At the same time, multi-channel signals (like the six-dimensional accelerometer signal) can still benefit from the designed circuit, achieving a reduction factor up to 80% with minor performance degradation.
It achieves a detection Gmean accuracy of 94. 9%, 98. 6%, 90. 3%, and 84. 3% for the B, C, X, and O points, respectively.
Next, we classify each individual's POTP regardless of the task at hand, achieving an F-1 score of 77. 1% when distinguishing time passing faster rather than slower than usual.
Furthermore, we evaluate a post-processing strategy to adjust the predictions to the dynamics of epileptic seizures, showing that performance is significantly improved in all the approaches and also that after post-processing, differences in performance are much smaller between approaches.
Continuous and multimodal stress detection has been performed recently through wearable devices and machine learning algorithms.
RECIPE (REliable power and time-ConstraInts-aware Predictive management of heterogeneous Exascale systems) is a recently started project funded within the H2020 FETHPC programme, which is expressly targeted at exploring new High-Performance Computing (HPC) technologies.
Weather Forecasting Distributed, Parallel, and Cluster Computing
Specifically, we focused the discussion on three main aspects: 1) how to aggregate the classification results on signal segments provided by the DL model into a larger time scale, at the seizure-level; 2) what are the relevant frequency patterns learned in the first convolutional layer of different models, and their relation with the delta, theta, alpha, beta and gamma frequency bands on which the visual interpretation of EEG is based; and 3) the identification of the signal waveforms with larger contribution towards the ictal class, according to the activation differences highlighted using the DeepLIFT method.
In this work, we generate synthetic seizure-like brain electrical activities, i. e., EEG signals, that can be used to train seizure detection algorithms, alleviating the need for recorded data.