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Found 14 papers, 5 papers with code
Machine Learning Discovery of Optimal Quadrature Rules for Isogeometric Analysis
The quadrature rule search is posed as an optimization problem and solved by a machine learning strategy based on gradient-descent.
Combining General and Personalized Models for Epilepsy Detection with Hyperdimensional Computing
In this work, we demonstrate a few additional aspects in which HD computing, and the way its models are built and stored, can be used for further understanding, comparing, and creating more advanced machine learning models for epilepsy detection.
Importance of methodological choices in data manipulation for validating epileptic seizure detection models
Epilepsy is a chronic neurological disorder that affects a significant portion of the human population and imposes serious risks in the daily life of patients.
A Semi-Supervised Algorithm for Improving the Consistency of Crowdsourced Datasets: The COVID-19 Case Study on Respiratory Disorder Classification
In this work, we use a semi-supervised learning (SSL) approach to improve the labeling consistency of the COUGHVID dataset and the robustness of COVID-19 versus healthy cough sound classification.
Many-to-One Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Internet of Things Systems
Integrating low-power wearable Internet of Things (IoT) systems into routine health monitoring is an ongoing challenge.
Event-based sampled ECG morphology reconstruction through self-similarity
In this work, we leverage the self-similarity of the electrocardiogram (ECG) signal to recover missing features in event-based sampled ECG signals, dynamically selecting patient-representative templates together with a novel dynamic time warping algorithm to infer the morphology of event-based sampled heartbeats.
HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration
However, its accuracy is not yet on par with other Machine Learning (ML) approaches.
Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection
As a result, we believe it can support the ML community to further foster the research in multiple directions related to feature and channel selection, as well as model interpretability.
Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure Detection
Yet, most of them have not been tested on the challenging task of epileptic seizure detection, and it stays unclear whether they can increase the HD computing performance to the level of the current state-of-the-art algorithms, such as random forests.
Adaptive R-Peak Detection on Wearable ECG Sensors for High-Intensity Exercise
Additionally, the online adaptive process achieves an F1 score of 99% across five different exercise intensities, with a total energy consumption of 1. 55+-0. 54~mJ.
Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection
At the same time, the total number of sub-classes is not significantly increased compared to the balanced dataset.
Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection
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.
Interpreting Deep Learning Models for Epileptic Seizure Detection on EEG signals
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.
Construe: a software solution for the explanation-based interpretation of time series
This paper presents a software implementation of a general framework for time series interpretation based on abductive reasoning.
