Article
Respiratory System
Michael G. Crooks, Albertus C. den Brinker, Susannah Thackray-Nocera, Ralph van Dinther, Caroline E. Wright, Alyn H. Morice
Summary: This study compared the effectiveness of cough monitoring and questionnaire surveys in identifying prodromal symptoms of AE-COPD, with results showing that the alert system based on cough monitoring is more effective in predicting AE-COPD in advance, while the utility of questionnaire-based symptom monitoring is limited by frequent false alerts.
Article
Computer Science, Information Systems
Jungyoon Kim, Songhee Cheon, Jihye Lim
Summary: Mental health disorders, specifically dementia, are common among elderly populations, and early diagnosis and control are crucial. This study proposes an unobtrusive dementia-prediction system that monitors physical activities using passive infrared motion sensors. The system effectively predicts dementia risk using various classification models, making it valuable for long-term monitoring and early symptom detection systems.
Article
Engineering, Electrical & Electronic
Joshua Di Tocco, Daniela Lo Presti, Martina Zaltieri, Giacomo D'Alesio, Mariangela Filosa, Luca Massari, Andrea Aliperta, Marco Di Rienzo, Maria Chiara Carrozza, Maurizio Ferrarin, Carlo Massaroni, Calogero Maria Oddo, Emiliano Schena
Summary: Monitoring respiratory frequency using wearable devices equipped with flexible sensors can help maintain Occupational Health and Safety. The performance of the sensors in laboratory tests showed promising results, encouraging the use of the system in real workplaces to collect quantitative information on workers' psychophysical state and stress level.
IEEE SENSORS JOURNAL
(2021)
Article
Respiratory System
Peter S. P. Cho, Hannah Fletcher, Irem S. Patel, Richard D. Turner, Caroline J. Jolley, Surinder S. Birring
Summary: Patients with COPD-chronic cough and CRC both exhibited heightened cough reflex sensitivity, but only those with CRC were unable to suppress capsaicin-evoked cough. This indicates differing mechanisms of cough between patients with COPD and CRC, highlighting the need for disease-specific approaches to management.
EUROPEAN RESPIRATORY JOURNAL
(2021)
Article
Automation & Control Systems
Tiina Vuorinen, Kai Noponen, Vala Jeyhani, Muhammad Awais Aslam, Matti Juhani Junttila, Mikko Paavo Tulppo, Kari Sakari Kaikkonen, Heikki Veli Huikuri, Tapio Seppanen, Matti Mantysalo, Antti Vehkaoja
Summary: Continuous monitoring of vital signs is crucial for various patient groups. The development is moving towards more intelligent and unobtrusive systems for body-worn monitoring devices to enhance usability.
ADVANCED INTELLIGENT SYSTEMS
(2021)
Article
Engineering, Biomedical
Akshay Paul, Min S. Lee, Yuchen Xu, Stephen R. Deiss, Gert Cauwenberghs
Summary: To enable continuous, mobile health monitoring, this work presents a versatile wireless electrophysiology data acquisition system called weDAQ. It provides 16 recording channels, driven right leg (DRL), a 3-axis accelerometer, local data storage, and adaptable data transmission modes. The weDAQ device leverages in-band impedance scanning and an input multiplexer to dynamically select good skin contacting electrodes for reference and sensing channels, enabling measurements of EEG, EOG, and EMG.
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Jung-Yoon Kim, Youngchan Jang, Eunshin Byon, Dawn M. Tilbury, Milo Engoren, Satya Krishna Ramachandran, Mi-Sun Kang
Summary: A respiration monitoring system based on Wi-Fi signal has been proposed in this paper, which shows strong and robust estimation performance in various monitoring conditions through testing with a human patient simulator and a real human subject.
IEEE SENSORS JOURNAL
(2021)
Article
Engineering, Electrical & Electronic
Salvatore Andrea Pullano, Venkata Deepa Kota, Karthik Kakaraparty, Antonino S. Fiorillo, Ifana Mahbub
Summary: In this paper, an optically unobtrusive dry electrode based on the iono-electric properties of zeolite is developed and proposed for continuous electrocardiogram (ECG) monitoring. The electrical and bio-electrical characterizations are evaluated for different types of electrodes, and the results show that certain parameters lead to the best performance.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Jung-Yoon Kim, Chao-Hsien Chu, Mi-Sun Kang
Summary: The aging population globally has led to a societal crisis with increasing healthcare costs and decreasing caregivers. This study proposes an unobtrusive sensing environment for monitoring elderly sleep quality, showing promising results in detecting sleep disorders.
