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The Power of Biosignal and Bioimage Processing in Human Healthcare:...
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The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems

A special issue of Bioengineering (ISSN 2306-5354).

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 44683

Special Issue Editors

Dr. Alfonso Maria Ponsiglione

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Guest Editor
Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80125 Naples, Italy
Interests: nanomedicine; microfluidics; drug delivery systems; biomaterials
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Francesco Amato

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Guest Editor
Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80134 Naples, Italy
Interests: applications of systems and control theory to bioengineering; computational biology; modeling and control of biomedical devices; computational analysis and robust control of dynamic systems in biomedical engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maria Romano

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80134 Naples, Italy
Interests: processing of biomedical signals and data; biomedical imaging; statistical and nonlinear biomedical signal analysis and processing; electrocardiography; heart rate variability; electromyography; electrical impedance spectroscopy; healthcare management; telemedicine
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Giovanni Improta

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Guest Editor
Department of Public Health, University of Naples Federico II, 80134 Naples, Italy
Interests: bioengineering; public health; healthcare decision making; machine learning and data mining for healthcare; modeling and analysis of biomedical data; health technology assessment; quality improvement in healthcare; lean six sigma; biomedical signal processing and analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

If you are unable to meet the current submission deadline, please consider our Volume II:

The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems (Volume II)

Biosignals are generated by mostly vital physiologic phenomena and provide valuable information regarding the status and function of a biological system. Being time or space–time records of biological events, biosignals as well as bioimages come from diversified sources (such as cardiovascular, muscular, respiratory, cerebral, etc.) and find a wide range of applications: from the identification of digital biomarkers and indicators of health outcome to the design of biosensors, wearable devices, and computational tools aimed at detecting, processing, and analyzing biomedical signals and images.

The growth in these research fields, together with the rapid development of new artificial intelligence techniques, have further boosted interest toward the study and use of biosignals and bioimages to give insights into the complex dynamics involved in the control of physiological systems in both healthy and diseased states.

In this regard, it becomes extremely important to develop robust methodological approaches and innovative algorithms and tools to enhance the clinical value of biosignals and biomages in order to take advantage of their full potential in health monitoring, enriching the information that can be extracted from them and used in the healthcare context.

We therefore invite you to submit original research papers and comprehensive reviews on advances in biosignal and bioimage processing, from acquisition to theoretical analysis, computational simulations, and clinical applications.

Topics of interest for this Special Issue include but are not limited to the following:

  • New approaches for biosignal/bioimage processing and analysis;
  • Methods for the classification of biosignals;
  • Biosignal/bioimage feature extraction;
  • Algorithms to improve the quality of biosignals;
  • Artificial Intelligence tools for biosignal/bioimage analysis;
  • Modeling and simulation of physiological signals;
  • Biosensors and wearable devices for biosignal acquisition and measurements;
  • Biomedical signal-based biomarkers.

Your contributions will help to improve and advance methodologies for biomedical signal and image processing with key implications in the fields of medicine and healthcare. 

Dr. Alfonso Maria Ponsiglione
Prof. Dr. Francesco Amato
Prof. Dr. Maria Romano
Prof. Dr. Giovanni Improta
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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
  • Bioimages
  • Biomedical signal/image processing and analysis
  • Artificial Intelligence for biosignal/bioimage analysis and classification
  • Pattern recognition
  • Feature extraction
  • Biosensors
  • Wearable devices
  • Modeling and simulation of biosignals

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Published Papers (12 papers)

