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

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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: closed (16 September 2023) | Viewed by 36925

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Prof. Dr. Alfredo Rosado

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Department of Electronic Engineering, University of Valencia, 46100 Burjassot, Spain
Interests: hardware implementation of algorithms; wood moisture sensing; IoT for sensor networks; hyperspectral sensing and processing
Special Issues, Collections and Topics in MDPI journals

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

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Published Papers (10 papers)

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Research

22 pages, 1247 KiB

Open AccessArticle

Ventricular Fibrillation and Tachycardia Detection Using Features Derived from Topological Data Analysis

by Azeddine Mjahad, Jose V. Frances-Villora, Manuel Bataller-Mompean and Alfredo Rosado-Muñoz

Appl. Sci. 2022, 12(14), 7248; https://doi.org/10.3390/app12147248 - 19 Jul 2022

Cited by 7 | Viewed by 1992

Abstract

A rapid and accurate detection of ventricular arrhythmias is essential to take appropriate therapeutic actions when cardiac arrhythmias occur. Furthermore, the accurate discrimination between arrhythmias is also important, provided that the required shocking therapy would not be the same. In this work, the main novelty is the use of the mathematical method known as Topological Data Analysis (TDA) to generate new types of features which can contribute to the improvement of the detection and classification performance of cardiac arrhythmias such as Ventricular Fibrillation (VF) and Ventricular Tachycardia (VT). The electrocardiographic (ECG) signals used for this evaluation were obtained from the standard MIT-BIH and AHA databases. Two input data to the classify are evaluated: TDA features, and Persistence Diagram Image (PDI). Using the reduced TDA-obtained features, a high average accuracy near 99% was observed when discriminating four types of rhythms (98.68% to VF; 99.05% to VT; 98.76% to normal sinus; and 99.09% to Other rhythms) with specificity values higher than 97.16% in all cases. In addition, a higher accuracy of 99.51% was obtained when discriminating between shockable (VT/VF) and non-shockable rhythms (99.03% sensitivity and 99.67% specificity). These results show that the use of TDA-derived geometric features, combined in this case this the k-Nearest Neighbor (kNN) classifier, raises the classification performance above results in previous works. Considering that these results have been achieved without preselection of ECG episodes, it can be concluded that these features may be successfully introduced in Automated External Defibrillation (AED) and Implantable Cardioverter Defibrillation (ICD) therapies. Full article

(This article belongs to the Special Issue Real-Time Diagnosis Algorithms in Biomedical Applications and Decision Support Tools)

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19 pages, 6250 KiB

Open AccessArticle

A Machine-Learning Model Based on Morphogeometric Parameters for RETICS Disease Classification and GUI Development

by José M. Bolarín, F. Cavas, J.S. Velázquez and J.L. Alió

Appl. Sci. 2020, 10(5), 1874; https://doi.org/10.3390/app10051874 - 09 Mar 2020

Cited by 10 | Viewed by 2872

Abstract

This work pursues two objectives: defining a new concept of risk probability associated with suffering early-stage keratoconus, classifying disease severity according to the RETICS (Thematic Network for Co-Operative Research in Health) scale. It recruited 169 individuals, 62 healthy and 107 keratoconus diseased, grouped according to the RETICS classification: 44 grade I; 18 grade II; 15 grade III; 15 grade IV; 15 grade V. Different demographic, optical, pachymetric and eometrical parameters were measured. The collected data were used for training two machine-learning models: a multivariate logistic regression model for early keratoconus detection and an ordinal logistic regression model for RETICS grade assessments. The early keratoconus detection model showed very good sensitivity, specificity and area under ROC curve, with around 95% for training and 85% for validation. The variables that made the most significant contributions were gender, coma-like, central thickness, high-order aberrations and temporal thickness. The RETICS grade assessment also showed high-performance figures, albeit lower, with a global accuracy of 0.698 and a 95% confidence interval of 0.623–0.766. The most significant variables were CDVA, central thickness and temporal thickness. The developed web application allows the fast, objective and quantitative assessment of keratoconus in early diagnosis and RETICS grading terms. Full article

