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The main objective of this project is to identify and to classify the heart abnormalities in an ECG signal. Based on the peak amplitude values can perform the classification operation.
Cardiac arrhythmia indicates abnormal electrical activity of heart that can be a great threat to human. So it needs to be identified for clinical diagnosis and treatment. Analysis of ECG signal plays an important role in diagnosing cardiac diseases. An efficient method of analyzing ECG signal and predicting heart abnormalities have been proposed in this paper. In the proposed scheme, the collected ECG Signal Database will Convert 1D ECG signals to Image using CWT scalogram and classify with Transfer Learning via pre-trained Alex Net deep CNN. The goal is to train a CNN to distinguish between Arrhythmias(ARR), Congestive Heart Failure (CHF), Normal Sinus Rhythm (NSR). The ECG Signals are obtained from 162 ECG recordings from three Physio Net databases (https://github.com/mathworks/physionet_ECG_data).
- SVM classifier
- K means clustering
- High Computational load
- Poor discriminatory power
- Less accuracy in classification
- A CNN passes an image through the network layers and outputs a final class. The network can have tens or hundreds of layers, with each layer learning to detect different features. Filters are applied to each training image at different resolutions, and the output of each convolved image is used as the input to the next layer. The filters can start as very simple features, such as brightness and edges, and increase in complexity to features that uniquely define the object as the layers progress.
- Medical applications
- MATLAB 2018b
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