Recently, it has become very important to analyze atrial activity (AA) and to detect arrhythmic AAs and, for this, complete ventricular activity (VA) cancellation is prerequisite. There have been several VA cancellation algorithms for multi-lead ECG but VA cancellation algorithm for single-lead is quite a few. In this study, we have modeled thoracic ECG and, based on this model, proposed a novel VA cancellation algorithm based on event synchronous adaptive filter (ESAF). In this ESAF, the AF ECG was treated as a primary input and event-synchronous impulse train (ESIT) as a reference. And, ESIT was generated so to be synchronized with the ventricular activity by detecting QRS complex. To evaluate the performance, it was applied to the AA estimation problem in atrial fibrillation electrocardiograms. As results, even with low computational cost, this ESAF based algorithm showed better performance than the ABS method and comparable performance to algorithm based on PCA (principal component analysis) or SVD (singular value decomposition). We also proposed an expanded version of ESAF for some AF ECGs with bimorphic VAs and this also showed reasonable performance. Ultimately, our proposed algorithm was found to estimate AA precisely even though it is possible to implement in real-time. We expect our algorithm to replace the most widely used method, that is, the ABS (averaged beat subtraction) method.