4.6 Article

Comparison of symbolization strategies for complexity assessment of spontaneous variability in individuals with signs of cardiovascular control impairment

Journal

BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Volume 62, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2020.102128

Keywords

Heart rate variability; Arterial blood pressure; Autonomic nervous system; Symbolic dynamics; Complexity analysis; Parkinson's disease; Head-up tilt

Funding

  1. Ricerca Corrente from the Italian Ministry of Health to IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy

Ask authors/readers for more resources

Symbolic analysis was frequently utilized in cardiovascular control assessment from spontaneous variability of heart period (HP) and systolic arterial pressure (SAP). However, due to different symbolization approaches comparison among studies present in literature is difficult especially on pathological groups with signs of autonomic dysfunction. We performed HP and SAP symbolic analyses via different symbolization methods over a group of Parkinson's disease (PD) patients with no sign of orthostatic hypotension and over age- and gender-matched healthy (H) controls at rest in supine position (REST) and during head-up tilt (HUT). The most frequently exploited symbolization methods applied directly to the original values, referred to as amplitude-based (AB) techniques, and to their first variations, labeled as variation-based (VB) methods, were compared. The rates of pattern families featuring different amount of variability among symbols were computed. In agreement with the inclusion criteria PD patients could maintain steady SAP during HUT via a tachycardic response. In spite of similar trends symbolic markers exhibited subtle differences. The level of consistency among different symbolic approaches is more dependent on the type of series and pattern family than on symbolization strategy (i.e. AB or VB). Consistency was higher over SAP than HP symbolic indexes and over highly variable patterns than more stable ones. All the symbolic methods detected an increased complexity of cardiac and vascular controls in PD patients compared to H subjects more evident during HUT than at REST. Symbolic markers of highly variable patterns provide interpretation independent of the symbolization technique exploited for their computation.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

Article Rehabilitation

Development of the Italian version of the Consultation and Relational Empathy (CARE) measure: translation, internal reliability, and construct validity in patients undergoing rehabilitation after total hip and knee arthroplasty

Fabrizio Natali, Laura Corradini, Cristiano Sconza, Patricia Taylor, Raffaello Furlan, Stewart W. Mercer, Roberto Gatti

Summary: This study translated and cross-culturally adapted the CARE measure into Italian, and examined its validity and reliability in a rehabilitation setting. The results showed that the Italian version of the CARE measure had high face validity, internal reliability, and construct validity.

DISABILITY AND REHABILITATION (2023)

Article Computer Science, Interdisciplinary Applications

Cardiorespiratory coupling in mechanically ventilated patients studied via synchrogram analysis

Davide Ottolina, Beatrice Cairo, Tommaso Fossali, Claudio Mazzucco, Antonio Castelli, Roberto Rech, Emanuele Catena, Alberto Porta, Riccardo Colombo

Summary: The study aimed to differentiate the impact of three ventilatory modes on cardiorespiratory phase coupling in critically ill patients. The highest synchronization was found during PCV ventilation, while the lowest was observed with NAVA. PCV induced a significant amount of cardiorespiratory phase synchronization, while patient-driven ventilatory modes had weaker synchronization, reaching the minimum with NAVA.

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2023)

Article Physiology

Changes of the cardiac baroreflex bandwidth during postural challenges

Alberto Porta, Francesca Gelpi, Vlasta Bari, Beatrice Cairo, Beatrice De Maria, Anielle C. M. Takahashi, Aparecida M. M. Catai, Riccardo Colombo

Summary: This study proposes a model-based parametric approach for estimating the baroreflex bandwidth, which provides different information compared to baroreflex sensitivity. The method takes into account the action of mechanisms changing heart rate irrespective of systolic arterial pressure. The study found that the baroreflex bandwidth changes with different degrees of head-up tilt.

AMERICAN JOURNAL OF PHYSIOLOGY-REGULATORY, INTEGRATIVE AND COMPARATIVE PHYSIOLOGY (2023)

Article Physics, Multidisciplinary

On the Different Abilities of Cross-Sample Entropy and K-Nearest-Neighbor Cross-Unpredictability in Assessing Dynamic Cardiorespiratory and Cerebrovascular Interactions

Alberto Porta, Vlasta Bari, Francesca Gelpi, Beatrice Cairo, Beatrice De Maria, Davide Tonon, Gianluca Rossato, Luca Faes

Summary: Nonlinear markers of coupling strength are utilized to typify cardiorespiratory and cerebrovascular regulations, and the computation of these indices requires techniques describing nonlinear interactions between respiration and heart period, and between mean arterial pressure and mean cerebral blood velocity. Two model-free methods, cross-sample entropy (CSampEn) and k-nearest-neighbor cross-unpredictability (KNNCUP), were compared for assessing dynamic interactions. The study found that KNNCUP is more reliable than CSampEn in evaluating coupling strength when interactions occur according to a causal structure, and it is more powerful in characterizing cardiorespiratory and cerebrovascular variability interactions in healthy subjects.

ENTROPY (2023)

Article Biophysics

Evaluation of cardiovascular and cerebrovascular control mechanisms in postural orthostatic tachycardia syndrome via conditional transfer entropy: the impact of the respiratory signal type

Francesca Gelpi, Vlasta Bari, Beatrice Cairo, Beatrice De Maria, Rachel Wells, Mathias Baumert, Alberto Porta

Summary: Transfer entropy was used to assess the interactions between cardiovascular and cerebrovascular variabilities, revealing an exaggerated sympathetic response in POTS subjects and impairments in baroreflex activation. This study highlights the importance of evaluating specific regulatory mechanisms and sensitivity to different respiratory aspects.

