Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Browser

Advances in Biosignal Processing and Biomedical Data Analysis

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Related Special Issue
Published Papers

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 11859

Share This Special Issue

Special Issue Editor

Prof. Dr. Sung Bum Pan

E-Mail Website
Guest Editor

IT Research Institute, Chosun University, 309 Pilmun-daero, Dong-gu, Gwang-Ju 61452, Republic of Korea
Interests: biosignal processing; biometrics; pattern recognition; wearable embedded system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biosignals have unique characteristics for each individual and are mainly used for disease judgment, prediction, and health status monitoring. As such, they play an important role in diagnosis. Using these characteristics, personal recognition using biosignals has been recently successfully performed. Biosignals are generated inside the body, which is advantageous for security.

Recent progress in machine learning techniques, and in particular deep learning, has revolutionized various fields of artificial vision, significantly pushing the state of the art of artificial intelligence systems into a wide range of high-level tasks. Such progress can help address problems in applications of biosignal data based on embedded systems.

We invite authors to submit original research articles, review articles, and significant preliminary communications covering (but not limited to) the following topics and scopes:

Big data processing for biometrics;
Biometric feature extraction based on deep learning;
Biometrics based on deep learning;
Advanced technologies in biosignal processing;
Deep learning architecture modeling for biosignals;
Analysis and utilization of various biosignals;
Biosignal processing based on wearable devices;
Architectures and applications in wearable devices.

Prof. Sung Bum Pan
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. Applied Sciences 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 2400 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

biosignal processing
biomedical data analysis
deep learning
wearable system

Related Special Issue

Advances in Biosignal Processing and Biomedical Data Analysis, 2nd Edition in Applied Sciences (1 article)

Published Papers (6 papers)

Download All Papers

Research

17 pages, 6951 KiB

Open AccessArticle

Application of Wavelet Transform and Fractal Analysis for Esophageal pH-Metry to Determine a New Method to Diagnose Gastroesophageal Reflux Disease

by Piotr Mateusz Tojza, Łukasz Doliński, Grzegorz Redlarski, Jacek Szkopek, Mariusz Dąbkowski and Maria Janiak

Appl. Sci. 2023, 13(1), 214; https://doi.org/10.3390/app13010214 - 24 Dec 2022

Viewed by 1248

Abstract

In this paper, a new method for analysing gastroesophageal reflux disease (GERD) is shown. This novel method uses wavelet transform (WT) and wavelet-based fractal analysis (WBFA) on esophageal pH-metry measurements. The esophageal pH-metry is an important diagnostic tool supporting the physician’s work in diagnosing some forms of reflux diseases. Interpreting the results of 24-h pH-metry monitoring is time-consuming, and the conclusions of such an analysis can sometimes be too subjective. There is no strict procedure or reference values to follow when the impedance measurements are assessed. Therefore, an attempt was made to develop a point of reference for the assessment process, helping to distinguish healthy patients from GERD patients. In this approach, wavelet transform (WT) and wavelet-based fractal analysis (WBFA) were used to aid the diagnostic process. With this approach, it was possible to develop two efficient computer methods to classify healthy and sick patients based on the pH measurement data alone. The WT method provided a sensitivity value of 93.33%, with 75% specificity. The results of the fractal analysis confirmed that the tested signals have features that enable their automatic classification and assignment to a group of sick or healthy people. The article will be interesting for those studying the application of wavelet and fractal analysis in biomedical waveforms. The authors included in the work a description of the implementation of the fractal and wavelet analysis, the descriptions of the results of the analyses, and the conclusions drawn from them. The work will also be of interest to those who study the methods of using machine learning and artificial intelligence in computer-aided, automatic medical diagnostics. Full article

(This article belongs to the Special Issue Advances in Biosignal Processing and Biomedical Data Analysis)

► Show Figures

Figure 1

12 pages, 1670 KiB

Open AccessArticle

Mobile Health App for Adolescents: Motion Sensor Data and Deep Learning Technique to Examine the Relationship between Obesity and Walking Patterns

by Sungchul Lee, Eunmin Hwang, Yanghee Kim, Fatih Demir, Hyunhwa Lee, Joshua J. Mosher, Eunyoung Jang and Kiho Lim

