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@article{146373,
        author = {Dhanya V K and Chinchu S},
        title = {Classifier Based Detection of Myocardial Infarction and Atrial Fibrillation on ECG Signals},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {12},
        pages = {356-364},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=146373},
        abstract = {Myocardial Infarction (MI) and Atrial Fibrillation (AF) are serious heart diseases. The number of patients related to the  heart failure due to MI and AF is increasing day by day. Early detection of MI and AF may reduce the risk of death due to the heart failure. In this, a novel technique is proposed for the detection of Atrial fibrillation and Myocardial infarction from electrocardiogram (ECG). In myocardial infarctioninversion of T-wave, changes occurring on ST elevation, hypercute T-waves or pathological Q-wave are the pathological characteristics  seen in  the ECG signals. Wavelet decomposition of the ECG signals segments the components at different sub bands for feature extraction.  In this work, entropy and covariance values are used for diagnostic features. Probabilistic Neural Network (PNN) is used as a classifier to detect the MI. In atrial fibrillation, the irregularity in the R-R intervals and the absence of  P-wave are the characteristics seen in ECG signals. For the detection of AF use the algorithm that follows the parametric statistic such as RMSSD, SE, and non-parametric statistic is TPR. Then check the result of RMSSD, TPRand SE of every beat, whether it crosses the threshold level or not. If all these parameters will cross the threshold level then the beat affected by AF. The accuracy, the sensitivity and the specificity values higher compared to other methods},
        keywords = {AF, MI, Probabilistic neural network classifier, RMSSD,SE,TPR},
        month = {},
        }