Review
Neurosciences
Konstantin G. Heimrich, Thomas Lehmann, Peter Schlattmann, Tino Prell
Summary: Recent evidence suggests that autonomic dysfunction, particularly in the vagus nerve, plays a significant role in the pathogenesis of Parkinson's disease. Heart rate variability analysis can be used to investigate cardiac activity regulation. Studies have shown that there may be decreased parasympathetic tone in patients with Parkinson's disease based on heart rate variability analysis.
Article
Clinical Neurology
Masashi Suzuki, Tomohiko Nakamura, Masaaki Hirayama, Masamichi Ueda, Mai Hatanaka, Yumiko Harada, Masahiro Nakatochi, Daisuke Nakatsubo, Satoshi Maesawa, Ryuta Saito, Koichi Fujiwara, Masahisa Katsuno
Summary: This study examined the HRV in Parkinson's disease patients using wearable sensors and found that the minimum values of SDNN and CVRR were significantly decreased in PD. Analyzing these minimum values in long-term recordings may be appropriate for detecting the decrease in HRV in PD.
JOURNAL OF NEURAL TRANSMISSION
(2022)
Article
Environmental Sciences
Minna Tang, Yu He, Xiaochun Zhang, Huichu Li, Chang Huang, Cuiping Wang, Ya Gao, Yinliang Li, Haidong Kan, Jialu Hu, Renjie Chen
Summary: The study found that temperature variability is negatively associated with heart rate variability, especially on the same day. The exposure-response relationships were almost linear for most parameters. The increase in temperature variability is significantly associated with the decrease in heart rate variability, with females being more affected.
ENVIRONMENTAL RESEARCH
(2021)
Article
Psychology, Biological
Dalbyeol Bae, Jacob J. L. Matthews, J. Jean Chen, Linda Mah
Summary: In this study, the impact of manipulating exhalation to inhalation ratio (E:I) on heart rate variability (HRV) was examined. The findings suggest that a longer exhalation relative to inhalation, without altering breathing rate, acutely increased HRV metrics, pointing towards an enhancement of cardiac vagal tone.
Review
Neurosciences
Jeann L. Sabino-Carvalho, Barbara Falquetto, Ana C. Takakura, Lauro C. Vianna
Summary: The incidence of Parkinson's disease is increasing worldwide, with nonmotor dysfunctions gaining recognition. Research suggests that baroreflex dysfunction may be an underlying mechanism of cardiovascular dysregulation in PD patients. This review summarizes potential altered central and peripheral mechanisms affecting the feedback-controlled loops in PD patients.
JOURNAL OF NEUROPHYSIOLOGY
(2021)
Article
Clinical Neurology
Timo Siepmann, Martin Arndt, Annahita Sedghi, Szabolcs Szatmari Jr, Tamas Horvath, Annamaria Takats, Daniel Bereczki, Mats Leif Moskopp, Sylvia Buchmann, Cornelia Skowronek, Wagner Zago, Warunya Woranush, Razvan Lapusca, Marie Luise H. Weidemann, Christopher Gibbons, Roy Freeman, Heinz Reichmann, Volker Puetz, Kristian Barlinn, Alexandra Pinter, Ben Min-Woo Illigens
Summary: This study characterized autonomic pilomotor and sudomotor skin function in early Parkinson's disease (PD) longitudinally. The results showed that pilomotor function and sympathetic skin response (SSR) were impaired in PD, indicating sympathetic pathophysiology. However, cholinergic sudomotor function and parasympathetic neurocardiac function remained unchanged. This finding suggests that a pilomotor axon-reflex test may be useful for monitoring PD-related pathology.
EUROPEAN JOURNAL OF NEUROLOGY
(2023)
Article
Cardiac & Cardiovascular Systems
Amit J. Shah, Matthew T. Wittbrodt, J. Douglas Bremner, Viola Vaccarino
Summary: Coronary heart disease and psychological stress factors like depression are highly prevalent and difficult to manage. Recent research suggests that adopting an integrated approach to managing the heart and neurological network may be effective. This article describes an extensive cardioneural network that includes the heart, brain, spinal cord, and ganglia throughout the body, and discusses non-invasive measures that can assess both psychological stress and severity of heart disease. Finally, the article explores the potential clinical and public health applications of these measures and potential cardioneural interventions.
