CardioView: a framework for detection Premature Ventricular Contractions with eXplainable Artificial Intelligence

Artificial Intelligence plays a vital role in disease diagnosis, but effectively classifying diverse Premature Ventricular Contraction (PVC) subtypes remains a challenge. While computer-aided systems demonstrate high performance, human oversight remains crucial for reliability. This study introduces Explainable AI algorithms utilizing the GRADient-weighted Class Activation Mapping algorithm, as part of the proposed framework CardioView, providing insights into the diagnosis process. With high accuracy, recall, precision, and AUC (96.21%, 98.09%, 94.74%, 99.28% respectively), the system enhances understanding of PVC classification. CardioView allows individuals to gain insights into the discrimination process, revealing its operations and visualizing the components of the electrocardiogram waveform that aid in distinguishing between PVC and non-PVC classes, as well as within various PVC subclasses. Furthermore, CardioView integrates a human-in-the-loop approach, ensuring active involvement of cardiologists throughout the diagnostic process and reinforcement learning mechanisms.