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Advances in Machine Learning for Physiological Signal Processing Applications
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Advances in Machine Learning for Physiological Signal Processing Applications

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

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 8219

Special Issue Editor

Dr. Vassilis S. Kodogiannis

E-Mail Website
Guest Editor
School of Computer Science and Engineering, University of Westminster, London, UK
Interests: computational intelligent systems; signal & image processing; system identification & control; energy forecasting systems; robotics; chemometrics; biomedicine; food safety/quality; traffic prediction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical signals are very useful for evaluating the well-being of a human. The rapid increase in the generation of physiological data, together with the development of big data intelligence, has enabled us to extract new insights from massive physiological signals. These include, among others, bioelectrical signals such as electrocardiogram (ECG), electroencephalogram (EEG), electromyography (EMG), and electrooculogram (EOG) signals as well as biochemical signals such as oxygen and carbon dioxide pressure during respiration. In order to analyse biomedical signals, biomedical engineers utilise different types of signal processing and machine learning techniques that will eventually assist physicians in order to gain greater insight and to make better decisions during clinical assessments. Machine learning (ML) and deep learning (DL) methods play an essential role in developing automated diagnostic systems for the accurate detection of various diseases using physiological signals.

The aim of this Special Issue is to bring together researchers and scientists in the fields of biomedical signal processing and machine learning to present and discuss the recent advances in learning methods and intelligent approaches for biomedical signal processing applications.

The focus will be on the use and elaboration of the latest techniques, such as deep learning schemes, traditional machine learning, and novel features extracted from experimental physiological signals in order to understand their usefulness in healthcare.

Dr. Vassilis S. Kodogiannis
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. 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

  • computational measurement of electroencephalogram (EEG), electromyography (EMG), and electrocardiogram (ECG) bio-signals and other electrophysiological signals for progressive disease detection and monitoring
  • analysis of physiological signals with a low signal-to-noise ratio
  • biomedical signal analysis using machine learning and modelling
  • data mining in biomedical applications
  • data fusion strategies and their applications in applied physiology
  • deep learning in physiological signals
  • novel supervised learning, semi-supervised learning, clustering approaches for sensory data processing, and classification
  • application of nonlinear features of physiological data
  • advanced signal processing techniques for nonstationary or multi-scale data analysis
  • scalable, robust, data-driven, and ensemble learning for biomedical data mining
  • IoT-based wearable sensors and trackers for healthcare

Published Papers (4 papers)

