Research on signals collected from the human heart has been a core subject area as the heart displays a rich set of dynamical information that needs careful analysis for medical diagnosis and treatment. The acquisition of the electrical activity signals is a convenient way to analyze, control, evaluate and understand the heart. Electrocardiography (ECG) measurements are used to categorize the heartbeat behaviors to achieve classification. ECG heartbeat signal classification methods range from classical signal processing to convolutional neural networks. Heterogeneous Harmonization of Heartbeat Signals for Arrhythmia Detection (H3-SAD) method based CNN is proposed in this study. H3-SAD method differs from other methods in the literature with its robust and tempered classification ability against heterogeneous spectrums of ECG Signal by targeting being a part of high mobility lifestyle. Literature studies have reasonable estimation rates for MIT-BIH Dataset but not for the heterogeneous acquisition of data in real-life applications. The key point that tempers our classification algorithm is applied dynamic augmentation details towards different signal sources and input values that adduct data to real-life, and heterogeneous augmentation-based CNN architecture.