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Hong S, Coté G. Arterial Pulse Wave Velocity Signal Reconstruction Using Low Sampling Rates. Biosensors (Basel) 2024; 14:92. [PMID: 38392011 PMCID: PMC10887207 DOI: 10.3390/bios14020092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 12/28/2023] [Accepted: 02/06/2024] [Indexed: 02/24/2024]
Abstract
Pulse Wave Velocity (PWV) analysis is valuable for assessing arterial stiffness and cardiovascular health and potentially for estimating blood pressure cufflessly. However, conventional PWV analysis from two transducers spaced closely poses challenges in data management, battery life, and developing the device for continuous real-time applications together along an artery, which typically need data to be recorded at high sampling rates. Specifically, although a pulse signal consists of low-frequency components when used for applications such as determining heart rate, the pulse transit time for transducers near each other along an artery takes place in the millisecond range, typically needing a high sampling rate. To overcome this issue, in this study, we present a novel approach that leverages the Nyquist-Shannon sampling theorem and reconstruction techniques for signals produced by bioimpedance transducers closely spaced along a radial artery. Specifically, we recorded bioimpedance artery pulse signals at a low sampling rate, reducing the data size and subsequently algorithmically reconstructing these signals at a higher sampling rate. We were able to retain vital transit time information and achieved enhanced precision that is comparable to the traditional high-rate sampling method. Our research demonstrates the viability of the algorithmic method for enabling PWV analysis from low-sampling-rate data, overcoming the constraints of conventional approaches. This technique has the potential to contribute to the development of cardiovascular health monitoring and diagnosis using closely spaced wearable devices for real-time and low-resource PWV assessment, enhancing patient care and cardiovascular disease management.
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Affiliation(s)
- Sungcheol Hong
- Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA
| | - Gerard Coté
- Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA
- Department of Electrical Engineering, Texas A&M University, College Station, TX 77843, USA
- Center for Remote Health Technologies and Systems, Texas A&M Engineering Experiment Station, Texas A&M University, College Station, TX 77843, USA
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Gruenwald J, Sieghartsleitner S, Kapeller C, Scharinger J, Kamada K, Brunner P, Guger C. Characterization of High-Gamma Activity in Electrocorticographic Signals. Front Neurosci 2023; 17:1206120. [PMID: 37609450 PMCID: PMC10440607 DOI: 10.3389/fnins.2023.1206120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 07/10/2023] [Indexed: 08/24/2023] Open
Abstract
Introduction Electrocorticographic (ECoG) high-gamma activity (HGA) is a widely recognized and robust neural correlate of cognition and behavior. However, fundamental signal properties of HGA, such as the high-gamma frequency band or temporal dynamics of HGA, have never been systematically characterized. As a result, HGA estimators are often poorly adjusted, such that they miss valuable physiological information. Methods To address these issues, we conducted a thorough qualitative and quantitative characterization of HGA in ECoG signals. Our study is based on ECoG signals recorded from 18 epilepsy patients while performing motor control, listening, and visual perception tasks. In this study, we first categorize HGA into HGA types based on the cognitive/behavioral task. For each HGA type, we then systematically quantify three fundamental signal properties of HGA: the high-gamma frequency band, the HGA bandwidth, and the temporal dynamics of HGA. Results The high-gamma frequency band strongly varies across subjects and across cognitive/behavioral tasks. In addition, HGA time courses have lowpass character, with transients limited to 10 Hz. The task-related rise time and duration of these HGA time courses depend on the individual subject and cognitive/behavioral task. Task-related HGA amplitudes are comparable across the investigated tasks. Discussion This study is of high practical relevance because it provides a systematic basis for optimizing experiment design, ECoG acquisition and processing, and HGA estimation. Our results reveal previously unknown characteristics of HGA, the physiological principles of which need to be investigated in further studies.
