Real-time Recognition of ECG  

a new Strategy for Risk Management in Heart-Circulation Diseases

For optimal application of the portable PhysioCord the physician uses a two or multi step strategy:

In the first step, the recognition procedure runs on the stationary system with high performance and visual evaluation by a qualified expert, a cardiologist. The common recognition system recognizes the ECG and builds classes automatically. The experienced physician evaluates the ECG and the classes, marks significant and interesting signal structures in the ECG and in the classes as well as using a powerful station with graphical user interface by using the HeartScope. This process generates the formal description of the signal in detail, ECG-substructures (P, Q, R, S, T) up to heart beats and classes. The critical pattern may be failing to identify proper ECG’s, e.g. by cardiac arrest, appearance of iterative ECG’s with an elevated S-T, P-Q segments, e.g. acute myocardial infarction (AMI), or grouped ECG’s with desperate elements, e.g. torsade de points. All of these critical patterns can be adapted individually to properly fit the properties of the individual patients. A permanent monitoring of patients at risks, e.g. after an AMI or at the beginning, during changes or respective dosage adaptation of a specific drug treatment (anti-arrhythmica) gives new insight in these processes. Each of these patterns with its descriptive details is stored in a data base permitting further analysis. If a pattern does not match with previously described patterns a new class of pattern will be opened. In this way several classes of patterns of usually occurring ECG’s arise and can be offered for matching. Note that patterns indicating harmful situation or being critical for surviving can be introduced in this pattern recognition program by the physician.

These patterns which characterize the ECG and the risk situations are the basis for the next step: The portable recognition system with low power technology is taught by templates for marked details, ECG-substructures, heart beats and class descriptions of the same patient with the same electrode positions. The recognition rate using this strategy is asymptotic near 100%.

In this way, the system can recognize risk situations in real-time with highest accuracy, while connecting the rescue organization by handy-function and transmit relevant data for evaluation and decision making by the physician. The receiver has a powerful Database & Knowledge Management System with direct access. The physician can evaluate the received signals in context with the course of the illness and the stored signals. He can decide with high accuracy in minimum time duration. The optimisation of this decision process is the key process in this step. This creates a high chance for saving risk patients in optimal time.