AI-Based Biomedical Signal Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 8959

Special Issue Editors

Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy
Interests: cardiac signal processing; biostatistics applied to cardiac signals; artificial intelligence in medicine and biology; clinical decision support systems; wearable and portable sensors; cardiorespiratory monitoring in sport; serial electrocardiography; atrial fibrillation; fetal and newborn monitoring
Special Issues, Collections and Topics in MDPI journals
Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy
Interests: brain-computer interfacing; electroencephalography; human-machine interaction; motor imagery; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Artificial intelligence (AI) is spreading, and now influences all fields of healthcare. Typically used as statistical methods, these AI-based innovative tools are also promising in the other steps of the biomedical signal processing blockchain, including biomedical signal (i) acquisition, (ii) preprocessing, (iii) feature engineering and (iv) classification/interpretation. AI-based methods may find solutions to biomedical signal processing challenges by integrating sensors and acquisition systems, as well as preprocessing, characterizing, classifying, and interpreting biomedical signals. These solutions may be essential in all fields of healthcare, including cardiology, neurology, endocrinology, movement analysis, physical activity monitoring, assistive robotics, telemedicine, and others. Thus, this Special Issue aims to collect original research papers and/or reviews on AI-based methods for biomedical signal processing. Main topics include, but are not limited to:

  • Intelligent sensors, devices and instruments for biomedical signal acquisition;
  • AI-based biomedical signal preprocessing;
  • Machine learning for biomedical feature extraction and selection;
  • Knowledge engineering for feature interpretation;
  • AI-based clinical decision making in healthcare;
  • AI-based precision medicine;
  • Data analytics and mining for clinical decision support;
  • Ethics of AI in healthcare.

Dr. Agnese Sbrollini
Dr. Aurora Saibene
Guest Editors

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Keywords

  • artificial intelligence
  • biomedical signal processing
  • filtering and denoising
  • machine and deep learning
  • clinical decision support systems
  • cognitive computing
  • computer vision
  • interpretability

Published Papers (7 papers)

