Conventional electrocardiograms (ECG) are displayed in one dimension. Reading one-dimensional ECG waveform becomes challenging when one wants to visualize the heart rate variability with naked eye. Some ECG visualization techniques have been proposed. However, they rely on domain knowledge to comprehend the heart rate variability. To improve the readability for patients and non-experts, we introduce Star-ECG, a novel ECG visualization approach. Such approach projects ECG waveforms onto a two-dimensional plane in a circular form. We demonstrate that Star-ECG offers not only easily deciphered visualization of cardiac abnormalities and heart rate variability, but also the application of state-of-the-art arrhythmia classification with integrated deep neural networks. We also report positive user feedback from both experts and non-experts that Star-ECG can provide readable and helpful information to monitor cardiac activities.