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Biosignals Processing and Analysis in Biomedicine

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

Deadline for manuscript submissions: closed (15 March 2022) | Viewed by 28483

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Special Issue Editor

Dr. Giorgos Giannakakis

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Guest Editor

Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH), 700 13 Heraklion, Greece
Interests: biosignals analysis; image analysis; biomedical engineering; EEG; ECG; EDA; speech analysis; MRI; PET; affective computing; computational neuroscience

Special Issue Information

Dear colleagues,

Biosignals are physiological and physical measures of the human body’s functions. They provide useful information about one’s physiological, pathophysiological, and emotional states, playing a key role in health monitoring and clinical diagnosis. The processing and analysis of biosignals is an interdisciplinary and dynamic area of specialization, covering biology, physics, medicine, engineering, and computer science. Recent advances in signal computational methods enables the proper signal preprocessing/noise removal, the improvement of analysis algorithms, and the extraction of representative features for effective utilization of biosignals in clinical environments.

Multimodal biosignals analysis provide a more complete image integrating the specific information from each modality. Fusion approaches aim at integrating data analyses establishing synergic relationships for improved diagnostic accuracy. Recent advances in machine learning (ML) and artificial intelligence (AI) analyze the dataset and provide a computational model for automatic classification or decision making for the problem under investigation. The data analysis interpretation and the connection with the underlying physiological mechanisms may lead to a deeper understanding of the pathophysiological states.

The Special Issue “Biosignals Processing and Analysis in Biomedicine” aims to provide a collection of contributions showing new advancements and applications of advanced biosignals analysis for biomedical applications. Topics relevant to biosignals processing and analysis for biomedical applications and affective computing are invited for this Special Issue. Topics of interest include, but are not limited to, the following:

Biomedical Signal Processing and Analysis
Biomedical Image Processing and Analysis
Electroencephalogram (EEG), Electrocardiogram (ECG), Heart Rate Variability (HRV), Electromyogram (EMG), Electrodermal Activity (EDA), Thermal Infrared Imaging (TII), Photoplethysmography (PPG)
Medical Imaging (MRI, PET, CT, SPECT)
Video Analysis
Speech analysis
Computational Neuroscience
Biomedical/Human body sensing
Signal Analysis for Neurological disorders
Signal Analysis for Affective Computing
Application of machine learning and artificial intelligence in medicine
Deep learning for biosignal analysis

Dr. Giorgos Giannakakis
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

biosignals; biomedical image; biomedical engineering; EEG; ECG; EDA; speech; MRI; PET; affective computing; computational neuroscience

Published Papers (6 papers)

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Research

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17 pages, 3305 KiB

Open AccessArticle

Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition

by Francesca Gasparini, Alessandra Grossi, Marta Giltri and Stefania Bandini

Signals 2022, 3(2), 249-265; https://0-doi-org.brum.beds.ac.uk/10.3390/signals3020016 - 18 Apr 2022

Cited by 3 | Viewed by 2927

Abstract

Physiological responses are currently widely used to recognize the affective state of subjects in real-life scenarios. However, these data are intrinsically subject-dependent, making machine learning techniques for data classification not easily applicable due to inter-subject variability. In this work, the reduction of inter-subject heterogeneity was considered in the case of Photoplethysmography (PPG), which was successfully used to detect stress and evaluate experienced cognitive load. To face the inter-subject heterogeneity, a novel personalized PPG normalization is herein proposed. A subject-normalized discrete domain where the PPG signals are properly re-scaled is introduced, considering the subject’s heartbeat frequency in resting state conditions. The effectiveness of the proposed normalization was evaluated in comparison to other normalization procedures in a binary classification task, where cognitive load and relaxed state were considered. The results obtained on two different datasets available in the literature confirmed that applying the proposed normalization strategy permitted increasing the classification performance. Full article

(This article belongs to the Special Issue Biosignals Processing and Analysis in Biomedicine)

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23 pages, 8186 KiB

Open AccessArticle

Effect of Exposure Time on Thermal Behaviour: A Psychophysiological Approach

by Bilge Kobas, Sebastian Clark Koth, Kizito Nkurikiyeyezu, Giorgos Giannakakis and Thomas Auer

