrupt the normal cardiac functioning and leads to irregular cardiac cyclicity. . It may be too fast (above 100 beats per minute ) – Tachycardia or may be too slow (below 60 beats per minute) – Bradycardia. The abnormality of arrhythmias can be assessed medically using Electrocardiography (ECG). The ECG basically represents the P – QRS – T cardiac cyclicity. If any hinderance affects this cyclicity the results might become severe in some cases. For this, timely detection and diagnosis of cardiac arrhythmias is of essence. Such a detection is carried out in this paper. For this, initially the mathematical model that describes the ECG signal is presented and the P, Q, R, S and T features of a normal person is compared with the person who is suffering from one of the arrhythmias. To improve the accuracy of detection, the fuzzy classifier is developed using MATLAB. The ECG signals are the standardised MIT – BIH database. The fuzzification is carried out so that the proper manipulation of imprecise data can be carried out. But, to further improve the accuracy, Genetic Algorithm is applied that is known for finding the optimal solution for a given problem by following various iterations. On comparing, the results obtained from both fuzzy and genetic classifier, it is seen that the Genetic Algorithm gives the best results and improves the accuracy of classification and detection of Cardiac Arrhythmias.
Keywords- Electrocardiography, Arrhythmias, Fuzzy logic, Genetic Algorithm.
Cite this Article
Manbir Kaur,  Prof. Karamjeet Singh,   "Segregation of Cardiac Arrhythmias in Electrocardiography employing Fuzzy- Genetic classifier"
, International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.4, Issue 2, pp.1443-1449, June 2016, Available at :http://www.ijedr.org/papers/IJEDR1602257.pdf