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Sensors
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Artificial Neural Networks-Based Sensing and Biomedical Signal Processing Technology
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Artificial Neural Networks-Based Sensing and Biomedical Signal Processing Technology

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 2831

Special Issue Editors

Prof. Dr. Armando Barreto

E-Mail Website
Guest Editor
Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
Interests: digital signal processing; image processing utilizing adaptive systems; trainable classifiers for the analysis; understanding of biomedical signals; the development of assistive technologies
Prof. Dr. Malek Adjouadi

E-Mail Website
Guest Editor
Center for Advanced Technology and Education (CATE), Florida International University, Miami, FL 33174, USA
Interests: imaging; signal processing and machine learning with a focus on brain research (EEG, fMRI analysis) in applications related to epilepsy, autism, Alzheimer’s disease and assistive technology

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the use of learning models, such as artificial neural networks, and their applications to the extraction of meaningful results (e.g., classification) from biomedical signals. The scope of this Special Issue is broad, contemplating the use of shallow and deep neural networks with the inclusion of standard layers of processing elements, as well as the involvement of specialized layers, such as convolutional and recurrent layers. Similarly, the signals to which the processing is applied may be one-dimensional (e.g., a voltage varying through time), or multidimensional (e.g., static or dynamic images). All of these forms of processing are becoming a necessary complement to the use of biomedical sensors (e.g., biopotential electrodes) as contemporary advances in processing provide significant advances regarding the capability of sensing devices used to monitor biomedical variables. This Special Issue seeks to present the most recent progress in the use of neural-networks processing systems to expand the capabilities of biomedical sensors.

Prof. Dr. Armando Barreto
Prof. Dr. Malek Adjouadi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neural networks
  • deep learning
  • biomedical signals
  • biomedical images
  • trainable classifiers

Published Papers (3 papers)

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Research

10 pages, 788 KiB  
Article
Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images
by Steven Fussner, Aidan Boyne, Albert Han, Lauren A. Nakhleh and Zulfi Haneef
Sensors 2024, 24(9), 2823; https://doi.org/10.3390/s24092823 - 29 Apr 2024
Viewed by 435
Abstract
The treatment of epilepsy, the second most common chronic neurological disorder, is often complicated by the failure of patients to respond to medication. Treatment failure with anti-seizure medications is often due to the presence of non-epileptic seizures. Distinguishing non-epileptic from epileptic seizures requires [...] Read more.
The treatment of epilepsy, the second most common chronic neurological disorder, is often complicated by the failure of patients to respond to medication. Treatment failure with anti-seizure medications is often due to the presence of non-epileptic seizures. Distinguishing non-epileptic from epileptic seizures requires an expensive and time-consuming analysis of electroencephalograms (EEGs) recorded in an epilepsy monitoring unit. Machine learning algorithms have been used to detect seizures from EEG, typically using EEG waveform analysis. We employed an alternative approach, using a convolutional neural network (CNN) with transfer learning using MobileNetV2 to emulate the real-world visual analysis of EEG images by epileptologists. A total of 5359 EEG waveform plot images from 107 adult subjects across two epilepsy monitoring units in separate medical facilities were divided into epileptic and non-epileptic groups for training and cross-validation of the CNN. The model achieved an accuracy of 86.9% (Area Under the Curve, AUC 0.92) at the site where training data were extracted and an accuracy of 87.3% (AUC 0.94) at the other site whose data were only used for validation. This investigation demonstrates the high accuracy achievable with CNN analysis of EEG plot images and the robustness of this approach across EEG visualization software, laying the groundwork for further subclassification of seizures using similar approaches in a clinical setting. Full article
(This article belongs to the Special Issue Artificial Neural Networks-Based Sensing and Biomedical Signal Processing Technology)
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18 pages, 573 KiB  
Article
Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks
by Lehel Dénes-Fazakas, Barbara Simon, Ádám Hartvég, Levente Kovács, Éva-Henrietta Dulf, László Szilágyi and György Eigner
Sensors 2024, 24(8), 2412; https://doi.org/10.3390/s24082412 - 10 Apr 2024
Viewed by 576
Abstract
Diabetes mellitus (DM) is a persistent metabolic disorder associated with the hormone insulin. The two main types of DM are type 1 (T1DM) and type 2 (T2DM). Physical activity plays a crucial role in the therapy of diabetes, benefiting both types of patients. [...] Read more.
Diabetes mellitus (DM) is a persistent metabolic disorder associated with the hormone insulin. The two main types of DM are type 1 (T1DM) and type 2 (T2DM). Physical activity plays a crucial role in the therapy of diabetes, benefiting both types of patients. The detection, recognition, and subsequent classification of physical activity based on type and intensity are integral components of DM treatment. The continuous glucose monitoring system (CGMS) signal provides the blood glucose (BG) level, and the combination of CGMS and heart rate (HR) signals are potential targets for detecting relevant physical activity from the BG variation point of view. The main objective of the present research is the developing of an artificial intelligence (AI) algorithm capable of detecting physical activity using these signals. Using multiple recurrent models, the best-achieved performance of the different classifiers is a 0.99 area under the receiver operating characteristic curve. The application of recurrent neural networks (RNNs) is shown to be a powerful and efficient solution for accurate detection and analysis of physical activity in patients with DM. This approach has great potential to improve our understanding of individual activity patterns, thus contributing to a more personalized and effective management of DM. Full article
(This article belongs to the Special Issue Artificial Neural Networks-Based Sensing and Biomedical Signal Processing Technology)
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9 pages, 292 KiB  
Article
A Deep Learning Model for Correlation Analysis between Electroencephalography Signal and Speech Stimuli
by Michele Alessandrini, Laura Falaschetti, Giorgio Biagetti, Paolo Crippa, Simona Luzzi and Claudio Turchetti
Sensors 2023, 23(19), 8039; https://doi.org/10.3390/s23198039 - 23 Sep 2023
Cited by 1 | Viewed by 1406
Abstract
In recent years, the use of electroencephalography (EEG) has grown as a tool for diagnostic and brain function monitoring, being a simple and non-invasive method compared with other procedures like histological sampling. Typically, in order to extract functional brain responses from EEG signals, [...] Read more.
In recent years, the use of electroencephalography (EEG) has grown as a tool for diagnostic and brain function monitoring, being a simple and non-invasive method compared with other procedures like histological sampling. Typically, in order to extract functional brain responses from EEG signals, prolonged and repeated stimuli are needed because of the artifacts generated in recordings which adversely impact the stimulus-response analysis. To mitigate the artifact effect, correlation analysis (CA) methods are applied in the literature, where the predominant approaches focus on enhancing stimulus-response correlations through the use of linear analysis methods like canonical correlation analysis (CCA). This paper introduces a novel CA framework based on a neural network with a loss function specifically designed to maximize correlation between EEG and speech stimuli. Compared with other deep learning CA approaches (DCCAs) in the literature, this framework introduces a single multilayer perceptron (MLP) network instead of two networks for each stimulus. To validate the proposed approach, a comparison with linear CCA (LCCA) and DCCA was performed, using a dataset containing the EEG traces of subjects listening to speech stimuli. The experimental results show that the proposed method improves the overall Pearson correlation by 10.56% compared with the state-of-the-art DCCA method. Full article
(This article belongs to the Special Issue Artificial Neural Networks-Based Sensing and Biomedical Signal Processing Technology)
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