Dynamical Feature Extraction from Brain Activity Time Series

Dynamical Feature Extraction from Brain Activity Time Series

Chang-Chia Liu (University of Florida, USA), W. Art Chaovalitwongse (Rutgers University, USA), Panos M. Pardalos (University of Florida, USA) and Basim M. Uthman (NF/SG VHS & University of Florida, USA)
Copyright: © 2009 |Pages: 7
DOI: 10.4018/978-1-60566-010-3.ch113
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Neurologists typically study the brain activity through acquired biomarker signals such as Electroencephalograms (EEGs) which have been widely used to capture the interactions between neurons or groups of neurons. Detecting and identifying the abnormal patterns through visual inspection of EEG signals are extremely challenging and require constant attention for well trained and experienced specialists. To resolve this problem, data mining techniques have been successfully applied to the analysis for EEG recordings and other biomarker data sets. For example, Chaovalitwongse et al., (2006; 2007), Prokopyev et al., (2007) and Pardalos et al., (2007) reported the EEG patterns can be classified through dynamical features extracted from the underlying EEG dynamical characteristics. Moreover, in the area of cancer research, Busygin et al., (2006) showed promising results via Bi-clustering data classification technique using selected features from DNA microarrays. Ceci et al., (2007); Krishnamoorthy et al., (2007) also reported that data mining techniques enable protein structure characterization and protein structure prediction. From data mining aspects, feature extraction and selection for time series data sets not only play an important role in data preprocessing but also provide opportunities to uncover the underlying mechanisms of data under study. It also keeps the essential data structure and makes a better representation of acquired data sets that need to be classified. In this work, the properties and descriptions of the most common neurological biomarker namely EEG data sets will be given as well as the motivations and challenges for abnormal EEG classification. The dynamical features for EEG classification will be reviewed and described in the second part of this work. The described dynamical features can also be applied to other types of classification applications for discovering the useful knowledge from obtained data sets. Finally, the potential applications for EEG classification will be discussed and comments for further research directions will be given in the future trends and conclusion sections.
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Epilepsy is a common neurological disorder, affecting about 50 million people worldwide (WHO, 2005). Anti antiepileptic drugs (AEDs) are the mainstay of contemporary treatment for epilepsy, it can reduce frequency of seizure and prevent seizure recurrence in most cases. For subjects with uncontrolled seizures, ablative surgery is considered after two or more AEDs have failed to result for seizure freedom. The EEG (see Fig. 1 for an example) is a key tool in diagnosing seizure disorders and contributing tremendously to surgical decisions in patients with intractable epilepsy. The EEG recordings provide information about underlying interactions among neurons around the recording electrodes as a function of time. By carefully investigating EEG patterns, the spatio-temporal neuronal electrical activities can be decoded and abnormal patterns can be captured for diagnostic purposes (Berger, 1929). Through nonlinear dynamical features, data mining techniques have made progresses in shedding light on hidden patterns in EEG recordings for such neurological disorders (Chaovalitwongse et al., 2006; 2007).

Figure 1.

An example of 10 second, 32-channel intracranial EEG display


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