IEEE SENSORS JOURNAL
(2021)
Article
Chemistry, Analytical
Joshua Di Tocco, Luigi Raiano, Riccardo Sabbadini, Carlo Massaroni, Domenico Formica, Emiliano Schena
Summary: The study introduces a wearable device for continuous monitoring of respiratory rate and heart rate, using chest conductive sensors and an IMU sensor. Comparison of the estimated respiratory rate and heart rate in different postures demonstrates the system's reliability and low error rates across scenarios.
Review
Chemistry, Analytical
Ju Wang, Nicolai Spicher, Joana M. Warnecke, Mostafa Haghi, Jonas Schwartze, Thomas M. Deserno
Summary: This research provides a guide to current sensor technology for unobtrusive in-home monitoring through a literature review, addressing questions related to sensor types, placement, monitored data, and monitoring functions. The study identified 25 types of sensors that can be used for various monitoring purposes, but clear evidence of their impact on clinical outcomes is still lacking.
Article
Biology
Thomas Kronborg, Stine Hangaard, Simon L. Cichosz, Ole Hejlesen
Summary: This study demonstrates that a two-layer probabilistic model can improve classification rates for predicting COPD exacerbations compared to a one-layer model. The comparison across nine different classification algorithms showed a significant increase in the area under the receiver operating characteristics curve and sensitivity at a specificity of 0.95.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Karandeep Kaur, Harsh K. Verma
Summary: Internet of Things (IoT), the most significant technological advancement in the past decade, has had a profound impact on our daily lives. Its application in healthcare has enabled continuous monitoring of patients with chronic conditions, leading to extended lifespans and reduced risk of unexpected deaths. Additionally, the integration of IoT principles in Intelligent Transportation Systems (ITS) highlights the importance of IoV in healthcare for improved road safety. This study proposes an IoV-Health system that utilizes IoV concepts to detect emergency phases based on the driver's health and send emergency messages to surrounding vehicles, ambulances, and hospitals. Simulation results demonstrate that our proposed IoV-Health system outperforms existing healthcare systems in terms of accuracy, precision, sensitivity, score, energy consumption, throughput, and end-to-end delay.
Article
Computer Science, Hardware & Architecture
Manju Lata Sahu, Mithilesh Atulkar, Mitul Kumar Ahirwal, Afsar Ahamad
Summary: This study presents a remote patient monitoring system that utilizes IoT and cloud computing technologies to address the challenge of limited healthcare services in remote areas. The system can continuously measure physiological parameters and enable remote monitoring and data analysis through a mobile application, while generating alert notifications in case of abnormalities.
MOBILE NETWORKS & APPLICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Terence E. Taylor, Frank Keane, Yaniv Zigel
Summary: This study developed an audio-based speech obfuscation system that can detect and obfuscate intelligible speech while retaining cough events. This system is important for protecting data privacy and providing objective measures of respiratory clinical features.
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
(2022)
Article
Respiratory System
Michael G. Crooks, Albertus den Brinker, Yvette Hayman, James D. Williamson, Andrew Innes, Caroline E. Wright, Peter Hill, Alyn H. Morice
Article
Biophysics
Wenjin Wang, Albertus C. den Brinker, Sander Stuijk, Gerard de Haan
PHYSIOLOGICAL MEASUREMENT
(2017)
Article
Biochemical Research Methods
Wenjin Wang, Albertus C. den Brinker, Gerard De Haan
BIOMEDICAL OPTICS EXPRESS
(2018)
Article
Engineering, Biomedical
Wenjin Wang, Albertus C. den Brinker, Gerard de Haan
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2019)
Article
Engineering, Biomedical
Wenjin Wang, Albertus C. den Brinker, Gerard de Haan
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2020)
Article
Chemistry, Analytical
Wenjin Wang, Luc Vosters, Albertus C. den Brinker
Article
Respiratory System
Michael G. Crooks, Albertus C. den Brinker, Susannah Thackray-Nocera, Ralph van Dinther, Caroline E. Wright, Alyn H. Morice
Summary: This study compared the effectiveness of cough monitoring and questionnaire surveys in identifying prodromal symptoms of AE-COPD, with results showing that the alert system based on cough monitoring is more effective in predicting AE-COPD in advance, while the utility of questionnaire-based symptom monitoring is limited by frequent false alerts.