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Research

13 pages, 1337 KiB  
Article
Identification of Electrocardiographic Patterns Related to Mortality with COVID-19
Appl. Sci. 2024, 14(2), 817; https://doi.org/10.3390/app14020817 - 18 Jan 2024
Viewed by 660
Abstract
COVID-19 is an infectious disease that has greatly affected worldwide healthcare systems, due to the high number of cases and deaths. As COVID-19 patients may develop cardiac comorbidities that can be potentially fatal, electrocardiographic monitoring can be crucial. This work aims to identify [...] Read more.
COVID-19 is an infectious disease that has greatly affected worldwide healthcare systems, due to the high number of cases and deaths. As COVID-19 patients may develop cardiac comorbidities that can be potentially fatal, electrocardiographic monitoring can be crucial. This work aims to identify electrocardiographic and vectorcardiographic patterns that may be related to mortality in COVID-19, with the application of the Advanced Repeated Structuring and Learning Procedure (AdvRS&LP). The procedure was applied to data from the “automatic computation of cardiovascular arrhythmic risk from electrocardiographic data of COVID-19 patients” (COVIDSQUARED) project to obtain neural networks (NNs) that, through 254 electrocardiographic and vectorcardiographic features, could discriminate between COVID-19 survivors and deaths. The NNs were validated by a five-fold cross-validation procedure and assessed in terms of the area under the curve (AUC) of the receiver operating characteristic. The features’ contribution to the classification was evaluated through the Local-Interpretable Model-Agnostic Explanations (LIME) algorithm. The obtained NNs properly discriminated between COVID-19 survivors and deaths (AUC = 84.31 ± 2.58% on hold-out testing datasets); the classification was mainly affected by the electrocardiographic-interval-related features, thus suggesting that changes in the duration of cardiac electrical activity might be related to mortality in COVID-19 cases. Full article
(This article belongs to the Special Issue AI-Based Biomedical Signal Processing)
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23 pages, 3065 KiB  
Article
An Improved Approach for Atrial Fibrillation Detection in Long-Term ECG Using Decomposition Transforms and Least-Squares Support Vector Machine
Appl. Sci. 2023, 13(22), 12187; https://doi.org/10.3390/app132212187 - 09 Nov 2023
Cited by 1 | Viewed by 483
Abstract
Atrial fibrillation is a common heart rhythm disorder that is now becoming a significant healthcare challenge as it affects more and more people in developed countries. This paper proposes a novel approach for detecting this disease. For this purpose, we examined the ECG [...] Read more.
Atrial fibrillation is a common heart rhythm disorder that is now becoming a significant healthcare challenge as it affects more and more people in developed countries. This paper proposes a novel approach for detecting this disease. For this purpose, we examined the ECG signal by detecting QRS complexes and then selecting 30 successive R-peaks and analyzing the atrial activity segment with a variety of indices, including the entropy change, the variance of the wavelet transform indices, and the distribution of energy in bands determined by the dual-Q tunable Q-factor wavelet transform and coefficients of the Hilbert transform of ensemble empirical mode decomposition. These transformations provided a vector of 21 features that characterized the relevant part of the electrocardiography signal. The MIT-BIH Atrial Fibrillation Database was used to evaluate the proposed method. Then, using the K-fold cross-validation method, the sets of features were fed into the LS-SVM and SVM classifiers and a trilayered neural network classifier. Training and test subsets were set up to avoid sampling from a single participant and to maintain the balance between classes. In addition, individual classification quality scores were analyzed for each signal to determine the dependencies of the classification quality on the subject. The results obtained during the testing procedure showed a sensitivity of 98.86%, a positive predictive value of 99.04%, and a classification accuracy of 98.95%. Full article
(This article belongs to the Special Issue AI-Based Biomedical Signal Processing)
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25 pages, 8241 KiB  
Article
Heart Sound Classification Using Wavelet Analysis Approaches and Ensemble of Deep Learning Models
Appl. Sci. 2023, 13(21), 11942; https://doi.org/10.3390/app132111942 - 31 Oct 2023
Viewed by 902
Abstract
Analyzing the condition and function of the heart is very important because cardiovascular diseases (CVDs) are responsible for high mortality rates worldwide and can lead to strokes and heart attacks; thus, early diagnosis and treatment are important. Phonocardiogram (PCG) signals can be used [...] Read more.