TRENDS IN CARDIOVASCULAR MEDICINE
(2022)
Review
Cardiac & Cardiovascular Systems
Karina Carvalho Marques, Juarez Antonio Simoes Quaresma, Luiz Fabio Magno Falcao
Summary: Long COVID refers to the persistence of signs and symptoms for more than 4 weeks after acute infection. Patients with Long COVID experience autonomic imbalance, possibly due to neurotropism, cytokine storms, and inflammation. Immunological factors and heart rate variability can be used to identify and assess the risk in Long COVID patients. Inflammatory markers are helpful in understanding the mechanisms underlying the inflammatory response.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2023)
Article
Health Care Sciences & Services
Daniela Lucini, Mara Malacarne, Wolfgang Gatzemeier, Eleonora Pagani, Giuseppina Bernardelli, Gianfranco Parati, Massimo Pagani
Summary: The increased cardiometabolic risk in breast cancer survivors is attributed to multiple mechanisms, including hormonal and immunological dysfunction as well as cardiac autonomic regulation. This study found that physical activity can improve cardiac autonomic regulation, metabolism, and psychological well-being in breast cancer survivors.
JOURNAL OF PERSONALIZED MEDICINE
(2022)
Article
Neurosciences
Jacob Horsager, Karoline Knudsen, Michael Sommerauer
Summary: Braak's hypothesis has had a significant influence on Parkinson's disease research. However, a new model suggests that the existing model does not apply to all patients. By using REM-sleep behavior disorder as a clinical identifier, the disease can be divided into two subtypes: body-first PD and brain-first PD. These subtypes show differences in clinical symptoms and imaging features.
NEUROBIOLOGY OF DISEASE
(2022)
Review
Biochemistry & Molecular Biology
Lorena Cuenca-Bermejo, Pilar Almela, Javier Navarro-Zaragoza, Emiliano Fernandez Villalba, Ana-Maria Gonzalez-Cuello, Maria-Luisa Laorden, Maria-Trinidad Herrero
Summary: Dysautonomia is a common non-motor symptom in Parkinson's disease, mainly characterized by alterations in the sympathetic and parasympathetic nervous systems. The imbalance of these nervous system components may lead to cardiovascular abnormalities, particularly tachycardia and vasoconstriction.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Biology
Yi-Chia Shan, Wei Fang, Jih-Huah Wu
Summary: A feasible and integrated system was proposed to measure and affect the autonomic nervous system (ANS) status. The test results show that stimulating the Neiguan (PC6) acupoint can inhibit the sympathetic nervous system (SNS), while stimulating the Shenmen (HT7) acupoint can activate the SNS.
Article
Orthopedics
T. D. Yeater, J. Zubcevic, K. D. Allen
Summary: The study aimed to evaluate autonomic nervous system shifts in rat knee joint injury and osteoarthritis (OA) models. The results showed that injured animals had a slower heart rate during low activity and mechanical stimuli caused an immediate decrease in heart rate and blood pressure in all groups. Furthermore, the damaged groups exhibited a larger drop in heart rate following pharmacological stimulation. These findings provide preliminary evidence of potential functional shifts in the autonomic nervous system in models of joint injury and OA.
OSTEOARTHRITIS AND CARTILAGE
(2022)
Article
Endocrinology & Metabolism
Inha Jung, Da Young Lee, Mi Yeon Lee, Hyemi Kwon, Eun-Jung Rhee, Cheol-Young Park, Ki-Won Oh, Won-Young Lee, Sung-Woo Park, Se Eun Park
Summary: Overall autonomic imbalance, decreased parasympathetic activity, and recently increased sympathetic activity may increase the risk of nonalcoholic fatty liver disease (NAFLD) according to heart rate variability evaluation.
FRONTIERS IN ENDOCRINOLOGY
(2021)
Article
Medicine, General & Internal
Ahsan A. Khan, Rehan T. Junejo, Graham N. Thomas, James P. Fisher, Gregory Y. H. Lip
Summary: AF, regardless of hypertension, is associated with higher HRV, and may be related to vagal tone. Permanent AF has a stronger influence on HRV than paroxysmal AF, indicating autonomic involvement in permanent AF pathophysiology. Exploration of autonomic modulation on cardiovascular system is recommended for future studies.
EUROPEAN JOURNAL OF CLINICAL INVESTIGATION
(2021)
Article
Neurosciences
Chiara Marzi, Alessandro d'Ambrosio, Stefano Diciotti, Alvino Bisecco, Manuela Altieri, Massimo Filippi, Maria Assunta Rocca, Loredana Storelli, Patrizia Pantano, Silvia Tommasin, Rosa Cortese, Nicola De Stefano, Gioacchino Tedeschi, Antonio Gallo
Summary: This study used machine learning techniques to assess the relationship between brain MRI structural volumes and cognitive deficits in MS patients, and found that damage to gray matter structures is most closely related to cognitive performance.
