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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 8727

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Special Issue Editors

Prof. Dr. Kemal Polat

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Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Abant Izzet Baysal University, Bolu, Turkey
Interests: biomedical signal processing; medical decision support systems; machine learning; pattern recognition; deep learning image processing; embedded systems; speech analysis; cloud computing; brain–computer interfaces; human–machine systems; ECG and PPG signal measurements

Dr. Ümit Şentürk

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Guest Editor

Department of Computer Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey
Interests: computer engineering; biomedical signal processing and analysis

Special Issue Information

Dear Colleagues,

Recently, biomedical signal processing has significantly improved in solving various problems in many areas of biomedical engineering. Today, more than ever, the extraction of information hidden in biosignals plays an important role in understanding the secrets of the functioning of our body. Despite the impressive progress of recent times, new diseases represent a future challenge, and biomedical signal processing will continue to play an irreplaceable role in early detection.

This Special Issue aims to present and discuss the latest biomedical signal analysis and processing developments. We invite original research works, including new theories, innovative methods, and advanced systems that significantly advance applied biosciences and bioengineering.

Prof. Dr. Kemal Polat
Dr. Ümit Şentürk
Guest Editors

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. Diagnostics 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

medical decision support systems with biomedical signal processing
deep learning
machine learning for biosignal processing
non-stationary biosignal analysis
multidimensional biosignal processing
EEG signal processing
automatic systems for artifact reduction in wearable medical devices
advanced systems for biosignal prediction

Published Papers (5 papers)

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Research

19 pages, 18409 KiB

Open AccessArticle

Brain–Computer Interface: The HOL–SSA Decomposition and Two-Phase Classification on the HGD EEG Data

by Mary Judith Antony, Baghavathi Priya Sankaralingam, Shakir Khan, Abrar Almjally, Nouf Abdullah Almujally and Rakesh Kumar Mahendran

Diagnostics 2023, 13(17), 2852; https://doi.org/10.3390/diagnostics13172852 - 03 Sep 2023

Viewed by 1063

Abstract

An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain–Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL–SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method’s ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Analysis)

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20 pages, 2956 KiB

Open AccessArticle

Optimizing 1D-CNN-Based Emotion Recognition Process through Channel and Feature Selection from EEG Signals

by Haya Aldawsari, Saad Al-Ahmadi and Farah Muhammad

Diagnostics 2023, 13(16), 2624; https://doi.org/10.3390/diagnostics13162624 - 08 Aug 2023

Cited by 1 | Viewed by 1334

Abstract

EEG-based emotion recognition has numerous real-world applications in fields such as affective computing, human-computer interaction, and mental health monitoring. This offers the potential for developing IOT-based, emotion-aware systems and personalized interventions using real-time EEG data. This study focused on unique EEG channel selection and feature selection methods to remove unnecessary data from high-quality features. This helped improve the overall efficiency of a deep learning model in terms of memory, time, and accuracy. Moreover, this work utilized a lightweight deep learning method, specifically one-dimensional convolutional neural networks (1D-CNN), to analyze EEG signals and classify emotional states. By capturing intricate patterns and relationships within the data, the 1D-CNN model accurately distinguished between emotional states (HV/LV and HA/LA). Moreover, an efficient method for data augmentation was used to increase the sample size and observe the performance deep learning model using additional data. The study conducted EEG-based emotion recognition tests on SEED, DEAP, and MAHNOB-HCI datasets. Consequently, this approach achieved mean accuracies of 97.6, 95.3, and 89.0 on MAHNOB-HCI, SEED, and DEAP datasets, respectively. The results have demonstrated significant potential for the implementation of a cost-effective IoT device to collect EEG signals, thereby enhancing the feasibility and applicability of the data. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Analysis)

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18 pages, 5371 KiB

Open AccessArticle

PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN–LSTM

by Nurul Qashri Mahardika T, Yunendah Nur Fuadah, Da Un Jeong and Ki Moo Lim

Diagnostics 2023, 13(15), 2566; https://doi.org/10.3390/diagnostics13152566 - 01 Aug 2023

