An ontology and rule based intelligent information system to detect and predict myocardial diseases

Elderly people have a high risk of myocardial diseases. Hence, here we propose an architecture for Ambient Assisted Living (AAL) and telemedicine that supports pre-hospital health emergencies, remote monitoring of patients with chronic conditions and medical collaboration through sharing health-related information resources. Furthermore, it is going to use medical data from vital signs for, on one hand, the detection of symptoms using a rule system based on Jess (tachycardia, arrhythmia ...) and, on the other hand, the prediction of illness using chronobiology algorithms. This paper proposes a knowledge base to represent general human information, heart details and electrocardiogram parameters based on ontologies and a Jess' rule system to detect anomalies and patterns from electrocardiogram information. This solution has been tested by the research team's staff, using a wearable electrocardiogram.