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.
It is desirable to have a monitoring system that can keep a constant surveillance on the conditions of the heart and its related patterns. This is particularly important in many patients with critical cardiac abnormalities. This can be very convenient in clinical settings but may not be possible for individuals who are not in hospital and are in their day-to-day activities. Wearable ECG-based systems have been proposed for such situations and can perform such monitoring in real life. However, detecting the abnormality in near real-time is still a challenge in these systems. Similarly, what information should be relayed to doctors or other caregivers and how soon this can be achieved is a very hot area of research at present.
This work presents a monitoring system that embeds an intelligent wearable data acquisition system with unique identification algorithms requiring very little computational time and simple threshold based classification. Once this is done, the related information is passed to a gateway system that can communicate the criticality flags as well as the actual ECG waveform data to the pre-defined data node that connects it to the doctor and/or other clinical representatives. We have used an Android-based cellphone as the gateway. The presented system focuses on intelligent health monitoring with possible wearable application for long-term monitoring and updating in real-time of patient's ECG conditions to the physician.
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