Automated detection and classification of electrocardiogram (ECG) noise sources can play a crucial role in reliable measurement of ECG parameters for accurate diagnosis of cardiovascular diseases (CVDs) under unsupervised telehealth monitoring and intensive care unit (ICU) applications. Although the methods had quite acceptable detection rates, most methods are too complicated for real-time implementation for wearable cardiac health monitoring devices. Therefore, in this paper, we present a low-complexity algorithm for automatically detecting and classifying the ECG noises including flat line (FL), time-varying noise (TVN), baseline wander (BW), abrupt change (ABC), muscle artifact (MA) and power line interference (PLI). The proposed method is based on the moving average (MAv) and derivative filters and the five temporal features including turning points, global and local amplitude estimates, zerocrossing and autocorrelation peak values. The proposed method is tested and validated using a wide variety of clean and noisy ECG signals taken from the MIT-BIH arrhythmia database and Physionet Challenge database. The method achieves an average sensitivity (Se) of 98.55%, positive productivity (+P) of 95.27% and overall accuracy (OA) of 94.19% for classifying the noises. Results show that the proposed method outperforms the other existing methods. © 2016 IEEE.