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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

A Web Based Cardiovascular Disease Detection System.

Hussam Alshraideh¹, Mwaffaq Otoom, Aseel Al-Araida

¹Department of Industrial Engineering, Jordan University of Science and Technology, Irbid, Jordan, haalshraideh@just.edu.jo.

Journal of Medical Systems

|August 22, 2015

Summary

This summary is machine-generated.

Early detection of cardiovascular disease (CVD) is crucial. This study presents an accurate CVD detection algorithm using ECG signals and patient data, achieving 98.29% accuracy via a web-based system.

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Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

Area of Science:

Biomedical Engineering
Medical Informatics
Cardiology

Background:

Cardiovascular disease (CVD) presents a significant global health challenge.
Early detection of CVD is critical for mitigating severe health outcomes.
Existing diagnostic methods can be time-consuming or inaccessible.

Purpose of the Study:

To develop and validate an efficient algorithm for early cardiovascular disease detection.
To integrate the algorithm into a user-friendly, web-based system for patient accessibility.
To leverage patient demographic data and electrocardiogram (ECG) signal features for enhanced diagnostic accuracy.

Main Methods:

Utilized signal processing techniques to automatically extract relevant features from ECG signals.
Employed a decision tree classification algorithm for CVD risk assessment.
Integrated the algorithm into a web-based platform with an Android application for data acquisition via ECG sensors.

Main Results:

The decision tree classification algorithm achieved a high accuracy of 98.29% in cross-validation.
The developed system enables real-time patient heart health monitoring.
Successful integration of ECG sensors, Android application, and the detection algorithm was demonstrated.

Conclusions:

The proposed algorithm offers an efficient and accurate method for cardiovascular disease detection.
The web-based system provides a accessible tool for patients to monitor their heart health.
This approach holds promise for improving early diagnosis and management of CVD.