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Biomedical Signal Processing and Health Monitoring Based on Sensors

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

Deadline for manuscript submissions: 20 June 2024 | Viewed by 14126

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Special Issue Editors

Dr. Sang Ho Choi

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Guest Editor

Department of Computer and Information Engineering, Kwangwoon University, 20 Gwangun-ro, Seoul, Republic of Korea
Interests: biomedical signal processing; artificial intelligence; healthcare system; wearable IoT

Prof. Dr. Heenam Yoon

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Guest Editor

Department of Human-Centered Artificial Intelligence, Sangmyung University, 20 Hongjimun 2-gil, Seoul, Republic of Korea
Interests: artificial intelligence; data science; sleep engineering; biomedical signal processing and control; cardiovascular engineering
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hyun Jae Baek

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Guest Editor

Department of Biomedical Engineering, Soonchunhyang University, Asan-si 31538, Republic of Korea
Interests: medical electronics; health IoT; sleep engineering; brain–computer interface; digital therapeutics
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Dr. Xi Long

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Guest Editor

Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
Interests: signal processing and machine learning; unobtrusive sensing; vital signs monitoring; sleep; neonatology & pregnancy; epilepsy & brain activity; clinical decision support
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The healthcare industry is growing at a rapid pace with the development of IoT technology. The global epidemic of COVID-19, an aging population, and an increase of chronic diseases are accelerating the development of various IoT services related to health monitoring. The paradigm of global healthcare is shifting from diagnosis and treatment to prevention, prediction, and personalization. The need for health care that can prevent and manage diseases is increasing, and sensor based IoT technology for this is evaluated as a key technology for health monitoring.

The goal of this Special Issue is to collect original contributions or critical reviews focusing on monitoring and predicting health conditions or diseases through biomedical signal processing based on sensors. The topics include, but are not limited to, biomedical signal processing algorithm, feature extraction and interpretation, sensor system, and artificial intelligence algorithms for health monitoring.

Potential topics include but are not limited to the following:

Biomedical signal processing algorithms;
Feature extraction and interpretation for biomedical signals;
Biomedical sensors and wearable systems;
IoT applications for health monitoring;
Artificial intelligence algorithms for monitoring and predicting health conditions or diseases;

Dr. Sang Ho Choi
Prof. Dr. Heenam Yoon
Prof. Dr. Hyun Jae Baek
Dr. Xi Long
Guest Editors

Manuscript Submission Information

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Keywords

biomedical signal processing algorithms
feature extraction and interpretation for biomedical signals
biomedical sensors and wearable systems
IoT applications for health monitoring
artificial intelligence algorithms for monitoring and predicting health conditions or diseases

Published Papers (10 papers)

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Research

14 pages, 1944 KiB

Open AccessArticle

Scented Solutions: Examining the Efficacy of Scent Interventions in Mitigating Driving Fatigue

by Xinyue Jiang, Kanesan Muthusamy, Jian Chen and Xueliang Fang

Sensors 2024, 24(8), 2384; https://doi.org/10.3390/s24082384 - 09 Apr 2024

Viewed by 286

Abstract

Fatigued driving threatens the safety of people’s lives and property. Scent countermeasures offer minimal disruption and high efficacy, making them a promising approach. The aim of this study was to explore the application of scent countermeasures in alleviating fatigued driving. This study explored changes in EEG frequency bands (alpha, beta, theta, and gamma) and the activity of EEG metrics (R_(α/β), R_θ_/(α+β) and R_{(α+θ)/(α+β)}) in the temporal lobe during driving tasks, selected fatigued driving identifiers, and aided validation by investigating subjective fatigue with the Karolinska Sleepiness Scale (KSS). The EEG indicators all increased, with a significant increase in R_(α/β). R_(α/β) was combined with the KSS to explore the effects of three scents, peppermint, grapefruit, and lavender, on driving fatigue. The subjective questionnaire results indicated that all three scents significantly improved driving fatigue, with significantly lower levels of driving fatigue compared to the control group. The analysis of EEG signals revealed a significant decrease in R_(α/β) after the implementation of scent countermeasures. Moreover, R_(α/β) was found to be lower in all three odor intervention groups compared to the control group. All three scents were found to significantly alleviate driving fatigue. The grapefruit scent had a better timely effect in relieving driving fatigue and the lavender scent had a longer effectiveness. This study provides further exploration for the application of odor interventions to alleviate driving fatigue. This study provides a practical reference for drivers to use odors to avoid fatigue in order to improve road safety. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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19 pages, 5977 KiB

