Accurate classification of ECG signals is vital for early detection of cardiovascular conditions, particularly in wearable healthcare devices with limited computational resources. This paper presents Emcnet, an ensemble multiscale convolutional neural network designed for single-lead ECG classification. By combining multiscale convolutional feature extraction with ensemble strategies, Emcnet captures both fine-grained and global temporal patterns in ECG signals. Experimental results on benchmark datasets demonstrate that Emcnet achieves superior accuracy and robustness compared to state-of-the-art baselines, while remaining efficient enough for deployment on wearable platforms.