%0 Journal Article %1 Sathya_2015 %A Sathya, R. %A Akilandeswari, K. %D 2015 %I Auricle Technologies, Pvt., Ltd. %J International Journal on Recent and Innovation Trends in Computing and Communication %K Classification ECG Evaluation Fitting Metrics NN Network Neural Signal %N 3 %P 1554--1557 %R 10.17762/ijritcc2321-8169.1503144 %T A Novel Neural Network based Classification for ECG Signals %U http://dx.doi.org/10.17762/ijritcc2321-8169.1503144 %V 3 %X Cardiac Arrhythmia represents heart abnormalities. This problem is faced by people, irrespective of age. Even the physicians feel difficulty in diagnosing the abnormal behavior of heart accurately. Accurate detection of cardiac abnormalities helps to provide right treatment. Classification plays an important role in predicting abnormal behaviors of heart and it helps the physician to treat the patients who are having cardiac arrhythmia. Extracted features from ECG (Electrocardiogram) signals are used for classification. It is possible to extract multiple features from ECG signal regardless of the features used for classification. Classification performed using all the extracted features leads to misclassification of abnormalities. So feature selection is an important concept in classifying the normal and abnormal behavior of heart. MIT BIH Arrhythmia dataset is used in our proposed work where the classification is done in MATLAB using Fitting Neural Network.

@article{Sathya_2015, abstract = {Cardiac Arrhythmia represents heart abnormalities. This problem is faced by people, irrespective of age. Even the physicians feel difficulty in diagnosing the abnormal behavior of heart accurately. Accurate detection of cardiac abnormalities helps to provide right treatment. Classification plays an important role in predicting abnormal behaviors of heart and it helps the physician to treat the patients who are having cardiac arrhythmia. Extracted features from ECG (Electrocardiogram) signals are used for classification. It is possible to extract multiple features from ECG signal regardless of the features used for classification. Classification performed using all the extracted features leads to misclassification of abnormalities. So feature selection is an important concept in classifying the normal and abnormal behavior of heart. MIT BIH Arrhythmia dataset is used in our proposed work where the classification is done in MATLAB using Fitting Neural Network.}, added-at = {2015-08-13T07:33:37.000+0200}, author = {Sathya, R. and Akilandeswari, K.}, biburl = {https://www.bibsonomy.org/bibtex/2461933e91f2618d73d110d2c82b7cfe1/ijritcc}, doi = {10.17762/ijritcc2321-8169.1503144}, interhash = {d489f313ab4a6eaad9e6995bd14a292c}, intrahash = {461933e91f2618d73d110d2c82b7cfe1}, journal = {International Journal on Recent and Innovation Trends in Computing and Communication}, keywords = {Classification ECG Evaluation Fitting Metrics NN Network Neural Signal}, month = {march}, number = 3, pages = {1554--1557}, publisher = {Auricle Technologies, Pvt., Ltd.}, timestamp = {2015-08-13T07:33:37.000+0200}, title = {A Novel Neural Network based Classification for {ECG} Signals}, url = {http://dx.doi.org/10.17762/ijritcc2321-8169.1503144}, volume = 3, year = 2015 }