OCADN: Improving Accuracy in Multi-Class Arrhythmia Detection From ECG Signals With a Hyperparameter-Optimized CNN

Project Code :TMMASP213

Objective

The objective of this study is to develop an optimized CNN-based model (OCADN) with advanced pre-processing and hyperparameter tuning for accurate and robust arrhythmia detection from ECG signals, outperforming traditional LSTM approaches in classification performance.

Abstract

Arrhythmia, a heart rhythm disorder, remains a serious global health problem due to its potential to cause complications such as stroke and heart failure. Early detection and accurate classification of arrhythmia are crucial for appropriate medical intervention. Although deep learning holds promise for automatic arrhythmia detection using ECG signals, several challenges need to be addressed. Previous studies often utilize limited and imbalanced datasets and lack exploration of optimal pre-processing and feature extraction methods. To overcome these limitations, this study proposes the Optimized Cardiac Arrhythmia Detection Network (OCADN), a CNN-based model with hyperparameter optimization and advanced pre-processing techniques such as Discrete Wavelet Transform (DWT) and Z-score normalization. As a comparison to OCADN, this research also develops an arrhythmia detection model using the LSTM algorithm. Experimental results demonstrate that OCADN outperforms LSTM, achieving high accuracy, precision, sensitivity, specificity, and F1-score on both training and test data. The consistent performance of OCADN on both datasets indicates its robustness and potential for clinical implementation.

Keywords: CNN, LSTM, deep learning, arrhythmia prediction.

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: Matlab R2022b.

Hardware:

Operating Systems:

• Windows 10

• Windows 7 Service Pack 1

• Windows Server 2019

• Windows Server 2016

Processors:

Minimum: Any Intel or AMD x86-64 processor

Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support.

Learning Outcomes

· Introduction to Matlab

· What is EISPACK & LINPACK

· How to start with MATLAB

· About Matlab language

· Matlab coding skills

· About tools & libraries

· Application Program Interface in Matlab

· About Matlab desktop

· How to use Matlab editor to create M-Files

· Features of Matlab

· Basics on Matlab

· What is Signal Processing?

· About Signal Processing

· Introduction to Signal Processing

· How analog and digital signal is formed

· Importing the signal via signal acquisition tools

· Analyzing and manipulation of signals.

· Phases of signal processing:

· Acquisition

· Signal enhancement

· Signal restoration

· Medical Signal Processing

· Medical Signal Analysis

· Medical Signal Diagnosis

· Filtering techniques

· Machine Learning Algorithms

· Deep Learning Algorithms etc.

· How to extend our work to another real time applications

· Project development Skills

o Problem analyzing skills

o Problem solving skills

o Creativity and imaginary skills

o Programming skills

o Deployment

o Testing skills

o Debugging skills

o Project presentation skills

o Thesis writing skills

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