Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection

Methodologies for automatic non-rapid eye movement and cyclic alternating pattern analysis were proposed to examine the signal from one electroencephalogram monopolar derivation for the A phase, cyclic alternating pattern cycles, and cyclic alternating pattern rate assessments. A population composed of subjects free of neurological disorders and subjects diagnosed with sleep-disordered breathing was studied. Parallel classifications were performed for non-rapid eye movement and A phase estimations, examining a one-dimension convolutional neural network (fed with the electroencephalogram signal), a long short-term memory (fed with the electroencephalogram signal or with proposed features), and a feed-forward neural network (fed with proposed features), along with a finite state machine for the cyclic alternating pattern cycle scoring. Two hyper-parameter tuning algorithms were developed to optimize the classifiers. The model with long short-term memory fed with proposed features was found to be the best, with accuracy and area under the receiver operating characteristic curve of 83% and 0.88, respectively, for the A phase classification, while for the non-rapid eye movement estimation, the results were 88% and 0.95, respectively. The cyclic alternating pattern cycle classification accuracy was 79% for the same model, while the cyclic alternating pattern rate percentage error was 22%.info:eu-repo/semantics/publishedVersio

Cyclic Alternating Pattern of Encephalopathy (Cape) In CNS Infection: A Case Report

Cyclic Alternating Pattern of Encephalopathy (CAPE) is rare EEG phenomenon first described in 1944 in a comatose patient. It is similar to sleep EEG pattern of cyclic alternating pattern (CAP) which is a periodic electroencephalogram activity of non-REM sleep. The cyclic alternating pattern (CAP) is defined by sequences of transient electrocortical events that are different from the tonic background and repeat at intervals of up to one minute. CAPE, however, is abnormal EEG pattern. In this pattern of EEG abnormality slow wave activity of 1-2Hz alternates with fast activity of 6-10 hertz. Here we present a case of patient with CNS infection on mechanical ventilation whose EEG showed the above-mentioned pattern

Visual and automatic cyclic alternating pattern (CAP) scoring

The classification of short duration events in the EEG during sleep, as the A stage of the cyclic alternating pattern (CAP) is a tedious and error prone task. the number of events under normal conditions is large (several hundreds), and it is necessary to mark the limits of the events with precision, otherwise the time sensitive classification of the CAP phases (A and B) and specially the scoring of different types of A phases will be compromised. the objective of this study is to verify the feasibility of visual CAP scoring with only one channel of EEG, the evaluation of the inter-scorer agreement in a variety of recordings, and the comparison of the visual scorings with a known automatic scoring system. Sixteen hours of one channel (C4-A1 or C3-A2) of NREM sleep were extracted from eight whole night recordings in European Data Format and presented to the different scorers. the average inter-scorer agreement for all scorers is above 70%, the pair wise inter-scorer agreement found was between 69% up to 77.5%. These values are similar to what has been reported in different type studies. the automatic scoring system has similar performance of the visual scorings. the study also has shown that it is possible to classify the CAP using only one channel of EEG.Univ Tecn Lisboa, Laseeb, ISR, IST,Lab Sistemas Evolut & Engn Biomed, P-1049001 Lisbon, PortugalUniversidade Federal de São Paulo, EPM, Inst Sono, Dept Psicobiol, São Paulo, BrazilUniversidade Federal de São Paulo, EPM, Inst Sono, Dept Psicobiol, São Paulo, BrazilWeb of Scienc

Automatic Cyclic Alternating Pattern (CAP) analysis: Local and multi-trace approaches

