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Download ECG Arrhythmia Classification Using Deep Convolution Neural Networks in Transfer Learning by ['Dayana Vieira'] in PDF format completely FREE. No registration required, no payment needed. Get instant access to this valuable resource on PDFdrive.to!
About ECG Arrhythmia Classification Using Deep Convolution Neural Networks in Transfer Learning
- The document is a master's thesis that examines using deep convolution neural networks and transfer learning for ECG arrhythmia (abnormal heart rhythm) classification. - The author acquires ECG data from a public database and uses Python libraries to preprocess the data, detect heartbeats, and convert the heartbeat data into images for use in convolutional neural networks. - Several CNN models including multilayer CNNs, ResNet-50, and VGG-16 are tested for classification. Transfer learning is applied using pre-trained ResNet-50 and VGG-16 models. Accuracies of 83% and 99% are achieved using ResNet-50 and VGG-16 respectively, showing transfer learning can be effective for this task
Detailed Information
| Author: | ['Dayana Vieira'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 54 |
| Language: | English |
| Format: | |
| Price: | FREE |
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