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Bioengineering
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Machine Learning-Based Heart, Brain and Nerve Tissue Engineering
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Machine Learning-Based Heart, Brain and Nerve Tissue Engineering

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Regenerative Engineering".

Deadline for manuscript submissions: closed (18 March 2022) | Viewed by 10725

Special Issue Editor

Dr. Chung Tin

E-Mail Website
Guest Editor
Department of Biomedical Engineering, City University of Hong Kong, Hong Kong
Interests: sensorimotor learning and control; brain–machine interface; machine learning; neural modeling; neural prosthetics

Special Issue Information

Dear Colleagues,

The last decade has seen a unprecedented growth in the success of machine learning in a variety of applications. We have also seen a fast growth in the use of advanced machine learning techniques in biomedical related applicatons. However, researchers face new challenges in dealing with these biomedical data. For example, labeled biomedical data is limited and the labeling is expensive. Furthermore, significant subject variability challenges the performance of the algorithms for real-life use.

In this Special Issue, we would like to invite submissions for innovative research in the development of advanced machine learning algorithms, efficient implementation of such algorithms and smart point-of-care and wireless devices for biomedical applications in heart, brain and nerve tissue.

Dr. Chung Tin
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. 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

  • Heart
  • Brain
  • Nerve
  • Machine learning
  • Biomedical applications

Published Papers (4 papers)

