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Smart Computing Systems for Biomedical Signal Processing

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

Deadline for manuscript submissions: closed (10 November 2021) | Viewed by 13136

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Special Issue Editors

Dr. Andrés Ortiz García

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Departamento de Ingeniería de Comunicaciones, Universidad de Málaga, 29071 Málaga, Spain
Interests: intelligence systems; neurosciences; signal processing with biomedical applications; high performance computing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Juan Manuel Gorriz

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Department of Signal Theory, Telematics and Communications, University of Granada, 18071 Granada, Spain
Interests: neuroimaging; statistical learning theory; biomedical signal processing; computer-aided diagnosis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Javier Ramírez

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Guest Editor

Department of Signal Theory, Telematics and Communications, University of Granada, 18071 Granada, Spain
Interests: biomedical signal processing; medical imaging; machine learning; computer aided diagnosis; neuroimaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, advances in biomedical sensors have increased the number of variables measured as well as the quality of the acquired signals. On the other hand, current computing architectures either process the acquired signals, aiming to extract features in search of specific patterns to identify different diseases or disabilities, or just obtain real time data for subjects monitoring. In addition, the synergic combination of advanced signal processing techniques and machine learning algorithms, enhances the extraction of relevant information from the signals even in the noisy conditions naturally present in biomedical data. Additionally, the small sample size is a common issue in biomedical problems that has to be properly addressed.

Current computing systems enable the use of computationally expensive signal processing techniques or learning-based approaches even in real time for a wide range of applications, usually related (but not only) to the early detection of health disorders by means of neurophysiological, electrophysiological or human movement-related signals among others. The application of these methods constitutes a new way for the early detection of different pathologies going towards a better understanding of the unknown origin or processes of some diseases.

This Special Issue aims to highlight advances in biomedical sensors, including applications, methods and algorithms for signal processing, modeling and classification.

Topics include, but are not limited to, signal processing- and learning-based algorithms in:

Medical imaging (CT, XR, MRI, functional)

Neurophysiological signals (EEG, MEG)

Electrophysoilogycal signals (ECG, EMG)

Human movement modeling (inertial sensors)

Body monitoring by thermal imaging

Glucose monitoring sensors

Prof. Dr. Andrés Ortiz García
Prof. Dr. Juan Manuel Gorriz
Prof. Dr. Javier Ramírez

Guest Editor

Manuscript Submission Information

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Keywords

biomedical signal processing
machine learning
biomedical sensors
EEG
MEG
ECG
MEG
human movement
body thermal imaging

Published Papers (5 papers)

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Research

20 pages, 1316 KiB

Open AccessArticle

Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis

by Marco A. Formoso, Andrés Ortiz, Francisco J. Martinez-Murcia, Nicolás Gallego and Juan L. Luque

Sensors 2021, 21(21), 7061; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217061 - 25 Oct 2021

Cited by 6 | Viewed by 2586

Abstract

Objective Dyslexia diagnosis is a challenging task, since traditional diagnosis methods are not based on biological markers but on behavioural tests. Although dyslexia diagnosis has been addressed by these tests in clinical practice, it is difficult to extract information about the brain processes involved in the different tasks and, then, to go deeper into its biological basis. Thus, the use of biomarkers can contribute not only to the diagnosis but also to a better understanding of specific learning disorders such as dyslexia. In this work, we use Electroencephalography (EEG) signals to discover differences among controls and dyslexic subjects using signal processing and artificial intelligence techniques. Specifically, we measure phase synchronization among channels, to reveal the functional brain network activated during auditory processing. On the other hand, to explore synchronicity patterns risen by low-level auditory processing, we used specific stimuli consisting in band-limited white noise, modulated in amplitude at different frequencies. The differential information contained in the functional (i.e., synchronization) network has been processed by an anomaly detection system that addresses the problem of subjects variability by an outlier-detection method based on vector quantization. The results, obtained for 7 years-old children, show that the proposed method constitutes an useful tool for clinical use, with the area under ROC curve (AUC) values up to 0.95 in differential diagnosis tasks. Full article

