394 research outputs found
Interpreting Deep Learning Models for Epileptic Seizure Detection on EEG signals
While Deep Learning (DL) is often considered the state-of-the art for
Artificial Intelligence-based medical decision support, it remains sparsely
implemented in clinical practice and poorly trusted by clinicians due to
insufficient interpretability of neural network models. We have tackled this
issue by developing interpretable DL models in the context of online detection
of epileptic seizure, based on EEG signal. This has conditioned the preparation
of the input signals, the network architecture, and the post-processing of the
output in line with the domain knowledge. 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. Results show that
the kernel size in the first layer determines the interpretability of the
extracted features and the sensitivity of the trained models, even though the
final performance is very similar after post-processing. Also, we found that
amplitude is the main feature leading to an ictal prediction, suggesting that a
larger patient population would be required to learn more complex frequency
patterns. Still, our methodology was successfully able to generalize patient
inter-variability for the majority of the studied population with a
classification F1-score of 0.873 and detecting 90% of the seizures.Comment: 28 pages, 11 figures, 12 table
EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications.
Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research
Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review
Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data
Knowledge-Distilled Graph Neural Networks for Personalized Epileptic Seizure Detection
Wearable devices for seizure monitoring detection could significantly improve
the quality of life of epileptic patients. However, existing solutions that
mostly rely on full electrode set of electroencephalogram (EEG) measurements
could be inconvenient for every day use. In this paper, we propose a novel
knowledge distillation approach to transfer the knowledge from a sophisticated
seizure detector (called the teacher) trained on data from the full set of
electrodes to learn new detectors (called the student). They are both providing
lightweight implementations and significantly reducing the number of electrodes
needed for recording the EEG. We consider the case where the teacher and the
student seizure detectors are graph neural networks (GNN), since these
architectures actively use the connectivity information. We consider two cases
(a) when a single student is learnt for all the patients using preselected
channels; and (b) when personalized students are learnt for every individual
patient, with personalized channel selection using a Gumbelsoftmax approach.
Our experiments on the publicly available Temple University Hospital EEG
Seizure Data Corpus (TUSZ) show that both knowledge-distillation and
personalization play significant roles in improving performance of seizure
detection, particularly for patients with scarce EEG data. We observe that
using as few as two channels, we are able to obtain competitive seizure
detection performance. This, in turn, shows the potential of our approach in
more realistic scenario of wearable devices for personalized monitoring of
seizures, even with few recordings
- …