  Heart diseases rank among the leading causes of global mortality,
demonstrating a crucial need for early diagnosis and intervention. Most
traditional electrocardiogram (ECG) based automated diagnosis methods are
trained at population level, neglecting the customization of personalized ECGs
to enhance individual healthcare management. A potential solution to address
this limitation is to employ digital twins to simulate symptoms of diseases in
real patients. In this paper, we present an innovative prospective learning
approach for personalized heart disease detection, which generates digital
twins of healthy individuals' anomalous ECGs and enhances the model sensitivity
to the personalized symptoms. In our approach, a vector quantized feature
separator is proposed to locate and isolate the disease symptom and normal
segments in ECG signals with ECG report guidance. Thus, the ECG digital twins
can simulate specific heart diseases used to train a personalized heart disease
detection model. Experiments demonstrate that our approach not only excels in
generating high-fidelity ECG signals but also improves personalized heart
disease detection. Moreover, our approach ensures robust privacy protection,
safeguarding patient data in model development.
