Knowledge based system with embedded intelligent heart sound analyser for diagnosing cardiovascular disorders

Area of Science:

• Cardiology
• Biomedical Engineering
• Artificial Intelligence

Background:

• Cardiovascular disease (CVD) is a leading global cause of mortality.
• Limited early detection methods contribute to the rising incidence of CVD.
• There is a need for accessible diagnostic tools, particularly in resource-limited settings.

Purpose of the Study:

• To develop and validate a knowledge-based system with an intelligent heart sound analyzer (KBHSA) for early cardiovascular disorder diagnosis.
• To provide a decision support tool for general physicians in diagnosing CVD.
• To improve diagnostic accuracy and reliability in emergency situations.

Main Methods:

• Digitized heart sounds were recorded using an electronic stethoscope.
• Advanced digital signal processing and artificial intelligence techniques were employed for analysis.
• The KBHSA integrated patient history, clinical findings, and diagnostic imaging data.
• System diagnoses were validated against expert cardiologist assessments.

Main Results:

• The KBHSA successfully analyzed abnormal heart sound samples and patient medical data.
• The system's diagnoses showed strong agreement with those of senior cardiologists.
• Validation involved data from 40 patients with abnormal heart sounds.

Conclusions:

• The KBHSA demonstrates potential as an effective tool for early cardiovascular disease diagnosis.
• The system can support general physicians in making timely and accurate diagnoses.
• KBHSA offers a viable solution for improving cardiac care accessibility.