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Introduction

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The electroencephalogram (EEG) provides a real-time measure of brain electrical activity and has proven to be useful in a wide range of clinical settings, for example, evaluation of seizures, mental status change, coma, and classification of epilepsy.1,2 The clinical applications of continuous EEG monitoring can involve recordings from scalp or intracranial electrodes obtained in the hospital (e.g., video scalp and intracranial EEG [IEEG] monitoring), on an outpatient basis (routine scalp EEG), and even in nonmedical settings (ambulatory EEG). More recently, a cranially implanted device (the RNS system, NeuroPace Inc.) using continuous IEEG for real-time seizure detection and electrical stimulation to abort seizures is undergoing clinical trial.3 This broad range of clinical applications underscores the usefulness of EEG for studying brain dysfunction and highlights the potential applications of automated EEG analysis. In this chapter, we give an overview of automated detection of epileptiform activity. The primary goal is to discuss epileptiform event detection in the context of modern data mining and pattern recognition.46 The chapter is not intended to be a comprehensive review of epileptiform spike and seizure detection.710

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Data Collection and EEG Recordings

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The quantity of data generated by continuous EEG recording can be significant. Historically, the analysis and storage of these data has been challenging due to relatively limited computational resources. These challenges persist today, despite the ubiquity of cheap gigaflop servers and terabyte storage systems, for a different reason: the established clinical work flow for EEG analysis breaks down for modern data sets. Current state-of-the-art recording systems now acquire hundreds of channels of EEG data at high sampling rates. These large-scale data sets can be thousands of times larger than traditionally acquired clinical EEG, which is nominally collected at 200 to 500 Hz. Although the hardware aspect of collecting, analyzing, and storing these data is feasible, the “gold standard” of analysis by human expert visual review of entire records is not. The sheer increase in data for modern recordings places a significant strain on epilepsy monitoring unit (EMU) staff and resources for all but the simplest of analyses. Moreover, the hunt for multiscale events ranging in duration from milliseconds to minutes requires more detailed and repeated review of EEG. The need for reliable automated analysis tools in clinical and research electrophysiology is clearly recognized, and there is a growing community of researchers pursuing these goals. The field should certainly benefit from parallel efforts in other clinical fields, such as genomics, that are driving efforts in database management, data mining, and pattern recognition.4

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Without automated analyses tools, clinical video-EEG evaluation continues to rely primarily on human expert visual review. Video-EEG monitoring (VEM) has long been a cornerstone in the evaluation of patients with seizure disorders.1,2 Generally, the primary goal of long-term VEM is to record the patient's habitual seizures. However, even when seizures are not captured, the ...

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