Special Issue "Real-Time Diagnosis Algorithms in Biomedical Applications and Decision Support Tools"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editor

Prof. Dr. Alfredo Rosado Muñoz
E-Mail Website
Guest Editor
Electronic Engineering Dpt. ETSE. University of Valencia
Interests: hardware implementation for biomedical applications: diagnosis and prediction using artificial intelligence

Special Issue Information

Dear Colleagues,

The use of automatic support tools for the medical practice is continuously increasing. From family doctors to surgeons, specialists are using a wide number of devices, which certainly increase the level of accuracy in their diagnoses. Deep learning algorithms and data analysis in general are providing new possibilities for doctors. Many doctors now use user-friendly tools and devices in their daily practice, even though they are necessarily skilled in advanced tools or research-level algorithms.

On the other hand, many of the newly proposed biomedical algorithms require offline tools, i.e., data must be saved, processed, and, finally, a result is provided to the doctor, to be used by him as a support for patient diagnosis. This process can be followed in many occasions where the medical test is done in advance and, after some hours or days, the result is sent to the doctor. However, other common situations exist where helping the doctor immediately by providing important data on the patient is required.

Currently, it is common that medical devices provide real-time monitoring of patients by providing basic data (cardiac rhythm, blood pressure, and body temperature), but due to the algorithm complexity, it is uncommon to find real-time diagnosis since most of the algorithms require a complex computation and real-time results cannot be provided.

Artificial intelligence algorithms have proven to be tools that improve the accuracy of detection in many medical problems; they provide predictions and can learn new features while being used. However, real-time implementation is still a challenge if we consider that the results should be given in a few seconds (at most) so that they can be helpful to the practitioner. So that this goal can be achieved, special hardware as well as parallel software implementations must be developed.

This Special Issue welcomes real-time implementations of algorithms that can serve as an immediate decision-support tools in medical situations. This Special Issue is open but not restricted to any of the following biomedical areas where a real-time implementation is proposed:

  • Signal processing algorithms for real-time pathology detection;
  • Image processing algorithms for real-time diagnosis;
  • Artificial intelligence algorithm for real-time implementation (this may include online training);
  • Real-time parallel software implementations;
  • Hardware implementations for computation speed-up, based on specific hardware: VLSI, FPGA, GPU, etc;
  • Hardware implementations for portability and/or low power;
  • User tools for real-time decision-support systems;
  • Implementations for real-time remote diagnosis.

Prof. Dr. Alfredo Rosado
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 papers will be 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 1500 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 (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Optimization of Physical Activity Recognition for Real-Time Wearable Systems: Effect of Window Length, Sampling Frequency and Number of Features
Appl. Sci. 2019, 9(22), 4833; https://doi.org/10.3390/app9224833 - 12 Nov 2019
Abstract
The aim of this study was to develop an optimized physical activity classifier for real-time wearable systems with the focus on reducing the requirements on device power consumption and memory buffer. Classification parameters evaluated in this study were the sampling frequency of the [...] Read more.
The aim of this study was to develop an optimized physical activity classifier for real-time wearable systems with the focus on reducing the requirements on device power consumption and memory buffer. Classification parameters evaluated in this study were the sampling frequency of the acceleration signal, window length of the classification fragment, and the number of classification features, found with different feature selection methods. For parameter evaluation, a decision tree classifier was created based on the acceleration signals recorded during tests, where 25 healthy test subjects performed various physical activities. Overall average F1-score achieved in this study was about 0.90. Similar F1-scores were achieved with the evaluated window lengths of 5 s (0.92 ± 0.02) and 3 s (0.91 ± 0.02), while classification performance with 1 s were lower (0.87 ± 0.02). Tested sampling frequencies of 50 Hz, 25 Hz, and 13 Hz had similar results with most classified activity types, with an exception of outdoor cycling, where differences were significant. Using forward sequential feature selection enabled the decreasing of the number of features from initial 110 features to about 12 features without lowering the classification performance. The results of this study have been used for developing more efficient real-time physical activity classifiers. Full article
Show Figures

Graphical abstract

Open AccessArticle
Analogy Study of Center-Of-Pressure and Acceleration Measurement for Evaluating Human Body Balance via Segmentalized Principal Component Analysis
Appl. Sci. 2019, 9(22), 4779; https://doi.org/10.3390/app9224779 - 08 Nov 2019
Abstract
The purpose of this research is to investigate the feasibility of evaluating the human’s balancing ability by means of the human body’s swaying acceleration measurements instead of the traditional center-of-pressure (COP) measurement. The COP measurement has been used broadly for assessing the balance [...] Read more.
The purpose of this research is to investigate the feasibility of evaluating the human’s balancing ability by means of the human body’s swaying acceleration measurements instead of the traditional center-of-pressure (COP) measurement. The COP measurement has been used broadly for assessing the balance ability of patients in hospitals. However, the force plate system which is employed to measure the COP signals of the human body is generally restrictive due to the very high cost as well as the bulky portability. In this study, the balancing ability of the human body was evaluated through the measurements of a capacitive accelerometer. The segmentalized principal components analysis (sPCA) was employed to reduce the influence of the gravity component in acceleration measurement projected onto the horizontal components while the accelerometer inevitably tilts. The signal relationship between the COP and the acceleration was derived, so that the swaying acceleration measurements of human body can be utilized to evaluate the human body’s balancing ability. Full article
Show Figures