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Research

27 pages, 2640 KiB  
Article
A Study of R-R Interval Transition Matrix Features for Machine Learning Algorithms in AFib Detection
by Sahil Patel, Maximilian Wang, Justin Guo, Georgia Smith and Cuixian Chen
Sensors 2023, 23(7), 3700; https://doi.org/10.3390/s23073700 - 3 Apr 2023
Cited by 1 | Viewed by 2139
Abstract
Atrial Fibrillation (AFib) is a heart condition that occurs when electrophysiological malformations within heart tissues cause the atria to lose coordination with the ventricles, resulting in “irregularly irregular” heartbeats. Because symptoms are subtle and unpredictable, AFib diagnosis is often difficult or delayed. One [...] Read more.
Atrial Fibrillation (AFib) is a heart condition that occurs when electrophysiological malformations within heart tissues cause the atria to lose coordination with the ventricles, resulting in “irregularly irregular” heartbeats. Because symptoms are subtle and unpredictable, AFib diagnosis is often difficult or delayed. One possible solution is to build a system which predicts AFib based on the variability of R-R intervals (the distances between two R-peaks). This research aims to incorporate the transition matrix as a novel measure of R-R variability, while combining three segmentation schemes and two feature importance measures to systematically analyze the significance of individual features. The MIT-BIH dataset was first divided into three segmentation schemes, consisting of 5-s, 10-s, and 25-s subsets. In total, 21 various features, including the transition matrix features, were extracted from these subsets and used for the training of 11 machine learning classifiers. Next, permutation importance and tree-based feature importance calculations determined the most predictive features for each model. In summary, with Leave-One-Person-Out Cross Validation, classifiers under the 25-s segmentation scheme produced the best accuracies; specifically, Gradient Boosting (96.08%), Light Gradient Boosting (96.11%), and Extreme Gradient Boosting (96.30%). Among eleven classifiers, the three gradient boosting models and Random Forest exhibited the highest overall performance across all segmentation schemes. Moreover, the permutation and tree-based importance results demonstrated that the transition matrix features were most significant with longer subset lengths. Full article
(This article belongs to the Special Issue Advances in Machine Learning for Physiological Signal Processing Applications)
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33 pages, 3811 KiB  
Article
Sleep Stage Classification in Children Using Self-Attention and Gaussian Noise Data Augmentation
by Xinyu Huang, Kimiaki Shirahama, Muhammad Tausif Irshad, Muhammad Adeel Nisar, Artur Piet and Marcin Grzegorzek
Sensors 2023, 23(7), 3446; https://doi.org/10.3390/s23073446 - 25 Mar 2023
Cited by 10 | Viewed by 2344
Abstract
The analysis of sleep stages for children plays an important role in early diagnosis and treatment. This paper introduces our sleep stage classification method addressing the following two challenges: the first is the data imbalance problem, i.e., the highly skewed class distribution with [...] Read more.
The analysis of sleep stages for children plays an important role in early diagnosis and treatment. This paper introduces our sleep stage classification method addressing the following two challenges: the first is the data imbalance problem, i.e., the highly skewed class distribution with underrepresented minority classes. For this, a Gaussian Noise Data Augmentation (GNDA) algorithm was applied to polysomnography recordings to seek the balance of data sizes for different sleep stages. The second challenge is the difficulty in identifying a minority class of sleep stages, given their short sleep duration and similarities to other stages in terms of EEG characteristics. To overcome this, we developed a DeConvolution- and Self-Attention-based Model (DCSAM) which can inverse the feature map of a hidden layer to the input space to extract local features and extract the correlations between all possible pairs of features to distinguish sleep stages. The results on our dataset show that DCSAM based on GNDA obtains an accuracy of 90.26% and a macro F1-score of 86.51% which are higher than those of our previous method. We also tested DCSAM on a well-known public dataset—Sleep-EDFX—to prove whether it is applicable to sleep data from adults. It achieves a comparable performance to state-of-the-art methods, especially accuracies of 91.77%, 92.54%, 94.73%, and 95.30% for six-stage, five-stage, four-stage, and three-stage classification, respectively. These results imply that our DCSAM based on GNDA has a great potential to offer performance improvements in various medical domains by considering the data imbalance problems and correlations among features in time series data. Full article
(This article belongs to the Special Issue Advances in Machine Learning for Physiological Signal Processing Applications)
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18 pages, 5539 KiB  
Article
Posthoc Interpretability of Neural Responses by Grouping Subject Motor Imagery Skills Using CNN-Based Connectivity
by Diego Fabian Collazos-Huertas, Andrés Marino Álvarez-Meza, David Augusto Cárdenas-Peña, Germán Albeiro Castaño-Duque and César Germán Castellanos-Domínguez