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Research

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14 pages, 1054 KiB  
Article
On Extracting Digitized Spiral Dynamics’ Representations: A Study on Transfer Learning for Early Alzheimer’s Detection
by Daniela Carfora, Suyeon Kim, Nesma Houmani, Sonia Garcia-Salicetti and Anne-Sophie Rigaud
Bioengineering 2022, 9(8), 375; https://doi.org/10.3390/bioengineering9080375 - 9 Aug 2022
Cited by 1 | Viewed by 1857
Abstract
This work proposes a decision-aid tool for detecting Alzheimer’s disease (AD) at an early stage, based on the Archimedes spiral, executed on a Wacom digitizer. Our work assesses the potential of the task as a dynamic gesture and defines the most pertinent methodology [...] Read more.
This work proposes a decision-aid tool for detecting Alzheimer’s disease (AD) at an early stage, based on the Archimedes spiral, executed on a Wacom digitizer. Our work assesses the potential of the task as a dynamic gesture and defines the most pertinent methodology for exploiting transfer learning to compensate for sparse data. We embed directly in spiral trajectory images, kinematic time functions. With transfer learning, we perform automatic feature extraction on such images. Experiments on 30 AD patients and 45 healthy controls (HC) show that the extracted features allow a significant improvement in sensitivity and accuracy, compared to raw images. We study at which level of the deep network features have the highest discriminant capabilities. Results show that intermediate-level features are the best for our specific task. Decision fusion of experts trained on such descriptors outperforms low-level fusion of hybrid images. When fusing decisions of classifiers trained on the best features, from pressure, altitude, and velocity images, we obtain 84% of sensitivity and 81.5% of accuracy, achieving an absolute improvement of 22% in sensitivity and 7% in accuracy. We demonstrate the potential of the spiral task for AD detection and give a complete methodology based on off-the-shelf features. Full article
(This article belongs to the Special Issue The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems)
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16 pages, 3434 KiB  
Article
Multi-Label Attribute Selection of Arrhythmia for Electrocardiogram Signals with Fusion Learning
by Jie Yang, Jinfeng Li, Kun Lan, Anruo Wei, Han Wang, Shigao Huang and Simon Fong
Bioengineering 2022, 9(7), 268; https://doi.org/10.3390/bioengineering9070268 - 22 Jun 2022
Cited by 6 | Viewed by 2464
Abstract
There are three primary challenges in the automatic diagnosis of arrhythmias by electrocardiogram (ECG): the significant variation among individual patients, the multiple pathologies in the ECG signal and the high cost in annotating clinical ECG with the corresponding labels. Traditional ECG processing approaches [...] Read more.
There are three primary challenges in the automatic diagnosis of arrhythmias by electrocardiogram (ECG): the significant variation among individual patients, the multiple pathologies in the ECG signal and the high cost in annotating clinical ECG with the corresponding labels. Traditional ECG processing approaches rely heavily on prior knowledge, such as those from feature extraction and waveform analysis. The preprocessing for prior knowledge incurs computational overhead. Furthermore, standard deep learning methods do not fully consider the dynamic temporal, spatial and multi-labeling characteristics of ECG data. In clinical ECG waveforms, it is common to see multi-labeling in which a patient is labeled with multiple classes of arrhythmias. However, multiclass approaches in current research mainly solve the multi-label machine learning problem, ignoring the correlation between diseases, resulting in information loss. In this paper, an arrhythmia detection and classification scheme called multi-label fusion deep learning is proposed. The objective is to build a unified system with automatic feature learning which supports effective multi-label classification. First, a multi-label ECG-based feature selection method is combined with a matrix decomposition and sparse learning theory. The optimal feature subset is selected as a preprocessing algorithm for ECG data. A multi-label classifier is then constructed by fusing CNN and RNN networks to fully exploit the interactions and features of the time and space dimensions. The experimental result demonstrates that the proposed method can achieve a state-of-the-art performance compared to other algorithms in multi-label database experiments. Full article
(This article belongs to the Special Issue The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems)
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16 pages, 3490 KiB  
Article
