Heart Failure Assessment Using Multiparameter Polar Representations and Deep Learning.

Area of Science:

• Cardiology
• Artificial Intelligence
• Biomedical Engineering

Background:

• Heart failure affects millions annually, with current screening lacking circadian and patient-specific data.
• Echocardiography is the standard but does not integrate heart rate variability or patient profiles.
• There is a need for advanced diagnostic tools to improve cardiovascular disease assessment.

Purpose of the Study:

• To develop a novel approach integrating 24-hour heart rate variability (HRV) and patient profile information.
• To create a multi-parameter, color-coded polar representation for cardiovascular disease assessment.
• To validate a deep learning model for predicting heart failure groups using this novel representation.

Main Methods:

• Integration of 24-hour HRV features and patient profile data.
• Development of a multi-parameter, color-coded polar representation.
• Training a deep learning model on 7,575 generated images to predict heart failure categories.

Main Results:

• The deep learning model achieved 93% overall accuracy, 88% sensitivity, and 95% specificity.
• High performance metrics including AUROC of 0.88 and AUPR of 0.79 were recorded.
• The novel approach demonstrated effectiveness in classifying heart failure groups.

Conclusions:

• The proposed polar representation and deep learning model offer a new protocol for cardiovascular disease assessment.
• This method complements echocardiography by incorporating circadian heart rhythm and personalized patient data.
• Clinical implementation can enhance personalized cardiovascular medicine and reduce healthcare provider burden.