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Bioengineering
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Monitoring and Analysis of Human Biosignals, Volume II
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Monitoring and Analysis of Human Biosignals, Volume II

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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 7180

Special Issue Editor

Dr. Antonio Lanata

E-Mail Website
Guest Editor
Department of Information Engineering, Università degli Studi di Firenze, Firenze, Italy
Interests: wearable system for non-invasive physiological monitoring; statistical and nonlinear biomedical signal processing; affective computing; mood/mental/neurological disorders; human–animal–robot interaction; autonomic nervous system investigation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biosignals are evidence of biosystems' communication and are our primary source of information on their behaviour, playing a pivotal role in health care monitoring and clinical diagnosis. Among the best-known biosignals are the following: ECG, EEG, EMG, EOG, ERG and GSR. Biosignals also refer to non-electrical signals such as acoustic signals and optical signals. Recent advances in artificial intelligence (AI) and machine learning (ML) make it possible to gather more information on biosignals, and this may lead to a deeper understanding of the pathophysiological states. 

The Special Issue "Monitoring and Analysis of Human Biosignals, Volume II" aims to provide a collection of contributions showing the new advancements and applications of biosignal monitoring and analysis. Topics may include, but are not limited to, the following:
- Biosignal acquisition;
- Biosignal quality analysis;
- Biosignal processing and analysis;
- Deep learning for biosignal analysis;
- Human body sensing;
- Biomedical image processing and analysis;
- Computational neuroscience;
- Emotion analysis;
- Speech analysis.

Dr. Antonio Lanata
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.

Published Papers (6 papers)