Cited by 3 | Viewed by 2380

Abstract

Researchers commonly use continuous noninvasive blood-pressure measurement (cNIBP) based on photoplethysmography (PPG) signals to monitor blood pressure conveniently. However, the performance of the system still needs to be improved. Accuracy and precision in blood-pressure measurements are critical factors in diagnosing and managing patients’ health conditions. Therefore, we propose a convolutional long short-term memory neural network (CNN–LSTM) with grid search ability, which provides a robust blood-pressure estimation system by extracting meaningful information from PPG signals and reducing the complexity of hyperparameter optimization in the proposed model. The multiparameter intelligent monitoring for intensive care III (MIMIC III) dataset obtained PPG and arterial-blood-pressure (ABP) signals. We obtained 75,226 signal segments, with 60,180 signals allocated for training data, 12,030 signals allocated for the validation set, and 15,045 signals allocated for the test data. During training, we applied five-fold cross-validation with a grid-search method to select the best model and determine the optimal hyperparameter settings. The optimized configuration of the CNN–LSTM layers consisted of five convolutional layers, one long short-term memory (LSTM) layer, and two fully connected layers for blood-pressure estimation. This study successfully achieved good accuracy in assessing both systolic blood pressure (SBP) and diastolic blood pressure (DBP) by calculating the standard deviation (SD) and the mean absolute error (MAE), resulting in values of 7.89 ± 3.79 and 5.34 ± 2.89 mmHg, respectively. The optimal configuration of the CNN–LSTM provided satisfactory performance according to the standards set by the British Hypertension Society (BHS), the Association for the Advancement of Medical Instrumentation (AAMI), and the Institute of Electrical and Electronics Engineers (IEEE) for blood-pressure monitoring devices. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Analysis)

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14 pages, 2259 KiB

Open AccessArticle

Altered Functional Brain Network Structure between Patients with High and Low Generalized Anxiety Disorder

by Xuchen Qi, Jiaqi Fang, Yu Sun, Wanxiu Xu and Gang Li

Diagnostics 2023, 13(7), 1292; https://doi.org/10.3390/diagnostics13071292 - 29 Mar 2023

Cited by 6 | Viewed by 1453

Abstract

To investigate the differences in functional brain network structures between patients with a high level of generalized anxiety disorder (HGAD) and those with a low level of generalized anxiety disorder (LGAD), a resting-state electroencephalogram (EEG) was recorded in 30 LGAD patients and 21 HGAD patients. Functional connectivity between all pairs of brain regions was determined by the Phase Lag Index (PLI) to construct a functional brain network. Then, the characteristic path length, clustering coefficient, and small world were calculated to estimate functional brain network structures. The results showed that the PLI values of HGAD were significantly increased in alpha2, and significantly decreased in the theta and alpha1 rhythms, and the small-world attributes for both HGAD patients and LGAD patients were less than one for all the rhythms. Moreover, the small-world values of HGAD were significantly lower than those of LGAD in the theta and alpha2 rhythms, which indicated that the brain functional network structure would deteriorate with the increase in generalized anxiety disorder (GAD) severity. Our findings may play a role in the development and understanding of LGAD and HGAD to determine whether interventions that target these brain changes may be effective in treating GAD. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Analysis)

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18 pages, 4583 KiB

Open AccessArticle

A Novel Cuffless Blood Pressure Prediction: Uncovering New Features and New Hybrid ML Models

by Majid Nour, Kemal Polat, Ümit Şentürk and Murat Arıcan

Diagnostics 2023, 13(7), 1278; https://doi.org/10.3390/diagnostics13071278 - 28 Mar 2023

Cited by 3 | Viewed by 1872

Abstract

This paper investigates new feature extraction and regression methods for predicting cuffless blood pressure from PPG signals. Cuffless blood pressure is a technology that measures blood pressure without needing a cuff. This technology can be used in various medical applications, including home health monitoring, clinical uses, and portable devices. The new feature extraction method involves extracting meaningful features (time and chaotic features) from the PPG signals in the prediction of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. These extracted features are then used as inputs to regression models, which are used to predict cuffless blood pressure. The regression model performances were evaluated using root mean squared error (RMSE), R², mean square error (MSE), and the mean absolute error (MAE). The obtained RMSE was 4.277 for systolic blood pressure (SBP) values using the Matérn 5/2 Gaussian process regression model. The obtained RMSE was 2.303 for diastolic blood pressure (DBP) values using the rational quadratic Gaussian process regression model. The results of this study have shown that the proposed feature extraction and regression models can predict cuffless blood pressure with reasonable accuracy. This study provides a novel approach for predicting cuffless blood pressure and can be used to develop more accurate models in the future. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Analysis)

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