Analyzing the condition and function of the heart is very important because cardiovascular diseases (CVDs) are responsible for high mortality rates worldwide and can lead to strokes and heart attacks; thus, early diagnosis and treatment are important. Phonocardiogram (PCG) signals can be used to analyze heart rate characteristics to detect heart health and detect heart-related diseases. In this paper, we propose a method for designing using wavelet analysis techniques and an ensemble of deep learning models from phonocardiogram (PCG) for heart sound classification. For this purpose, we use wavelet scattering transform (WST) and continuous wavelet transform (CWT) as the wavelet analysis approaches for 1D-convolutional neural network (CNN) and 2D-CNN modeling, respectively. These features are insensitive to translations of the input on an invariance scale and are continuous with respect to deformations. Furthermore, the ensemble model is combined with 1D-CNN and 2D-CNN. The proposed method consists of four stages: a preprocessing stage for dividing signals at regular intervals, a feature extraction stage through wavelet scattering transform (WST) and continuous wavelet transform (CWT), a design stage of individual 1D-CNN and 2D-CNN, and a classification stage of heart sound by the ensemble model. The datasets used for the experiment were the PhysioNet/CinC 2016 challenge dataset and the PASCAL classifying heart sounds challenge dataset. The performance evaluation is performed by precision, recall, F1-score, sensitivity, and specificity. The experimental results revealed that the proposed method showed good performance on two datasets in comparison to the previous methods. The ensemble method of the proposed deep learning model surpasses the performance of recent studies and is suitable for predicting and diagnosing heart-related diseases by classifying heart sounds through phonocardiogram (PCG) signals. Full article
(This article belongs to the Special Issue AI-Based Biomedical Signal Processing)
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16 pages, 3137 KiB  
Article
ChroSegNet: An Attention-Based Model for Chromosome Segmentation with Enhanced Processing
Appl. Sci. 2023, 13(4), 2308; https://doi.org/10.3390/app13042308 - 10 Feb 2023
Cited by 2 | Viewed by 2168
Abstract
In modern medical diagnosis, the karyotype analysis for human chromosome is clinically significant for the diagnosis and treatment of genetic diseases. In such an analysis, it is critically important to segment the banded chromosomes. Chromosome segmentation, however, is technically challenging due to the [...] Read more.
In modern medical diagnosis, the karyotype analysis for human chromosome is clinically significant for the diagnosis and treatment of genetic diseases. In such an analysis, it is critically important to segment the banded chromosomes. Chromosome segmentation, however, is technically challenging due to the variable chromosome features, the complex background noise, and the uneven image quality of the chromosome images. Owing to these technical challenges, the existing deep-learning-based algorithms would have severe overfitting problems and are ineffective in the segmentation task. In this paper, we propose a novel chromosome segmentation model with our enhanced chromosome processing, namely ChroSegNet. First, we develop enhanced chromosome processing techniques to realize the quality and quantity enhancement of the chromosome data, leading to the chromosome segmentation dataset for our subsequent network training. Second, we propose our novel chromosome segmentation model “ChroSegNet" based on U-Net. According to the characteristics of chromosome data, we have not only improved the baseline structure but also incorporate the hybrid attention module to ChroSegNet, which can extract the key feature information and location information of chromosome. Finally, we evaluated ChroSegNet on our chromosome segmentation dataset and obtained the MPA of 93.31% and the F1-score of 92.99%. Experimental results show that ChroSegNet not only outperforms the representative segmentation models in chromosome segmentation performance but also has a lightweight model structure. We believe that our proposed ChroSegNet is highly promising in future applications of genetic measurement and diagnosis. Full article
(This article belongs to the Special Issue AI-Based Biomedical Signal Processing)
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16 pages, 2698 KiB  
Article
Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities
Appl. Sci. 2023, 13(3), 1712; https://doi.org/10.3390/app13031712 - 29 Jan 2023
Viewed by 760
Abstract
Classification of normal sinus rhythm (NSR), paroxysmal atrial fibrillation (PAF), and persistent atrial fibrillation (AF) is crucial in order to diagnose and effectively plan treatment for patients. Current classification models were primarily developed by electrocardiogram (ECG) signal databases, which may be unsuitable for [...] Read more.
Classification of normal sinus rhythm (NSR), paroxysmal atrial fibrillation (PAF), and persistent atrial fibrillation (AF) is crucial in order to diagnose and effectively plan treatment for patients. Current classification models were primarily developed by electrocardiogram (ECG) signal databases, which may be unsuitable for local patients. Therefore, this research collected ECG signals from 60 local Thai patients (age 52.53 ± 23.92) to create a classification model. The coefficient of variance (CV), the median absolute deviation (MAD), and the root mean square of the successive differences (RMSSD) are ordinary feature variables of RR irregularities used by existing models. The square of average variation (SAV) is a newly proposed feature that extracts from the irregularity of RR intervals. All variables were found to be statistically different using ANOVA tests and Tukey’s method with a p-value less than 0.05. The methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were also tested and compared to find the best classification model. Finally, SAV showed the best performance using the ANFIS model with trapezoidal membership function, having the highest system accuracy (ACC) at 89.33%, sensitivity (SE), specificity (SP), and positive predictivity (PPR) for NSR at 100.00%, 94.00%, and 89.29%, PAF at 88.00%, 90.57%, and 81.48%, and AF at 80.00%, 96.00%, and 90.91%, respectively. Full article
