UNSUPERVISED REAL-TIME DIAGNOSIS SYSTEM FOR ECG STREAMING DAT
Maghawry, E., Ismail, R., Gharib, T. (2021). UNSUPERVISED REAL-TIME DIAGNOSIS SYSTEM FOR ECG STREAMING DAT. EKB Journal Management System, 21(1), 180-195. doi: 10.21608/ijicis.2021.69762.1077
Eman Maghawry; Rasha Ismail; Tarek Gharib. "UNSUPERVISED REAL-TIME DIAGNOSIS SYSTEM FOR ECG STREAMING DAT". EKB Journal Management System, 21, 1, 2021, 180-195. doi: 10.21608/ijicis.2021.69762.1077
Maghawry, E., Ismail, R., Gharib, T. (2021). 'UNSUPERVISED REAL-TIME DIAGNOSIS SYSTEM FOR ECG STREAMING DAT', EKB Journal Management System, 21(1), pp. 180-195. doi: 10.21608/ijicis.2021.69762.1077
Maghawry, E., Ismail, R., Gharib, T. UNSUPERVISED REAL-TIME DIAGNOSIS SYSTEM FOR ECG STREAMING DAT. EKB Journal Management System, 2021; 21(1): 180-195. doi: 10.21608/ijicis.2021.69762.1077

UNSUPERVISED REAL-TIME DIAGNOSIS SYSTEM FOR ECG STREAMING DAT

International Journal of Intelligent Computing and Information Sciences
Article 1, Volume 21, Issue 1, February 2021, Page 180-195  XML PDF (866.66 K)
Document Type: Original Article
DOI: 10.21608/ijicis.2021.69762.1077
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Authors
Eman Maghawry email 1; Rasha Ismailorcid 2; Tarek Ghariborcid 3
1Faculty of computer and information science, Ain Shams University
2Vice Dean for Postgraduate Studies & Research, Faculty of Computer and Information Sciences, Ain Shams University
3Head of Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
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.
Keywords
Anomaly detection; ECG signals streaming; ECG diagnosis system; Hierarchical temporal memory
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