Simplified Convolutional Neural Network Application for Cervix Type Classification via Colposcopic Images
by Vitalii Pavlov, Stanislav Fyodorov, Sergey Zavjalov, Tatiana Pervunina, Igor Govorov, Eduard Komlichenko, Viktor Deynega and Veronika Artemenko
Bioengineering 2022, 9(6), 240; https://doi.org/10.3390/bioengineering9060240 - 30 May 2022
Cited by 3 | Viewed by 6320
Abstract
The inner parts of the human body are usually inspected endoscopically using special equipment. For instance, each part of the female reproductive system can be examined endoscopically (laparoscopy, hysteroscopy, and colposcopy). The primary purpose of colposcopy is the early detection of malignant lesions [...] Read more.
The inner parts of the human body are usually inspected endoscopically using special equipment. For instance, each part of the female reproductive system can be examined endoscopically (laparoscopy, hysteroscopy, and colposcopy). The primary purpose of colposcopy is the early detection of malignant lesions of the cervix. Cervical cancer (CC) is one of the most common cancers in women worldwide, especially in middle- and low-income countries. Therefore, there is a growing demand for approaches that aim to detect precancerous lesions, ideally without quality loss. Despite its high efficiency, this method has some disadvantages, including subjectivity and pronounced dependence on the operator’s experience. The objective of the current work is to propose an alternative to overcoming these limitations by utilizing the neural network approach. The classifier is trained to recognize and classify lesions. The classifier has a high recognition accuracy and a low computational complexity. The classification accuracies for the classes normal, LSIL, HSIL, and suspicious for invasion were 95.46%, 79.78%, 94.16%, and 97.09%, respectively. We argue that the proposed architecture is simpler than those discussed in other articles due to the use of the global averaging level of the pool. Therefore, the classifier can be implemented on low-power computing platforms at a reasonable cost. Full article
(This article belongs to the Special Issue The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems)
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12 pages, 2240 KiB  
Article
A Comparison of Heart Pulsations Provided by Forcecardiography and Double Integration of Seismocardiogram
by Emilio Andreozzi, Jessica Centracchio, Daniele Esposito and Paolo Bifulco
Bioengineering 2022, 9(4), 167; https://doi.org/10.3390/bioengineering9040167 - 9 Apr 2022
Cited by 19 | Viewed by 2864
Abstract
Seismocardiography (SCG) is largely regarded as the state-of-the-art technique for continuous, long-term monitoring of cardiac mechanical activity in wearable applications. SCG signals are acquired via small, lightweight accelerometers fixed on the chest. They provide timings of important cardiac events, such as heart valves [...] Read more.
Seismocardiography (SCG) is largely regarded as the state-of-the-art technique for continuous, long-term monitoring of cardiac mechanical activity in wearable applications. SCG signals are acquired via small, lightweight accelerometers fixed on the chest. They provide timings of important cardiac events, such as heart valves openings and closures, thus allowing the estimation of cardiac time intervals of clinical relevance. Forcecardiography (FCG) is a novel technique that records the cardiac-induced vibrations of the chest wall by means of specific force sensors, which proved capable of monitoring respiration, heart sounds and infrasonic cardiac vibrations, simultaneously from a single contact point on the chest. A specific infrasonic component captures the heart walls displacements and looks very similar to the Apexcardiogram. This low-frequency component is not visible in SCG recordings, nor it can be extracted by simple filtering. In this study, a feasible way to extract this information from SCG signals is presented. The proposed approach is based on double integration of SCG. Numerical double integration is usually very prone to large errors, therefore a specific numerical procedure was devised. This procedure yields a new displacement signal (DSCG) that features a low-frequency component (LF-DSCG) very similar to that of the FCG (LF-FCG). Experimental tests were carried out using an FCG sensor and an off-the-shelf accelerometer firmly attached to each other and placed onto the precordial region. Simultaneous recordings were acquired from both sensors, together with an electrocardiogram lead (used as a reference). Quantitative morphological comparison confirmed the high similarity between LF-FCG and LF-DSCG (normalized cross-correlation index >0.9). Statistical analyses suggested that LF-DSCG, although achieving a fair sensitivity in heartbeat detection (about 90%), has not a very high consistency within the cardiac cycle, leading to inaccuracies in inter-beat intervals estimation. Future experiments with high-performance accelerometers and improved processing methods are envisioned to investigate the potential enhancement of the accuracy and reliability of the proposed method. Full article