Article
Mathematics
Albertus C. den Brinker
Summary: The study proposes an algorithm for stable determination of orthogonal basis for Krawtchouk functions, achieved by defining proper initial points, balancing the order of recursion execution, and adaptively restricting the range of equation application. The adaptation is controlled by user-specified deviation from unit norm, with theoretical background provided, algorithmic concept explained, and the effect of controlled accuracy demonstrated through examples.
Article
Biophysics
Wenjin Wang, Albertus C. den Brinker
Summary: This study aims to investigate and evaluate the accuracy and performance of video-based respiratory signal measurement algorithms based on body motion by designing a physical phantom. According to the experimental results, the recommended approach achieves high precision and recall rates even in scenarios with significant variations in respiratory motion intensity.
PHYSIOLOGICAL MEASUREMENT
(2022)
Article
Multidisciplinary Sciences
Albertus C. den Brinker
Summary: This article discusses the generation of discrete orthogonal polynomials from recurrence or difference equations. It proposes a strategy to handle the deviation and successfully generates the orthonormal basis for a large range of supports and polynomial degrees. It pays special attention to the application of symmetry in simplifying the algorithm and saving computational power.
Article
Computer Science, Information Systems
Wenjin Wang, Steffen Weiss, Albertus C. den Brinker, Jan Hendrik Wuelbern, Albert Garcia i Tormo, Ioannis Pappous, Julien Senegas
Summary: Camera-based photoplethysmography (PPG) is a feasible alternative for cardiac triggering in Magnetic Resonance Imaging (MRI). Compared to traditional Electrocardiogram (ECG) signals, camera-based PPG provides more stable and less delayed signal.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Proceedings Paper
Engineering, Biomedical
Wenjin Wang, Luc Vosters, Albertus C. den Brinker
Summary: This paper explores suitable camera configurations for pulse-rate monitoring during both day and night (24/7), finding that it is best to deploy the full spectral band of an RGB camera for continuous monitoring without compromising performance at night.
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)
(2021)
Meeting Abstract
Respiratory System
Michael Crooks, Albertus Den Brinker, Ralph Van Dinther, Susannah Thackray-Nocera, Caroline Wright, Alyn Morice
EUROPEAN RESPIRATORY JOURNAL
(2020)
Meeting Abstract
Respiratory System
Nico Willard, Bert Den Brinker, Marian Dekker, Daniele De Massari, Mareike Klee, Fik Van Lint
EUROPEAN RESPIRATORY JOURNAL
(2018)
Article
Engineering, Biomedical
Wenwen Wu, Yanqi Huang, Xiaomei Wu
Summary: In this study, a 2D deep learning classification network SRT was proposed to improve automatic ECG analysis. The model structure was enhanced with the CNN and Transformer-encoder modules, and a novel attention module and Dilated Stem structure were introduced to improve feature extraction. Comparative experiments showed that the proposed model outperformed several advanced methods.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Chiheb Jamazi, Ghaith Manita, Amit Chhabra, Houssem Manita, Ouajdi Korbaa
Summary: In this study, a new dynamic and intelligent clustering method for brain tumor segmentation is proposed by combining the improved Aquila Optimizer (AO) and the K-Means algorithm. The proposed MAO-Kmeans approach aims to automatically extract the correct number and location of cluster centers and the number of pixels in each cluster in abnormal MRI images, and the experimental results demonstrate its effectiveness in improving the performance of conventional K-means clustering.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Alberto Hernando, Maria Dolores Pelaez-Coca, Eduardo Gil
Summary: This study applied a new algorithm to decompose the photoplethysmogram (PPG) pulse and identified changes in PPG pulse morphology due to pressure. The results showed that there was an increase in amplitude, width, and area values of the PPG pulse, and a decrease in ratios when pressure increased, indicating vasoconstriction. Furthermore, some parameters were found to be related to the pulse-to-pulse interval.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Jens Moeller, Eveline Popanda, Nuri H. Aydin, Hubert Welp, Iris Tischoff, Carsten Brenner, Kirsten Schmieder, Martin R. Hofmann, Dorothea Miller
Summary: In this study, a method based on texture features is proposed, which can classify healthy gray and white matter against glioma degrees 4 samples with reasonable classification performance using a relatively low number of samples for training. The method achieves high classification performance without the need for large datasets and complex machine learning approaches.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Amrutha Bhaskaran, Manish Arora
Summary: The study evaluates a cyclic repetition frequency-based algorithm for fetal heart rate estimation. The algorithm improves accuracy and reliability for poor-quality signals and performs well for different gestation weeks and clinical settings.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Manan Patel, Harsh Bhatt, Manushi Munshi, Shivani Pandya, Swati Jain, Priyank Thakkar, Sangwon Yoon
