The electrocardiogram (ECG) is a highly cost-effective and convenient diagnostic tool that can aid in the diagnosis of a wide range of cardiovascular conditions, such as arrhythmias , myocardial ischemia , myocardial infarction, and heart failure. Its usefulness lies in its ability to provide information about the electrical activity and rhythm of the heart, making it an essential component of the diagnostic process for many cardiac conditions. However, the interpretation of ECG signals often requires a high level of expertise from medical professionals. When reading complex ECGs, noncardiologists often need to consult with cardiologists. Even cardiologists may make errors in their judgments after prolonged reading of ECGs. Therefore, the accurate and timely diagnosis of cardiovascular diseases using ECG signals is a complex task. In this paper, we categorize this problem as a multilabel, multidimensional time-series data anomaly detection task. We propose a method called T w o 3 -AnoECG for ECG anomaly detection, which improves upon existing ECG feature extraction and data segmentation methods and introduces a two-stream network that is trained in two stages using two double-throw switches to control the input and model structure of each stage. We further propose T w o 3 -EnsECG, which combines the anomaly score generated by T w o 3 -AnoECG and multiple baseline methods, to improve the overall performance of anomaly detection in ECG signals. The experimental results demonstrate the effectiveness of our proposed method.

Anomaly detection for multivariate time series is an essential task in the modern industrial field. Although several methods have been developed for anomaly detection, they usually fail to effectively exploit the metrical-temporal correlation and the other dependencies among multiple variables. To address this problem, we propose a stacked attention autoencoder for anomaly detection in multivariate time series (SA2E-AD); it focuses on fully utilizing the metrical and temporal relationships among multivariate time series. We design a multiattention block, alternately containing the temporal attention and metrical attention components in a hierarchical structure to better reconstruct normal time series, which is helpful in distinguishing the anomalies from the normal time series. Meanwhile, a two-stage training strategy is designed to further separate the anomalies from the normal data. Experiments on three publicly available datasets show that SA2E-AD outperforms the advanced baseline methods in detection performance and demonstrate the effectiveness of each part of the process in our method.

Time series anomaly detection (TSAD) is an essential problem faced in several fields, e.g., fault detection, fraud detection, and intrusion detection, etc. Although TSAD is a crucial problem in anomaly detection, few solutions in anomaly detection are suitable for it at present. Recently, some researchers use GAN-based methods such as TAnoGAN and TadGAN to solve TSAD problem. However, problems such as model collapse, low generalization capability and poor accuracy still exist. In this article, we proposed a Dilated Convolutional Transformer-based GAN (DCT-GAN) to enhance accuracy and improve generalization capability of the model. Specifically, DCT-GAN utilize several generators and a single discriminator to alleviate the mode collapse problem. Each generator consists of a dilated convolutional neural network and a Transformer block to obtain fine-grained and coarse-grained information of the time series, which is a useful component to improve generalization capability. We also use weight-based mechanism to balance these generators. Experiments verify the effectiveness of our method and each part of DCT-GAN.