Computer-aided morphological analysis of Holter ECG recordings based on support vector learning system

The paper presents a new approach to computer-aided analysis of ECG Holter recordings. In contrast to existing tools it is a learning system: the pertinent features of the signal shape are automatically discovered upon the examples carefully selected and commented by cardiologists. Mathematical basis of our system is the theory of support vector machines that are applied for two tasks: signal approximation and pattern classification. Numerical procedures implement the algorithm of sequential minimal optimisation. The computer program is developed in Borland C++ Builder environment. The excellent performances of our approach, high rate of successful pattern recognition and computational efficiency, make use of our tools possible in clinical practice. The system is tested at the Chair and Department of Internal Medicine and Cardiology, Central Teaching Hospital in Warsaw, Poland.