Use of Hierarchical Temporal Memory Algorithm in Heart Attack Detection

In order to reduce the number of deaths due to heart
problems, we propose the use of Hierarchical Temporal Memory
Algorithm (HTM) which is a real time anomaly detection algorithm.
HTM is a cortical learning algorithm based on neocortex used for
anomaly detection. In other words, it is based on a conceptual theory
of how the human brain can work. It is powerful in predicting unusual
patterns, anomaly detection and classification. In this paper, HTM
have been implemented and tested on ECG datasets in order to detect
cardiac anomalies. Experiments showed good performance in terms
of specificity, sensitivity and execution time.

Authors:

• Tesnim Charrad
• Kaouther Nouira
• Ahmed Ferchichi

Keywords:

• HTM
• Real time anomaly detection
• ECG
• Cardiac Anomalies.

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