Appl. Sci. 2022, 12(2), 850; https://doi.org/10.3390/app12020850 - 14 Jan 2022

Cited by 3 | Viewed by 2465

Abstract

With the prevalence of obesity in adolescents, and its long-term influence on their overall health, there is a large body of research exploring better ways to reduce the rate of obesity. A traditional way of maintaining an adequate body mass index (BMI), calculated by measuring the weight and height of an individual, is no longer enough, and we are in need of a better health care tool. Therefore, the current research proposes an easier method that offers instant and real-time feedback to the users from the data collected from the motion sensors of a smartphone. The study utilized the mHealth application to identify participants presenting the walking movements of the high BMI group. Using the feedforward deep learning models and convolutional neural network models, the study was able to distinguish the walking movements between nonobese and obese groups, at a rate of 90.5%. The research highlights the potential use of smartphones and suggests the mHealth application as a way to monitor individual health. Full article

(This article belongs to the Special Issue Advances in Biosignal Processing and Biomedical Data Analysis)

► Show Figures

Figure 1

10 pages, 2554 KiB

Open AccessArticle

The Auto-Regressive Model and Spectrum Information Entropy Judgment Method for High Intensity Focused Ultrasound Echo Signal

by Shang-Qu Yan, Zheng Huang, Bei Liu, Xu-Sheng Ni, Han Zhang, Xiao Zou and Sheng-You Qian

Appl. Sci. 2021, 11(20), 9558; https://doi.org/10.3390/app11209558 - 14 Oct 2021

Cited by 1 | Viewed by 1080

Abstract

For accurate evaluation of high intensity focused ultrasound (HIFU) treatment effect, it is of great importance to effectively judge whether the sampled signal is the HIFU echo signal or the noise signal. In this paper, a judgment method based on an auto-regressive (AR) model and spectrum information entropy is proposed. In total, 188 groups of data are obtained while the HIFU source is on or off through experiments, and these sampled signals are judged by this method. The judgment results of this method are compared with empirical judgments. It is found that when the segment number for the power spectrum estimated by AR model is 14 to 17, the judgment results of this method have a higher consistency with empirical judgments, and Accuracy, Sensitivity and Specificity all have good values. Moreover, after comparing and analyzing this method with the classic power spectrum estimation method, it is found that the recognition rate of the two sampled signals of this method is higher than that of the classic power spectrum estimation method. Therefore, this method can effectively judge the different types of sampled signals. Full article

(This article belongs to the Special Issue Advances in Biosignal Processing and Biomedical Data Analysis)

► Show Figures

Figure 1

18 pages, 1051 KiB

Open AccessArticle

Framework for Privacy-Preserving Wearable Health Data Analysis: Proof-of-Concept Study for Atrial Fibrillation Detection

by Anamaria Vizitiu, Cosmin-Ioan Nita, Radu Miron Toev, Tudor Suditu, Constantin Suciu and Lucian Mihai Itu

Appl. Sci. 2021, 11(19), 9049; https://doi.org/10.3390/app11199049 - 28 Sep 2021

Cited by 5 | Viewed by 1878

Abstract

Medical wearable devices monitor health data and, coupled with data analytics, cloud computing, and artificial intelligence (AI), enable early detection of disease. Privacy issues arise when personal health information is sent or processed outside the device. We propose a framework that ensures the privacy and integrity of personal medical data while performing AI-based homomorphically encrypted data analytics in the cloud. The main contributions are: (i) a privacy-preserving cloud-based machine learning framework for wearable devices, (ii) CipherML—a library for fast implementation and deployment of deep learning-based solutions on homomorphically encrypted data, and (iii) a proof-of-concept study for atrial fibrillation (AF) detection from electrocardiograms recorded on a wearable device. In the context of AF detection, two approaches are considered: a multi-layer perceptron (MLP) which receives as input the ECG features computed and encrypted on the wearable device, and an end-to-end deep convolutional neural network (1D-CNN), which receives as input the encrypted raw ECG data. The CNN model achieves a lower mean F1-score than the hand-crafted feature-based model. This illustrates the benefit of hand-crafted features over deep convolutional neural networks, especially in a setting with a small training data. Compared to state-of-the-art results, the two privacy-preserving approaches lead, with reasonable computational overhead, to slightly lower, but still similar results: the small performance drop is caused by limitations related to the use of homomorphically encrypted data instead of plaintext data. The findings highlight the potential of the proposed framework to enhance the functionality of wearables through privacy-preserving AI, by providing, within a reasonable amount of time, results equivalent to those achieved without privacy enhancing mechanisms. While the chosen homomorphic encryption scheme prioritizes performance and utility, certain security shortcomings remain open for future development. Full article

