Background and purposes: Stroke is the second leading cause of death globally after ischemic heart disease, also a risk factor of cardioembolic stroke. Thus, we postulate that heartbeats encapsulate vital signals related to stroke. With the rapid advancement of deep neural networks (DNNs), it emerges as a powerful tool to decipher intriguing heartbeat patterns associated with post-stroke patients. In this study, we propose the use of a one-dimensional convolutional network (1D-CNN) architecture to build a binary classifier that distinguishes electrocardiogram s (ECGs) between the post-stroke and the stroke-free. Methods: We have built two 1D-CNNs that were used to identify distinct patterns from an openly accessible ECG dataset collected from elderly post-stroke patients. In addition to prediction accuracy, which is the primary focus of existing ECG deep neural network methods, we have utilized Gradient-weighted Class Activation Mapping (GRAD-CAM) to ease model interpretation by uncovering ECG patterns captured by our model. Results: Our stroke model has achieved ~90% accuracy and 0.95 area under the Receiver Operating Characteristic curve. Findings suggest that the core PQRST complex alone is important but not sufficient to differentiate the post-stroke and the stroke-free. Conclusions: We have developed an accurate stroke model using the latest DNN method. Importantly, our work has illustrated an approach to enhance model interpretation, overcoming the black-box issue facing DNN, fostering higher user confidence and adoption of DNN in medicine.