Arrhythmia Classification using 2D CNN
Objective
The primary objective of this project is to develop an efficient and accurate arrhythmia classification system using CNN with a focus on exploring smaller input sizes. The goal is to improve upon existing methodologies by simplifying the image classifier and grouping classes based on transformed ECG signals from the MIT-BIH and PTB Arrhythmia database. The project aims to identify the optimal input size and achieve high accuracy in classifying ECG signals for enhanced diagnostic capabilities in arrhythmia detection.
Abstract
In the context of cardiac health, this research undertakes a focused exploration into arrhythmia classification, leveraging advanced Deep Learning technology, specifically through the implementation of a 2D Convolutional Neural Network (CNN). The study is strategically designed to harness the nuanced capacities of this state-of-the-art approach, facilitating the precise identification and categorization of diverse arrhythmia patterns inherent in electrocardiogram (ECG) data. The overarching objective of this inquiry is to significantly contribute to the refinement of diagnostic tools, thereby elevating precision in cardiovascular health monitoring through the integration of sophisticated computational intelligence methodologies.
Keywords: Convolutional neural network, CNN, CNN 2D, Mobile Net image classifier, electrocardiogram, ECG, arrhythmia.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
SOFTWARE REQUIREMENS
Operating System : Windows 7/8/10
Server side Script : HTML, CSS, Bootstrap & JS
Programming Language : Python
Libraries : Flask, Pandas, Tensorflow, Keras, Sklearn, Numpy
IDE/Workbench : VSCode
Technology : Python 3.6+
Server Deployment : Xampp Server
Database : MySQL
HARDWARE REQUIREMENTS
Processor - I3/Intel Processor
RAM - 8GB (min)
Hard Disk - 128 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Paper Publishing
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