(This article belongs to the Special Issue The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems)
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14 pages, 3943 KiB  
Article
Detection of Aortic Valve Opening and Estimation of Pre-Ejection Period in Forcecardiography Recordings
by Jessica Centracchio, Emilio Andreozzi, Daniele Esposito, Gaetano Dario Gargiulo and Paolo Bifulco
Bioengineering 2022, 9(3), 89; https://doi.org/10.3390/bioengineering9030089 - 22 Feb 2022
Cited by 22 | Viewed by 2997
Abstract
Forcecardiography (FCG) is a novel technique that measures the local forces induced on the chest wall by the mechanical activity of the heart. Specific piezoresistive or piezoelectric force sensors are placed on subjects’ thorax to measure these very small forces. The FCG signal [...] Read more.
Forcecardiography (FCG) is a novel technique that measures the local forces induced on the chest wall by the mechanical activity of the heart. Specific piezoresistive or piezoelectric force sensors are placed on subjects’ thorax to measure these very small forces. The FCG signal can be divided into three components: low-frequency FCG, high-frequency FCG (HF-FCG) and heart sound FCG. HF-FCG has been shown to share a high similarity with the Seismocardiogram (SCG), which is commonly acquired via small accelerometers and is mainly used to locate specific fiducial markers corresponding to essential events of the cardiac cycle (e.g., heart valves opening and closure, peaks of blood flow). However, HF-FCG has not yet been demonstrated to provide the timings of these markers with reasonable accuracy. This study addresses the detection of the aortic valve opening (AO) marker in FCG signals. To this aim, simultaneous recordings from FCG and SCG sensors were acquired, together with Electrocardiogram (ECG) recordings, from a few healthy subjects at rest, both during quiet breathing and apnea. The AO markers were located in both SCG and FCG signals to obtain pre-ejection periods (PEP) estimates, which were compared via statistical analyses. The PEPs estimated from FCG and SCG showed a strong linear relationship (r > 0.95) with a practically unit slope, and 95% of their differences were found to be distributed within ± 4.6 ms around small biases of approximately 1 ms, corresponding to percentage differences lower than 5% of the mean measured PEP. These preliminary results suggest that FCG can provide accurate AO timings and PEP estimates. Full article
(This article belongs to the Special Issue The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems)
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16 pages, 1230 KiB  
Article
Top-Down Detection of Eating Episodes by Analyzing Large Windows of Wrist Motion Using a Convolutional Neural Network
by Surya Sharma and Adam Hoover
Bioengineering 2022, 9(2), 70; https://doi.org/10.3390/bioengineering9020070 - 11 Feb 2022
Cited by 8 | Viewed by 2773
Abstract
In this work, we describe a new method to detect periods of eating by tracking wrist motion during everyday life. Eating uses hand-to-mouth gestures for ingestion, each of which lasts a few seconds. Previous works have detected these gestures individually and then aggregated [...] Read more.
In this work, we describe a new method to detect periods of eating by tracking wrist motion during everyday life. Eating uses hand-to-mouth gestures for ingestion, each of which lasts a few seconds. Previous works have detected these gestures individually and then aggregated them to identify meals. The novelty of our approach is that we analyze a much longer window (0.5–15 min) using a convolutional neural network. Longer windows can contain other gestures related to eating, such as cutting or manipulating food, preparing foods for consumption, and resting between ingestion events. The context of these other gestures can improve the detection of periods of eating. We test our methods on the public Clemson all-day dataset, which consists of 354 recordings containing 1063 eating episodes. We found that accuracy at detecting eating increased by 15% in ≥4 min windows compared to ≤15 s windows. Using a 6 min window, we detected 89% of eating episodes, with 1.7 false positives for every true positive (FP/TP). These are the best results achieved to date on this dataset. Full article
(This article belongs to the Special Issue The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems)
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23 pages, 6078 KiB  
