Multi-stage classification of abnormal patterns in EEG and e-ECG using model-free methods

In this study, computer based pattern recognition and classification systems are proposed for EEG and ECG patterns which are one dimensional biomedical signals. In the first phase of the study, artificial neural network based automatic recognition system for epileptiform events in EEG is proposed. Recognition process is performed both using single MLP based classifier and using multi-stage classifier. Different methods are used to increase the classification accuracy of the single MLP based system. In the second phase of the study, a novel multi-stage automatic arrhythmia recognition and classification system is proposed. The system performs beat-based classification and classifies 16 different beat types. The first stage of the system classifies five main groups then, in the second stage of the system each main group is classified into subgroups. In both classification stages the best feature set for each main group and subgroup is determined and used in classification process. With this approach, the curse of dimensionality effect is reduced. In addition, selecting and using the most discriminative features for each group increases the classification performance of the system. Furthermore, the third stage is added to the system for classifying beats that are labeled as unclassified beats in the first two classification stages. KNN classifier and raw data as input vector is used in this stage. The performances of the proposed systems are finally evaluated using real EEG and ECG data and results are discussed.