An Ontology driven model for detection and classification of cardiac arrhythmias using ECG data

摘要

Cardiac arrhythmias are not life-threatening straight away but can cause serious heart-related complications if not medically handled appropriately. An electrocardiogram (ECG) captures the heart’s electric activity and has widespread usage due to its easy deployment and non-invasive aspect. Arrhythmia classification through manual analysis of electrocardiogram (ECG) is troublesome, tedious, and prone to human errors that can lead to serious repercussions. Hence, it’s a more effective alternative to deploy computational techniques to automatically perform the classification. Traditional techniques are data-driven and require an immense amount of data to train and then perform identification. This paper presents an ontology-driven knowledge model to automatically diagnose arrhythmias based on the patient’s sensor-based ECG data. The proposed approach models the arrhythmia domain knowledge and the conceptual relationships relevant to classification of heartbeat into corresponding cardiac arrhythmias and to facilitate decision making with respect to the patients. The newly developed arrhythmia ontology consists of three different modules, each semantically annotating a different aspect of the arrhythmia detection process. A SWRL (Semantic Web Rule Language) based ontology classifier performs classification of patient’s ECG data into corresponding cardiac arrhythmia types. The constructed knowledge base is ontologically aligned with some benchmarked top-level ontologies that promotes the semantic interoperability across multiple domains. The resultant ontological model is validated with a real-world ECG dataset and compared with the existing approaches showing higher precision rate and comparable performance. The developed model establishes a standardized ontology, that promotes the exchange and shareability of consensual domain knowledge about arrhythmic conditions, supports information retrieval and knowledge discovery.