Signals 2021, 2(4), 863-885; https://0-doi-org.brum.beds.ac.uk/10.3390/signals2040050 - 02 Dec 2021

Cited by 8 | Viewed by 3204

Abstract

This paper presents the findings of a 6-week long, five-participant experiment in a controlled climate chamber. The experiment was designed to understand the effect of time on thermal behaviour, electrodermal activity (EDA) and the adaptive behavior of occupants in response to a thermal non-uniform indoor environment were continuously logged. The results of the 150 h-long longitudinal study suggested a significant difference in tonic EDA levels between “morning” and “afternoon” clusters although the environmental parameters were the same, suggesting a change in the human body’s thermal reception over time. The correlation of the EDA and temperature was greater for the afternoon cluster (r = 0.449, p < 0.001) in relation to the morning cluster (r = 0.332, p < 0.001). These findings showed a strong temporal dependency of the skin conductance level of the EDA to the operative temperature, following the person’s circadian rhythm. Even further, based on the person’s chronotype, the beginning of the “afternoon” cluster was observed to have shifted according to the person’s circadian rhythm. Furthermore, the study is able to show how the body reacts differently under the same PMV values, both within and between subjects; pointing to the lack of temporal parameter in the PMV model. Full article

(This article belongs to the Special Issue Biosignals Processing and Analysis in Biomedicine)

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32 pages, 5734 KiB

Open AccessArticle

Fluorescent Imaging and Multifusion Segmentation for Enhanced Visualization and Delineation of Glioblastomas Margins

by Aditi Deshpande, Thomas Cambria, Charles Barnes, Alexandros Kerwick, George Livanos, Michalis Zervakis, Anthony Beninati, Nicolas Douard, Martin Nowak, James Basilion, Jennifer L. Cutter, Gloria Bauman, Suman Shrestha, Zoe Giakos, Wafa Elmannai, Yi Wang, Paniz Foroutan, Tannaz Farrahi and George C. Giakos

Signals 2021, 2(2), 304-335; https://0-doi-org.brum.beds.ac.uk/10.3390/signals2020020 - 13 May 2021

Cited by 2 | Viewed by 2834

Abstract

This study investigates the potential of fluorescence imaging in conjunction with an original, fused segmentation framework for enhanced detection and delineation of brain tumor margins. By means of a test bed optical microscopy system, autofluorescence is utilized to capture gray level images of brain tumor specimens through slices, obtained at various depths from the surface, each of 10 µm thickness. The samples used in this study originate from tumor cell lines characterized as Gli36ϑEGRF cells expressing a green fluorescent protein. An innovative three-step biomedical image analysis framework is presented aimed at enhancing the contrast and dissimilarity between the malignant and the remaining tissue regions to allow for enhanced visualization and accurate extraction of tumor boundaries. The fluorescence image acquisition system implemented with an appropriate unsupervised pipeline of image processing and fusion algorithms indicates clear differentiation of tumor margins and increased image contrast. Establishing protocols for the safe administration of fluorescent protein molecules, these would be introduced into glioma tissues or cells either at a pre-surgery stage or applied to the malignant tissue intraoperatively; typical applications encompass areas of fluorescence-guided surgery (FGS) and confocal laser endomicroscopy (CLE). As a result, this image acquisition scheme could significantly improve decision-making during brain tumor resection procedures and significantly facilitate brain surgery neuropathology during operation. Full article

(This article belongs to the Special Issue Biosignals Processing and Analysis in Biomedicine)

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24 pages, 2698 KiB

Open AccessArticle

A Study on the Essential and Parkinson’s Arm Tremor Classification

by Vasileios Skaramagkas, George Andrikopoulos, Zinovia Kefalopoulou and Panagiotis Polychronopoulos

Signals 2021, 2(2), 201-224; https://0-doi-org.brum.beds.ac.uk/10.3390/signals2020016 - 19 Apr 2021

