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Sensors
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Biomedical Signals, Images and Healthcare Data Analysis
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Biomedical Signals, Images and Healthcare Data Analysis

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 3130

Special Issue Editors

Dr. Shivaram Poigai Arunachalam

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Guest Editor
Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55902, USA
Interests: physiology; clinical medicine; biomedical engineering; clinical imaging; artificial intelligence
Dr. Kanchan Kulkarni

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Guest Editor
L'Institut de Rythmologie et modélisation Cardiaque, University of Bordeaux, Bordeaux, France
Interests: cardiology; smart health; signal processing
Dr. Anup Kumar Mishra

E-Mail Website
Guest Editor
Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA
Interests: machine learning; artificial intelligence; fall detection; pancreatic cancer; cancer; assistive living

Special Issue Information

Dear Colleagues,

With the digital health era revolutionizing patient care, health systems are focusing on identifying areas of improvement for both clinical practice changes as well as improved patient outcomes using biomedical data. In recent years, there has been an exponential growth of healthcare data ranging from Electronic Health Records (EHR), medical imaging data, and data from in-home and in-hospital health tracking and diagnostic sensors. Healthcare data analytics have been shown to improve patient outcomes such as reducing mortality, providing opportunities for personalized and early interventions, and operational benefits such as identifying waste and optimizing healthcare spending. However, there are several unmet clinical needs and the utilization of biomedical data is still sub-optimal. Therefore, we have an opportunity to build efficient biomedical data-driven digital tools to improve patient care. Advanced biomedical data processing and analytics techniques leveraging the potential of artificial intelligence (AI), cloud computing, data mining, and data visualization, in addition to a multidisciplinary collaborative research environment with clinicians are essential to enhance the ability of health care providers to optimize care delivery and improve patient outcomes. In the future, novel data analysis methods will be crucial to help transform healthcare systems from a reactive, treatment-based approach to a more integrated, preventive model. Novel machine learning-based clinical decision tools will become inevitable for an enriched healthcare system to provide much-needed care to patients in a timely fashion.

This Special Issue will provide an opportunity to showcase novel methodological innovation and translational efforts related to the analysis of various healthcare data including EHR data, biomedical signals, and imaging data to enhance patient care.

Dr. Shivaram Poigai Arunachalam
Dr. Kanchan Kulkarni
Dr. Anup Kumar Mishra
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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.

Published Papers (2 papers)

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17 pages, 14072 KiB  
Article
Integrating Spatial and Temporal Information for Violent Activity Detection from Video Using Deep Spiking Neural Networks
by Xiang Wang, Jie Yang and Nikola K. Kasabov
Sensors 2023, 23(9), 4532; https://doi.org/10.3390/s23094532 - 06 May 2023
Cited by 1 | Viewed by 1668
Abstract
Increasing violence in workplaces such as hospitals seriously challenges public safety. However, it is time- and labor-consuming to visually monitor masses of video data in real time. Therefore, automatic and timely violent activity detection from videos is vital, especially for small monitoring systems. [...] Read more.
Increasing violence in workplaces such as hospitals seriously challenges public safety. However, it is time- and labor-consuming to visually monitor masses of video data in real time. Therefore, automatic and timely violent activity detection from videos is vital, especially for small monitoring systems. This paper proposes a two-stream deep learning architecture for video violent activity detection named SpikeConvFlowNet. First, RGB frames and their optical flow data are used as inputs for each stream to extract the spatiotemporal features of videos. After that, the spatiotemporal features from the two streams are concatenated and fed to the classifier for the final decision. Each stream utilizes a supervised neural network consisting of multiple convolutional spiking and pooling layers. Convolutional layers are used to extract high-quality spatial features within frames, and spiking neurons can efficiently extract temporal features across frames by remembering historical information. The spiking neuron-based optical flow can strengthen the capability of extracting critical motion information. This method combines their advantages to enhance the performance and efficiency for recognizing violent actions. The experimental results on public datasets demonstrate that, compared with the latest methods, this approach greatly reduces parameters and achieves higher inference efficiency with limited accuracy loss. It is a potential solution for applications in embedded devices that provide low computing power but require fast processing speeds. Full article
(This article belongs to the Special Issue Biomedical Signals, Images and Healthcare Data Analysis)
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28 pages, 1351 KiB  
Systematic Review
Time-Series Modeling and Forecasting of Cerebral Pressure–Flow Physiology: A Scoping Systematic Review of the Human and Animal Literature
by Nuray Vakitbilir, Logan Froese, Alwyn Gomez, Amanjyot Singh Sainbhi, Kevin Y. Stein, Abrar Islam, Tobias J. G. Bergmann, Izabella Marquez, Fiorella Amenta, Younis Ibrahim and Frederick A. Zeiler
Sensors 2024, 24(5), 1453; https://doi.org/10.3390/s24051453 - 23 Feb 2024
Viewed by 588
Abstract
The modeling and forecasting of cerebral pressure–flow dynamics in the time–frequency domain have promising implications for veterinary and human life sciences research, enhancing clinical care by predicting cerebral blood flow (CBF)/perfusion, nutrient delivery, and intracranial pressure (ICP)/compliance behavior in advance. Despite its potential, [...] Read more.
The modeling and forecasting of cerebral pressure–flow dynamics in the time–frequency domain have promising implications for veterinary and human life sciences research, enhancing clinical care by predicting cerebral blood flow (CBF)/perfusion, nutrient delivery, and intracranial pressure (ICP)/compliance behavior in advance. Despite its potential, the literature lacks coherence regarding the optimal model type, structure, data streams, and performance. This systematic scoping review comprehensively examines the current landscape of cerebral physiological time-series modeling and forecasting. It focuses on temporally resolved cerebral pressure–flow and oxygen delivery data streams obtained from invasive/non-invasive cerebral sensors. A thorough search of databases identified 88 studies for evaluation, covering diverse cerebral physiologic signals from healthy volunteers, patients with various conditions, and animal subjects. Methodologies range from traditional statistical time-series analysis to innovative machine learning algorithms. A total of 30 studies in healthy cohorts and 23 studies in patient cohorts with traumatic brain injury (TBI) concentrated on modeling CBFv and predicting ICP, respectively. Animal studies exclusively analyzed CBF/CBFv. Of the 88 studies, 65 predominantly used traditional statistical time-series analysis, with transfer function analysis (TFA), wavelet analysis, and autoregressive (AR) models being prominent. Among machine learning algorithms, support vector machine (SVM) was widely utilized, and decision trees showed promise, especially in ICP prediction. Nonlinear models and multi-input models were prevalent, emphasizing the significance of multivariate modeling and forecasting. This review clarifies knowledge gaps and sets the stage for future research to advance cerebral physiologic signal analysis, benefiting neurocritical care applications. Full article
(This article belongs to the Special Issue Biomedical Signals, Images and Healthcare Data Analysis)
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