Article
Weighted Brain Network Analysis on Different Stages of Clinical Cognitive Decline
by Majd Abazid, Nesma Houmani, Bernadette Dorizzi, Jerome Boudy, Jean Mariani and Kiyoka Kinugawa
Bioengineering 2022, 9(2), 62; https://doi.org/10.3390/bioengineering9020062 - 4 Feb 2022
Cited by 9 | Viewed by 3089
Abstract
This study addresses brain network analysis over different clinical severity stages of cognitive dysfunction using electroencephalography (EEG). We exploit EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients and Alzheimer’s disease (AD) patients. We propose a new framework to [...] Read more.
This study addresses brain network analysis over different clinical severity stages of cognitive dysfunction using electroencephalography (EEG). We exploit EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients and Alzheimer’s disease (AD) patients. We propose a new framework to study the topological networks with a spatiotemporal entropy measure for estimating the connectivity. Our results show that functional connectivity and graph analysis are frequency-band dependent, and alterations start at the MCI stage. In delta, the SCI group exhibited a decrease of clustering coefficient and an increase of path length compared to MCI and AD. In alpha, the opposite behavior appeared, suggesting a rapid and high efficiency in information transmission across the SCI network. Modularity analysis showed that electrodes of the same brain region were distributed over several modules, and some obtained modules in SCI were extended from anterior to posterior regions. These results demonstrate that the SCI network was more resilient to neuronal damage compared to that of MCI and even more compared to that of AD. Finally, we confirm that MCI is a transitional stage between SCI and AD, with a predominance of high-strength intrinsic connectivity, which may reflect the compensatory response to the neuronal damage occurring early in the disease process. Full article
(This article belongs to the Special Issue The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems)
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17 pages, 3641 KiB  
Article
Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals
by Alfonso Maria Ponsiglione, Francesco Amato and Maria Romano
Bioengineering 2022, 9(1), 8; https://doi.org/10.3390/bioengineering9010008 - 28 Dec 2021
Cited by 43 | Viewed by 3260
Abstract
In the field of electronic fetal health monitoring, computerized analysis of fetal heart rate (FHR) signals has emerged as a valid decision-support tool in the assessment of fetal wellbeing. Despite the availability of several approaches to analyze the variability of FHR signals (namely [...] Read more.
In the field of electronic fetal health monitoring, computerized analysis of fetal heart rate (FHR) signals has emerged as a valid decision-support tool in the assessment of fetal wellbeing. Despite the availability of several approaches to analyze the variability of FHR signals (namely the FHRV), there are still shadows hindering a comprehensive understanding of how linear and nonlinear dynamics are involved in the control of the fetal heart rhythm. In this study, we propose a straightforward processing and modeling route for a deeper understanding of the relationships between the characteristics of the FHR signal. A multiparametric modeling and investigation of the factors influencing the FHR accelerations, chosen as major indicator of fetal wellbeing, is carried out by means of linear and nonlinear techniques, blockwise dimension reduction, and artificial neural networks. The obtained results show that linear features are more influential compared to nonlinear ones in the modeling of HRV in healthy fetuses. In addition, the results suggest that the investigation of nonlinear dynamics and the use of predictive tools in the field of FHRV should be undertaken carefully and limited to defined pregnancy periods and FHR mean values to provide interpretable and reliable information to clinicians and researchers. Full article
(This article belongs to the Special Issue The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems)
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13 pages, 3295 KiB  
Article
Machine Learning Evaluation of Biliary Atresia Patients to Predict Long-Term Outcome after the Kasai Procedure
by Martina Caruso, Carlo Ricciardi, Gregorio Delli Paoli, Fabiola Di Dato, Leandro Donisi, Valeria Romeo, Mario Petretta, Raffaele Iorio, Giuseppe Cesarelli, Arturo Brunetti and Simone Maurea
Bioengineering 2021, 8(11), 152; https://doi.org/10.3390/bioengineering8110152 - 22 Oct 2021
Cited by 6 | Viewed by 2680
Abstract