Cited by 7 | Viewed by 3759

Abstract

In this article, the challenge of discriminating between essential and Parkinson’s tremor is addressed. Although a variety of methods have been proposed for diagnosing the severity of these highly occurring tremor types, their rapid and effective identification, especially in their early stages, proves particularly difficult and complicated due to their wide range of causes and similarity of symptoms. To this goal, a clinical analysis was performed, where a number of volunteers including essential and Parkinson’s tremor-diagnosed patients underwent a series of pre-defined motion patterns, during which a wearable sensing setup was used to measure their lower arm tremor characteristics from multiple selected points. Extracted features from the acquired accelerometer signals were used to train classification algorithms, including decision trees, discriminant analysis, support vector machine (SVM), K-nearest neighbor (KNN) and ensemble learning algorithms, for providing a comparative study and evaluating the potential of utilizing machine learning to accurately discriminate among different tremor types. Overall, SVM related classifiers proved to be the most successful in terms of classifying between Parkinson’s, essential and no tremor diagnosed with percentages reaching up to 100% for a single accelerometer measurement at the metacarpal area. In general and in motion while holding an object position, Coarse Gaussian SVM classifier reached 82.62% accuracy. Full article

(This article belongs to the Special Issue Biosignals Processing and Analysis in Biomedicine)

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17 pages, 2035 KiB

Open AccessArticle

Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis

by Alexandra I. Korda, Giorgos Giannakakis, Errikos Ventouras, Pantelis A. Asvestas, Nikolaos Smyrnis, Kostas Marias and George K. Matsopoulos

Signals 2021, 2(1), 55-71; https://0-doi-org.brum.beds.ac.uk/10.3390/signals2010006 - 23 Jan 2021

Cited by 12 | Viewed by 6340

Abstract

This paper investigates eye behaviour through blinks activity during stress conditions. Although eye blinking is a semi-voluntary action, it is considered to be affected by one’s emotional states such as arousal or stress. The blinking rate provides information towards this direction, however, the analysis on the entire eye aperture timeseries and the corresponding blinking patterns provide enhanced information on eye behaviour during stress conditions. Thus, two experimental protocols were established to induce affective states (neutral, relaxed and stress) systematically through a variety of external and internal stressors. The study populations included 24 and 58 participants respectively performing 12 experimental affective trials. After the preprocessing phase, the eye aperture timeseries and the corresponding features were extracted. The behaviour of inter-blink intervals (IBI) was investigated using the Markovian Analysis to quantify incidence dynamics in sequences of blinks. Moreover, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) network models were employed to discriminate stressed versus neutral tasks per cognitive process using the sequence of IBI. The classification accuracy reached a percentage of 81.3% which is very promising considering the unimodal analysis and the noninvasiveness modality used. Full article

(This article belongs to the Special Issue Biosignals Processing and Analysis in Biomedicine)

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Review

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14 pages, 1130 KiB

Open AccessReview

Advances in Electrical Source Imaging: A Review of the Current Approaches, Applications and Challenges

by Ioannis Zorzos, Ioannis Kakkos, Errikos M. Ventouras and George K. Matsopoulos

Signals 2021, 2(3), 378-391; https://0-doi-org.brum.beds.ac.uk/10.3390/signals2030024 - 24 Jun 2021

Cited by 14 | Viewed by 4053

Abstract

Brain source localization has been consistently implemented over the recent years to elucidate complex brain operations, pairing the high temporal resolution of the EEG with the high spatial estimation of the estimated sources. This review paper aims to present the basic principles of Electrical source imaging (ESI) in the context of the recent progress for solving the forward and the inverse problems, and highlight the advantages and limitations of the different approaches. As such, a synthesis of the current state-of-the-art methodological aspects is provided, offering a complete overview of the present advances with regard to the ESI solutions. Moreover, the new dimensions for the analysis of the brain processes are indicated in terms of clinical and cognitive ESI applications, while the prevailing challenges and limitations are thoroughly discussed, providing insights for future approaches that could help to alleviate methodological and technical shortcomings. Full article

(This article belongs to the Special Issue Biosignals Processing and Analysis in Biomedicine)

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