Open AccessArticle

A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks

by Sevda Zafarmandi Ardabili, Soufia Bahmani, Lida Zare Lahijan, Nastaran Khaleghi, Sobhan Sheykhivand and Sebelan Danishvar

Sensors 2024, 24(2), 364; https://doi.org/10.3390/s24020364 - 07 Jan 2024

Cited by 2 | Viewed by 1188

Abstract

Nowadays, the automatic detection of driver fatigue has become one of the important measures to prevent traffic accidents. For this purpose, a lot of research has been conducted in this field in recent years. However, the diagnosis of fatigue in recent research is binary and has no operational capability. This research presents a multi-class driver fatigue detection system based on electroencephalography (EEG) signals using deep learning networks. In the proposed system, a standard driving simulator has been designed, and a database has been collected based on the recording of EEG signals from 20 participants in five different classes of fatigue. In addition to self-report questionnaires, changes in physiological patterns are used to confirm the various stages of weariness in the suggested model. To pre-process and process the signal, a combination of generative adversarial networks (GAN) and graph convolutional networks (GCN) has been used. The proposed deep model includes five convolutional graph layers, one dense layer, and one fully connected layer. The accuracy obtained for the proposed model is 99%, 97%, 96%, and 91%, respectively, for the four different considered practical cases. The proposed model is compared to one developed through recent methods and research and has a promising performance. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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24 pages, 8390 KiB

Open AccessArticle

Salient Arithmetic Data Extraction from Brain Activity via an Improved Deep Network

by Nastaran Khaleghi, Shaghayegh Hashemi, Sevda Zafarmandi Ardabili, Sobhan Sheykhivand and Sebelan Danishvar

Sensors 2023, 23(23), 9351; https://doi.org/10.3390/s23239351 - 23 Nov 2023

Cited by 1 | Viewed by 730

Abstract

Interpretation of neural activity in response to stimulations received from the surrounding environment is necessary to realize automatic brain decoding. Analyzing the brain recordings corresponding to visual stimulation helps to infer the effects of perception occurring by vision on brain activity. In this paper, the impact of arithmetic concepts on vision-related brain records has been considered and an efficient convolutional neural network-based generative adversarial network (CNN-GAN) is proposed to map the electroencephalogram (EEG) to salient parts of the image stimuli. The first part of the proposed network consists of depth-wise one-dimensional convolution layers to classify the brain signals into 10 different categories according to Modified National Institute of Standards and Technology (MNIST) image digits. The output of the CNN part is fed forward to a fine-tuned GAN in the proposed model. The performance of the proposed CNN part is evaluated via the visually provoked 14-channel MindBigData recorded by David Vivancos, corresponding to images of 10 digits. An average accuracy of 95.4% is obtained for the CNN part for classification. The performance of the proposed CNN-GAN is evaluated based on saliency metrics of SSIM and CC equal to 92.9% and 97.28%, respectively. Furthermore, the EEG-based reconstruction of MNIST digits is accomplished by transferring and tuning the improved CNN-GAN’s trained weights. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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20 pages, 2284 KiB

Open AccessArticle

Instrumented Gait Classification Using Meaningful Features in Patients with Impaired Coordination

by Zeus T. Dominguez-Vega, Mariano Bernaldo de Quiros, Jan Willem J. Elting, Deborah A. Sival and Natasha M. Maurits

Sensors 2023, 23(20), 8410; https://doi.org/10.3390/s23208410 - 12 Oct 2023

Viewed by 710

Abstract

Early onset ataxia (EOA) and developmental coordination disorder (DCD) both affect cerebellar functioning in children, making the clinical distinction challenging. We here aim to derive meaningful features from quantitative SARA-gait data (i.e., the gait test of the scale for the assessment and rating of ataxia (SARA)) to classify EOA and DCD patients and typically developing (CTRL) children with better explainability than previous classification approaches. We collected data from 18 EOA, 14 DCD and 29 CTRL children, while executing both SARA gait tests. Inertial measurement units were used to acquire movement data, and a gait model was employed to derive meaningful features. We used a random forest classifier on 36 extracted features, leave-one-out-cross-validation and a synthetic oversampling technique to distinguish between the three groups. Classification accuracy, probabilities of classification and feature relevance were obtained. The mean classification accuracy was 62.9% for EOA, 85.5% for DCD and 94.5% for CTRL participants. Overall, the random forest algorithm correctly classified 82.0% of the participants, which was slightly better than clinical assessment (73.0%). The classification resulted in a mean precision of 0.78, mean recall of 0.70 and mean F1 score of 0.74. The most relevant features were related to the range of the hip flexion–extension angle for gait, and to movement variability for tandem gait. Our results suggest that classification, employing features representing different aspects of movement during gait and tandem gait, may provide an insightful tool for the differential diagnoses of EOA, DCD and typically developing children. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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23 pages, 8392 KiB