Sensors 2023, 23(5), 2750; https://doi.org/10.3390/s23052750 - 2 Mar 2023
Cited by 1 | Viewed by 1459
Abstract
Motor Imagery (MI) refers to imagining the mental representation of motor movements without overt motor activity, enhancing physical action execution and neural plasticity with potential applications in medical and professional fields like rehabilitation and education. Currently, the most promising approach for implementing the [...] Read more.
Motor Imagery (MI) refers to imagining the mental representation of motor movements without overt motor activity, enhancing physical action execution and neural plasticity with potential applications in medical and professional fields like rehabilitation and education. Currently, the most promising approach for implementing the MI paradigm is the Brain-Computer Interface (BCI), which uses Electroencephalogram (EEG) sensors to detect brain activity. However, MI-BCI control depends on a synergy between user skills and EEG signal analysis. Thus, decoding brain neural responses recorded by scalp electrodes poses still challenging due to substantial limitations, such as non-stationarity and poor spatial resolution. Also, an estimated third of people need more skills to accurately perform MI tasks, leading to underperforming MI-BCI systems. As a strategy to deal with BCI-Inefficiency, this study identifies subjects with poor motor performance at the early stages of BCI training by assessing and interpreting the neural responses elicited by MI across the evaluated subject set. Using connectivity features extracted from class activation maps, we propose a Convolutional Neural Network-based framework for learning relevant information from high-dimensional dynamical data to distinguish between MI tasks while preserving the post-hoc interpretability of neural responses. Two approaches deal with inter/intra-subject variability of MI EEG data: (a) Extracting functional connectivity from spatiotemporal class activation maps through a novel kernel-based cross-spectral distribution estimator, (b) Clustering the subjects according to their achieved classifier accuracy, aiming to find common and discriminative patterns of motor skills. According to the validation results obtained on a bi-class database, an average accuracy enhancement of 10% is achieved compared to the baseline EEGNet approach, reducing the number of “poor skill” subjects from 40% to 20%. Overall, the proposed method can be used to help explain brain neural responses even in subjects with deficient MI skills, who have neural responses with high variability and poor EEG-BCI performance. Full article
(This article belongs to the Special Issue Advances in Machine Learning for Physiological Signal Processing Applications)
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16 pages, 1227 KiB  
Article
Towards Generating Realistic Wrist Pulse Signals Using Enhanced One Dimensional Wasserstein GAN
by Jiaxing Chang, Fei Hu, Huaxing Xu, Xiaobo Mao, Yuping Zhao and Luqi Huang
Sensors 2023, 23(3), 1450; https://doi.org/10.3390/s23031450 - 28 Jan 2023
Cited by 2 | Viewed by 1494
Abstract
For the past several years, there has been an increasing focus on deep learning methods applied into computational pulse diagnosis. However, one factor restraining its development lies in the small wrist pulse dataset, due to privacy risks or lengthy experiments cost. In this [...] Read more.
For the past several years, there has been an increasing focus on deep learning methods applied into computational pulse diagnosis. However, one factor restraining its development lies in the small wrist pulse dataset, due to privacy risks or lengthy experiments cost. In this study, for the first time, we address the challenging by presenting a novel one-dimension generative adversarial networks (GAN) for generating wrist pulse signals, which manages to learn a mapping strategy from a random noise space to the original wrist pulse data distribution automatically. Concretely, Wasserstein GAN with gradient penalty (WGAN-GP) is employed to alleviate the mode collapse problem of vanilla GANs, which could be able to further enhance the performance of the generated pulse data. We compared our proposed model performance with several typical GAN models, including vanilla GAN, deep convolutional GAN (DCGAN) and Wasserstein GAN (WGAN). To verify the feasibility of the proposed algorithm, we trained our model with a dataset of real recorded wrist pulse signals. In conducted experiments, qualitative visual inspection and several quantitative metrics, such as maximum mean deviation (MMD), sliced Wasserstein distance (SWD) and percent root mean square difference (PRD), are examined to measure performance comprehensively. Overall, WGAN-GP achieves the best performance and quantitative results show that the above three metrics can be as low as 0.2325, 0.0112 and 5.8748, respectively. The positive results support that generating wrist pulse data from a small ground truth is possible. Consequently, our proposed WGAN-GP model offers a potential innovative solution to address data scarcity challenge for researchers working with computational pulse diagnosis, which are expected to improve the performance of pulse diagnosis algorithms in the future. Full article
(This article belongs to the Special Issue Advances in Machine Learning for Physiological Signal Processing Applications)
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