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Algorithms
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Machine Learning Algorithms for Biomedical Signal Processing
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Machine Learning Algorithms for Biomedical Signal Processing

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 16617

Special Issue Editors

Dr. Maryam Ravan

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, New York Institute of Technology (NYIT), NYC Campus, Room 810, 1855 Broadway, New York, NY 10023-7692, USA
Interests: signal processing; machine learning; biomedical engineering; microwave imaging; non-destructive testing
Special Issues, Collections and Topics in MDPI journals
Dr. Ganesh R. Naik

E-Mail Website1 Website2
Guest Editor
College of Medicine and Public Health, Flinders University, Adelaide, SA 5042, Australia
Interests: biomedical engineering; signal processing; sleep, cardio respiratory research; BCI; wearables
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical signal processing aims to provide greater insights into the analysis of the information flows from physiological signals using a variety of mathematical formulae and algorithms.

Many researchers have proposed various novel algorithms and mathematical methods to analyze biomedical signals that significantly pushing the state of the art of biomeasurement tools into a wide range of high-level tasks. Such progress can help to gain a greater perception and to make better decisions in clinical assessments.

The purpose of this Special Issue is to present recent advances in signal processing and machine learning for biomedical signal analysis. We are focusing on original research works in this field, covering new theories, implementations, and mathematical analysis and modeling of time series in living systems and biomedical signals. Potential topics of interest are related to recent advances in machine learning and signal analysis and processing but are not limited to:

  • Biomedical signal processing and analysis;
  • Biomedical image processing and analysis;
  • Neural rehabilitation engineering;
  • Biomedical big data processing;
  • Signal/image processing for human machine interface;
  • Time-frequency and nonstationary biosignal analysis;
  • Machine learning in biomedical applications;
  • Biometrics with biomedical signals;
  • Statistical pattern recognition.

Dr. Maryam Ravan
Dr. Ganesh Naik
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. Algorithms 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 1800 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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

15 pages, 1400 KB  
Article
Diagnosing Schizophrenia Using Effective Connectivity of Resting-State EEG Data
by Claudio Ciprian, Kirill Masychev, Maryam Ravan, Akshaya Manimaran and AnkitaAmol Deshmukh
Algorithms 2021, 14(5), 139; https://doi.org/10.3390/a14050139 - 27 Apr 2021
Cited by 24 | Viewed by 6076
Abstract
Schizophrenia is a serious mental illness associated with neurobiological deficits. Even though the brain activities during tasks (i.e., P300 activities) are considered as biomarkers to diagnose schizophrenia, brain activities at rest have the potential to show an inherent dysfunctionality in schizophrenia and can [...] Read more.
Schizophrenia is a serious mental illness associated with neurobiological deficits. Even though the brain activities during tasks (i.e., P300 activities) are considered as biomarkers to diagnose schizophrenia, brain activities at rest have the potential to show an inherent dysfunctionality in schizophrenia and can be used to understand the cognitive deficits in these patients. In this study, we developed a machine learning algorithm (MLA) based on eyes closed resting-state electroencephalogram (EEG) datasets, which record the neural activity in the absence of any tasks or external stimuli given to the subjects, aiming to distinguish schizophrenic patients (SCZs) from healthy controls (HCs). The MLA has two steps. In the first step, symbolic transfer entropy (STE), which is a measure of effective connectivity, is applied to resting-state EEG data. In the second step, the MLA uses the STE matrix to find a set of features that can successfully discriminate SCZ from HC. From the results, we found that the MLA could achieve a total accuracy of 96.92%, with a sensitivity of 95%, a specificity of 98.57%, precision of 98.33%, F1-score of 0.97, and Matthews correlation coefficient (MCC) of 0.94 using only 10 out of 1900 STE features, which implies that the STE matrix extracted from resting-state EEG data may be a promising tool for the clinical diagnosis of schizophrenia. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Biomedical Signal Processing)
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15 pages, 1361 KB  
Article
Feature Selection from Lyme Disease Patient Survey Using Machine Learning
by Joshua Vendrow, Jamie Haddock, Deanna Needell and Lorraine Johnson
Algorithms 2020, 13(12), 334; https://doi.org/10.3390/a13120334 - 11 Dec 2020
Cited by 8 | Viewed by 4968
Abstract
Lyme disease is a rapidly growing illness that remains poorly understood within the medical community. Critical questions about when and why patients respond to treatment or stay ill, what kinds of treatments are effective, and even how to properly diagnose the disease remain [...] Read more.
Lyme disease is a rapidly growing illness that remains poorly understood within the medical community. Critical questions about when and why patients respond to treatment or stay ill, what kinds of treatments are effective, and even how to properly diagnose the disease remain largely unanswered. We investigate these questions by applying machine learning techniques to a large scale Lyme disease patient registry, MyLymeData, developed by the nonprofit LymeDisease.org. We apply various machine learning methods in order to measure the effect of individual features in predicting participants’ answers to the Global Rating of Change (GROC) survey questions that assess the self-reported degree to which their condition improved, worsened, or remained unchanged following antibiotic treatment. We use basic linear regression, support vector machines, neural networks, entropy-based decision tree models, and k-nearest neighbors approaches. We first analyze the general performance of the model and then identify the most important features for predicting participant answers to GROC. After we identify the “key” features, we separate them from the dataset and demonstrate the effectiveness of these features at identifying GROC. In doing so, we highlight possible directions for future study both mathematically and clinically. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Biomedical Signal Processing)
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20 pages, 3707 KB  
Article
An EEG Feature Extraction Method Based on Sparse Dictionary Self-Organizing Map for Event-Related Potential Recognition
by Shang Feng, Haifeng Li, Lin Ma and Zhongliang Xu
Algorithms 2020, 13(10), 259; https://doi.org/10.3390/a13100259 - 13 Oct 2020
Cited by 3 | Viewed by 3769
Abstract
In the application of the brain-computer interface, feature extraction is an important part of Electroencephalography (EEG) signal classification. Using sparse modeling to extract EEG signal features is a common approach. However, the features extracted by common sparse decomposition methods are only of analytical [...] Read more.
In the application of the brain-computer interface, feature extraction is an important part of Electroencephalography (EEG) signal classification. Using sparse modeling to extract EEG signal features is a common approach. However, the features extracted by common sparse decomposition methods are only of analytical meaning, and cannot relate to actual EEG waveforms, especially event-related potential waveforms. In this article, we propose a feature extraction method based on a self-organizing map of sparse dictionary atoms, which can aggregate event-related potential waveforms scattered inside an over-complete sparse dictionary into the code book of neurons in the self-organizing map network. Then, the cosine similarity between the EEG signal sample and the code vector is used as the classification feature. Compared with traditional feature extraction methods based on sparse decomposition, the classification features obtained by this method have more intuitive electrophysiological meaning. The experiment conducted on a public auditory event-related potential (ERP) brain-computer interface dataset showed that, after the self-organized mapping of dictionary atoms, the neurons’ code vectors in the self-organized mapping network were remarkably similar to the ERP waveform obtained after superposition and averaging. The feature extracted by the proposed method used a smaller amount of data to obtain classification accuracy comparable to the traditional method. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Biomedical Signal Processing)
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