Multi-modal Heart Failure Risk Estimation based on Short ECG and Sampled
  Long-Term HRV
Sergio González Abel Ko-Chun Yi Wan-Ting Hsieh Wei-Chao Chen Chun-Li Wang Victor Chien-Chia Wu Shang-Hung Chang
Cardiovascular diseases, including Heart Failure (HF), remain a leading
global cause of mortality, often evading early detection. In this context,
accessible and effective risk assessment is indispensable. Traditional
approaches rely on resource-intensive diagnostic tests, typically administered
after the onset of symptoms. The widespread availability of electrocardiogram
(ECG) technology and the power of Machine Learning are emerging as viable
alternatives within smart healthcare. In this paper, we propose several
multi-modal approaches that combine 30-second ECG recordings and approximate
long-term Heart Rate Variability (HRV) data to estimate the risk of HF
hospitalization. We introduce two survival models: an XGBoost model with
Accelerated Failure Time (AFT) incorporating comprehensive ECG features and a
ResNet model that learns from the raw ECG. We extend these with our novel
long-term HRVs extracted from the combination of ultra-short-term beat-to-beat
measurements taken over the day. To capture their temporal dynamics, we propose
a survival model comprising ResNet and Transformer architectures (TFM-ResNet).
Our experiments demonstrate high model performance for HF risk assessment with
a concordance index of 0.8537 compared to 14 survival models and competitive
discrimination power on various external ECG datasets. After transferability
tests with Apple Watch data, our approach implemented in the myHeartScore App
offers cost-effective and highly accessible HF risk assessment, contributing to
its prevention and management.