: The Cyclic Alternating Pattern (CAP) is composed of cycles of two different electroencephalographic features: an activation A-phase followed by a B-phase representing the background activity. CAP is considered a physiological marker of sleep instability. Despite its informative nature, the clinical applications remain limited as CAP analysis is a time-consuming activity. In order to overcome this limit, several automatic detection methods were recently developed. In this paper, two new dimensions were investigated in the attempt to optimize novel, efficient and automatic detection algorithms: 1) many electroencephalographic leads were compared to identify the best local performance, and 2) the global contribution of the concurrent detection across several derivations to CAP identification. The developed algorithms were tested on 41 polysomnographic recordings from normal (n = 8) and pathological (n = 33) subjects. In comparison with the visual CAP analysis as the gold standard, the performance of each algorithm was evaluated. Locally, the detection on the F4-C4 derivation showed the best performance in comparison with all other leads, providing practical suggestions of electrode montage when a lean and minimally invasive approach is preferable. A further improvement in the detection was achieved by a multi-trace method, the Global Analysis-Common Events, to be applied when several recording derivations are available. Moreover, CAP time and CAP rate obtained with these algorithms positively correlated with the ones identified by the scorer. These preliminary findings support efficient automated ways for the evaluation of the sleep instability, generalizable to both normal and pathological subjects affected by different sleep disorders

Automatic Cyclic Alternating Pattern (CAP) analysis: Local and multi-trace approaches

The Cyclic Alternating Pattern (CAP) is composed of cycles of two different electroencephalographic features: an activation A-phase followed by a B-phase representing the background activity. CAP is considered a physiological marker of sleep instability. Despite its informative nature, the clinical applications remain limited as CAP analysis is a time-consuming activity. In order to overcome this limit, several automatic detection methods were recently developed. In this paper, two new dimensions were investigated in the attempt to optimize novel, efficient and automatic detection algorithms: 1) many electroencephalographic leads were compared to identify the best local performance, and 2) the global contribution of the concurrent detection across several derivations to CAP identification. The developed algorithms were tested on 41 polysomnographic recordings from normal (n = 8) and pathological (n = 33) subjects. In comparison with the visual CAP analysis as the gold standard, the performance of each algorithm was evaluated. Locally, the detection on the F4-C4 derivation showed the best performance in comparison with all other leads, providing practical suggestions of electrode montage when a lean and minimally invasive approach is preferable. A further improvement in the detection was achieved by a multi-trace method, the Global Analysis—Common Events, to be applied when several recording derivations are available. Moreover, CAP time and CAP rate obtained with these algorithms positively correlated with the ones identified by the scorer. These preliminary findings support efficient automated ways for the evaluation of the sleep instability, generalizable to both normal and pathological subjects affected by different sleep disorders

Characterization of A phases during the Cyclic Alternating Pattern of sleep

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The Correlation between Sleep and Creativity

Fredrich August von Kekule, a famous German chemist, was attempting to determine the shape of the benzene molecule, which was known to have six carbon atoms. In 1865, reflecting upon his discovery of the hexagonal-ring like structure, he asserted that the solution came to him in a dream1; however, it is not clear if he was in rapid eye movement (REM) sleep dreaming or if he was in non-REM (NREM) sleep imagery. It is possible to think of this type of discoveries as an expression of creativity, i.e. the ability to use existing pieces of information and combine them in novel patterns leading to greater understanding and new solutions. Preliminary support of the role of sleep in creative thinking comes from a recent study by Wagner et al.2; these authors asked normal participants to perform a cognitive task, the Number Reduction Task. In this task, participants are required to understand a set of stimulus-response sequences and supply a single representative numerical answer. Improvement in task performance may be gradual (i.e., by slowly increasing response speed), or abrupt (after insight into an abstract rule underlying all sequences). They found that 59% of the participants that were allowed to sleep were able to perform the task in a time that was 70% shorter than the other group that did not sleep and suggested that sleep may facilitate insight-related problem solving. Here we report the results of the first study showing a direct complex correlation between sleep architecture or microstructure and creativity in normal controls

Non-REM dreaming in relation to the cyclic alternating pattern an exploratory study

Includes bibliographical references.Dreaming is yet to be studied in relation to sleep microstructure. By endeavouring to study mentation in relation to the finer neurophysiological processes underlying the rhythmicity of the sleep cycles, dream science stands to benefit from the wealth of knowledge of these processes. While relationships between dreaming and certain of these processes have been identified in the literature, a comprehensive study of dreaming in relation to all of the recognized components of the sleep microstructure is completely lacking. With this in mind, the main aim of this study was to examine sleep microstructure in relation to dreaming and determine whether there is any relationship between dream recall and the various types of phasic arousal phenomena during NREM sleep, as systematised within the global framework of the cyclic alternating pattern (CAP)