Electrocardiographic (ECG) anomaly detection aims to detect abnormal cardiac behaviors in the ECG. However, existing time-series anomaly detection methods have challenges in extracting key ECG features. To enhance the model’s ability to extract ECG features, we propose a simple yet effective self-supervised anomaly detection technique, named Noise Distribution Aware Autoencoder (NDAAE). This method utilizes mixed noisy ECG data to increase the feature extraction difficulty and aids model learning through a self-supervised task to capture more effective ECG features. Specifically, we first randomly inject six different types of noise signals into the raw ECG data. Then, using an autoencoder, we reconstruct the noisy ECG data back to the original noise-free ECG data. Finally, we establish a self-supervised task from the perspectives of low-dimensional feature space similarity measurement and noise classification, in order to improve the model’s ability to extract features. The method enables the model to obtain more effective ECG representations and improve the anomaly detection accuracy. We evaluate our proposed method on the real ECG dataset, and empirical results show that our proposal outperforms existing methods in detecting anomalies.