Heart-circulation-disease is a civilization disease with a high risk of fatality especially in industrial regions like Europe. At the same time, such industrialized regions exhibit frequently high unemployment patterns coupled with stress related syndromes on the persons considered which might contribute to such diseases. This is the motivation for innovative product development using national and international co-operation to overcome unemployment on the one hand and to recognize persons under increased health risk on the other. 

The real-time recognition of the electrical activities of the heart, known as the electrocardiogram (ECG), is a unique feature of the developed system which uses powerful information technologies as its technical basis for continuous monitoring of patients at risk, and recognizes critical situations in real-time with highest accuracy. In addition, state-of-the-art communication technologies are also used for the transmission of relevant data to present the risk situation of the patient to a qualified physician for making decisions. The scientific background for this work is related to the project "Methods of Nonlinear Dynamic for analysis of the ECG, for risk stratification and therapy assessment for heart patients", supported by the German Ministry of Education and Research (Grant 13N7129).

The ECG system HeartScope consists of a special amplifier system for high quality signal detection in open field conditions. A high performance multi-processor system which is capable of processing the huge amounts of data produced by a multichannel ECG record to gain information in real-time has also been developed. Algorithms for recognition of events in single channels are implemented in the first level of the multi-processor system. We use high performance image processing algorithms in the second level, interpreting the sampled values of each channel as pixels of the image, 256 up to 2.000 times per second. This new and patented method is based on information theory and describes the ECG activity as sequences of so called virtual sources in parameters of amplitude, time and space.

Fuzzy logic and methods of AI are used to define and recognise sequences of virtual sources as QRS-complexes or heart beats in real-time. The network of two or more Personal Computers (PC's) is co-ordinated through the multi-processor system for presentation of ECG activity and controlling. These methods are integrated into the powerful graphic user interface and uses a database system. Incorporated into this user interface are state of the art algorithms from the NIMH (Washington / USA) for mappings, FFT, etc.  

The innovative method for the real-time recognition of signals worked - in contrast to the established frequency domain methods such as the Discrete Fourier Transform (DFT) -  in the time domain. This new method is based on the “old” peak measurement and evaluates each event in the signal.

Each event is transformed into a data structure named Virtual Source (in German: Virtuelle Quelle – VQ). The result of this transformation is the description of the signal as a sequence of VQs. Further steps build up a hierarchical system of chained lists of  VQs.

This description of the signal can be easily manipulated by mathematical methods and can be easily recognized.

This high performance requires the application of time domain methods for signal recognition of the highest accuracy, which employs the latest technologies in the fields of Data Base (DBS) and Knowledge Management Systems (KMS). Each incoming signal  is stored in the DBS. These signals  are transformed in the VQs, segmented and indexed by the time and by the segment number. The classification is achieved using DB&KMS for the acquisition of personal knowledge in direct communication between the user and the DB&KMS.

Publication HeartScope               PatiMon                    CARiMan                  Herz                                      EKGNLD