Open AccessArticle

Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach

by Mohammad Peivandi, Sevda Zafarmandi Ardabili, Sobhan Sheykhivand and Sebelan Danishvar

Sensors 2023, 23(19), 8171; https://doi.org/10.3390/s23198171 - 29 Sep 2023

Cited by 3 | Viewed by 1200

Abstract

A large share of traffic accidents is related to driver fatigue. In recent years, many studies have been organized in order to diagnose and warn drivers. In this research, a new approach was presented in order to detect multi-level driver fatigue. A multi-level driver tiredness diagnostic database based on physiological signals including ECG, EEG, EMG, and respiratory effort was developed for this aim. The EEG signal was used for processing and other recorded signals were used to confirm the driver’s fatigue so that fatigue was not confirmed based on self-report questionnaires. A customized architecture based on adversarial generative networks and convolutional neural networks (end-to-end) was utilized to select/extract features and classify different levels of fatigue. In the customized architecture, with the objective of eliminating uncertainty, type 2 fuzzy sets were used instead of activation functions such as Relu and Leaky Relu, and the performance of each was investigated. The final accuracy obtained in the three scenarios considered, two-level, three-level, and five-level, were 96.8%, 95.1%, and 89.1%, respectively. Given the suggested model’s optimal performance, which can identify five various levels of driver fatigue with high accuracy, it can be employed in practical applications of driver fatigue to warn drivers. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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11 pages, 1647 KiB

Open AccessArticle

Closed-Loop Auditory Stimulation to Guide Respiration: Preliminary Study to Evaluate the Effect on Time Spent in Sleep Initiation during a Nap

by Heenam Yoon and Sang Ho Choi

Sensors 2023, 23(14), 6468; https://doi.org/10.3390/s23146468 - 17 Jul 2023

Viewed by 896

Abstract

Various stimulation systems to modulate sleep structure and function have been introduced. However, studies on the time spent in sleep initiation (TSSI) are limited. This study proposes a closed-loop auditory stimulation (CLAS) to gradually modulate respiratory rhythm linked to the autonomic nervous system (ANS) activity directly associated with sleep. CLAS is continuously updated to reflect the individual’s current respiratory frequency and pattern. Six participants took naps on different days with and without CLAS. The average values of the TSSI are 14.00 ± 4.24 and 9.67 ± 5.31 min in the control and stimulation experiments (p < 0.03), respectively. Further, the values of respiratory instability and heart rate variability differ significantly between the control and stimulation experiments. Based on our findings, CLAS supports the individuals to gradually modulate their respiratory rhythms to have similar characteristics observed near sleep initiation, and the changed respiratory rhythms influence ANS activities, possibly influencing sleep initiation. Our approach aims to modulate the respiratory rhythm, which can be controlled intentionally. Therefore, this method can probably be used for sleep initiation and daytime applications. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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16 pages, 6022 KiB

Open AccessArticle

Experimental Verification of the Possibility of Reducing Photoplethysmography Measurement Time for Stress Index Calculation

by Seung-Gun Lee, Young Do Song and Eui Chul Lee

Sensors 2023, 23(12), 5511; https://doi.org/10.3390/s23125511 - 12 Jun 2023

Cited by 2 | Viewed by 1369

Abstract

Stress is a direct or indirect cause of reduced work efficiency in daily life. It can damage physical and mental health, leading to cardiovascular disease and depression. With increased interest and awareness of the risks of stress in modern society, there is a growing demand for quick assessment and monitoring of stress levels. Traditional ultra-short-term stress measurement classifies stress situations using heart rate variability (HRV) or pulse rate variability (PRV) information extracted from electrocardiogram (ECG) or photoplethysmography (PPG) signals. However, it requires more than one minute, making it difficult to monitor stress status in real-time and accurately predict stress levels. In this paper, stress indices were predicted using PRV indices acquired at different lengths of time (60 s, 50 s, 40 s, 30 s, 20 s, 10 s, and 5 s) for the purpose of real-time stress monitoring. Stress was predicted with Extra Tree Regressor, Random Forest Regressor, and Gradient Boost Regressor models using a valid PRV index for each data acquisition time. The predicted stress index was evaluated using an

R^{2}

score between the predicted stress index and the actual stress index calculated from one minute of the PPG signal. The average

