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Biomedical Signal Processing for Healthcare Applications
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Biomedical Signal Processing for Healthcare Applications

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

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 11962

Special Issue Editors

Dr. Nan Liu

E-Mail Website
Guest Editor
Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore
Interests: artificial intelligence; machine learning; medical informatics; physiological signal analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chengyu Liu

E-Mail Website
Guest Editor
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Interests: wearable device; signal processing; mHealth; database
Special Issues, Collections and Topics in MDPI journals
Dr. Jessilyn Dunn

E-Mail Website
Guest Editor
Pratt School of Engineering, Duke University, Durham, NC 27708, USA
Interests: Machine learning; data science; cardiometabolic diseases; wearable devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With advances in signal processing and computational intelligence, biomedical signal processing is playing a vital role in ushering a new era of healthcare innovation. Beyond traditional signal analysis tools, artificial intelligence and machine learning have gained popularity in analyzing biomedical data, ranging from ECG, EEG, and PPG to medical images such as CT and MRI. Furthermore, biomedical signal processing seamlessly aligns with research and innovation in wearable devices and internet of things.

This special issue aims to report the latest scholar updates in all aspects of biomedical signal processing and their applications in healthcare.

Dr. Nan Liu
Prof. Chengyu Liu
Dr. Jessilyn Dunn
Guest Editors

If you want to learn more information or need any advice, you can contact the Special Issue Editor Iris Shen via <iris.shen@mdpi.com> directly.

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.

Keywords

  • biomedical signal processing
  • physiological measurement
  • medical image analysis
  • artificial intelligence
  • machine learning
  • wearable device

Published Papers (3 papers)

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Research

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20 pages, 4150 KiB  
Article
A Wearable Multimodal Sensing System for Tracking Changes in Pulmonary Fluid Status, Lung Sounds, and Respiratory Markers
by Jesus Antonio Sanchez-Perez, John A. Berkebile, Brandi N. Nevius, Goktug C. Ozmen, Christopher J. Nichols, Venu G. Ganti, Samer A. Mabrouk, Gari D. Clifford, Rishikesan Kamaleswaran, David W. Wright and Omer T. Inan
Sensors 2022, 22(3), 1130; https://doi.org/10.3390/s22031130 - 02 Feb 2022
Cited by 14 | Viewed by 3257
Abstract
Heart failure (HF) exacerbations, characterized by pulmonary congestion and breathlessness, require frequent hospitalizations, often resulting in poor outcomes. Current methods for tracking lung fluid and respiratory distress are unable to produce continuous, holistic measures of cardiopulmonary health. We present a multimodal sensing system [...] Read more.
Heart failure (HF) exacerbations, characterized by pulmonary congestion and breathlessness, require frequent hospitalizations, often resulting in poor outcomes. Current methods for tracking lung fluid and respiratory distress are unable to produce continuous, holistic measures of cardiopulmonary health. We present a multimodal sensing system that captures bioimpedance spectroscopy (BIS), multi-channel lung sounds from four contact microphones, multi-frequency impedance pneumography (IP), temperature, and kinematics to track changes in cardiopulmonary status. We first validated the system on healthy subjects (n = 10) and then conducted a feasibility study on patients (n = 14) with HF in clinical settings. Three measurements were taken throughout the course of hospitalization, and parameters relevant to lung fluid status—the ratio of the resistances at 5 kHz to those at 150 kHz (K)—and respiratory timings (e.g., respiratory rate) were extracted. We found a statistically significant increase in K (p < 0.05) from admission to discharge and observed respiratory timings in physiologically plausible ranges. The IP-derived respiratory signals and lung sounds were sensitive enough to detect abnormal respiratory patterns (Cheyne–Stokes) and inspiratory crackles from patient recordings, respectively. We demonstrated that the proposed system is suitable for detecting changes in pulmonary fluid status and capturing high-quality respiratory signals and lung sounds in a clinical setting. Full article
(This article belongs to the Special Issue Biomedical Signal Processing for Healthcare Applications)
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19 pages, 3537 KiB  
Article
Orthogonality-Constrained CNMF-Based Noise Reduction with Reduced Degradation of Biological Sound
by Naoto Murakami, Shota Nakashima, Katsuma Fujimoto, Shoya Makihira, Seiji Nishifuji, Keiko Doi, Xianghong Li, Tsunahiko Hirano and Kazuto Matsunaga
Sensors 2021, 21(23), 7981; https://doi.org/10.3390/s21237981 - 29 Nov 2021
Viewed by 1766
Abstract
The number of deaths due to cardiovascular and respiratory diseases is increasing annually. Cardiovascular diseases with high mortality rates, such as strokes, are frequently caused by atrial fibrillation without subjective symptoms. Chronic obstructive pulmonary disease is another condition in which early detection is [...] Read more.
The number of deaths due to cardiovascular and respiratory diseases is increasing annually. Cardiovascular diseases with high mortality rates, such as strokes, are frequently caused by atrial fibrillation without subjective symptoms. Chronic obstructive pulmonary disease is another condition in which early detection is difficult owing to the slow progression of the disease. Hence, a device that enables the early diagnosis of both diseases is necessary. In our previous study, a sensor for monitoring biological sounds such as vascular and respiratory sounds was developed and a noise reduction method based on semi-supervised convolutive non-negative matrix factorization (SCNMF) was proposed for the noisy environments of users. However, SCNMF attenuated part of the biological sound in addition to the noise. Therefore, this paper proposes a novel noise reduction method that achieves less distortion by imposing orthogonality constraints on the SCNMF. The effectiveness of the proposed method was verified experimentally using the biological sounds of 21 subjects. The experimental results showed an average improvement of 1.4 dB in the signal-to-noise ratio and 2.1 dB in the signal-to-distortion ratio over the conventional method. These results demonstrate the capability of the proposed approach to measure biological sounds even in noisy environments. Full article
(This article belongs to the Special Issue Biomedical Signal Processing for Healthcare Applications)
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Review

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24 pages, 2478 KiB  
Review
A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications
by Will Ke Wang, Ina Chen, Leeor Hershkovich, Jiamu Yang, Ayush Shetty, Geetika Singh, Yihang Jiang, Aditya Kotla, Jason Zisheng Shang, Rushil Yerrabelli, Ali R. Roghanizad, Md Mobashir Hasan Shandhi and Jessilyn Dunn
Sensors 2022, 22(20), 8016; https://doi.org/10.3390/s22208016 - 20 Oct 2022
Cited by 7 | Viewed by 5346
Abstract
Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) [...] Read more.
Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used for modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for time series classification in biomedical applications that can achieve high performance. Objective: We performed a systematic review to characterize the techniques that are used in time series classification of digital clinical measures throughout all the stages of data processing and model building. Methods: We conducted a literature search on PubMed, as well as the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles that report on the academic research about digital clinical measures from a five-year period between June 2016 and June 2021. We identified and categorized the research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely used machine learning classifiers were the most commonly used technique, and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also the second-best classification technique. Conclusions: In this review paper, summaries of the time series classification models and interpretation methods for biomedical applications are summarized and categorized. While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation, however many different methods have proven successful. Full article
(This article belongs to the Special Issue Biomedical Signal Processing for Healthcare Applications)
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