Short:VANet: An Intuitive Light-Weight Deep Learning Solution Towards Ventricular Arrhythmia Detection

Ventricular Arrhythmia (VA) is a leading cause of sudden cardiac death (SCD), which kills an average of 180,000 to 350,000 people annually, accounting for 15%–20% of all deaths. Furthermore, fewer than 6% of those who experience sudden cardiac arrest outside the hospital survive, compared to 24% of those who experience SCD inside a hospital. To aid in earlier detection and improve outcomes for out-of-hospital cardiac events, an automated passive detection system for these events could be used. Such automated detection would allow users to raise their self-awareness of potential cardiac risks in life-threatening situations. Diagnosis and detection of heart dysfunctions at early stages can help to prevent complications of a patient’s condition.