Machine Learning Characterization of Ictal and Interictal States in EEG Aimed at Automated Seizure Detection

Background: The development of automated seizure detection methods using EEG signals could be of great importance for the diagnosis and the monitoring of patients with epilepsy. These methods are often patient-specific and require high accuracy in detecting seizures but also very low false-positive rates. The aim of this study is to evaluate the performance of a seizure detection method using EEG signals by investigating its performance in correctly identifying seizures and in minimizing false alarms and to determine if it is generalizable to different patients. Methods: We tested the method on about two hours of preictal/ictal and about ten hours of interictal EEG recordings of one patient from the Freiburg Seizure Prediction EEG database using machine learning techniques for data mining. Then, we tested the obtained model on six other patients of the same database. Results: The method achieved very high performance in detecting seizures (close to 100% of correctly classified positive elements) with a very low false-positive rate when tested on one patient. Furthermore, the model portability or transfer analysis revealed that the method achieved good performance in one out of six patients from the same dataset. Conclusions: This result suggests a strategy to discover clusters of similar patients, for which it would be possible to train a general-purpose model for seizure detection

A Deep Learning Ensemble Approach for Automated COVID-19 Detection from Chest CT Images

Background: The aim of this study was to evaluate the performance of an automated COVID-19 detection method based on a transfer learning technique that makes use of chest computed tomography (CT) images. Method: In this study, we used a publicly available multiclass CT scan dataset containing 4171 CT scans of 210 different patients. In particular, we extracted features from the CT images using a set of convolutional neural networks (CNNs) that had been pretrained on the ImageNet dataset as feature extractors, and we then selected a subset of these features using the Information Gain filter. The resulting feature vectors were then used to train a set of k Nearest Neighbors classifiers with 10-fold cross validation to assess the classification performance of the features that had been extracted by each CNN. Finally, a majority voting approach was used to classify each image into two different classes: COVID-19 and NO COVID-19. Results: A total of 414 images of the test set (10% of the complete dataset) were correctly classified, and only 4 were misclassified, yielding a final classification accuracy of 99.04%. Conclusions: The high performance that was achieved by the method could make it feasible option that could be used to assist radiologists in COVID-19 diagnosis through the use of CT images

Neural Network based architecture for Fault Detection and Isolation in air data systems

This paper presents the design, development, integration and flight testing of a Fault Detection and Isolation architecture for an air data computer based on Artificial Neural Networks. A lot of Networks have been trained using Knowledge Discovery in Data Base Process in order to identify faults on air data measurements such as airspeed, sideslip angle, and angle of attack. The proposed methodology makes use of a huge number of flight data for training and testing in the Neural Network design. Flight data have been recorded during flight trials carried out using the experimental aircraft of the Italian Aerospace Research Centre. The proposed architecture tested on flight data gathered during an autonomous mission of an Unmanned Aerial Vehicle (UAV) shows good performance in identifying fault occurrences

EEG signal analysis for epileptic seizures detection by applying Data Mining techniques

seizures, which severely impact the quality of life of epilepsy patients and sometimes are accompanied by loss of consciousness. The most widely accepted and used tool by epileptologists to identify seizures and diagnose epilepsy is the ElectroEncephaloGram (EEG). Seizure detection on EEG signals is a long process, which is done manually by epileptologists. This paper describes how to analyze EEG signal using Data Mining methods and techniques with the main objective of automatically detect a seizure within EEG signals. We have designed and developed a multipurpose and extendable tool for feature extraction from time series data, named Training Builder. Our trained classifier, based on signal processing, sliding window paradigm, features extraction and selection, and Support Vector Machines, showed excellent results, reaching an accuracy of over 99% during the test made on publicly available EEG datasets

EEG signal analysis for epileptic seizures detection by applying Data Mining techniques

seizures, which severely impact the quality of life of epilepsy patients and sometimes are accompanied by loss of consciousness. The most widely accepted and used tool by epileptologists to identify seizures and diagnose epilepsy is the ElectroEncephaloGram (EEG). Seizure detection on EEG signals is a long process, which is done manually by epileptologists. This paper describes how to analyze EEG signal using Data Mining methods and techniques with the main objective of automatically detect a seizure within EEG signals. We have designed and developed a multipurpose and extendable tool for feature extraction from time series data, named Training Builder. Our trained classifier, based on signal processing, sliding window paradigm, features extraction and selection, and Support Vector Machines, showed excellent results, reaching an accuracy of over 99% during the test made on publicly available EEG datasets