Volume & Issue no: Volume 11, Issue 4, July - August 2022
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Title: |
ECG Feature Extraction and Classifications using Deep Learning techniques |
Author Name: |
Vijendra V, Meghana Kulkarni |
Abstract: |
Abstract: ECG Pattern recognition for features is most crucial
factor in diagnostic systems. Identifying the diseases based on
morphological features: Widely used techniques include ECG
beats annotation and classification, Spatial domain
Classification, time-frequency intra-domain Classification. The
Optimized Computational deep learning Algorithms based on
the parameters of Sensitivity, Specificity, Positive Predictivity
Accuracy, True positive detection, True negative detection and
false positive detection, false negative detection of ECG beats.
The detection and Classification of beats based on Machine
learning algorithms and comparison on convolutional neural
networks (CNNs) by using Tensor Flow Platform. The
implemented of ECG feature extraction and classification using
Binary neural networks on Jupyter Notebook. The Classification
of Artificial Neural Networks as 98.39% Accuracy with Adaptive
thresholding on fiducial mean square algorithm. Different
techniques and their accuracy parameters are compared with
Advance Neuro Fuzzy Interface System, Autoencoders,
Convolution neural network and Recurrent network Layers,
Long short term memory.
Keywords: Supervised Learning, Unsupervised Learning,
Convolutional Neural Networks (CNNs), Lifting based
Discrete Wavelet Transform (DWT), Positive Prediction,
Sensitivity |
Cite this article: |
Vijendra V, Meghana Kulkarni , "
ECG Feature Extraction and Classifications using Deep Learning techniques " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 11, Issue 4, July - August 2022 , pp.
011-015 , ISSN 2278-6856.
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