HUMAN BRAIN MAPPING
(2023)
Review
Business
Shobhit Kakaria, Enrique Bigne, Vincenzo Catrambone, Gaetano Valenza
Summary: Heart rate variability is an emerging physiological measurement used in marketing research. This literature review provides an overview of its applications and methodological considerations. The findings suggest that 42% of studies focus on promotion-related topics and most studies combine heart rate variability with Galvanic skin response. Six research avenues are identified, categorized using the theory characteristics methodology framework.
PSYCHOLOGY & MARKETING
(2023)
Article
Neurosciences
Allegra Conti, Constantina Andrada Treaba, Ambica Mehndiratta, Valeria Teresa Barletta, Caterina Mainero, Nicola Toschi
Summary: The relationship between central hallmarks of multiple sclerosis (MS), such as white matter (WM)/cortical demyelinated lesions and cortical gray matter atrophy, remains unclear. A machine learning model was employed to predict mean cortical thinning in different brain regions using demographic and lesion-related characteristics. The study found that volume and rimless WM lesions, patient age, and volume of intracortical lesions have the most predictive power.
Article
Biotechnology & Applied Microbiology
Chiara Marzi, Daniela Marfisi, Andrea Barucci, Jacopo Del Meglio, Alessio Lilli, Claudio Vignali, Mario Mascalchi, Giancarlo Casolo, Stefano Diciotti, Antonio Claudio Traino, Carlo Tessa, Marco Giannelli
Summary: Radiomics and artificial intelligence have the potential to be valuable tools in clinical applications. This study assessed the effect of preprocessing, such as voxel size resampling, discretization, and filtering, on correlation-based dimensionality reduction of radiomic features from cardiac T1 and T2 maps. The results showed that the percentage of eliminated radiomic features was more dependent on resampling voxel size and discretization bin width for textural features. Correlation-based dimensionality reduction was less sensitive to preprocessing when considering T2 features compared to T1 features.
BIOENGINEERING-BASEL
(2023)
Article
Physiology
Diego Candia-Rivera, Kian Norouzi, Thomas Zoega Ramsoy, Gaetano Valenza
Summary: Dynamic information exchange between the central and autonomic nervous systems occurs during emotional and physical arousal. Mental stress induces variability in sympathovagal markers and directional brain-heart interplay, suggesting bidirectional interactions at a brain-heart level.
AMERICAN JOURNAL OF PHYSIOLOGY-REGULATORY, INTEGRATIVE AND COMPARATIVE PHYSIOLOGY
(2023)
Article
Computer Science, Interdisciplinary Applications
Antonio Luca Alfeo, Antonio G. Zippo, Vincenzo Catrambone, Mario G. C. A. Cimino, Nicola Toschi, Gaetano Valenza
Summary: This study proposes a new method for computing feature importance by aggregating local counterfactual explanations, which overcomes the limitations of traditional methods. Experimental results demonstrate that this approach is more robust and computationally efficient in handling high-dimensional and highly correlated brain signals. This is crucial for medical decision support systems.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Letter
Cardiac & Cardiovascular Systems
Gaetano Valenza
JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY
(2023)
Article
Immunology
Elena Azzolini, Maximiliano Mollura, Chiara Pozzi, Leonardo Ubaldi, Alberto Mantovani, Carlo Selmi, Riccardo Barbieri, Maria Rescigno
Summary: An important issue often ignored is the discrepancy in medical treatment response between male and female genders. COVID-19 vaccine administration has revealed that females tend to experience more adverse events compared to males despite following identical protocols. Through logistic regression analysis of adverse events in 2385 healthcare workers receiving the Comirnaty vaccine, we found that age, sex, COVID-19 history, and BMI may contribute to the development of adverse events, particularly in young subjects, females, and individuals with a BMI below 25 kg/m(2). Furthermore, partial dependence plots indicate a 50% probability of mild adverse events lasting for a long period (>= 7 days) or any duration of severe adverse events in women below 40 years old with a BMI < 20 kg/m(2). Given that this effect is more prominent after the second vaccine dose, we propose adjusting the vaccine dosage for additional booster doses based on age, sex, and BMI to reduce adverse events without compromising vaccine efficacy.
