Application of a variation of empirical mode decomposition and Teager energy operator to EEG signals for mental task classification

A knowledge discovery methodology from EEG data for cyclic alternating pattern detection

Background

Detection and quantification of cyclic alternating patterns (CAP) components has the potential to serve as a disease bio-marker. Few methods exist to discriminate all the different CAP components, they do not present appropriate sensitivities, and often they are evaluated based on accuracy (AC) that is not an appropriate measure for imbalanced datasets.

Methods

We describe a knowledge discovery methodology in data (KDD) aiming the development of automatic CAP scoring approaches. Automatic CAP scoring was faced from two perspectives: the binary distinction between A-phases and B-phases, and also for multi-class classification of the different CAP components. The most important KDD stages are: extraction of 55 features, feature ranking/transformation, and classification. Classification is performed by (i) support vector machine (SVM), (ii) k-nearest neighbors (k-NN), and (iii) discriminant analysis. We report the weighted accuracy (WAC) that accounts for class imbalance.

Results

The study includes 30 subjects from the CAP Sleep Database of Physionet. The best alternative for the discrimination of the different A-phase subtypes involved feature ranking by the minimum redundancy maximum relevance algorithm (mRMR) and classification by SVM, with a WAC of 51%. Concerning the binary discrimination between A-phases and B-phases, k-NN with mRMR ranking achieved the best WAC of 80%.

Conclusions

We describe a KDD that, to the best of our knowledge, was for the first time applied to CAP scoring. In particular, the fully discrimination of the three different A-phases subtypes is a new perspective, since past works tried multi-class approaches but based on grouping of different sub-types. We also considered the weighted accuracy, in addition to simple accuracy, resulting in a more trustworthy performance assessment. Globally, better subtype sensitivities than other published approaches were achieved.

EEG seizure detection and epilepsy diagnosis using a novel variation of Empirical Mode Decomposition

Epileptic seizure detection and epilepsy diagnosis based on feature extraction and classification using electroencephalography (EEG) signals is an important area of research. In this paper, we present a simple and effective approach based on signal decomposition, using a novel variation of the Empirical Mode Decomposition called Empirical Mode Decomposition-Modified Peak Selection (EMD-MPS). EMD-MPS allows time-scale based de-trending of signals, allowing signals to be separated directly into a de-trended component, and a trend, according to a frequency separation criterion. Features are extracted from the decomposed components, and a simple classifier, namely the 1-NN classifier is used for three classification tasks. The technique is tested on a publicly available EEG database, and a classification accuracy of 99% for epilepsy diagnosis task, and 100% and 98.2% for two seizure detection tasks is obtained. These results are better than, or comparable to previous results using the same EEG database, but have been obtained with a simpler and computationally fast signal analysis and classification method.