Figure 1

Open AccessArticle
Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review
Appl. Sci. 2019, 9(21), 4666; https://doi.org/10.3390/app9214666 - 01 Nov 2019
Abstract
Studying the effects of neurodegeneration on handwriting has emerged as an interdisciplinary research topic and has attracted considerable interest from psychologists to neuroscientists and from physicians to computer scientists. The complexity of handwriting, in fact, appears to be sensitive to age-related impairments in [...] Read more.
Studying the effects of neurodegeneration on handwriting has emerged as an interdisciplinary research topic and has attracted considerable interest from psychologists to neuroscientists and from physicians to computer scientists. The complexity of handwriting, in fact, appears to be sensitive to age-related impairments in cognitive functioning; thus, analyzing handwriting in elderly people may facilitate the diagnosis and monitoring of these impairments. A large body of knowledge has been collected in the last thirty years thanks to the advent of new technologies which allow researchers to investigate not only the static characteristics of handwriting but also especially the dynamic aspects of the handwriting process. The present paper aims at providing an overview of the most relevant literature investigating the application of dynamic handwriting analysis in neurodegenerative disease assessment. The focus, in particular, is on Parkinon’s disease (PD) and Alzheimer’s disease (AD), as the two most widespread neurodegenerative disorders. More specifically, the studies taken into account are grouped in accordance with three main research questions: disease insight, disease monitoring, and disease diagnosis. The net result is that dynamic handwriting analysis is a powerful, noninvasive, and low-cost tool for real-time diagnosis and follow-up of PD and AD. In conclusion of the paper, open issues still demanding further research are highlighted. Full article
Show Figures

Figure 1

Open AccessArticle
Automatic Detection of a Standard Line for Brain Magnetic Resonance Imaging Using Deep Learning
Appl. Sci. 2019, 9(18), 3849; https://doi.org/10.3390/app9183849 - 13 Sep 2019
Abstract
Recently, deep learning technology has been applied to medical images. This study aimed to create a detector able to automatically detect an anatomical structure presented in a brain magnetic resonance imaging (MRI) scan to draw a standard line. A total of 1200 brain [...] Read more.
Recently, deep learning technology has been applied to medical images. This study aimed to create a detector able to automatically detect an anatomical structure presented in a brain magnetic resonance imaging (MRI) scan to draw a standard line. A total of 1200 brain sagittal MRI scans were used for training and validation. Two sizes of regions of interest (ROIs) were drawn on each anatomical structure measuring 64 × 64 pixels and 32 × 32 pixels, respectively. Data augmentation was applied to these ROIs. The faster region-based convolutional neural network was used as the network model for training. The detectors created were validated to evaluate the precision of detection. Anatomical structures detected by the model created were processed to draw the standard line. The average precision of anatomical detection, detection rate of the standard line, and accuracy rate of achieving a correct drawing were evaluated. For the 64 × 64-pixel ROI, the mean average precision achieved a result of 0.76 ± 0.04, which was higher than the outcome achieved with the 32 × 32-pixel ROI. Moreover, the detection and accuracy rates of the angle of difference at 10 degrees for the orbitomeatal line were 93.3 ± 5.2 and 76.7 ± 11.0, respectively. The automatic detection of a reference line for brain MRI can help technologists improve this examination. Full article
Show Figures

Figure 1

Open AccessCommunication
A Prototype of a Portable Gas Analyzer for Exhaled Acetone Detection
Appl. Sci. 2019, 9(13), 2605; https://doi.org/10.3390/app9132605 - 27 Jun 2019
Abstract
The paper presents the development of a portable gas analyzer prototype for exhaled acetone detection, employing an application-suited gas sensor array and 3D printing technology. The device provides the functionality to monitor exhaled acetone levels, which could be used as a potential tool [...] Read more.
The paper presents the development of a portable gas analyzer prototype for exhaled acetone detection, employing an application-suited gas sensor array and 3D printing technology. The device provides the functionality to monitor exhaled acetone levels, which could be used as a potential tool for non-invasive diabetes monitoring. The relationship between exhaled acetone concentrations and glucose in blood is confirmed in the literature, including research carried out by the authors. The design process is presented including a general consideration for the sensor array construction, which is the core for sensing gases, as well as requirements for the measurement chamber it is to be placed in. Moreover, the mechanical design of the 3D-printed housing is discussed to ensure the ergonomics of use as a hand-held device while keeping the hardware integrity. Also, the processing hardware is discussed to provide sufficient computing power to handle the stand-alone operation while being energy efficient, enabling long battery-powered operation. Finally, calibration and measurement, as well as the analyzer operation, are shown, validating the proposed class of exhaled acetone-detection capable meters. Full article
Show Figures

Figure 1

Back to TopTop