(This article belongs to the Special Issue Smart Computing Systems for Biomedical Signal Processing)

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35 pages, 49238 KiB

Open AccessArticle

Application of Dense Neural Networks for Detection of Atrial Fibrillation and Ranking of Augmented ECG Feature Set

by Vessela Krasteva, Ivaylo Christov, Stefan Naydenov, Todor Stoyanov and Irena Jekova

Sensors 2021, 21(20), 6848; https://0-doi-org.brum.beds.ac.uk/10.3390/s21206848 - 15 Oct 2021

Cited by 23 | Viewed by 2506

Abstract

Considering the significant burden to patients and healthcare systems globally related to atrial fibrillation (AF) complications, the early AF diagnosis is of crucial importance. In the view of prominent perspectives for fast and accurate point-of-care arrhythmia detection, our study optimizes an artificial neural network (NN) classifier and ranks the importance of enhanced 137 diagnostic ECG features computed from time and frequency ECG signal representations of short single-lead strips available in 2017 Physionet/CinC Challenge database. Based on hyperparameters’ grid search of densely connected NN layers, we derive the optimal topology with three layers and 128, 32, 4 neurons per layer (DenseNet-3@128-32-4), which presents maximal F1-scores for classification of Normal rhythms (0.883, 5076 strips), AF (0.825, 758 strips), Other rhythms (0.705, 2415 strips), Noise (0.618, 279 strips) and total F1 relevant to the CinC Challenge of 0.804, derived by five-fold cross-validation. DenseNet-3@128-32-4 performs equally well with 137 to 32 features and presents tolerable reduction by about 0.03 to 0.06 points for limited input sets, including 8 and 16 features, respectively. The feature reduction is linked to effective application of a comprehensive method for computation of the feature map importance based on the weights of the activated neurons through the total path from input to specific output in DenseNet. The detailed analysis of 20 top-ranked ECG features with greatest importance to the detection of each rhythm and overall of all rhythms reveals DenseNet decision-making process, noticeably corresponding to the cardiologists’ diagnostic point of view. Full article

(This article belongs to the Special Issue Smart Computing Systems for Biomedical Signal Processing)

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18 pages, 2294 KiB

Open AccessArticle

Detection of Mild Cognitive Impairment with MEG Functional Connectivity Using Wavelet-Based Neuromarkers

by Su Yang, Jose Miguel Sanchez Bornot, Ricardo Bruña Fernandez, Farzin Deravi, Sanaul Hoque, KongFatt Wong-Lin and Girijesh Prasad

Sensors 2021, 21(18), 6210; https://0-doi-org.brum.beds.ac.uk/10.3390/s21186210 - 16 Sep 2021

Cited by 1 | Viewed by 2032

Abstract

Studies on developing effective neuromarkers based on magnetoencephalographic (MEG) signals have been drawing increasing attention in the neuroscience community. This study explores the idea of using source-based magnitude-squared spectral coherence as a spatial indicator for effective regions of interest (ROIs) localization, subsequently discriminating the participants with mild cognitive impairment (MCI) from a group of age-matched healthy control (HC) elderly participants. We found that the cortical regions could be divided into two distinctive groups based on their coherence indices. Compared to HC, some ROIs showed increased connectivity (hyper-connected ROIs) for MCI participants, whereas the remaining ROIs demonstrated reduced connectivity (hypo-connected ROIs). Based on these findings, a series of wavelet-based source-level neuromarkers for MCI detection are proposed and explored, with respect to the two distinctive ROI groups. It was found that the neuromarkers extracted from the hyper-connected ROIs performed significantly better for MCI detection than those from the hypo-connected ROIs. The neuromarkers were classified using support vector machine (SVM) and k-NN classifiers and evaluated through Monte Carlo cross-validation. An average recognition rate of 93.83% was obtained using source-reconstructed signals from the hyper-connected ROI group. To better conform to clinical practice settings, a leave-one-out cross-validation (LOOCV) approach was also employed to ensure that the data for testing was from a participant that the classifier has never seen. Using LOOCV, we found the best average classification accuracy was reduced to 83.80% using the same set of neuromarkers obtained from the ROI group with functional hyper-connections. This performance surpassed the results reported using wavelet-based features by approximately 15%. Overall, our work suggests that (1) certain ROIs are particularly effective for MCI detection, especially when multi-resolution wavelet biomarkers are employed for such diagnosis; (2) there exists a significant performance difference in system evaluation between research-based experimental design and clinically accepted evaluation standards. Full article

