The electrocardiogram (ECG) signal is adopted extensively as a low-cost diagnostic procedure to provide information concerning the healthy status of the heart. Heart rate variability (HRV) is a physiological phenomenon where the time interval between heart beats varies. It is measured by the variation in the beat-to-beat interval. Based on the HRV analysis, the medical experts can make a health assessment for our body, especially for the heart. This chapter presents a fuzzy markup language (FML)-based HRV ontology to apply to ECG domain knowledge. The ontology technologies are used to construct the personalized HRV ontology, and the FML is used to describe the knowledge base and rule base of the HRV. The ECG signals were collected from the involved students before they took exams and after they finished the exams. Based on the constructed FML-based HRV ontology, the variation in the nervous level between before exams and after exams could be inferred when implementing the proposed approach. An experimental platform has been constructed to test the performance of the ontology. The results indicate that the proposed method can work feasibly.
|Titolo:||Electrocardiogram application based on heart rate variability ontology and fuzzy markup language|
|Data di pubblicazione:||2014|
|Appare nelle tipologie:||2.1 Contributo in volume|