Kasai portoenterostomy (KP) represents the first-line treatment for biliary atresia (BA). The purpose was to compare the accuracy of quantitative parameters extracted from laboratory tests, US imaging, and MR imaging studies using machine learning (ML) algorithms to predict the long-term medical outcome in [...] Read more.
Kasai portoenterostomy (KP) represents the first-line treatment for biliary atresia (BA). The purpose was to compare the accuracy of quantitative parameters extracted from laboratory tests, US imaging, and MR imaging studies using machine learning (ML) algorithms to predict the long-term medical outcome in native liver survivor BA patients after KP. Twenty-four patients were evaluated according to clinical and laboratory data at initial evaluation (median follow-up = 9.7 years) after KP as having ideal (n = 15) or non-ideal (n = 9) medical outcomes. Patients were re-evaluated after an additional 4 years and classified in group 1 (n = 12) as stable and group 2 (n = 12) as non-stable in the disease course. Laboratory and quantitative imaging parameters were merged to test ML algorithms. Total and direct bilirubin (TB and DB), as laboratory parameters, and US stiffness, as an imaging parameter, were the only statistically significant parameters between the groups. The best algorithm in terms of accuracy, sensitivity, specificity, and AUCROC was naive Bayes algorithm, selecting only laboratory parameters (TB and DB). This preliminary ML analysis confirms the fundamental role of TB and DB values in predicting the long-term medical outcome for BA patients after KP, even though their values may be within the normal range. Physicians should be alert when TB and DB values change slightly. Full article
(This article belongs to the Special Issue The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems)
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13 pages, 786 KiB  
Article
Extracting Features from Poincaré Plots to Distinguish Congestive Heart Failure Patients According to NYHA Classes
by Giovanni D’Addio, Leandro Donisi, Giuseppe Cesarelli, Federica Amitrano, Armando Coccia, Maria Teresa La Rovere and Carlo Ricciardi
Bioengineering 2021, 8(10), 138; https://doi.org/10.3390/bioengineering8100138 - 3 Oct 2021
Cited by 19 | Viewed by 3571
Abstract
Heart-rate variability has proved a valid tool in prognosis definition of patients with congestive heart failure (CHF). Previous research has documented Poincaré plot analysis as a valuable approach to study heart-rate variability performance among different subjects. In this paper, we explored the possibility [...] Read more.
Heart-rate variability has proved a valid tool in prognosis definition of patients with congestive heart failure (CHF). Previous research has documented Poincaré plot analysis as a valuable approach to study heart-rate variability performance among different subjects. In this paper, we explored the possibility to feed machine-learning (ML) algorithms using unconventional quantitative parameters extracted from Poincaré plots (generated from 24-h electrocardiogram recordings) to classify patients with CHF belonging to different New York Heart Association (NYHA) classes. We performed in sequence the following investigations: first, a statistical analysis was carried out on 9 morphological parameters, automatically measured from Poincaré plots. Subsequently, a feature selection through a wrapper with a 10-fold cross-validation method was performed to find the best subset of features which maximized the classification accuracy for each considered ML algorithm. Finally, patient classification was assessed through a ML analysis using AdaBoost of Decision Tree, k-Nearest Neighbors and Naive Bayes algorithms. A univariate statistical analysis proved 5 out of 9 parameters presented statistically significant differences among patients of distinct NYHA classes; similarly, a multivariate logistic regression confirmed the importance of the parameter ρy in the separability between low-risk and high-risk classes. The ML analysis achieved promising results in terms of evaluation metrics (especially the Naive Bayes algorithm), with accuracies greater than 80% and Area Under the Receiver Operating Curve indices greater than 0.7 for the overall three algorithms. The study indicates the proposed features have a predictive power to discriminate the NYHA classes, to which the features seem evenly correlated. Despite the NYHA classification being subjective and easily recognized by cardiologists, the potential relevance in the clinical cardiology of the proposed features and the promising ML results implies the methodology could be a valuable approach to automatically classify CHF. Future investigations on enriched datasets may further confirm the presented evidence. Full article
(This article belongs to the Special Issue The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems)
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Review

