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Detection of QRS Complexes Using Convolutional Neural Network | IEEE Conference Publication | IEEE Xplore
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Detection of QRS Complexes Using Convolutional Neural Network


Abstract:

This paper deals about electrocardiogram beat detection using the Convolution Neural Network model. The Convolutional Neural Network is modelling three classes. Each clas...Show More

Abstract:

This paper deals about electrocardiogram beat detection using the Convolution Neural Network model. The Convolutional Neural Network is modelling three classes. Each class represents normal, abnormal and another QRS like signal. We used the MIT-BIH Arrhythmia Database. This database consists of 48 electrocardiogram records of patients. Each record length is approximately 30 minutes. We split the records into four subsets. Each subset has records of 12 patients. The cardiovascular experts annotate this database also each record have more than 2000 labels. In our experiments, we trained four models With similar architecture. Difference between models is the selection of the training and testing subset.
Date of Conference: 01-03 July 2019
Date Added to IEEE Xplore: 25 July 2019
ISBN Information:
Conference Location: Budapest, Hungary

References

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