7 research outputs found
Automatic Detection of a Phases for CAP Classification
The aim of this study is to develop an automatic detector of the cyclic alternating pattern by first detecting
the activation phases (A phases) of this pattern, analysing the electroencephalogram during sleep, and then
applying a finite state machine to implement the final classification. A public database was used to test the
algorithms and a total of eleven features were analysed. Sequential feature selection was employed to select
the most relevant features and a post processing procedure was used for further improvement of the
classification. The classification of the A phases was produced using linear discriminant analysis and the
average accuracy, sensitivity and specificity was, respectively, 75%, 78% and 74%. The cyclic alternating
pattern detection accuracy was 75%. When comparing with the state of the art, the proposed method
achieved the highest sensitivity but a lower accuracy since the fallowed approach was to keep the REM
periods, contrary to the method that is used in the majority of the state of the art publications which leads to
an increase in the overall performance. However, the approach of this work is more suitable for automatic
system implementation since no alteration of the EEG data is needed.info:eu-repo/semantics/publishedVersio
Towards automatic EEG cyclic alternating pattern analysis: a systematic review
This study conducted a systematic review to determine the feasibility of automatic Cyclic Alternating Pattern (CAP)
analysis. Specifically, this review followed the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses
(PRISMA) guidelines to address the formulated research question: is automatic CAP analysis viable for clinical applica tion? From the identified 1,280 articles, the review included 35 studies that proposed various methods for examining CAP,
including the classification of A phase, their subtypes, or the CAP cycles. Three main trends were observed over time
regarding A phase classification, starting with mathematical models or features classified with a tuned threshold, followed
by using conventional machine learning models and, recently, deep learning models. Regarding the CAP cycle detection,
it was observed that most studies employed a finite state machine to implement the CAP scoring rules, which depended on
an initial A phase classifier, stressing the importance of developing suitable A phase detection models. The assessment of
A-phase subtypes has proven challenging due to various approaches used in the state-of-the-art for their detection, ranging
from multiclass models to creating a model for each subtype. The review provided a positive answer to the main research
question, concluding that automatic CAP analysis can be reliably performed. The main recommended research agenda
involves validating the proposed methodologies on larger datasets, including more subjects with sleep-related disorders,
and providing the source code for independent confirmationinfo:eu-repo/semantics/publishedVersio
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
Automatic detection of CAP on central and fronto-central EEG leads via Support Vector Machines.
The aim of this study is to implement a high-accuracy automatic detector of the Cyclic Alternating Pattern (CAP) during sleep. EEG data from four healthy subjects were used. Both the C4-A1 and the F4-C4 leads were analyzed for this study. Seven features were extracted from each of the two leads and two separate studies were performed for each set of descriptors. For both sets, a Support Vector Machine was trained and tested on the data with the Leave One Out cross-validation method. The two final classifications obtained on the two sets were merged, by considering a CAP A phase scored only if it had been recognized both on the central and on the frontal lead. The length of the A phase was then determined by the result on the fronto-central lead. This method leads to encouraging results, with a classification sensitivity on the whole dataset equal to 73.82\%, specificity equal to 85.93\%, accuracy equal to 84,05\% and Cohen's kappa equal to 0.50
Automatic detection of CAP on central and fronto-central EEG leads via support vector machines
The aim of this study is to implement a high-accuracy automatic detector of the Cyclic Alternating Pattern (CAP) during sleep. EEG data from four healthy subjects were used. Both the C4-A1 and the F4-C4 leads were analyzed for this study. Seven features were extracted from each of the two leads and two separate studies were performed for each set of descriptors. For both sets, a Support Vector Machine was trained and tested on the data with the Leave One Out cross-validation method. The two final classifications obtained on the two sets were merged, by considering a CAP A phase scored only if it had been recognized both on the central and on the frontal lead. The length of the A phase was then determined by the result on the fronto-central lead. This method leads to encouraging results, with a classification sensitivity on the whole dataset equal to 73.82\%, specificity equal to 85.93\%, accuracy equal to 84,05\% and Cohen's kappa equal to 0.50
Automatic Detection of CAP on central and fronto-central EEG leads via Support Vector Machines
The aim of this study is to implement a high-accuracy automatic detector of the Cyclic Alternating Pattern (CAP) during sleep. EEG data from four healthy subjects were used. Both the C4-A1 and the F4-C4 leads were analyzed for this study. Seven features were extracted from each of the two leads and two separate studies were performed for each set of descriptors. For both sets, a Support Vector Machine was trained and tested on the data with the Leave One Out cross-validation method. The two final classifications obtained on the two sets were merged, by considering a CAP A phase scored only if it had been recognized both on the central and on the frontal lead. The length of the A phase was then determined by the result on the fronto-central lead. This method leads to encouraging results, with a classification sensitivity on the whole dataset equal to 73.82%, specificity equal to 85.93%, accuracy equal to 84,05% and Cohen's kappa equal to 0.50