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Affiliation(s)
- Johannes Gruenwald
- g.tec medical engineering GmbH, Schiedlberg, Austria
- Institute of Computational Perception, Johannes Kepler University, Linz, Austria
| | - Sebastian Sieghartsleitner
- g.tec medical engineering GmbH, Schiedlberg, Austria
- Institute of Computational Perception, Johannes Kepler University, Linz, Austria
| | | | - Josef Scharinger
- Institute of Computational Perception, Johannes Kepler University, Linz, Austria
| | - Kyousuke Kamada
- Department for Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
- Hokashin Group Megumino Hospital, Sapporo, Japan
| | - Peter Brunner
- National Center for Adaptive Neurotechnologies, Albany, NY, United States
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States
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Strommen KJ, Tørresen J, Côté-Allard U. Latent space unsupervised semantic segmentation. Front Physiol 2023; 14:1151312. [PMID: 37179829 PMCID: PMC10166858 DOI: 10.3389/fphys.2023.1151312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 03/28/2023] [Indexed: 05/15/2023] Open
Abstract
The development of compact and energy-efficient wearable sensors has led to an increase in the availability of biosignals. To effectively and efficiently analyze continuously recorded and multidimensional time series at scale, the ability to perform meaningful unsupervised data segmentation is an auspicious target. A common way to achieve this is to identify change-points within the time series as the segmentation basis. However, traditional change-point detection algorithms often come with drawbacks, limiting their real-world applicability. Notably, they generally rely on the complete time series to be available and thus cannot be used for real-time applications. Another common limitation is that they poorly (or cannot) handle the segmentation of multidimensional time series. Consequently, the main contribution of this work is to propose a novel unsupervised segmentation algorithm for multidimensional time series named Latent Space Unsupervised Semantic Segmentation (LS-USS), which was designed to easily work with both online and batch data. Latent Space Unsupervised Semantic Segmentation addresses the challenge of multivariate change-point detection by utilizing an autoencoder to learn a 1-dimensional latent space on which change-point detection is then performed. To address the challenge of real-time time series segmentation, this work introduces the Local Threshold Extraction Algorithm (LTEA) and a "batch collapse" algorithm. The "batch collapse" algorithm enables Latent Space Unsupervised Semantic Segmentation to process streaming data by dividing it into manageable batches, while Local Threshold Extraction Algorithm is employed to detect change-points in the time series whenever the computed metric by Latent Space Unsupervised Semantic Segmentation exceeds a predefined threshold. By using these algorithms in combination, our approach is able to accurately segment time series data in real-time, making it well-suited for applications where timely detection of changes is critical. When evaluating Latent Space Unsupervised Semantic Segmentation on a variety of real-world datasets the Latent Space Unsupervised Semantic Segmentation systematically achieves equal or better performance than other state-of-the-art change-point detection algorithms it is compared to in both offline and real-time settings.
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Affiliation(s)
| | - Jim Tørresen
- Department of Informatics, University of Oslo, Oslo, Norway
- RITMO, University of Oslo, Oslo, Norway
| | - Ulysse Côté-Allard
- Department of Informatics, University of Oslo, Oslo, Norway
- RITMO, University of Oslo, Oslo, Norway
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Rodrigues J, Liu H, Folgado D, Belo D, Schultz T, Gamboa H. Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix: Focus on Automatic Segmentation. Biosensors (Basel) 2022; 12:1182. [PMID: 36551149 PMCID: PMC9776348 DOI: 10.3390/bios12121182] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 05/27/2023]
Abstract
Biosignal-based technology has been increasingly available in our daily life, being a critical information source. Wearable biosensors have been widely applied in, among others, biometrics, sports, health care, rehabilitation assistance, and edutainment. Continuous data collection from biodevices provides a valuable volume of information, which needs to be curated and prepared before serving machine learning applications. One of the universal preparation steps is data segmentation and labelling/annotation. This work proposes a practical and manageable way to automatically segment and label single-channel or multimodal biosignal data using a self-similarity matrix (SSM) computed with signals' feature-based representation. Applied to public biosignal datasets and a benchmark for change point detection, the proposed approach delivered lucid visual support in interpreting the biosignals with the SSM while performing accurate automatic segmentation of biosignals with the help of the novelty function and associating the segments grounded on their similarity measures with the similarity profiles. The proposed method performed superior to other algorithms in most cases of a series of automatic biosignal segmentation tasks; of equal appeal is that it provides an intuitive visualization for information retrieval of multimodal biosignals.
