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Mud Ring Optimization Algorithm With Deep Learning Model For Disease Diagnosis On Ecg Monitoring System.

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Mud Ring Optimization Algorithm With Deep Learning Model For Disease Diagnosis On Ecg Monitoring System.

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Mud Ring Optimization Algorithm with Deep Learning Model for Disease Diagnosis on ECG Monitoring System.

Ala Saleh Alluhaidan¹, Mashael Maashi², Munya A Arasi³

¹Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Sensors (Basel, Switzerland)

|August 12, 2023

View abstract on PubMed

Summary

This summary is machine-generated.

A new deep learning technique uses Mud Ring Optimization (MRO) to classify electrocardiogram (ECG) signals for heart disease detection. This MROA-DLECGSC approach improves accuracy in diagnosing cardiovascular disease (CVD) from ECG data.

Keywords:

ECG signals cardiovascular disease deep learning hyperparameter tuning

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Area of Science:

Biomedical Engineering
Artificial Intelligence in Medicine
Cardiology

Background:

The proliferation of Internet of Things (IoT), sensing technologies, and wearables has shifted medical services towards real-time monitoring.
Electrocardiogram (ECG) signals are crucial for noninvasive diagnosis of cardiovascular diseases (CVD).
Increasing patient numbers and variations in ECG patterns necessitate automated diagnostic tools for accurate ECG signal classification.

Purpose of the Study:

To introduce a novel Mud Ring Optimization with Deep Learning-based ECG Signal Classification (MROA-DLECGSC) technique.
To develop a computer-assisted tool for accurate identification of heart disease using ECG signals.
To enhance the performance of ECG signal classification for CVD detection.

Main Methods:

ECG signals were preprocessed to ensure a uniform format.
A Stacked Autoencoder Topographic Map (SAETM) was employed for ECG signal classification to detect CVDs.
Mud Ring Optimization (MROA) was utilized as a hyperparameter optimizer to improve classification performance.

Main Results:

The MROA-DLECGSC technique demonstrated effective recognition of heart disease through ECG signal analysis.
The SAETM approach successfully classified ECG signals for CVD identification.
Hyperparameter optimization using MROA led to enhanced overall performance of the classification model.
Experimental results on a benchmark database showed superior performance of MROA-DLECGSC compared to existing algorithms.

Conclusions:

The MROA-DLECGSC technique offers a promising approach for automated ECG signal classification in diagnosing cardiovascular diseases.
The integration of MROA for hyperparameter tuning significantly boosts the accuracy and efficiency of deep learning models for CVD detection.
This study highlights the potential of advanced AI techniques in real-time medical diagnostics, addressing the challenges posed by large patient populations and signal variability.