ABSTRACT
Networks are naturally occurring phenomena that are studied across many disciplines. The topological features of a network can provide insight into the dynamics of a system as it evolves, and can be used to predict changes in state. The brain is a complex network whose temporal and spatial behavior can be measured using electroencephalography (EEG). This data can be reconstructed to form a family of graphs that represent the state of the brain over time, and the evolution of these graphs can be used to predict changes in brain states, such as the transition from preictal to ictal in patients with epilepsy. This research proposes objective indications of seizure onset observed from minimally invasive scalp EEG. The approach considers the brain as a complex nonlinear dynamical system whose state can be derived through time-delay embedding of the EEG data and characterized to determine change in brain dynamics related to the preictal state. This method targets phase-space graph spectra as biomarkers for seizure prediction, correlates historical degrees of change in spectra, and makes accurate prediction of seizure onset. A significant trend of normalized dissimilarity over time indicates a departure from the norm, and thus a change in state. Our methods show high sensitivity (90-100%) and specificity (90%) on 241 h of scalp EEG training data, and sensitivity and specificity of 70%-90% on test data. Moreover, the algorithm was capable of processing 12.7 min of data per second on an Intel Core i3 CPU in Matlab, showing that real-time analysis is viable.
Subject(s)
Brain/physiopathology , Epilepsy/physiopathology , Seizures/physiopathology , Adolescent , Adult , Brain Mapping , Child , Child, Preschool , Electroencephalography , Female , Humans , Male , Middle Aged , Models, Neurological , Signal Processing, Computer-Assisted , Young AdultABSTRACT
Amnestic mild cognitive impairment (aMCI) often is an early stage of Alzheimer's disease (AD). MCI is characterized by cognitive decline departing from normal cognitive aging but that does not significantly interfere with daily activities. This study explores the potential of scalp EEG for early detection of alterations from cognitively normal status of older adults signifying MCI and AD. Resting 32-channel EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls (NC), 16 early MCI, and 17 early stage AD-are examined. Regional spectral and complexity features are computed and used in a support vector machine model to discriminate between groups. Analyses based on three-way classifications demonstrate overall discrimination accuracies of 83.3%, 85.4%, and 79.2% for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. These results demonstrate the great promise for scalp EEG spectral and complexity features as noninvasive biomarkers for detection of MCI and early AD.
Subject(s)
Alzheimer Disease/diagnosis , Cognitive Dysfunction/diagnosis , Diagnosis, Computer-Assisted/methods , Electroencephalography/statistics & numerical data , Aged , Aged, 80 and over , Aging/psychology , Alzheimer Disease/physiopathology , Alzheimer Disease/psychology , Case-Control Studies , Cognitive Dysfunction/physiopathology , Cognitive Dysfunction/psychology , Cohort Studies , Female , Humans , Male , Middle Aged , Neuropsychological Tests , Reference Values , ScalpABSTRACT
The authors extend the recent application of phase-space dissimilarity measures for scalp EEG data in two directions. First, a forewarning window of up to 8 hours was used, thereby providing more forewarning time of the seizure event. This window was limited to a maximum of 1 hour in their previous work. Second, they combined information from two channels via a multichannel phase-space to improve the quality and confidence limits of the forewarning. Combining these two enhancements, they obtained two-channel results that were superior to the single-channel ones.
Subject(s)
Electroencephalography/methods , Epilepsy/physiopathology , Seizures/prevention & control , Adolescent , Adult , Child , Child, Preschool , Humans , Middle AgedABSTRACT
Phase-space dissimilarity measures (PSDM) have been recently proposed to provide forewarning of impending epileptic events from scalp electroencephalographic (EEG) for eventual ambulatory settings. Despite high noise in scalp EEG, PSDM yield consistently superior performance over traditional nonlinear indicators, such as Kolmogorov entropy, Lyapunov exponents, and correlation dimension. However, blind application of PSDM may result in channel inconsistency, whereby multiple datasets from the same patient yield conflicting forewarning indications in the same channel. This paper presents a first attempt to solve this problem.
