Fuzzy detection of critical cardiac abnormalities using ECG data: A ubiquitous approach

Electrocardiogram (ECG) based health diagnosis of cardiac diseases has been a saturated area of research and almost any known heart-condition can be detected and diagnosed by doctors in the hospital setting. However, these approaches fall extremely short when attempting to design an automatic detection system to do the same. The situation becomes even more difficult when the measurement system is being designed for a ubiquitous application in which the patient is not confined to the hospital and the device is attached to him/her externally while the person is involved in daily chores. This paper presents the classification technique for one such system which is being built by the same team. Hence the presented work covers the initial findings related to some of the cardiac conditions that can be monitored in the ubiquitous scenario. This detection system produces warning signals that can be conveyed to the concerned healthcare personnel if signs of critical cardiac conditions begin to show. Due to the compact nature of such systems, the detection and classification techniques have to be extremely simple in order to be stored in the small memory of the microcontroller of the ubiquitous system. The paper presents one such technique that is a combination of digital filters and Fuzzy classifications implemented at look-up table level in order to preserve the simplicity of the system.