This paper presents an explainable, low-complexity binary electrocardiogram (ECG) classifier to be deployed in a resource-limited wearable edge device. The presented technique could be used as stand alone on an edge device or in a two-stage distributed edge-cloud classifier, where a preliminary two-class classification is done on the edge and a more comprehensive multi-class classification is done on the cloud. We used an Explainable Boosting Machine (EBM) classifier for the preliminary binary classification. EBMs can be implemented using a decision tree-like structure and therefore complexity is much lower than deep learning models and many traditional classifiers. We used the Physionet MIT-BIH Arrhythmia dataset for performance evaluation and the EBM classifier achieves an accuracy of 96.84%, F1 score of 91.38%, and sensitivity of 96.83%. When used in a distributed edge-cloud classifier configuration, our proposed work limits cloud transmission to only 19.37% of the total data, that in turn reduces the power consumed in the wearable edge device.