(This article belongs to the Special Issue Real-Time Diagnosis Algorithms in Biomedical Applications and Decision Support Tools)

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44 pages, 18261 KiB

Open AccessArticle

Wavelia Breast Imaging: The Optical Breast Contour Detection Subsystem

by Julio Daniel Gil Cano, Angie Fasoula, Luc Duchesne and Jean-Gael Bernard

Appl. Sci. 2020, 10(4), 1234; https://doi.org/10.3390/app10041234 - 12 Feb 2020

Cited by 7 | Viewed by 4500

Abstract

Wavelia is a low-power electromagnetic wave breast imaging device for breast cancer diagnosis, which consists of two subsystems, both performing non-invasive examinations: the Microwave Breast Imaging (MBI) subsystem and the Optical Breast Contour Detection (OBCD) subsystem. The Wavelia OBCD subsystem is a 3D scanning device using an infrared 3D stereoscopic camera, which performs an azimuthal scan to acquire 3D point clouds of the external surface of the breast. The OBCD subsystem aims at reconstructing fully the external envelope of the breast, with high precision, to provide the total volume of the breast and morphological data as a priori information to the MBI subsystem. This paper presents a new shape-based calibration procedure for turntable-based 3D scanning devices, a new 3D breast surface reconstruction method based on a linear stretching function, as well as the breast volume computation method that have been developed and integrated with the Wavelia OBCD subsystem, before its installation at the Clinical Research Facility of Galway (CRFG), in Ireland, for first-in-human clinical testing. Indicative results of the Wavelia OBCD subsystem both from scans of experimental breast phantoms and from patient scans are thoroughly presented and discussed in the paper. Full article

(This article belongs to the Special Issue Real-Time Diagnosis Algorithms in Biomedical Applications and Decision Support Tools)

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13 pages, 5472 KiB

Open AccessArticle

Low-Cost, Compact, and Rapid Bio-Impedance Spectrometer with Real-Time Bode and Nyquist Plots

by Didik R. Santoso, Bella Pitaloka, Chomsin S. Widodo and Unggul P. Juswono

Appl. Sci. 2020, 10(3), 878; https://doi.org/10.3390/app10030878 - 28 Jan 2020

Cited by 7 | Viewed by 4214

Abstract

Bioelectric impedance spectroscopy (BIS) has been widely used to study the electrical properties of biological tissue based on the characteristics of the complex electrical impedance dispersions. One of the problems in using the BIS method is the length of time required for the data acquisition process and possibly data analysis as well. In this research, a compact and work rapidly BIS instrumentation system has been developed at a low cost. It is designed to work in the frequency range of 100 Hz to 100 kHz, which is generally used in the fields of biophysics and medical physics. The BIS instrumentation system is built using several integrated modules. The modules are an AC current source to produce a selectable injection current; a data acquisition system to measure voltage, current, and phase difference rapidly and simultaneously; and software to calculate and display measurement results in the form of Bode and Nyquist plots in real time. The developed BIS system has been validated using a simple RC circuit as the sample being tested. The average time needed in the process of data acquisition and analysis until the formation of impedance dispersion curves in the form of Bode and Nyquist plots, for 54 sample frequencies, is less than one minute. The system is able to identify R and C values of the sample with a maximum error of 1.5%. In addition, some simple application examples are also presented in this paper. Full article

(This article belongs to the Special Issue Real-Time Diagnosis Algorithms in Biomedical Applications and Decision Support Tools)

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21 pages, 375 KiB

Open AccessArticle

Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation

by Jose V. Frances-Villora, Manuel Bataller-Mompean, Azeddine Mjahad, Alfredo Rosado-Muñoz, Antonio Gutierrez Martin, Vicent Teruel-Marti, Vicente Villanueva, Kevin G. Hampel and Juan F. Guerrero-Martinez

