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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: closed (31 May 2024) | Viewed by 9502

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

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Published Papers (6 papers)

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Research

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24 pages, 14553 KiB  
Article
Multiple-Point Metamaterial-Inspired Microwave Sensors for Early-Stage Brain Tumor Diagnosis
by Nantakan Wongkasem and Gabriel Cabrera
Sensors 2024, 24(18), 5953; https://doi.org/10.3390/s24185953 - 13 Sep 2024
Viewed by 736
Abstract
Simple, instantaneous, contactless, multiple-point metamaterial-inspired microwave sensors, composed of multi-band, low-profile metamaterial-inspired antennas, were developed to detect and identify meningioma tumors, the most common primary brain tumors. Based on a typical meningioma tumor size of 5–20 mm, a higher operating frequency, where the [...] Read more.
Simple, instantaneous, contactless, multiple-point metamaterial-inspired microwave sensors, composed of multi-band, low-profile metamaterial-inspired antennas, were developed to detect and identify meningioma tumors, the most common primary brain tumors. Based on a typical meningioma tumor size of 5–20 mm, a higher operating frequency, where the wavelength is similar or smaller than the tumor target, is crucial. The sensors, designed for the microwave Ku band range (12–18 GHz), where the electromagnetic property values of tumors are available, were implemented in this study. A seven-layered head phantom, including the meningioma tumors, was defined using actual electromagnetic parametric values in the frequency range of interest to mimic the actual human head. The reflection coefficients can be recorded and analyzed instantaneously, reducing high electromagnetic radiation consumption. It has been shown that a single-band detection point is not adequate to classify the nonlinear tumor and head model parameters. On the other hand, dual-band and tri-band metamaterial-inspired antennas, with additional detecting points, create a continuous function solution for the nonlinear problem by adding extra observation points using multiple-band excitation. The point mapping values can be used to enhance the tumor detection capability. Two-point mapping showed a consistent trend between the S11 value order and the tumor size, while three-point mapping can also be used to demonstrate the correlation between the S11 value order and the tumor size. This proposed multi-detection point technique can be applied to a sensor for other nonlinear property targets. Moreover, a set of antennas with different polarizations, orientations, and arrangements in a network could help to obtain the highest sensitivity and accuracy of the whole system. Full article
(This article belongs to the Special Issue Biomedical Signals, Images and Healthcare Data Analysis)
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18 pages, 703 KiB  
Article
Higher-Order Spectral Analysis Combined with a Convolution Neural Network for Atrial Fibrillation Detection-Preliminary Study
by Barbara Mika and Dariusz Komorowski
Sensors 2024, 24(13), 4171; https://doi.org/10.3390/s24134171 - 27 Jun 2024
Viewed by 1655
Abstract
The global burden of atrial fibrillation (AFIB) is constantly increasing, and its early detection is still a challenge for public health and motivates researchers to improve methods for automatic AFIB prediction and management. This work proposes higher-order spectra analysis, especially the bispectrum of [...] Read more.
The global burden of atrial fibrillation (AFIB) is constantly increasing, and its early detection is still a challenge for public health and motivates researchers to improve methods for automatic AFIB prediction and management. This work proposes higher-order spectra analysis, especially the bispectrum of electrocardiogram (ECG) signals combined with the convolution neural network (CNN) for AFIB detection. Like other biomedical signals, ECG is non-stationary, non-linear, and non-Gaussian in nature, so the spectra of higher-order cumulants, in this case, bispectra, preserve valuable features. The two-dimensional (2D) bispectrum images were applied as input for the two CNN architectures with the output AFIB vs. no-AFIB: the pre-trained modified GoogLeNet and the proposed CNN called AFIB-NET. The MIT-BIH Atrial Fibrillation Database (AFDB) was used to evaluate the performance of the proposed methodology. AFIB-NET detected atrial fibrillation with a sensitivity of 95.3%, a specificity of 93.7%, and an area under the receiver operating characteristic (ROC) of 98.3%, while for GoogLeNet results for sensitivity and specificity were equal to 96.7%, 82%, respectively, and the area under ROC was equal to 96.7%. According to preliminary studies, bispectrum images as input to 2D CNN can be successfully used for AFIB rhythm detection. Full article
(This article belongs to the Special Issue Biomedical Signals, Images and Healthcare Data Analysis)
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14 pages, 4462 KiB  
Article
Light and Displacement Compensation-Based iPPG for Heart-Rate Measurement in Complex Detection Conditions
by Shubo Bi, Haipeng Wang and Shuaishuai Zhang
Sensors 2024, 24(11), 3346; https://doi.org/10.3390/s24113346 - 23 May 2024
Viewed by 787
Abstract
A light and displacement-compensation-based iPPG algorithm is proposed in this paper for heart-rate measurement in complex detection conditions. Two compensation sub-algorithms, including light compensation and displacement compensation, are designed and integrated into the iPPG algorithm for more accurate heart-rate measurement. In the light-compensation [...] Read more.
A light and displacement-compensation-based iPPG algorithm is proposed in this paper for heart-rate measurement in complex detection conditions. Two compensation sub-algorithms, including light compensation and displacement compensation, are designed and integrated into the iPPG algorithm for more accurate heart-rate measurement. In the light-compensation sub-algorithm, the measurement deviation caused by the ambient light change is compensated by the mean filter-based light adjustment strategy. In the displacement-compensation sub-algorithm, the measurement deviation caused by the subject motion is compensated by the optical flow-based displacement calculation strategy. A series of heart-rate measurement experiments are conducted to verify the effectiveness of the proposed method. Compared with conventional iPPG, the average measurement accuracy increases by 3.8% under different detection distances and 5.0% under different light intensities. Full article
(This article belongs to the Special Issue Biomedical Signals, Images and Healthcare Data Analysis)
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13 pages, 2664 KiB  
Article
Stimulus–Response Plots as a Novel Bowel-Sound-Based Method for Evaluating Motor Response to Drinking in Healthy People
by Takeyuki Haraguchi and Takahiro Emoto
Sensors 2024, 24(10), 3054; https://doi.org/10.3390/s24103054 - 11 May 2024
Viewed by 1298
Abstract
Constipation is a common gastrointestinal disorder that impairs quality of life. Evaluating bowel motility via traditional methods, such as MRI and radiography, is expensive and inconvenient. Bowel sound (BS) analysis has been proposed as an alternative, with BS-time-domain acoustic features (BSTDAFs) being effective [...] Read more.
Constipation is a common gastrointestinal disorder that impairs quality of life. Evaluating bowel motility via traditional methods, such as MRI and radiography, is expensive and inconvenient. Bowel sound (BS) analysis has been proposed as an alternative, with BS-time-domain acoustic features (BSTDAFs) being effective for evaluating bowel motility via several food and drink consumption tests. However, the effect of BSTDAFs before drink consumption on those after drink consumption is yet to be investigated. This study used BS-based stimulus–response plots (BSSRPs) to investigate this effect on 20 participants who underwent drinking tests. A strong negative correlation was observed between the number of BSs per minute before carbonated water consumption and the ratio of that before and after carbonated water consumption. However, a similar trend was not observed when the participants drank cold water. These findings suggest that when carbonated water is drunk, bowel motility before ingestion affects motor response to ingestion. This study provides a non-invasive BS-based approach for evaluating motor response to food and drink, offering a new research window for investigators in this field. Full article
(This article belongs to the Special Issue Biomedical Signals, Images and Healthcare Data Analysis)
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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 - 6 May 2023
Cited by 3 | Viewed by 2362
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 1369
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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