(This article belongs to the Special Issue AI-Based Biomedical Signal Processing)
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18 pages, 3296 KiB  
Article
Mandarin Electro-Laryngeal Speech Enhancement Using Cycle-Consistent Generative Adversarial Networks
Appl. Sci. 2023, 13(1), 537; https://doi.org/10.3390/app13010537 - 30 Dec 2022
Viewed by 1241
Abstract
Electro-laryngeal (EL) speech has poor intelligibility and naturalness, which hampers the popular use of the electro-larynx. Voice conversion (VC) can enhance EL speech. However, if the EL speech to be enhanced is with complicated tone variation rules in Mandarin, the enhancement will be [...] Read more.
Electro-laryngeal (EL) speech has poor intelligibility and naturalness, which hampers the popular use of the electro-larynx. Voice conversion (VC) can enhance EL speech. However, if the EL speech to be enhanced is with complicated tone variation rules in Mandarin, the enhancement will be less effective. This is because the source speech (Mandarin EL speech) and the target speech (normal speech) are not strictly parallel. We propose using cycle-consistent generative adversarial networks (CycleGAN, a parallel-free VC framework) to enhance continuous Mandarin EL speech, which can solve the above problem. In the proposed framework, the generator is designed based on the neural networks of a 2D-Conformer-1D-Transformer-2D-Conformer. Then, we used Mel-Spectrogram instead of traditional acoustic features (fundamental frequency, Mel-Cepstrum parameters and aperiodicity parameters). At last, we converted the enhanced Mel-Spectrogram into waveform signals using WaveNet. We undertook both subjective and objective tests to evaluate the proposed approach. Compared with traditional approaches to enhance continuous Mandarin EL speech with variable tone (the average tone accuracy being 71.59% and average word error rate being 10.85%), our framework increases the average tone accuracy by 12.12% and reduces the average errors of word perception by 9.15%. Compared with the approaches towards continuous Mandarin EL speech with fixed tone (the average tone accuracy being 29.89% and the average word error rate being 10.74%), our framework increases the average tone accuracy by 42.38% and reduces the average errors of word perception by 8.59%. Our proposed framework can effectively address the problem that the source and target speech are not strictly parallel. The intelligibility and naturalness of Mandarin EL speech have been further improved. Full article
(This article belongs to the Special Issue AI-Based Biomedical Signal Processing)
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18 pages, 10719 KiB  
Article
Implementation of the XR Rehabilitation Simulation System for the Utilization of Rehabilitation with Robotic Prosthetic Leg
Appl. Sci. 2022, 12(24), 12659; https://doi.org/10.3390/app122412659 - 09 Dec 2022
Cited by 2 | Viewed by 1698
Abstract
With the recent development of a digital rehabilitation system, research on the rehabilitation of amputees is accelerating. However, research on rehabilitation systems for patients with amputation of the lower extremities is insufficient. For the rehabilitation of amputees, it is important to maintain muscle [...] Read more.
With the recent development of a digital rehabilitation system, research on the rehabilitation of amputees is accelerating. However, research on rehabilitation systems for patients with amputation of the lower extremities is insufficient. For the rehabilitation of amputees, it is important to maintain muscle mass through the improvement of muscle movement memory, continuous rehabilitation learning, and motivation to improve efficiency. The rehabilitation system in a virtual environment is convenient in that there is no restriction on time and space because rehabilitation training of amputees is possible without removing/attaching general prosthetic legs and robot prosthetic legs. In this paper, we propose an XR rehabilitation system for patients with lower extremity amputation to improve the motivational aspect of rehabilitation training. The proposed method is a system that allows patients and clinical experts to perform rehabilitation in the same environment using two XR equipment called HoloLens 2. The content was provided in the form of a game in which the number of movements of amputees was allocated as scores to enhance rehabilitation convenience and motivation aspects. The virtual 3D model prosthetic leg used in-game content worked through the acquisition and processing of the patient’s actual muscle EMG (ElectroMyoGraphy) signal. In order to improve reactivity, there was a time limit for completing the operation. The classified action should be completed by the amputee within the time limit, although the number of times set as the target. To complete the operation, the amputee must force the amputation area to exceed an arbitrarily set threshold. The evaluation results were evaluated through an independent sample t-test. we contribute to the development of digital rehabilitation simulation systems. XR rehabilitation training techniques, operated with EMG signals obtained from actual amputation sites, contribute to the promotion of rehabilitation content in patients with amputation of the lower extremities. It is expected that this paper will improve the convenience and rehabilitation of rehabilitation training in the future. Full article
(This article belongs to the Special Issue AI-Based Biomedical Signal Processing)
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