Introduction: In intensive care units (ICU), the most significant life support technology for patients with acute respiratory failure is mechanical ventilation. A mismatch between ventilatory support and patient demand is referred to as patient-ventilator asynchrony (PVA), and it is associated with a series of adverse clinical outcomes. Although the use of a reference signal for patient effort is critical in recognition of PVA, existing detection algorithms are frequently solely based on the ventilator’s airway pressure (Paw) and flow-time signals. The aim of this study was to develop an automated detection algorithm for PVA using the ventilator’s Paw, flow-time and esophageal pressure (Pes) signals. Methods: We proposed a two-dimensional convolutional neural network (2DCNN) to detect two types of PVA (reverse triggering (RT) and premature cycling) using a dataset of respiratory cycles recorded from 11 patients. Mechanical ventilation experts with access to the Pes signal annotated 12.337 respiratory cycles to create a gold standard dataset. Several techniques for a potential class imbalance problem, as well as several changes to the initial model architecture, were investigated. A leave-one-patient-out cross-validation technique was used to evaluate model performance. The proposed Pes-based 2DCNN (Pes_2DCNN) was compared to a similar model based solely on the ventilator Paw and flow-time signals (PF_2DCNN). Results: The proposed Pes_2DCNN exhibited superior performance in detecting RT as compared to PF_2DCNN in terms of area under the receiver operating characteristic (AUROC) (0.80 ± 0.07 vs. 0.75 ± 0.13, respectively; p < 0.01). Furthermore, the results indicate that the class imbalance solutions did not improve the performance for detection of RT. For detection of premature cycling, Pes_2DCNN also outperformed PF_2DCNN in terms of AUROC (0.88 ± 0.09 vs. 0.71 ± 0.24, respectively; p < 0.01). Conclusion: The findings of this study suggest the added value of the Pes signal in detection of RT and premature cycling. However, because this is a preliminary study, more research is required to further investigate the importance of the Pes signal in PVA detection.