Article
Immunology
Maria Elena Romero-Ibarguengoitia, Diego Rivera-Salinas, Riccardo Sarti, Riccardo Levi, Maximiliano Mollura, Arnulfo Garza-Silva, Andrea Rivera-Cavazos, Yodira Guadalupe Hernandez-Ruiz, Irene Antonieta Barco-Flores, Arnulfo Gonzalez-Cantu, Miguel Angel Sanz-Sanchez, Milton Henriques Guimaraes Junior, Chiara Pozzi, Riccardo Barbieri, Devany Paola Morales-Rodriguez, Mauro Martins Texeira, Maria Rescigno
Summary: This study evaluated the real-life efficacy of six different vaccines against SARS-CoV-2 and found that mRNA vaccines had the highest antibody levels during follow-up. Infection before vaccination and after complete vaccination scheme correlated with higher antibody titers. The CoronaVac vaccine was found to lower the risk of infection in the presence of certain comorbidities.
Article
Neurosciences
Vincenzo Catrambone, Gaetano Valenza
Summary: This study investigated whether microstates extend to the peripheral autonomic nerves and demonstrated the existence of microstates at the brain-heart axis level, which are associated with specific experimental conditions.
HUMAN BRAIN MAPPING
(2023)
Article
Medicine, General & Internal
Francesco Alfano, Francesca Cesari, Anna Maria Gori, Martina Berteotti, Emilia Salvadori, Betti Giusti, Alessia Bertelli, Ada Kura, Carmen Barbato, Benedetta Formelli, Francesca Pescini, Enrico Fainardi, Stefano Chiti, Chiara Marzi, Stefano Diciotti, Rossella Marcucci, Anna Poggesi
Summary: This study aims to evaluate the predictive capability of biomarkers for vascular damage and brain tissue injury in anticoagulated atrial fibrillation (AF) patients. The results show that circulating biomarkers MMP-2 and TIMP are associated with cerebral microbleeds, white matter hyperintensity, and small vessel disease. IL-8 and TIMP are associated with enlarged perivascular spaces.
JOURNAL OF CLINICAL MEDICINE
(2023)
Review
Medicine, General & Internal
Mario Mascalchi, Giulia Picozzi, Donella Puliti, Stefano Diciotti, Annalisa Deliperi, Chiara Romei, Fabio Falaschi, Francesco Pistelli, Michela Grazzini, Letizia Vannucchi, Simonetta Bisanzi, Marco Zappa, Giuseppe Gorini, Francesca Maria Carozzi, Laura Carrozzi, Eugenio Paci
Summary: The ITALUNG trial, conducted from 2004, compared the mortality rates of lung cancer and other causes in smokers and ex-smokers aged 55-69 who were randomly assigned to receive low-dose CT (LDCT) or usual care. After 13 years of follow-up, the ITALUNG trial demonstrated a lower mortality rate for lung cancer and cardiovascular diseases in the screened subjects, particularly in women. In addition, the trial generated multiple ancillary studies on various aspects of lung cancer screening, including software development, assessment of lung nodules and calcifications, and biomarker assays.
Article
Psychology, Multidisciplinary
Claudio Paolucci, Federica Giorgini, Riccardo Scheda, Flavio Valerio Alessi, Stefano Diciotti
Summary: This study proposes an AI pre-screening tool to identify potentially alarming signs of Autism Spectrum Disorder (ASD) in pre-verbal interactions. The effectiveness of these features in classifying individuals with ASD vs. controls is evaluated using explainable artificial intelligence, with a focus on body-related sensorimotor features. The results highlight the significance of early detection in ASD diagnosis.
COMPUTERS IN HUMAN BEHAVIOR
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Marianna Inglese, Matteo Ferrante, Andrea Duggento, Tommaso Boccato, Nicola Toschi
Summary: Positron emission tomography (PET) is a noninvasive imaging technology used to assess tissue metabolism and function. Dynamic PET acquisitions provide information about tracer delivery, target interaction, and physiological washout, which can be analyzed using time activity curves (TACs). Conventional PET analysis requires invasive arterial blood sampling, but this study demonstrates that deep learning models can accurately discriminate breast cancer lesions using TACs without arterial blood sampling, outperforming traditional SUV analysis.
IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES
(2023)
Article
Engineering, Electrical & Electronic
Andrea Scarciglia, Fulvio Gini, Vincenzo Catrambone, Claudio Bonanno, Gaetano Valenza
Summary: This article proposes a method based on nonlinear entropy profile to estimate the power of dynamic noise s(2) without requiring knowledge of the specific T function. Testing with time series generated from Logistic maps and Pomeau-Manneville systems under different conditions, the results demonstrate that the proposed estimation algorithm can properly discern different noise levels without any a priori information.
IEEE SIGNAL PROCESSING LETTERS
(2023)
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)