Appl. Sci. 2020, 10(3), 827; https://doi.org/10.3390/app10030827 - 23 Jan 2020

Cited by 4 | Viewed by 2731

Abstract

The epileptogenic focus is a brain area that may be surgically removed to control of epileptic seizures. Locating it is an essential and crucial step prior to the surgical treatment. However, given the difficulty of determining the localization of this brain region responsible of the initial seizure discharge, many works have proposed machine learning methods for the automatic classification of focal and non-focal electroencephalographic (EEG) signals. These works use automatic classification as an analysis tool for helping neurosurgeons to identify focal areas off-line, out of surgery, during the processing of the huge amount of information collected during several days of patient monitoring. In turn, this paper proposes an automatic classification procedure capable of assisting neurosurgeons online, during the resective epilepsy surgery, to refine the localization of the epileptogenic area to be resected, if they have doubts. This goal requires a real-time implementation with as low a computational cost as possible. For that reason, this work proposes both a feature set and a classifier model that minimizes the computational load while preserving the classification accuracy at 95.5%, a level similar to previous works. In addition, the classification procedure has been implemented on a FPGA device to determine its resource needs and throughput. Thus, it can be concluded that such a device can embed the whole classification process, from accepting raw signals to the delivery of the classification results in a cost-effective Xilinx Spartan-6 FPGA device. This real-time implementation begins providing results after a 5 s latency, and later, can deliver floating-point classification results at 3.5 Hz rate, using overlapped time-windows. Full article

(This article belongs to the Special Issue Real-Time Diagnosis Algorithms in Biomedical Applications and Decision Support Tools)

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14 pages, 2316 KiB

Open AccessArticle

Optimization of Physical Activity Recognition for Real-Time Wearable Systems: Effect of Window Length, Sampling Frequency and Number of Features

by Ardo Allik, Kristjan Pilt, Deniss Karai, Ivo Fridolin, Mairo Leier and Gert Jervan

Appl. Sci. 2019, 9(22), 4833; https://doi.org/10.3390/app9224833 - 12 Nov 2019

Cited by 7 | Viewed by 2045

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 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

(This article belongs to the Special Issue Real-Time Diagnosis Algorithms in Biomedical Applications and Decision Support Tools)

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Graphical abstract

17 pages, 4955 KiB

Open AccessArticle

Analogy Study of Center-Of-Pressure and Acceleration Measurement for Evaluating Human Body Balance via Segmentalized Principal Component Analysis

by Tian-Yau Wu and Ching-Ting Liou

Appl. Sci. 2019, 9(22), 4779; https://doi.org/10.3390/app9224779 - 08 Nov 2019

Cited by 2 | Viewed by 2390

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 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

(This article belongs to the Special Issue Real-Time Diagnosis Algorithms in Biomedical Applications and Decision Support Tools)

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33 pages, 501 KiB

Open AccessArticle

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

by Gennaro Vessio

Appl. Sci. 2019, 9(21), 4666; https://doi.org/10.3390/app9214666 - 01 Nov 2019

Cited by 54 | Viewed by 7434

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 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

(This article belongs to the Special Issue Real-Time Diagnosis Algorithms in Biomedical Applications and Decision Support Tools)

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9 pages, 2600 KiB

Open AccessArticle

Automatic Detection of a Standard Line for Brain Magnetic Resonance Imaging Using Deep Learning

by Hiroyuki Sugimori and Masashi Kawakami

Appl. Sci. 2019, 9(18), 3849; https://doi.org/10.3390/app9183849 - 13 Sep 2019

Cited by 16 | Viewed by 3784

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 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

(This article belongs to the Special Issue Real-Time Diagnosis Algorithms in Biomedical Applications and Decision Support Tools)

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13 pages, 2382 KiB

Open AccessCommunication

A Prototype of a Portable Gas Analyzer for Exhaled Acetone Detection

by Jakub Sorocki and Artur Rydosz

Appl. Sci. 2019, 9(13), 2605; https://doi.org/10.3390/app9132605 - 27 Jun 2019

Cited by 11 | Viewed by 3252

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 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

(This article belongs to the Special Issue Real-Time Diagnosis Algorithms in Biomedical Applications and Decision Support Tools)

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