Abstract: This study demonstrates a method of acquiring ECG multi lead waveform recognition along heart rate, blood pressure data of the patient and comparing these inputs in an artificially intelligent decision support system to perform cardiac diagnosis. This interpretation can be transmitted over the network to obtain expert analysis of the doctor. The study aims to introducing a technique of waveform recognition for the 12-lead ECG interpretation.By comparing the waveforms of the selected lead of the ECG -under diagnosis with the same leads of different ECG’s from the database, similarity of compared ECG beats is calculated using correlation function. This novel technique makes it possible to obtain the diagnosis for the unknown ECG from a comparison between the signal waveforms of this ECG with unknown diagnosis and an ECG of known diagnosis stored in the database, thus allowing automatic decision making. At the same time the blood pressure and heart rate values are compared with the normal values to interpret them as either normal or abnormal. Hence, by acquiring patient’s cardiac data through multiple sensors, like ECG,Heart-Rate and Blood-Pressure sensors and comparing these values from the corresponding values in the artificially intelligent decision support system maintained at the patient side, cardiac diagnosis is done. With the help of networking technology, the diagnosis so made can be sent to a remote doctor at a telemedicine centre for his expert opinion. The possible diagnostic statement along with the future course of action may then be apprised to a nurse available at the patient site for effective co-ordination of a cardiac unit. This requires the use of a database that is sufficiently representative, annotated and clinically validated. In the above developed decision support system the database acts like a knowledge base. This ensures that the system is volatile since knowledge can be added or deleted from the database easily. This saves both the time and money of the patient concerned and also enables the patient to receive immediate emergency treatment if, any needed.
J.Janet and Sofia Parveen , 2005. Design of Multi-sensored Cardiac Decision System to Aid Telemedicine . Asian Journal of Information Technology, 4: 1074-1079.