R^{2}

score of the three models by the data acquisition time was 0.2194 at 5 s, 0.7600 at 10 s, 0.8846 at 20 s, 0.9263 at 30 s, 0.9501 at 40 s, 0.9733 at 50 s, and 0.9909 at 60 s. Thus, when stress was predicted using PPG data acquired for 10 s or more, the

R^{2}

score was confirmed to be over 0.7. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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15 pages, 5554 KiB

Open AccessArticle

Convolutional Neural Networks for the Real-Time Monitoring of Vital Signs Based on Impulse Radio Ultrawide-Band Radar during Sleep

by Sang Ho Choi and Heenam Yoon

Sensors 2023, 23(6), 3116; https://doi.org/10.3390/s23063116 - 14 Mar 2023

Cited by 5 | Viewed by 2605

Abstract

Vital signs provide important biometric information for managing health and disease, and it is important to monitor them for a long time in a daily home environment. To this end, we developed and evaluated a deep learning framework that estimates the respiration rate (RR) and heart rate (HR) in real time from long-term data measured during sleep using a contactless impulse radio ultrawide-band (IR-UWB) radar. The clutter is removed from the measured radar signal, and the position of the subject is detected using the standard deviation of each radar signal channel. The 1D signal of the selected UWB channel index and the 2D signal applied with the continuous wavelet transform are entered as inputs into the convolutional neural-network-based model that then estimates RR and HR. From 30 recordings measured during night-time sleep, 10 were used for training, 5 for validation, and 15 for testing. The average mean absolute errors for RR and HR were 2.67 and 4.78, respectively. The performance of the proposed model was confirmed for long-term data, including static and dynamic conditions, and it is expected to be used for health management through vital-sign monitoring in the home environment. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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14 pages, 11190 KiB

Open AccessArticle

Feature Pyramid Networks and Long Short-Term Memory for EEG Feature Map-Based Emotion Recognition

by Xiaodan Zhang, Yige Li, Jinxiang Du, Rui Zhao, Kemeng Xu, Lu Zhang and Yichong She

Sensors 2023, 23(3), 1622; https://doi.org/10.3390/s23031622 - 02 Feb 2023

Cited by 5 | Viewed by 1668

Abstract

The original EEG data collected are the 1D sequence, which ignores spatial topology information; Feature Pyramid Networks (FPN) is better at small dimension target detection and insufficient feature extraction in the scale transformation than CNN. We propose a method of FPN and Long Short-Term Memory (FPN-LSTM) for EEG feature map-based emotion recognition. According to the spatial arrangement of brain electrodes, the Azimuth Equidistant Projection (AEP) is employed to generate the 2D EEG map, which preserves the spatial topology information; then, the average power, variance power, and standard deviation power of three frequency bands (

α

β

, and

γ

) are extracted as the feature data for the EEG feature map. BiCubic interpolation is employed to interpolate the blank pixel among the electrodes; the three frequency bands EEG feature maps are used as the G, R, and B channels to generate EEG feature maps. Then, we put forward the idea of distributing the weight proportion for channels, assign large weight to strong emotion correlation channels (AF3, F3, F7, FC5, and T7), and assign small weight to the others; the proposed FPN-LSTM is used on EEG feature maps for emotion recognition. The experiment results show that the proposed method can achieve Value and Arousal recognition rates of 90.05% and 90.84%, respectively. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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15 pages, 673 KiB

Open AccessArticle

An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets

by Smith K. Khare, Nikhil Gaikwad and Neeraj Dhanraj Bokde

Sensors 2022, 22(21), 8128; https://doi.org/10.3390/s22218128 - 24 Oct 2022

Cited by 7 | Viewed by 1856

Abstract

Classification of motor imagery (MI) tasks provides a robust solution for specially-abled people to connect with the milieu for brain-computer interface. Precise selection of uniform tuning parameters of tunable Q wavelet transform (TQWT) for electroencephalography (EEG) signals is arduous. Therefore, this paper proposes robust TQWT for automatically selecting optimum tuning parameters to decompose non-stationary EEG signals accurately. Three evolutionary optimization algorithms are explored for automating the tuning parameters of robust TQWT. The fitness function of the mean square error of decomposition is used. This paper also exploits channel selection using a Laplacian score for dominant channel selection. Important features elicited from sub-bands of robust TQWT are classified using different kernels of the least square support vector machine classifier. The radial basis function kernel has provided the highest accuracy of 99.78%, proving that the proposed method is superior to other state-of-the-art using the same database. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Health Monitoring Based on Sensors)

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