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Signals
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Advanced Methods of Biomedical Signal Processing
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Advanced Methods of Biomedical Signal Processing

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 9284

Special Issue Editor

Dr. Hugo Fernando Posada-Quintero

E-Mail Website
Guest Editor
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: biosensors; heart rate variability; autonomic nervous system; electrodermal activity; biomedical digital signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical sensing technology is most commonly perceived as wearable devices, such as smart glasses, smart watches, and smart clothing have become more and more popular in recent years. People have become more inclined to monitor themselves more closely than ever, and technology is enabling them to do so. The trend of wearable technology looks set to continue as technology improves. The challenges of new sensing technologies include quality control, data corruption detection and correction, as well as automatic interpretation of massive amounts of data. For this reason, in recent years many researchers have been working to advance the methods for processing biomedical signals, utilizing methods that include time-varying spectral analysis, entropy, adaptive filtering, multivariate probability distributions, machine learning, deep learning, nonlinear regression, Markov chains, Bayesian estimation, etc. In this Special Issue, we invite research papers presenting novel and advanced Methods of Biomedical Signal Processing, applied but not limited to EDA, ECG, EMG, EEG, PPG, and other biomedical signals or images, as well as their application in the detection and correction of data corruption, and interpretation, diagnosis or prediction of physiological conditions or diseases.

Dr. Hugo Fernando Posada-Quintero
Guest Editor

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. Signals is an international peer-reviewed open access quarterly 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 1000 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

  • biomedical signals
  • images
  • signal processing
  • signal analysis

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Published Papers (5 papers)

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Research

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15 pages, 1098 KiB  
Article
Automatic Detection of Electrodermal Activity Events during Sleep
by Jacopo Piccini, Elias August, Sami Leon Noel Aziz Hanna, Tiina Siilak and Erna Sif Arnardóttir
Signals 2023, 4(4), 877-891; https://doi.org/10.3390/signals4040048 - 18 Dec 2023
Cited by 1 | Viewed by 1470
Abstract
Currently, there is significant interest in developing algorithms for processing electrodermal activity (EDA) signals recorded during sleep. The interest is driven by the growing popularity and increased accuracy of wearable devices capable of recording EDA signals. If properly processed and analysed, they can [...] Read more.
Currently, there is significant interest in developing algorithms for processing electrodermal activity (EDA) signals recorded during sleep. The interest is driven by the growing popularity and increased accuracy of wearable devices capable of recording EDA signals. If properly processed and analysed, they can be used for various purposes, such as identifying sleep stages and sleep-disordered breathing, while being minimally intrusive. Due to the tedious nature of manually scoring EDA sleep signals, the development of an algorithm to automate scoring is necessary. In this paper, we present a novel scoring algorithm for the detection of EDA events and EDA storms using signal processing techniques. We apply the algorithm to EDA recordings from two different and unrelated studies that have also been manually scored and evaluate its performances in terms of precision, recall, and F1 score. We obtain F1 scores of about 69% for EDA events and of about 56% for EDA storms. In comparison to the literature values for scoring agreement between experts, we observe a strong agreement between automatic and manual scoring of EDA events and a moderate agreement between automatic and manual scoring of EDA storms. EDA events and EDA storms detected with the algorithm can be further processed and used as training variables in machine learning algorithms to classify sleep health. Full article
(This article belongs to the Special Issue Advanced Methods of Biomedical Signal Processing)
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20 pages, 6552 KiB  
Article
EEG-Based Seizure Detection Using Variable-Frequency Complex Demodulation and Convolutional Neural Networks
by Yedukondala Rao Veeranki, Riley McNaboe and Hugo F. Posada-Quintero
Signals 2023, 4(4), 816-835; https://doi.org/10.3390/signals4040045 - 28 Nov 2023
Cited by 10 | Viewed by 1795
Abstract
Epilepsy is a complex neurological disorder characterized by recurrent and unpredictable seizures that affect millions of people around the world. Early and accurate epilepsy detection is critical for timely medical intervention and improved patient outcomes. Several methods and classifiers for automated epilepsy detection [...] Read more.
Epilepsy is a complex neurological disorder characterized by recurrent and unpredictable seizures that affect millions of people around the world. Early and accurate epilepsy detection is critical for timely medical intervention and improved patient outcomes. Several methods and classifiers for automated epilepsy detection have been developed in previous research. However, the existing research landscape requires innovative approaches that can further improve the accuracy of diagnosing and managing patients. This study investigates the application of variable-frequency complex demodulation (VFCDM) and convolutional neural networks (CNN) to discriminate between healthy, interictal, and ictal states using electroencephalogram (EEG) data. For testing this approach, the EEG signals were collected from the publicly available Bonn dataset. A high-resolution time–frequency spectrum (TFS) of each EEG signal was obtained using the VFCDM. The TFS images were fed to the CNN classifier for the classification of the signals. The performance of CNN was evaluated using leave-one-subject-out cross-validation (LOSO CV). The TFS shows variations in its frequency for different states that correspond to variation in the neural activity. The LOSO CV approach yields a consistently high performance, ranging from 90% to 99% between different combinations of healthy and epilepsy states (interictal and ictal). The extensive LOSO CV validation approach ensures the reliability and robustness of the proposed method. As a result, the research contributes to advancing the field of epilepsy detection and brings us one step closer to developing practical, reliable, and efficient diagnostic tools for clinical applications. Full article
(This article belongs to the Special Issue Advanced Methods of Biomedical Signal Processing)
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15 pages, 3769 KiB  
Article
Improving Foraminifera Classification Using Convolutional Neural Networks with Ensemble Learning
by Loris Nanni, Giovanni Faldani, Sheryl Brahnam, Riccardo Bravin and Elia Feltrin
Signals 2023, 4(3), 524-538; https://doi.org/10.3390/signals4030028 - 17 Jul 2023
Viewed by 1257
Abstract
This paper presents a study of an automated system for identifying planktic foraminifera at the species level. The system uses a combination of deep learning methods, specifically convolutional neural networks (CNNs), to analyze digital images of foraminifera taken at different illumination angles. The [...] Read more.
This paper presents a study of an automated system for identifying planktic foraminifera at the species level. The system uses a combination of deep learning methods, specifically convolutional neural networks (CNNs), to analyze digital images of foraminifera taken at different illumination angles. The dataset is composed of 1437 groups of sixteen grayscale images, one group for each foraminifera specimen, that are then converted to RGB images with various processing methods. These RGB images are fed into a set of CNNs, organized in an ensemble learning (EL) environment. The ensemble is built by training different networks using different approaches for creating the RGB images. The study finds that an ensemble of CNN models trained on different RGB images improves the system’s performance compared to other state-of-the-art approaches. The main focus of this paper is to introduce multiple colorization methods that differ from the current cutting-edge techniques; novel strategies like Gaussian or mean-based techniques are suggested. The proposed system was also found to outperform human experts in classification accuracy. Full article
(This article belongs to the Special Issue Advanced Methods of Biomedical Signal Processing)
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18 pages, 5852 KiB  
Article
Breast Density Transformations Using CycleGANs for Revealing Undetected Findings in Mammograms
by Dionysios Anyfantis, Athanasios Koutras, George Apostolopoulos and Ioanna Christoyianni
Signals 2023, 4(2), 421-438; https://doi.org/10.3390/signals4020022 - 1 Jun 2023
Cited by 1 | Viewed by 2244
Abstract
Breast cancer is the most common cancer in women, a leading cause of morbidity and mortality, and a significant health issue worldwide. According to the World Health Organization’s cancer awareness recommendations, mammographic screening should be regularly performed on middle-aged or older women to [...] Read more.
Breast cancer is the most common cancer in women, a leading cause of morbidity and mortality, and a significant health issue worldwide. According to the World Health Organization’s cancer awareness recommendations, mammographic screening should be regularly performed on middle-aged or older women to increase the chances of early cancer detection. Breast density is widely known to be related to the risk of cancer development. The American College of Radiology Breast Imaging Reporting and Data System categorizes mammography into four levels based on breast density, ranging from ACR-A (least dense) to ACR-D (most dense). Computer-aided diagnostic (CAD) systems can now detect suspicious regions in mammograms and identify abnormalities more quickly and accurately than human readers. However, their performance is still influenced by the tissue density level, which must be considered when designing such systems. In this paper, we propose a novel method that uses CycleGANs to transform suspicious regions of mammograms from ACR-B, -C, and -D levels to ACR-A level. This transformation aims to reduce the masking effect caused by thick tissue and separate cancerous regions from surrounding tissue. Our proposed system enhances the performance of conventional CNN-based classifiers significantly by focusing on regions of interest that would otherwise be misidentified due to fatty masking. Extensive testing on different types of mammograms (digital and scanned X-ray film) demonstrates the effectiveness of our system in identifying normal, benign, and malignant regions of interest. Full article
(This article belongs to the Special Issue Advanced Methods of Biomedical Signal Processing)
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Other

