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Updated: Jun 30, 2025
Assessing Cardiac Reprogramming using High Content Imaging Analysis
Published on: October 26, 2020
Circadian assessment of heart failure using explainable deep learning and novel multi-parameter polar images
Mohanad Alkhodari1, Ahsan H Khandoker2, Herbert F Jelinek3
1Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering and Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates; Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
Insights
This study introduces a new heart failure (HF) screening method combining heart rate variability (HRV) and patient data. The novel approach shows high accuracy in detecting HF, offering a potential early detection tool.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomedical Engineering
Background:
- Heart failure (HF) affects over 64.3 million globally.
- Current echocardiography screening lacks circadian rhythm and patient profile data.
- A novel multi-parameter approach is proposed for HF assessment.
Purpose of the Study:
- To develop and validate a new method for heart failure assessment.
- To integrate heart rate variability (HRV) and clinical data for improved HF detection.
- To explore the potential of deep learning in analyzing complex cardiovascular data.
Main Methods:
- A 24-hour HRV and clinical information dataset was utilized.
- Features were combined into a single polar image representation.
- A 2D deep learning model was employed to infer HF presence.
Main Results:
- The model achieved high performance metrics: AUC 0.883, sensitivity 90.68%, specificity 95.19%, NMCC 0.93, accuracy 92.62%.
- Validation was performed on a multi-center cohort of 303 coronary artery disease patients.
- Model interpretation highlighted key temporal and clinical factors relevant to HF stages.
Conclusions:
- The proposed approach shows promise as an early HF screening tool.
- This method offers a circadian enhancement to traditional echocardiography.
- It lays the groundwork for next-generation personalized healthcare in cardiology.
Background And Objective:
Heart failure (HF) is a multi-faceted and life-threatening syndrome that affects more than 64.3 million people worldwide. Current gold-standard screening technique, echocardiography, neglects cardiovascular information regulated by the circadian rhythm and does not incorporate knowledge from patient profiles. In this study, we propose a novel multi-parameter approach to assess heart failure using heart rate variability (HRV) and patient clinical information.
Methods:
In this approach, features from 24-hour HRV and clinical information were combined as a single polar image and fed to a 2D deep learning model to infer the HF condition. The edges of the polar image correspond to the timely variation of different features, each of which carries information on the function of the heart, and internal illustrates color-coded patient clinical information.
Results:
Under a leave-one-subject-out cross-validation scheme and using 7,575 polar images from a multi-center cohort (American and Greek) of 303 coronary artery disease patients (median age: 58 years [50-65], median body mass index (BMI): 27.28 kg/m2 [24.91-29.41]), the model yielded mean values for the area under the receiver operating characteristics curve (AUC), sensitivity, specificity, normalized Matthews correlation coefficient (NMCC), and accuracy of 0.883, 90.68%, 95.19%, 0.93, and 92.62%, respectively. Moreover, interpretation of the model showed proper attention to key hourly intervals and clinical information for each HF stage.
Conclusions:
The proposed approach could be a powerful early HF screening tool and a supplemental circadian enhancement to echocardiography which sets the basis for next-generation personalized healthcare.
