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Found 24 papers, 10 papers with code
Synthetic Epileptic Brain Activities Using Generative Adversarial Networks
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.
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.
The RECIPE Approach to Challenges in Deeply Heterogeneous High Performance Systems
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
ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals
Continuous and multimodal stress detection has been performed recently through wearable devices and machine learning algorithms.
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.
Wearable and Continuous Prediction of Passage of Time Perception for Monitoring Mental Health
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.
ReBeatICG: Real-time Low-Complexity Beat-to-beat Impedance Cardiogram Delineation Algorithm
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.
An Error-Based Approximation Sensing Circuit for Event-Triggered, Low Power Wearable Sensors
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.
Semiparametric Bayesian Networks
In addition, we present modifications of two well-known algorithms (greedy hill-climbing and PC) to learn the structure of a semiparametric Bayesian network from data.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties
In this paper, we propose \textbf{Party-wise Dropout} to improve the VFL model's robustness against the unexpected exit of passive parties and a defense method called \textbf{DIMIP} to protect the active party's IP in the deployment phase.
Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures
Additionally, we address the overhead of non-GEMM operations in transformer models within the scope of this memory data arrangement.
TEE4EHR: Transformer Event Encoder for Better Representation Learning in Electronic Health Records
Furthermore, our results demonstrate that our approach can improve representation learning in EHRs and can be useful for clinical prediction tasks.
TimEHR: Image-based Time Series Generation for Electronic Health Records
Time series in Electronic Health Records (EHRs) present unique challenges for generative models, such as irregular sampling, missing values, and high dimensionality.
SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithms
Based on existing guidelines and recommendations, the framework introduces a set of recommendations and standards related to datasets, file formats, EEG data input content, seizure annotation input and output, cross-validation strategies, and performance metrics.
