No abstract

No abstract

No abstract

Detecting anomalies in time series data plays a vital role in the various applications of diagnosis systems. The importance of anomaly detection is increased by its ability to detect abnormalities in Electrocardiogram (ECG) signals to generate alerts for cardiac health problems. An ECG is a time series that provides essential information about the electrical activity of the heart and is used in the diagnosis of numerous heart diseases. An accurate ECG streaming analytics approach requires continuous learning and adaptation in changing data behaviors. We aim to diagnose ECG by investigating healthy ECG and ECG with cardiological disorders by detecting anomalies in ECG signals. The main objective of this paper is to develop an efficient unsupervised diagnosing system for ECG streaming data based on an online sequence memory algorithm called Hierarchical Temporal Memory (HTM). The HTM is based on neural network and machine learning algorithm for continuous learning tasks. The proposed customization of the HTM algorithm based on our problem domain provides a significant performance results of detection of anomalies in the ECG signals.

Paroxysmal Atrial Fibrillation (PAF) is a special class of Atrial Fibrillation. Predicting PAF events from electrocardiogram (ECG) signal streams plays a vital role in generating real-time alerts for cardiac disorders. These alerts are extremely important to cardiologists in taking precautions to prevent their patients from having a stroke. In this study, an effective predictive approach to PAF events using the Extreme Learning Machine classification technique is proposed. Besides, we propose a feature extraction method that integrates new ECG signal features to its time-domain ones. The new features are based on the construction of sparse vectors for peaks in ECG signals that provide high overlap between similar ECGs. The proposed prediction approach with the new ECG features representation were evaluated on a real PAF dataset using the five-fold cross-validation method. Experiments show promising results for predicting PAF in terms of accuracy and execution time compared to other existing studies. The proposed approach achieved classification accuracy of 97% for non-streaming ECG signals mode and 94.4% for streaming mode.