Variant Maps on Normal and Abnormal ECG Data Sequences

ECG data sequences are classical and most reliable clinical data for patients to provide complex physiological and pathological information diagnosing various heart diseases. Extracting dynamic information from ECG signal time sequences, Poincare maps have developed as classical assistant tools using two dimension maps as important basis for medical doctors to diagnose multiple cardiovascular diseases. Since simulation systems of human heart could be extremely complicated on chaos behaviors, Poincare maps based on paired measures have some limitations to excavate ECG data sequences on special physiological and pathological information. In this paper, we propose a new measuring model based on multi-dimensional measurements using variant maps to handle ECG data sequences in refined visual representations. System architecture of this model and their core components are discussed. Under this construction, normal and abnormal ECG data sequences can be represented as variant maps. Sample results are illustrated as a set of two dimensional variant maps for selected ECG data sequences.