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Special Issue "Biomedical Signal Processing and Analysis"

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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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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

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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

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Research

Open AccessArticle

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

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

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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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Open AccessArticle

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

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

Viewed by 707

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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Special Issue "Biomedical Signal Processing and Analysis"

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