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12 pages, 1236 KiB  
Technical Note
Evaluating the Feasibility of Euler Angles for Bed-Based Patient Movement Monitoring
by Jonathan Mayer, Rejath Jose, Gregory Kurgansky, Paramvir Singh, Chris Coletti, Timothy Devine and Milan Toma
Signals 2023, 4(4), 788-799; https://doi.org/10.3390/signals4040043 - 14 Nov 2023
Viewed by 1155
Abstract
In the field of modern healthcare, technology plays a crucial role in improving patient care and ensuring their safety. One area where advancements can still be made is in alert systems, which provide timely notifications to hospital staff about critical events involving patients. [...] Read more.
In the field of modern healthcare, technology plays a crucial role in improving patient care and ensuring their safety. One area where advancements can still be made is in alert systems, which provide timely notifications to hospital staff about critical events involving patients. These early warning systems allow for swift responses and appropriate interventions when needed. A commonly used patient alert technology is nurse call systems, which empower patients to request assistance using bedside devices. Over time, these systems have evolved to include features such as call prioritization, integration with staff communication tools, and links to patient monitoring setups that can generate alerts based on vital signs. There is currently a shortage of smart systems that use sensors to inform healthcare workers about the activity levels of patients who are confined to their beds. Current systems mainly focus on alerting staff when patients become disconnected from monitoring machines. In this technical note, we discuss the potential of utilizing cost-effective sensors to monitor and evaluate typical movements made by hospitalized bed-bound patients. To improve the care provided to unaware patients further, healthcare professionals could benefit from implementing trigger alert systems that are based on detecting patient movements. Such systems would promptly notify mobile devices or nursing stations whenever a patient displays restlessness or leaves their bed urgently and requires medical attention. Full article
(This article belongs to the Special Issue Advanced Methods of Biomedical Signal Processing)
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