PHYSIOLOGICAL MEASUREMENT (2023)

Article Biophysics

Model-based spectral causality of cardiovascular variability interactions during head-down tilt

Alberto Porta, Beatrice Cairo, Vlasta Bari, Francesca Gelpi, Beatrice De Maria, Riccardo Colombo

Summary: This study investigates the variability between heart rate (HP) and systolic arterial pressure (SAP) in healthy men during head-down tilt (HDT) using model-based spectral causality analysis. The findings suggest that HDT reduces the involvement of the baroreflex in regulating the relationship between HP and SAP in the low frequency band, but does not affect the action of mechanical feedforward mechanisms in both low and high frequency bands.

PHYSIOLOGICAL MEASUREMENT (2023)

Article Chemistry, Analytical

Video-based Goniometer Applications for Measuring Knee Joint Angles during Walking in Neurological Patients: A Validity, Reliability and Usability Study

Monica Parati, Matteo Gallotta, Beatrice De Maria, Annalisa Pirola, Matteo Morini, Luca Longoni, Emilia Ambrosini, Giorgio Ferriero, Simona Ferrante

Summary: This study discusses the use of smartphone applications to measure knee joint ROM during walking and tests the reliability, validity, and usability of the collected measurements. The results show that both applications have good reliability, validity, and usability, making them suitable for assessing knee ROM in neurological patients.

SENSORS (2023)

Article Clinical Neurology

Spiral drawing analysis with a smart ink pen to identify Parkinson's disease fine motor deficits

Simone Toffoli, Francesca Lunardini, Monica Parati, Matteo Gallotta, Beatrice De Maria, Luca Longoni, Maria Elisabetta Dell'Anna, Simona Ferrante

Summary: This study presents a novel smart ink pen for spiral drawing assessment, aiming to better characterize Parkinson's disease motor symptoms. The device, used on paper as a normal pen, is enriched with motion and force sensors. Machine learning classification models were applied to test the indicators' ability to discriminate between Parkinsonian patients and age-matched controls. The results showed that the indicators were able to significantly identify Parkinson's disease motor symptoms, supporting the introduction of the smart ink pen as a time-efficient tool to complement clinical assessment.

FRONTIERS IN NEUROLOGY (2023)

Article Biophysics

The degree of engagement of cardiac and sympathetic arms of the baroreflex does not depend on the absolute value and sign of arterial pressure variations

Beatrice De Maria, Laura Adelaide Dalla Vecchia, Vlasta Bari, Beatrice Cairo, Francesca Gelpi, Francesca Perego, Anielle Christine Medeiros Takahashi, Juliana Cristina Milan-Mattos, Vinicius Minatel, Patricia Rehder-Santos, Murray Esler, Elisabeth Lambert, Mathias Baumert, Aparecida Maria Catai, Alberto Porta

Summary: This study found that the level of cardiac and sympathetic baroreflex engagement decreases with age and increases with postural stimulus intensity. Additionally, postural challenge magnitude leads to an increase in the percentages of sympathetic baroreflex patterns. Interestingly, the involvement of the cardiac and sympathetic arms of the baroreflex is not influenced by the absolute value or direction of arterial pressure changes.

PHYSIOLOGICAL MEASUREMENT (2023)

Article Biophysics

Characterization of cardiovascular and cerebrovascular controls via spectral causality analysis in patients undergoing surgical aortic valve replacement during a three-month follow-up

Vlasta Bari, Francesca Gelpi, Beatrice Cairo, Martina Anguissola, Sara Pugliese, Beatrice De Maria, Enrico Giuseppe Bertoldo, Valentina Fiolo, Edward Callus, Carlo De Vincentiis, Marianna Volpe, Raffaella Molfetta, Marco Ranucci, Alberto Porta

Summary: This study aimed to characterize the cardiovascular (CV) and cerebrovascular (CBV) controls in aortic valve stenosis (AVS) patients before and after surgical aortic valve replacement (SAVR). The study found that CV regulation is impaired in AVS patients, worsens after SAVR, and recovers after a three-month follow-up. However, CBV regulation remains preserved in AVS patients and is not affected by SAVR. The findings suggest the importance of monitoring CV and CBV controls in AVS patients and evaluating the effects of SAVR on these controls.

PHYSIOLOGICAL MEASUREMENT (2023)

Article Engineering, Biomedical

SRT: Improved transformer-based model for classification of 2D heartbeat images

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

Mutated Aquila Optimizer for assisting brain tumor segmentation

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

Decomposing photoplethysmogram waveforms into systolic and diastolic waves, with to environments

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

Accurate OCT-based diffuse adult-type glioma WHO grade 4 tissue classification using comprehensible texture feature analysis

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

Evaluation of cyclic repetition frequency based algorithm for fetal heart rate extraction from fetal phonocardiography

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

CNN-FEBAC: A framework for attention measurement of autistic individuals

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

AYOLOv5: Improved YOLOv5 based on attention mechanism for blood cell detection

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

Hybrid model with optimal features for non-invasive blood glucose monitoring from breath biomarkers

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

The effect of individual stress on the signature verification system using muscle synergy

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

Diabetic retinopathy lesion segmentation using deep multi-scale framework

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

Skin cancer diagnosis: Leveraging deep hidden features and ensemble classifiers for early detection and classification

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

In-phase matrix profile: A novel method for the detection of major depressive disorder

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

ASO-DKELM: Alpine skiing optimization based deep kernel extreme learning machine for elderly stroke detection from EEG signal

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

Continuous blood pressure monitoring using photoplethysmography and electrocardiogram signals by random forest feature selection and GWO-GBRT prediction model

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

Enhanced spatial-temporal learning network for dynamic facial expression recognition

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)