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32 pages, 31533 KiB  
Review
Biomechanics of Transcatheter Aortic Valve Implant
by Francesco Nappi, Sanjeet Singh Avtaar Singh, Pierluigi Nappi and Antonio Fiore
Bioengineering 2022, 9(7), 299; https://doi.org/10.3390/bioengineering9070299 - 4 Jul 2022
Cited by 1 | Viewed by 3439
Abstract
Transcatheter aortic valve implantation (TAVI) has grown exponentially within the cardiology and cardiac surgical spheres. It has now become a routine approach for treating aortic stenosis. Several concerns have been raised about TAVI in comparison to conventional surgical aortic valve replacement (SAVR). The [...] Read more.
Transcatheter aortic valve implantation (TAVI) has grown exponentially within the cardiology and cardiac surgical spheres. It has now become a routine approach for treating aortic stenosis. Several concerns have been raised about TAVI in comparison to conventional surgical aortic valve replacement (SAVR). The primary concerns regard the longevity of the valves. Several factors have been identified which may predict poor outcomes following TAVI. To this end, the lesser-used finite element analysis (FEA) was used to quantify the properties of calcifications which affect TAVI valves. This method can also be used in conjunction with other integrated software to ascertain the functionality of these valves. Other imaging modalities such as multi-detector row computed tomography (MDCT) are now widely available, which can accurately size aortic valve annuli. This may help reduce the incidence of paravalvular leaks and regurgitation which may necessitate further intervention. Structural valve degeneration (SVD) remains a key factor, with varying results from current studies. The true incidence of SVD in TAVI compared to SAVR remains unclear due to the lack of long-term data. It is now widely accepted that both are part of the armamentarium and are not mutually exclusive. Decision making in terms of appropriate interventions should be undertaken via shared decision making involving heart teams. Full article
(This article belongs to the Special Issue The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems)
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21 pages, 15197 KiB  
Review
Measuring Biosignals with Single Circuit Boards
by Guido Ehrmann, Tomasz Blachowicz, Sarah Vanessa Homburg and Andrea Ehrmann
Bioengineering 2022, 9(2), 84; https://doi.org/10.3390/bioengineering9020084 - 21 Feb 2022
Cited by 12 | Viewed by 6793
Abstract
To measure biosignals constantly, using textile-integrated or even textile-based electrodes and miniaturized electronics, is ideal to provide maximum comfort for patients or athletes during monitoring. While in former times, this was usually solved by integrating specialized electronics into garments, either connected to a [...] Read more.
To measure biosignals constantly, using textile-integrated or even textile-based electrodes and miniaturized electronics, is ideal to provide maximum comfort for patients or athletes during monitoring. While in former times, this was usually solved by integrating specialized electronics into garments, either connected to a handheld computer or including a wireless data transfer option, nowadays increasingly smaller single circuit boards are available, e.g., single-board computers such as Raspberry Pi or microcontrollers such as Arduino, in various shapes and dimensions. This review gives an overview of studies found in the recent scientific literature, reporting measurements of biosignals such as ECG, EMG, sweat and other health-related parameters by single circuit boards, showing new possibilities offered by Arduino, Raspberry Pi etc. in the mobile long-term acquisition of biosignals. The review concentrates on the electronics, not on textile electrodes about which several review papers are available. Full article
(This article belongs to the Special Issue The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems)
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