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Research

18 pages, 1926 KiB  
Article
An Investigation of Left Ventricular Valve Disorders and the Mechano-Electric Feedback Using a Synergistic Lumped Parameter Cardiovascular Numerical Model
by Nicholas Pearce and Eun-jin Kim
Bioengineering 2022, 9(9), 454; https://0-doi-org.brum.beds.ac.uk/10.3390/bioengineering9090454 - 08 Sep 2022
Cited by 2 | Viewed by 1454
Abstract
Cardiac diseases and failure make up one of largest contributions to global mortality and significantly detriment the quality of life for millions of others. Disorders in the valves of the left ventricle are a prominent example of heart disease, with prolapse, regurgitation, and [...] Read more.
Cardiac diseases and failure make up one of largest contributions to global mortality and significantly detriment the quality of life for millions of others. Disorders in the valves of the left ventricle are a prominent example of heart disease, with prolapse, regurgitation, and stenoses—the three main valve disorders. It is widely known that mitral valve prolapse increases the susceptibility to cardiac arrhythmia. Here, we investigate stenoses and regurgitation of the mitral and aortic valves in the left ventricle using a synergistic low-order numerical model. The model synergy derives from the incorporation of the mechanical, chemical, and electrical elements. As an alternative framework to the time-varying elastance (TVE) method, it allows feedback mechanisms at work in the heart to be considered. The TVE model imposes the ventricular pressure–volume relationship using a periodic function rather than calculating it consistently. Using our synergistic approach, the effects of valve disorders on the mechano-electric-feedback (MEF) are investigated. The MEF is the influence of cellular mechanics on the electrical activity, and significantly contributes to the generation of arrhythmia. We further investigate stenoses and regurgitation of the mitral and aortic valves and their relationship with the MEF and generation of arrhythmia. Mitral valve stenosis is found to increase the sensitivity to arrhythmia-stimulating systolic stretch, and reduces the sensitivity to diastolic stretch. Aortic valve stenosis does not change the sensitivity to arrhythmia-stimulating stretch, and regurgitation reduces it. A key result is found when valve regurgitation is accompanied by diastolic stretch. In the presence of MEF disorder, ectopic beats become far more frequent when accompanied by valve regurgitation. Therefore, arrhythmia resulting from a disorder in the MEF will be more severe when valve regurgitation is present. Full article
(This article belongs to the Special Issue Machine Learning-Based Heart, Brain and Nerve Tissue Engineering)
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12 pages, 4102 KiB  
Article
Noise Power Minimization in CMOS Brain-Chip Interfaces
by Lorenzo Stevenazzi, Andrea Baschirotto, Giorgio Zanotto, Elia Arturo Vallicelli and Marcello De Matteis
Bioengineering 2022, 9(2), 42; https://0-doi-org.brum.beds.ac.uk/10.3390/bioengineering9020042 - 18 Jan 2022
Viewed by 2086
Abstract
This paper presents specific noise minimization strategies to be adopted in silicon–cell interfaces. For this objective, a complete and general model for the analog processing of the signal coming from cell–silicon junctions is presented. This model will then be described at the level [...] Read more.
This paper presents specific noise minimization strategies to be adopted in silicon–cell interfaces. For this objective, a complete and general model for the analog processing of the signal coming from cell–silicon junctions is presented. This model will then be described at the level of the single stages and of the fundamental parameters that characterize them (bandwidth, gain and noise). Thanks to a few design equations, it will therefore be possible to simulate the behavior of a time-division multiplexed acquisition channel, including the most relevant parameters for signal processing, such as amplification (or power of the analog signal) and noise. This model has the undoubted advantage of being particularly simple to simulate and implement, while maintaining high accuracy in estimating the signal quality (i.e., the signal-to-noise ratio, SNR). Thanks to the simulation results of the model, it will be possible to set an optimal operating point for the front-end to minimize the artifacts introduced by the time-division multiplexing (TDM) scheme and to maximize the SNR at the a-to-d converter input. The proposed results provide an SNR of 12 dB at 10 µVRMS of noise power and 50 µVRMS of signal power (both evaluated at input of the analog front-end, AFE). This is particularly relevant for cell–silicon junctions because it demonstrates that it is possible to detect weak extracellular events (of the order of few µVRMS) without necessarily increasing the total amplification of the front-end (and, therefore, as a first approximation, the dissipated electrical power), while adopting a specific gain distribution through the acquisition chain. Full article
(This article belongs to the Special Issue Machine Learning-Based Heart, Brain and Nerve Tissue Engineering)
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14 pages, 10366 KiB  
Article
Machine Learning Prediction Models for Mitral Valve Repairability and Mitral Regurgitation Recurrence in Patients Undergoing Surgical Mitral Valve Repair
by Marco Penso, Mauro Pepi, Valentina Mantegazza, Claudia Cefalù, Manuela Muratori, Laura Fusini, Paola Gripari, Sarah Ghulam Ali, Enrico G. Caiani and Gloria Tamborini
Bioengineering 2021, 8(9), 117; https://0-doi-org.brum.beds.ac.uk/10.3390/bioengineering8090117 - 25 Aug 2021
Cited by 9 | Viewed by 3650
Abstract
Background: Mitral valve regurgitation (MR) is the most common valvular heart disease and current variables associated with MR recurrence are still controversial. We aim to develop a machine learning-based prognostic model to predict causes of mitral valve (MV) repair failure and MR recurrence. [...] Read more.
Background: Mitral valve regurgitation (MR) is the most common valvular heart disease and current variables associated with MR recurrence are still controversial. We aim to develop a machine learning-based prognostic model to predict causes of mitral valve (MV) repair failure and MR recurrence. Methods: 1000 patients who underwent MV repair at our institution between 2008 and 2018 were enrolled. Patients were followed longitudinally for up to three years. Clinical and echocardiographic data were included in the analysis. Endpoints were MV repair surgical failure with consequent MV replacement or moderate/severe MR (>2+) recurrence at one-month and moderate/severe MR recurrence after three years. Results: 817 patients (DS1) had an echocardiographic examination at one-month while 295 (DS2) also had one at three years. Data were randomly divided into training (DS1: n = 654; DS2: n = 206) and validation (DS1: n = 164; DS2 n = 89) cohorts. For intra-operative or early MV repair failure assessment, the best area under the curve (AUC) was 0.75 and the complexity of mitral valve prolapse was the main predictor. In predicting moderate/severe recurrent MR at three years, the best AUC was 0.92 and residual MR at six months was the most important predictor. Conclusions: Machine learning algorithms may improve prognosis after MV repair procedure, thus improving indications for correct candidate selection for MV surgical repair. Full article
(This article belongs to the Special Issue Machine Learning-Based Heart, Brain and Nerve Tissue Engineering)
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12 pages, 1027 KiB  
Article
Supervised Analysis for Phenotype Identification: The Case of Heart Failure Ejection Fraction Class
by Cristina Lopez, Jose Luis Holgado, Raquel Cortes, Inma Sauri, Antonio Fernandez, Jose Miguel Calderon, Julio Nuñez and Josep Redon
Bioengineering 2021, 8(6), 85; https://0-doi-org.brum.beds.ac.uk/10.3390/bioengineering8060085 - 21 Jun 2021
Cited by 2 | Viewed by 2613
Abstract
Artificial Intelligence is creating a paradigm shift in health care, with phenotyping patients through clustering techniques being one of the areas of interest. Objective: To develop a predictive model to classify heart failure (HF) patients according to their left ventricular ejection fraction (LVEF), [...] Read more.
Artificial Intelligence is creating a paradigm shift in health care, with phenotyping patients through clustering techniques being one of the areas of interest. Objective: To develop a predictive model to classify heart failure (HF) patients according to their left ventricular ejection fraction (LVEF), by using available data from Electronic Health Records (EHR). Subjects and methods: 2854 subjects over 25 years old with a diagnosis of HF and LVEF, measured by echocardiography, were selected to develop an algorithm to predict patients with reduced EF using supervised analysis. The performance of the developed algorithm was tested in heart failure patients from Primary Care. To select the most influentual variables, the LASSO algorithm setting was used, and to tackle the issue of one class exceeding the other one by a large amount, we used the Synthetic Minority Oversampling Technique (SMOTE). Finally, Random Forest (RF) and XGBoost models were constructed. Results: The full XGBoost model obtained the maximum accuracy, a high negative predictive value, and the highest positive predictive value. Gender, age, unstable angina, atrial fibrillation and acute myocardial infarct are the variables that most influence EF value. Applied in the EHR dataset, with a total of 25,594 patients with an ICD-code of HF and no regular follow-up in cardiology clinics, 6170 (21.1%) were identified as pertaining to the reduced EF group. Conclusion: The obtained algorithm was able to identify a number of HF patients with reduced ejection fraction, who could benefit from a protocol with a strong possibility of success. Furthermore, the methodology can be used for studies using data extracted from the Electronic Health Records. Full article
(This article belongs to the Special Issue Machine Learning-Based Heart, Brain and Nerve Tissue Engineering)
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