(This article belongs to the Special Issue Smart Computing Systems for Biomedical Signal Processing)

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15 pages, 2241 KiB

Open AccessArticle

Automatic Detection of Short-Term Atrial Fibrillation Segments Based on Frequency Slice Wavelet Transform and Machine Learning Techniques

by Yaru Yue, Chengdong Chen, Pengkun Liu, Ying Xing and Xiaoguang Zhou

Sensors 2021, 21(16), 5302; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165302 - 05 Aug 2021

Cited by 13 | Viewed by 2486

Abstract

Atrial fibrillation (AF) is the most frequently encountered cardiac arrhythmia and is often associated with other cardiovascular and cerebrovascular diseases, such as ischemic heart disease, chronic heart failure, and stroke. Automatic detection of AF by analyzing electrocardiogram (ECG) signals has an important application value. Using the contaminated and actual ECG signals, it is not enough to only analyze the atrial activity of disappeared P wave and appeared F wave in the TQ segment. Moreover, the best analysis method is to combine nonlinear features analyzing ventricular activity based on the detection of R peak. In this paper, to utilize the information of the P-QRS-T waveform generated by atrial and ventricular activity, frequency slice wavelet transform (FSWT) is adopted to conduct time-frequency analysis on short-term ECG segments from the MIT-BIH Atrial Fibrillation Database. The two-dimensional time-frequency matrices are obtained. Furthermore, an average sliding window is used to convert the two-dimensional time-frequency matrices to the one-dimensional feature vectors, which are classified using five machine learning (ML) techniques. The experimental results show that the classification performance of the Gaussian-kernel support vector machine (GKSVM) based on the Bayesian optimizer is better. The accuracy of the training set and validation set are 100% and 93.4%. The accuracy, sensitivity, and specificity of the test set without training are 98.15%, 96.43%, and 100%, respectively. Compared with previous research results, our proposed FSWT-GKSVM model shows stability and robustness, and it could achieve the purpose of automatic detection of AF. Full article

(This article belongs to the Special Issue Smart Computing Systems for Biomedical Signal Processing)

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16 pages, 4272 KiB

Open AccessArticle

Sensor-Level Wavelet Analysis Reveals EEG Biomarkers of Perceptual Decision-Making

by Alexander Kuc, Vadim V. Grubov, Vladimir A. Maksimenko, Natalia Shusharina, Alexander N. Pisarchik and Alexander E. Hramov

Sensors 2021, 21(7), 2461; https://0-doi-org.brum.beds.ac.uk/10.3390/s21072461 - 02 Apr 2021

Cited by 7 | Viewed by 2459

Abstract

Perceptual decision-making requires transforming sensory information into decisions. An ambiguity of sensory input affects perceptual decisions inducing specific time-frequency patterns on EEG (electroencephalogram) signals. This paper uses a wavelet-based method to analyze how ambiguity affects EEG features during a perceptual decision-making task. We observe that parietal and temporal beta-band wavelet power monotonically increases throughout the perceptual process. Ambiguity induces high frontal beta-band power at 0.3–0.6 s post-stimulus onset. It may reflect the increasing reliance on the top-down mechanisms to facilitate accumulating decision-relevant sensory features. Finally, this study analyzes the perceptual process using mixed within-trial and within-subject design. First, we found significant percept-related changes in each subject and then test their significance at the group level. Thus, observed beta-band biomarkers are pronounced in single EEG trials and may serve as control commands for brain-computer interface (BCI). Full article

(This article belongs to the Special Issue Smart Computing Systems for Biomedical Signal Processing)

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