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Research

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16 pages, 3031 KiB  
Article
Two-Stream Convolutional Neural Networks for Breathing Pattern Classification: Real-Time Monitoring of Respiratory Disease Patients
by Jinho Park, Thien Nguyen, Soongho Park, Brian Hill, Babak Shadgan and Amir Gandjbakhche
Bioengineering 2024, 11(7), 709; https://doi.org/10.3390/bioengineering11070709 - 12 Jul 2024
Viewed by 454
Abstract
A two-stream convolutional neural network (TCNN) for breathing pattern classification has been devised for the continuous monitoring of patients with infectious respiratory diseases. The TCNN consists of a convolutional neural network (CNN)-based autoencoder and classifier. The encoder of the autoencoder generates deep compressed [...] Read more.
A two-stream convolutional neural network (TCNN) for breathing pattern classification has been devised for the continuous monitoring of patients with infectious respiratory diseases. The TCNN consists of a convolutional neural network (CNN)-based autoencoder and classifier. The encoder of the autoencoder generates deep compressed feature maps, which contain the most important information constituting data. These maps are concatenated with feature maps generated by the classifier to classify breathing patterns. The TCNN, single-stream CNN (SCNN), and state-of-the-art classification models were applied to classify four breathing patterns: normal, slow, rapid, and breath holding. The input data consisted of chest tissue hemodynamic responses measured using a wearable near-infrared spectroscopy device on 14 healthy adult participants. Among the classification models evaluated, random forest had the lowest classification accuracy at 88.49%, while the TCNN achieved the highest classification accuracy at 94.63%. In addition, the proposed TCNN performed 2.6% better in terms of classification accuracy than an SCNN (without an autoencoder). Moreover, the TCNN mitigates the issue of declining learning performance with increasing network depth, as observed in the SCNN model. These results prove the robustness of the TCNN in classifying breathing patterns despite using a significantly smaller number of parameters and computations compared to state-of-the-art classification models. Full article
(This article belongs to the Special Issue Monitoring and Analysis of Human Biosignals, Volume II)
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12 pages, 1138 KiB  
Article
Enhancing Early Detection of Sepsis in Neonates through Multimodal Biosignal Integration: A Study of Pulse Oximetry, Near-Infrared Spectroscopy (NIRS), and Skin Temperature Monitoring
by Nicoleta Lungu, Daniela-Eugenia Popescu, Ana Maria Cristina Jura, Mihaela Zaharie, Mihai-Andrei Jura, Ioana Roșca and Mărioara Boia
Bioengineering 2024, 11(7), 681; https://doi.org/10.3390/bioengineering11070681 - 4 Jul 2024
Viewed by 360
Abstract
Sepsis continues to be challenging to diagnose due to its non-specific clinical signs and symptoms, emphasizing the importance of early detection. Our study aimed to enhance the accuracy of sepsis diagnosis by integrating multimodal monitoring technologies with conventional diagnostic methods. The research included [...] Read more.
Sepsis continues to be challenging to diagnose due to its non-specific clinical signs and symptoms, emphasizing the importance of early detection. Our study aimed to enhance the accuracy of sepsis diagnosis by integrating multimodal monitoring technologies with conventional diagnostic methods. The research included a total of 121 newborns, with 39 cases of late-onset sepsis, 35 cases of early-onset sepsis, and 47 control subjects. Continuous monitoring of biosignals, including pulse oximetry (PO), near-infrared spectroscopy (NIRS), and skin temperature (ST), was conducted. An algorithm was then developed in Python to identify early signs of sepsis. The model demonstrated the capability to detect sepsis 6 to 48 h in advance with an accuracy rate of 87.67 ± 7.42%. Sensitivity and specificity were recorded at 76% and 90%, respectively, with NIRS and ST having the most significant impact on predictive accuracy. Despite the promising results, limitations such as sample size, data variability, and potential biases were noted. These findings highlight the critical role of non-invasive biosensing methods in conjunction with conventional biomarkers and cultures, offering a strong foundation for early sepsis detection and improved neonatal care. Further research should be conducted to validate these results across different clinical settings. Full article
(This article belongs to the Special Issue Monitoring and Analysis of Human Biosignals, Volume II)
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19 pages, 3408 KiB  
Article
Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks
by Lida Zare, Mahsan Rahmani, Nastaran Khaleghi, Sobhan Sheykhivand and Sebelan Danishvar
Bioengineering 2024, 11(7), 644; https://doi.org/10.3390/bioengineering11070644 - 24 Jun 2024
Viewed by 616
Abstract
Leukemia is a malignant disease that impacts explicitly the blood cells, leading to life-threatening infections and premature mortality. State-of-the-art machine-enabled technologies and sophisticated deep learning algorithms can assist clinicians in early-stage disease diagnosis. This study introduces an advanced end-to-end approach for the automated [...] Read more.
Leukemia is a malignant disease that impacts explicitly the blood cells, leading to life-threatening infections and premature mortality. State-of-the-art machine-enabled technologies and sophisticated deep learning algorithms can assist clinicians in early-stage disease diagnosis. This study introduces an advanced end-to-end approach for the automated diagnosis of acute leukemia classes acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML). This study gathered a complete database of 44 patients, comprising 670 ALL and AML images. The proposed deep model’s architecture consisted of a fusion of graph theory and convolutional neural network (CNN), with six graph Conv layers and a Softmax layer. The proposed deep model achieved a classification accuracy of 99% and a kappa coefficient of 0.85 for ALL and AML classes. The suggested model was assessed in noisy conditions and demonstrated strong resilience. Specifically, the model’s accuracy remained above 90%, even at a signal-to-noise ratio (SNR) of 0 dB. The proposed approach was evaluated against contemporary methodologies and research, demonstrating encouraging outcomes. According to this, the suggested deep model can serve as a tool for clinicians to identify specific forms of acute leukemia. Full article
(This article belongs to the Special Issue Monitoring and Analysis of Human Biosignals, Volume II)
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14 pages, 3617 KiB  