Summary: Electroencephalogram (EEG) signals have been effectively used to measure and analyze neurological data and brain-related ailments. Artificial Intelligence (AI) algorithms, specifically the proposed CNN-FEBAC framework, show promising results in studying the EEG signals of autistic patients and predicting their response to stimuli with 91% accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Wencheng Gu, Kexue Sun
Summary: This research proposes an improved version of YOLOv5 (AYOLOv5) based on the attention mechanism to address the issue of low recognition rate in cell detection. Experimental results demonstrate that AYOLOv5 can accurately identify cell targets and improve the quality and recognition performance of cell pictures.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Anita Gade, V. Vijaya Baskar, John Panneerselvam
Summary: Analysis of exhaled breath is an increasingly used diagnostic technique in medicine. This study introduces a new NICBGM-based model that utilizes various features and weight optimization for accurate data interpretation and result optimization.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Arsalan Asemi, Keivan Maghooli, Fereidoun Nowshiravan Rahatabad, Hamid Azadeh
Summary: Biometric authentication systems can perform identity verification with optimal accuracy in various environments and emotional changes, while the performance of signature verification systems can be affected when people are under stress. This study examines the performance of a signature verification system based on muscle synergy patterns as biometric characteristics for stressed individuals. EMG signals from hand and arm muscles were recorded and muscle synergies were extracted using Non-Negative Matrix Factorization. The extracted patterns were classified using Support Vector Machine for authentication of stressed individuals.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Tianjiao Guo, Jie Yang, Qi Yu
Summary: This paper proposes a CNN-based approach for segmenting four typical DR lesions simultaneously, achieving competitive performance. This approach is significant for DR lesion segmentation and has potential in other segmentation tasks.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
G. Akilandasowmya, G. Nirmaladevi, S. U. Suganthi, A. Aishwariya
Summary: This study proposes a technique for skin cancer detection and classification using deep hidden features and ensemble classifiers. By optimizing features to reduce data dimensionality and combining ensemble classifiers, the proposed method outperforms in skin cancer classification and improves prediction accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Tuuli Uudeberg, Juri Belikov, Laura Paeske, Hiie Hinrikus, Innar Liiv, Maie Bachmann
Summary: This article introduces a novel feature extraction method, the in-phase matrix profile (pMP), specifically adapted for electroencephalographic (EEG) signals, for detecting major depressive disorder (MDD). The results show that pMP outperforms Higuchi's fractal dimension (HFD) in detecting MDD, making it a promising method for future studies and potential clinical use for diagnosing MDD.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
P. Nancy, M. Parameswari, J. Sathya Priya
Summary: Stroke is the third leading cause of mortality worldwide, and early detection is crucial to avoid health risks. Existing research on disease detection using machine learning techniques has limitations, so a new stroke detection system is proposed. The experimental results show that the proposed method achieves a high accuracy rate in stroke detection.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Shimin Liu, Zhiwen Huang, Jianmin Zhu, Baolin Liu, Panyu Zhou
Summary: In this study, a continuous blood pressure (BP) monitoring method based on random forest feature selection (RFFS) and a gray wolf optimization-gradient boosting regression tree (GWO-GBRT) prediction model was developed. The method extracted features from electrocardiogram (ECG) and photoplethysmography (PPG) signals, and employed RFFS to select sensitive features highly correlated with BP. A hybrid prediction model of gray wolf optimization (GWO) technique and gradient boosting regression tree (GBRT) algorithm was established to learn the relationship between BP and sensitive features. Experimental results demonstrated the effectiveness and advancement of the proposed method.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Weijun Gong, Yurong Qian, Weihang Zhou, Hongyong Leng
Summary: The recognition of dynamic facial expressions is challenging due to various factors, and obtaining discriminative expression features has been difficult. Traditional deep learning networks lack understanding of global and temporal expressions. This study proposes an enhanced spatial-temporal learning network to improve dynamic facial expression recognition.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)