Pattern- and Network-Based Classification Techniques for Multichannel Medical Data Signals to Improve Brain Diagnosis

There is an urgent need for a quick screening process that could help neurologists diagnose and determine whether a patient is epileptic versus simply demonstrating symptoms linked to epilepsy but actually stemming from a different illness. An inaccurate diagnosis could have fatal consequences, particularly in operating rooms and intensive care units. Electroencephalogram (EEG) has been traditionally used, as a gold standard, to diagnose patients by evaluating those brain functions that might correspond to epilepsy and other brain disorders. This research therefore focuses on developing new classification techniques for multichannel EEG recordings. Two time-series classification techniques, namely, Support Feature Machine (SFM) and Network-Based Support Vector Machine (SVM) (NSVM), are proposed in this paper to predict from EEG readings whether a person is epileptic or nonepileptic. The SFM approach is an optimization model that maximizes classification accuracy by selecting a group of electrodes (features) that has strong class separability based on time-series similarity measures and correctly classifies EEG samples in the training phase. The NSVM approach integrates a new network-based model for multidimensional time-series data with traditional SVMs to exploit both the spatial and temporal characteristics of EEG data. The proposed techniques are tested on two EEG data sets acquired from ten and five patients, respectively. Compared with other commonly used classification techniques such as SVM and decision trees, the proposed SFM and NSVM techniques provide very promising and practical results and require much less time and memory resources than traditional techniques. This study is a necessary application of data mining to advance the diagnosis and treatment of human epilepsy.