Article
Online Ergonomic Evaluation in Realistic Manual Material Handling Task: Proof of Concept
by Sergio Leggieri, Vasco Fanti, Darwin G. Caldwell and Christian Di Natali
Bioengineering 2024, 11(1), 14; https://doi.org/10.3390/bioengineering11010014 - 23 Dec 2023
Viewed by 1262
Abstract
Work-related musculoskeletal disorders are globally one of the leading causes of work-related injuries. They significantly impact worker health and business costs. Work task ergonomic risk indices have been developed that use observational assessments to identify potential injuries, and allow safety managers to promptly [...] Read more.
Work-related musculoskeletal disorders are globally one of the leading causes of work-related injuries. They significantly impact worker health and business costs. Work task ergonomic risk indices have been developed that use observational assessments to identify potential injuries, and allow safety managers to promptly intervene to mitigate the risks. However, these assessments are very subjective and difficult to perform in real time. This work provides a technique that can digitalize this process by developing an online algorithm to calculate the NIOSH index and provide additional data for ergonomic risk assessment. The method is based on the use of inertial sensors, which are easily found commercially and can be integrated into the industrial environment without any other sensing technology. This preliminary study demonstrates the effectiveness of the first version of the Online Lifting Index (On-LI) algorithm on a common industrial logistic task. The effectiveness is compared to the standard ergonomic assessment method. The results report an average error of 3.6% compared to the NIOSH parameters used to calculate the ergonomic risk and a relative error of the Lifting Index of 2.8% when compared to observational methods. Full article
(This article belongs to the Special Issue Monitoring and Analysis of Human Biosignals, Volume II)
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13 pages, 2155 KiB  
Article
Multitask Attention-Based Neural Network for Intraoperative Hypotension Prediction
by Meng Shi, Yu Zheng, Youzhen Wu and Quansheng Ren
Bioengineering 2023, 10(9), 1026; https://doi.org/10.3390/bioengineering10091026 - 31 Aug 2023
Viewed by 1244
Abstract
Timely detection and response to Intraoperative Hypotension (IOH) during surgery is crucial to avoid severe postoperative complications. Although several methods have been proposed to predict IOH using machine learning, their performance still has space for improvement. In this paper, we propose a ResNet-BiLSTM [...] Read more.
Timely detection and response to Intraoperative Hypotension (IOH) during surgery is crucial to avoid severe postoperative complications. Although several methods have been proposed to predict IOH using machine learning, their performance still has space for improvement. In this paper, we propose a ResNet-BiLSTM model based on multitask training and attention mechanism for IOH prediction. We trained and tested our proposed model using bio-signal waveforms obtained from patient monitoring of non-cardiac surgery. We selected three models (WaveNet, CNN, and TCN) that process time-series data for comparison. The experimental results demonstrate that our proposed model has optimal MSE (43.83) and accuracy (0.9224) compared to other models, including WaveNet (51.52, 0.9087), CNN (318.52, 0.5861), and TCN (62.31, 0.9045), which suggests that our proposed model has better regression and classification performance. We conducted ablation experiments on the multitask and attention mechanisms, and the experimental results demonstrated that the multitask and attention mechanisms improved MSE and accuracy. The results demonstrate the effectiveness and superiority of our proposed model in predicting IOH. Full article
(This article belongs to the Special Issue Monitoring and Analysis of Human Biosignals, Volume II)
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14 pages, 842 KiB  
Systematic Review
Non-Invasive Wearable Devices for Monitoring Vital Signs in Patients with Type 2 Diabetes Mellitus: A Systematic Review
by Artur Piet, Lennart Jablonski, Jennifer I. Daniel Onwuchekwa, Steffen Unkel, Christian Weber, Marcin Grzegorzek, Jan P. Ehlers, Olaf Gaus and Thomas Neumann
Bioengineering 2023, 10(11), 1321; https://doi.org/10.3390/bioengineering10111321 - 16 Nov 2023
Cited by 1 | Viewed by 2277
Abstract
Type 2 diabetes mellitus (T2D) poses a significant global health challenge and demands effective self-management strategies, including continuous blood glucose monitoring (CGM) and lifestyle adaptations. While CGM offers real-time glucose level assessment, the quest for minimizing trauma and enhancing convenience has spurred the [...] Read more.
Type 2 diabetes mellitus (T2D) poses a significant global health challenge and demands effective self-management strategies, including continuous blood glucose monitoring (CGM) and lifestyle adaptations. While CGM offers real-time glucose level assessment, the quest for minimizing trauma and enhancing convenience has spurred the need to explore non-invasive alternatives for monitoring vital signs in patients with T2D. Objective: This systematic review is the first that explores the current literature and critically evaluates the use and reporting of non-invasive wearable devices for monitoring vital signs in patients with T2D. Methods: Employing the PRISMA and PICOS guidelines, we conducted a comprehensive search to incorporate evidence from relevant studies, focusing on randomized controlled trials (RCTs), systematic reviews, and meta-analyses published since 2017. Of the 437 publications identified, seven were selected based on predetermined criteria. Results: The seven studies included in this review used various sensing technologies, such as heart rate monitors, accelerometers, and other wearable devices. Primary health outcomes included blood pressure measurements, heart rate, body fat percentage, and cardiorespiratory endurance. Non-invasive wearable devices demonstrated potential for aiding T2D management, albeit with variations in efficacy across studies. Conclusions: Based on the low number of studies with higher evidence levels (i.e., RCTs) that we were able to find and the significant differences in design between these studies, we conclude that further evidence is required to validate the application, efficacy, and real-world impact of these wearable devices. Emphasizing transparency in bias reporting and conducting in-depth research is crucial for fully understanding the implications and benefits of wearable devices in T2D management. Full article
(This article belongs to the Special Issue Monitoring and Analysis of Human Biosignals, Volume II)
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