EEG Signal Processing and Machine Learning. Saeid Sanei

EEG Signal Processing and Machine Learning - Saeid Sanei


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morphology very similar to ictal patterns. In old age patients other similar patterns such as subclinical rhythmic EEG discharges of adults (SREDA) over the 4–7 Hz frequency band around the centroparietal region, and a wide frequency range (2–120 Hz) temporal minor sharp transient and wicket spikes over anterior temporal and midtemporal lobes of the brain may occur. These waves are also nonepileptic but with seizure‐type waveform [47].

      The epileptic seizure patterns, called ictal wave patterns, appear during the onset of epilepsy. Although the next chapters of this book focus on analysis of these waveforms from signal processing and machine learning points of view, here a brief explanation of morphology of these waveforms is given. Researchers in signal processing may exploit these concepts in the development of their algorithms. Although these waveform patterns are often highly obscured by the muscle movements, they normally maintain certain key characteristics.

Schematic illustration of bursts of 3–7 Hz seizure activity in a set of adult EEG signals. Schematic illustration of generalized tonic–clonic (grand mal) seizure. The seizure appears in almost all the electrodes.

      Generation of epileptiform brain discharges from deeper brain layers such as the hippocampus during pre‐ictal or interictal periods is an indication of upcoming seizure. These discharges which are spike‐type and have particular morphology which can be seen by inserting electrodes such as multichannel foramen ovale electrodes deep into the hippocampus. More than 90% of these discharges cannot be seen over the scalp due to their attenuation and smearing. A comprehensive overview of epileptic seizure disorders and nonepileptic attacks can be found in many books and publications such as [53, 56]. In this book a chapter is dedicated to the methods for analyzing intracranial and scalp EEGs.

      2.9.3 Psychiatric Disorders

      Not only can functional and certain anatomical brain abnormalities be investigated using EEG signals, pathophysiological brain disorders can also be studied by analyzing such signals. According to the ‘Diagnostic and Statistical Manual (DSM) of Mental Disorders’ of the American Psychiatric Association, changes in psychiatric education have evolved considerably since the 1970s. These changes have mainly resulted from physical and neurological laboratory studies based upon EEG signals [57].

      There have been evidences from EEG coherence measures suggesting differential patterns of maturation between normal and learning disabled children [58]. This finding can lead to the establishment of some methodology in monitoring learning disorders.

      Several psychiatric disorders are diagnosed by analysis of EPs achieved by simply averaging a number of consecutive trails having the same stimuli.

      Some pervasive mental disorders such as: dyslexia which is a developmental reading disorder; autistic disorder which is related to abnormal social interaction, communication, and restricted interests and activities, and starts appearing from the age of three; Rett's disorder, characterized by the development of multiple deficits following a period of normal postnatal functioning; and Asperger's disorder which leads to severe and sustained impairments in social interaction and restricted repetitive patterns of behaviour, interests, and activities; cause significant losses in multiple functioning areas [57].

      ADHD and attention‐deficit disorder (ADD), conduct disorder, oppositional defiant disorder, and disruptive behaviour disorder have also been under investigation and considered within the DSM. Most of these abnormalities appear during childhood and often prevent children from learning and socializing well. The associated EEG features have been rarely analytically investigated, but the EEG observations are often reported in the literature [59–63]. However, most of such abnormalities tend to disappear with advancing age.

      EEG has also been analyzed recently for the study of delirium [64, 65], dementia [66, 67], and many other cognitive disorders [68]. In EEGs, characteristics of delirium include slowing or dropout of the posterior dominant rhythm, generalized theta or delta slow‐wave activity, poor organization of the background rhythm, and loss of reactivity of the EEG to eye opening and closing. In parallel with that, the quantitative EEG (QEEG) shows increased absolute and relative slow‐wave (theta and delta) power, reduced ratio of fast‐to‐slow band power, reduced mean frequency, and reduced occipital peak frequency [65].


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