A review of epileptic seizure detection using machine learning classifiers
Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain
signals produced by brain neurons. Neurons are connected to each other in a complex way …
signals produced by brain neurons. Neurons are connected to each other in a complex way …
Explainable AI for time series classification: a review, taxonomy and research directions
Time series data is increasingly used in a wide range of fields, and it is often relied on in
crucial applications and high-stakes decision-making. For instance, sensors generate time …
crucial applications and high-stakes decision-making. For instance, sensors generate time …
HIVE-COTE 2.0: a new meta ensemble for time series classification
Abstract The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE)
is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its …
is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its …
Operations research in healthcare: a survey
A Rais, A Viana - International transactions in operational …, 2011 - Wiley Online Library
Optimisation problems in Healthcare have received considerable attention for more than
three decades. More recently, however, with decreasing birth rates in nearly all of the …
three decades. More recently, however, with decreasing birth rates in nearly all of the …
[BOG][B] Temporal data mining
T Mitsa - 2010 - taylorfrancis.com
Temporal data mining deals with the harvesting of useful information from temporal data.
New initiatives in health care and business organizations have increased the importance of …
New initiatives in health care and business organizations have increased the importance of …
On the Time Series -Nearest Neighbor Classification of Abnormal Brain Activity
Epilepsy is one of the most common brain disorders, but the dynamical transitions to
neurological dysfunctions of epilepsy are not well understood in current neuroscience …
neurological dysfunctions of epilepsy are not well understood in current neuroscience …
Predictive data mining in clinical medicine: a focus on selected methods and applications
Predictive data mining in clinical medicine deals with learning models to predict patients'
health. The models can be devoted to support clinicians in diagnostic, therapeutic, or …
health. The models can be devoted to support clinicians in diagnostic, therapeutic, or …
Machine learning based novel cost-sensitive seizure detection classifier for imbalanced EEG data sets
Epilepsy is one of the most prevalent neurological disorders. Its accurate detection is a
challenge since sometimes patients do not experience any prior alert to identify a seizure …
challenge since sometimes patients do not experience any prior alert to identify a seizure …
Seizure prediction in patients with focal hippocampal epilepsy
Objective We evaluated the performance of our previously developed seizure prediction
approach on thirty eight seizures from ten patients with focal hippocampal epilepsy. Methods …
approach on thirty eight seizures from ten patients with focal hippocampal epilepsy. Methods …
Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine
Y Song, J Zhang - Journal of neuroscience methods, 2016 - Elsevier
Background Epilepsy is one of the most common neurological disorders approximately one
in every 100 people worldwide are suffering from it. Uncontrolled epilepsy poses a …
in every 100 people worldwide are suffering from it. Uncontrolled epilepsy poses a …