Advances in Biomedical Engineering from the Annual Conference of SEIB 2021

Topic Information

Dear Colleagues,

The Annual Congress of the Spanish Society of Biomedical Engineering, CASEIB2021, was held from November 25 to 26, 2021, and provided the opportunity for scholars to share their research work with other researchers, students, and professionals working in biomedical engineering.

As with previous editions, a Topic will be set up in the Entropy and Sensors journals and all works submitted to the contest for the José María Ferrero Corral awards will be eligible. This Topic will collect articles referring to the theoretical development and application of basic and advanced aspects of entropy, complexity, and information theory, among others. It will also collect articles related to sensor manufacturing technologies and processes, as well as their packaging and applications.

Topics of interest include, but are not limited to, the following:

• Entropy and complexity metrics;
• Information theory tools;
• Nonlinear analysis of temporal dynamics;
• Pattern recognition, encoding, and compression;
• Artificial intelligence, deep learning, machine learning, big data;
• New technologies for biosignal sensing;
• Lab-on-a-chip;
• Telemedicine and telemonitoring of patients;
• Sensor technology and applications in biomedical engineering;
• Micro and nanosensors in biomedical engineering;
• Wearable sensors, devices, and electronics;
• Signal processing, data fusion, and deep learning in sensor systems;
• Processing algorithms of medical imaging;
• New technologies in simulation, monitoring, and surgical planning;
• Localization and object tracking;
• Sensing and imaging.

The original article presented at CASEIB 2021 must be cited and the final manuscript must extend the original one, with both differing by at least 50%. The deadline for submitting papers to this Topic is August 30, 2022.

We hope that this initiative will be of interest to you and that we can help to disseminate the excellent works sent to CASEIB2021.

Prof. Dr. Raúl Alcaraz
Dr. Elisabete Aramendi
Dr. Raimon Jane
Dr. Gema García-Sáez
Dr. Gema Prats-Boluda
Dr. Javier Reina-Tosina
Prof. Dr. Roberto Hornero
Dr. Patricia Sánchez-González
Topic Editors

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Entropy entropy	2.1	4.9	1999	22.4 Days	CHF 2600
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600
Bioengineering bioengineering	3.8	4.0	2014	15.6 Days	CHF 2700
Diagnostics diagnostics	3.0	4.7	2011	20.5 Days	CHF 2600

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Published Papers (6 papers)

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All Entropy Sensors Bioengineering Diagnostics

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14 pages, 3278 KiB  

Open AccessArticle

Comparative Study of the Impact of Human Leukocyte Antigens on Renal Transplant Survival in Andalusia and the United States

by Alejandro Talaminos Barroso, Javier Reina Tosina, Laura M. Roa, Jorge Calvillo Arbizu, Miguel Angel Pérez Valdivia, Rafael Medina, Jose Luis Rocha Castilla and Pablo Castro-de-la-Nuez

Diagnostics 2023, 13(4), 608; https://doi.org/10.3390/diagnostics13040608 - 7 Feb 2023

Viewed by 1343

Abstract

Renal transplantation is the treatment of choice for patients suffering from chronic renal disease, one of the leading causes of death worldwide. Among the biological barriers that may increase the risk of acute renal graft rejection is the presence of human leukocyte antigen [...] Read more.

Renal transplantation is the treatment of choice for patients suffering from chronic renal disease, one of the leading causes of death worldwide. Among the biological barriers that may increase the risk of acute renal graft rejection is the presence of human leukocyte antigen (HLA) incompatibilities between donor and recipient. This work presents a comparative study of the influence of HLA incompatibilities on renal transplantation survival in the Andalusian (South of Spain) and United States (US) population. The main objective is to analyse the extent to which results about the influence of different factors on renal graft survival can be generalised to different populations. The Kaplan–Meier estimator and the Cox model have been used to identify and quantify the impact on the survival probability of HLA incompatibilities, both in isolation and in conjunction with other factors associated with the donor and recipient. According to the results obtained, HLA incompatibilities considered in isolation have negligible impact on renal survival in the Andalusian population and a moderate impact in the US population. Grouping by HLA score presents some similarities for both populations, while the sum of all HLA scores (aHLA) only has an impact on the US population. Finally, the graft survival probability of the two populations differs when aHLA is considered in conjunction with blood type. The results suggest that the disparities in the renal graft survival probability between the two populations under study are due not only to biological and transplantation-associated factors, but also to social–health factors and ethnic heterogeneity between populations. Full article

(This article belongs to the Topic Advances in Biomedical Engineering from the Annual Conference of SEIB 2021)

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13 pages, 1597 KiB  

Open AccessArticle

Classification of Kinematic and Electromyographic Signals Associated with Pathological Tremor Using Machine and Deep Learning

by Alejandro Pascual-Valdunciel, Víctor Lopo-Martínez, Alberto J. Beltrán-Carrero, Rafael Sendra-Arranz, Miguel González-Sánchez, Javier Ricardo Pérez-Sánchez, Francisco Grandas, Dario Farina, José L. Pons, Filipe Oliveira Barroso and Álvaro Gutiérrez

Entropy 2023, 25(1), 114; https://doi.org/10.3390/e25010114 - 5 Jan 2023

Cited by 2 | Viewed by 2816

Abstract

Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological [...] Read more.

Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological tremors to accurately enable the stimulation only if a tremor is present. This study explores different machine learning (K-Nearest Neighbors, Random Forest and Support Vector Machines) and deep learning (Long Short-Term Memory neural networks) models in order to provide a binary (Tremor; No Tremor) classification of kinematic (angle displacement) and electromyography (EMG) signals recorded from patients diagnosed with essential tremors and healthy subjects. Three types of signal sequences without any feature extraction were used as inputs for the classifiers: kinematics (wrist flexion–extension angle), raw EMG and EMG envelopes from wrist flexor and extensor muscles. All the models showed high classification scores (Tremor vs. No Tremor) for the different input data modalities, ranging from 0.8 to 0.99 for the f₁ score. The LSTM models achieved 0.98 f₁ scores for the classification of raw EMG signals, showing high potential to detect tremors without any processed features or preliminary information. These models may be explored in real-time closed-loop PES strategies to detect tremors and enable stimulation with minimal signal processing steps. Full article

(This article belongs to the Topic Advances in Biomedical Engineering from the Annual Conference of SEIB 2021)

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10 pages, 3634 KiB  

Open AccessArticle

Differences in Striatal Metabolism in [¹⁸F]FDG PET in Parkinson’s Disease and Atypical Parkinsonism

by Alexander P. Seiffert, Adolfo Gómez-Grande, Laura Alonso-Gómez, Antonio Méndez-Guerrero, Alberto Villarejo-Galende, Enrique J. Gómez and Patricia Sánchez-González

Diagnostics 2023, 13(1), 6; https://doi.org/10.3390/diagnostics13010006 - 20 Dec 2022

Cited by 10 | Viewed by 2993

Abstract

Neurodegenerative parkinsonisms affect mainly cognitive and motor functions and are syndromes of overlapping symptoms and clinical manifestations such as tremor, rigidness, and bradykinesia. These include idiopathic Parkinson’s disease (PD) and the atypical parkinsonisms, namely progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), multiple system [...] Read more.

Neurodegenerative parkinsonisms affect mainly cognitive and motor functions and are syndromes of overlapping symptoms and clinical manifestations such as tremor, rigidness, and bradykinesia. These include idiopathic Parkinson’s disease (PD) and the atypical parkinsonisms, namely progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), multiple system atrophy (MSA) and dementia with Lewy body (DLB). Differences in the striatal metabolism among these syndromes are evaluated using [¹⁸F]FDG PET, caused by alterations to the dopaminergic activity and neuronal loss. A study cohort of three patients with PD, 29 with atypical parkinsonism (10 PSP, 6 CBD, 2 MSA, 7 DLB, and 4 non-classifiable), and a control group of 25 patients with normal striatal metabolism is available. Standardized uptake value ratios (SUVR) are extracted from the striatum, and the caudate and the putamen separately. SUVRs are compared among the study groups. In addition, hemispherical and caudate-putamen differences are evaluated in atypical parkinsonisms. Striatal hypermetabolism is detected in patients with PD, while atypical parkinsonisms show hypometabolism, compared to the control group. Hemispherical differences are observed in CBD, MSA and DLB, with the latter also showing statistically significant caudate–putamen asymmetry (p = 0.018). These results indicate disease-specific metabolic uptake patterns in the striatum that can support the differential diagnosis. Full article

(This article belongs to the Topic Advances in Biomedical Engineering from the Annual Conference of SEIB 2021)

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13 pages, 2189 KiB  

Open AccessArticle

Automatic Assessment of Procedural Skills Based on the Surgical Workflow Analysis Derived from Speech and Video

by Carmen Guzmán-García, Patricia Sánchez-González, Ignacio Oropesa and Enrique J. Gómez

Bioengineering 2022, 9(12), 753; https://doi.org/10.3390/bioengineering9120753 - 2 Dec 2022

Cited by 3 | Viewed by 1793

Abstract

Automatic surgical workflow analysis (SWA) plays an important role in the modelling of surgical processes. Current automatic approaches for SWA use videos (with accuracies varying from 0.8 and 0.9), but they do not incorporate speech (inherently linked to the ongoing cognitive process). The [...] Read more.