(This article belongs to the Special Issue Advances in Biosignal Processing and Biomedical Data Analysis)

► Show Figures

Figure 1

14 pages, 3779 KiB

Open AccessArticle

Two-Step Biometrics Using Electromyogram Signal Based on Convolutional Neural Network-Long Short-Term Memory Networks

by Jin-Su Kim, Min-Gu Kim and Sung-Bum Pan

Appl. Sci. 2021, 11(15), 6824; https://doi.org/10.3390/app11156824 - 25 Jul 2021

Cited by 11 | Viewed by 2456

Abstract

Electromyogram (EMG) signals cannot be forged and have the advantage of being able to change the registered data as they are characterized by the waveform, which varies depending on the gesture. In this paper, a two-step biometrics method was proposed using EMG signals based on a convolutional neural network–long short-term memory (CNN-LSTM) network. After preprocessing of the EMG signals, the time domain features and LSTM network were used to examine whether the gesture matched, and single biometrics was performed if the gesture matched. In single biometrics, EMG signals were converted into a two-dimensional spectrogram, and training and classification were performed through the CNN-LSTM network. Data fusion of the gesture recognition and single biometrics was performed in the form of an AND. The experiment used Ninapro EMG signal data as the proposed two-step biometrics method, and the results showed 83.91% gesture recognition performance and 99.17% single biometrics performance. In addition, the false acceptance rate (FAR) was observed to have been reduced by 64.7% through data fusion. Full article

(This article belongs to the Special Issue Advances in Biosignal Processing and Biomedical Data Analysis)

► Show Figures

Figure 1

13 pages, 445 KiB

Open AccessArticle

The Effects of Compression on the Detection of Atrial Fibrillation in ECG Signals

by Raquel Cervigón, Brian McGinley, Darren Craven, Martin Glavin and Edward Jones

Appl. Sci. 2021, 11(13), 5908; https://doi.org/10.3390/app11135908 - 25 Jun 2021

Cited by 4 | Viewed by 1514

Abstract

Although Atrial Fibrillation (AF) is the most frequent cause of cardioembolic stroke, the arrhythmia remains underdiagnosed, as it is often asymptomatic or intermittent. Automated detection of AF in ECG signals is important for patients with implantable cardiac devices, pacemakers or Holter systems. Such resource-constrained systems often operate by transmitting signals to a central server where diagnostic decisions are made. In this context, ECG signal compression is being increasingly investigated and employed to increase battery life, and hence the storage and transmission efficiency of these devices. At the same time, the diagnostic accuracy of AF detection must be preserved. This paper investigates the effects of ECG signal compression on an entropy-based AF detection algorithm that monitors R-R interval regularity. The compression and AF detection algorithms were applied to signals from the MIT-BIH AF database. The accuracy of AF detection on reconstructed signals is evaluated under varying degrees of compression using the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) compression algorithm. Results demonstrate that compression ratios (CR) of up to 90 can be obtained while maintaining a detection accuracy, expressed in terms of the area under the receiver operating characteristic curve, of at least 0.9. This highlights the potential for significant energy savings on devices that transmit/store ECG signals for AF detection applications, while preserving the diagnostic integrity of the signals, and hence the detection performance. Full article

(This article belongs to the Special Issue Advances in Biosignal Processing and Biomedical Data Analysis)

► Show Figures