Manab Kumar Das

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Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper,(More)
Electrocardiogram (ECG), which is a noninvasive technique, is used generally as a primary diagnostic tool for cardiovascular diseases. In real-time scenario, noises like channel noise, muscle artifacts, electrode motion and baseline wander are often embedded with ECG signals during acquisition and transmission. In this paper, an automatic ECG signal(More)
Electrocardiogram (ECG) beat classification plays an important role in the timely diagnosis of the critical heart condition. An automated diagnostic system is proposed to classify five types of ECG classes, namely normal (N), ventricular ectopic beat (V), supra ventricular ectopic beat (S), fusion (F) and unknown (Q) as recommended by the Association for(More)
In this paper, a potential application of Stock-ewell transforms (S-transform) is proposed to classify the ECG beats of the MIT-BIH database arrhythmias. Feature extraction is the important component of designing the system based on pattern recognition since even the best classifier will not perform better if the good features are not chosen properly. In(More)
The identification of the electrocardiogram (ECG) signal into different pathological categories is a complex pattern recognition task. In this paper, a classifier model is designed to classify the beat from ECG signal of the MIT-BIB ECG database. The classifier model consists of three important stages (i) feature extraction (ii) selection of qualitative(More)
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