Automatic surgical workflow analysis (SWA) plays an important role in the modelling of surgical processes. Current automatic approaches for SWA use videos (with accuracies varying from 0.8 and 0.9), but they do not incorporate speech (inherently linked to the ongoing cognitive process). The approach followed in this study uses both video and speech to classify the phases of laparoscopic cholecystectomy, based on neural networks and machine learning. The automatic application implemented in this study uses this information to calculate the total time spent in surgery, the time spent in each phase, the number of occurrences, the minimal, maximal and average time whenever there is more than one occurrence, the timeline of the surgery and the transition probability between phases. This information can be used as an assessment method for surgical procedural skills. Full article

(This article belongs to the Topic Advances in Biomedical Engineering from the Annual Conference of SEIB 2021)

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15 pages, 2185 KiB  

Open AccessArticle

Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data

by Lucía Cubero, Joël Castelli, Antoine Simon, Renaud de Crevoisier, Oscar Acosta and Javier Pascau

Entropy 2022, 24(11), 1661; https://doi.org/10.3390/e24111661 - 15 Nov 2022

Cited by 5 | Viewed by 2352

Abstract

Radiotherapy is one of the main treatments for localized head and neck (HN) cancer. To design a personalized treatment with reduced radio-induced toxicity, accurate delineation of organs at risk (OAR) is a crucial step. Manual delineation is time- and labor-consuming, as well as [...] Read more.

Radiotherapy is one of the main treatments for localized head and neck (HN) cancer. To design a personalized treatment with reduced radio-induced toxicity, accurate delineation of organs at risk (OAR) is a crucial step. Manual delineation is time- and labor-consuming, as well as observer-dependent. Deep learning (DL) based segmentation has proven to overcome some of these limitations, but requires large databases of homogeneously contoured image sets for robust training. However, these are not easily obtained from the standard clinical protocols as the OARs delineated may vary depending on the patient’s tumor site and specific treatment plan. This results in incomplete or partially labeled data. This paper presents a solution to train a robust DL-based automated segmentation tool exploiting a clinical partially labeled dataset. We propose a two-step workflow for OAR segmentation: first, we developed longitudinal OAR-specific 3D segmentation models for pseudo-contour generation, completing the missing contours for some patients; with all OAR available, we trained a multi-class 3D convolutional neural network (nnU-Net) for final OAR segmentation. Results obtained in 44 independent datasets showed superior performance of the proposed methodology for the segmentation of fifteen OARs, with an average Dice score coefficient and surface Dice similarity coefficient of 80.59% and 88.74%. We demonstrated that the model can be straightforwardly integrated into the clinical workflow for standard and adaptive radiotherapy. Full article

(This article belongs to the Topic Advances in Biomedical Engineering from the Annual Conference of SEIB 2021)

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17 pages, 1652 KiB  

Open AccessArticle

Practical Considerations for the Application of Nonlinear Indices Characterizing the Atrial Substrate in Atrial Fibrillation

by Emanuela Finotti, Aurelio Quesada, Edward J. Ciaccio, Hasan Garan, Fernando Hornero, Raúl Alcaraz and José J. Rieta

Entropy 2022, 24(9), 1261; https://doi.org/10.3390/e24091261 - 8 Sep 2022

Viewed by 1707

Abstract

Atrial fibrillation (AF) is the most common cardiac arrhythmia, and in response to increasing clinical demand, a variety of signals and indices have been utilized for its analysis, which include complex fractionated atrial electrograms (CFAEs). New methodologies have been developed to characterize the [...] Read more.

Atrial fibrillation (AF) is the most common cardiac arrhythmia, and in response to increasing clinical demand, a variety of signals and indices have been utilized for its analysis, which include complex fractionated atrial electrograms (CFAEs). New methodologies have been developed to characterize the atrial substrate, along with straightforward classification models to discriminate between paroxysmal and persistent AF (ParAF vs. PerAF). Yet, most previous works have missed the mark for the assessment of CFAE signal quality, as well as for studying their stability over time and between different recording locations. As a consequence, an atrial substrate assessment may be unreliable or inaccurate. The objectives of this work are, on the one hand, to make use of a reduced set of nonlinear indices that have been applied to CFAEs recorded from ParAF and PerAF patients to assess intra-recording and intra-patient stability and, on the other hand, to generate a simple classification model to discriminate between them. The dominant frequency (DF), AF cycle length, sample entropy (SE), and determinism (DET) of the Recurrence Quantification Analysis are the analyzed indices, along with the coefficient of variation (CV) which is utilized to indicate the corresponding alterations. The analysis of the intra-recording stability revealed that discarding noisy or artifacted CFAE segments provoked a significant variation in the CV(%) in any segment length for the DET and SE, with deeper decreases for longer segments. The intra-patient stability provided large variations in the CV(%) for the DET and even larger for the SE at any segment length. To discern ParAF versus PerAF, correlation matrix filters and Random Forests were employed, respectively, to remove redundant information and to rank the variables by relevance, while coarse tree models were built, optimally combining high-ranked indices, and tested with leave-one-out cross-validation. The best classification performance combined the SE and DF, with an accuracy (Acc) of 88.3%, to discriminate ParAF versus PerAF, while the highest single Acc was provided by the DET, reaching 82.2%. This work has demonstrated that due to the high variability of CFAEs data averaging from one recording place or among different recording places, as is traditionally made, it may lead to an unfair oversimplification of the CFAE-based atrial substrate characterization. Furthermore, a careful selection of reduced sets of features input to simple classification models is helpful to accurately discern the CFAEs of ParAF versus PerAF. Full article

(This article belongs to the Topic Advances in Biomedical Engineering from the Annual Conference of SEIB 2021)

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