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Affiliation(s)
- João Rodrigues
- Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics, NOVA School of Science and Technology, Campus da Caparica, 2829-516 Caparica, Portugal
- Cognitive Systems Lab, University of Bremen, Bibliothekstraße 1, 28359 Bremen, Germany
| | - Hui Liu
- Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics, NOVA School of Science and Technology, Campus da Caparica, 2829-516 Caparica, Portugal
- Cognitive Systems Lab, University of Bremen, Bibliothekstraße 1, 28359 Bremen, Germany
| | - Duarte Folgado
- Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics, NOVA School of Science and Technology, Campus da Caparica, 2829-516 Caparica, Portugal
- Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135, Porto, Portugal
| | - David Belo
- Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135, Porto, Portugal
| | - Tanja Schultz
- Cognitive Systems Lab, University of Bremen, Bibliothekstraße 1, 28359 Bremen, Germany
| | - Hugo Gamboa
- Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics, NOVA School of Science and Technology, Campus da Caparica, 2829-516 Caparica, Portugal
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Kim T, Nguyen P, Pham N, Bui N, Truong H, Ha S, Vu T. Epileptic Seizure Detection and Experimental Treatment: A Review. Front Neurol 2020; 11:701. [PMID: 32849189 PMCID: PMC7396638 DOI: 10.3389/fneur.2020.00701] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 07/09/2020] [Indexed: 01/18/2023] Open
Abstract
One-fourths of the patients have medication-resistant seizures and require seizure detection and treatment continuously to cope with sudden seizures. Seizures can be detected by monitoring the brain and muscle activities, heart rate, oxygen level, artificial sounds, or visual signatures through EEG, EMG, ECG, motion, or audio/video recording on the human head and body. In this article, we first discuss recent advances in seizure sensing, signal processing, time- or frequency-domain analysis, and classification algorithms to detect and classify seizure stages. Then, we show a strong potential of applying recent advancements in non-invasive brain stimulation technology to treat seizures. In particular, we explain the fundamentals of brain stimulation approaches, including (1) transcranial magnetic stimulation (TMS), (2) transcranial direct current stimulation (tDCS), (3) transcranial focused ultrasound stimulation (tFUS), and how to use them to treat seizures. Through this review, we intend to provide a broad view of both recent seizure diagnoses and treatments. Such knowledge would help fresh and experienced researchers to capture the advancements in sensing, detection, classification, and treatment seizures. Last but not least, we provide potential research directions that would attract seizure researchers/engineers in the field.
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Affiliation(s)
- Taeho Kim
- Department of Computer Science, University of Colorado, Boulder, CO, United States
| | - Phuc Nguyen
- Department of Computer Science, University of Colorado, Boulder, CO, United States
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, United States
| | - Nhat Pham
- Department of Computer Science, University of Colorado, Boulder, CO, United States
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Nam Bui
- Department of Computer Science, University of Colorado, Boulder, CO, United States
| | - Hoang Truong
- Department of Computer Science, University of Colorado, Boulder, CO, United States
| | - Sangtae Ha
- Department of Computer Science, University of Colorado, Boulder, CO, United States
| | - Tam Vu
- Department of Computer Science, University of Colorado, Boulder, CO, United States
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
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Roland T, Amsuess S, Russold MF, Baumgartner W. Ultra-Low-Power Digital Filtering for Insulated EMG Sensing. Sensors (Basel) 2019; 19:E959. [PMID: 30813494 DOI: 10.3390/s19040959] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 02/14/2019] [Accepted: 02/23/2019] [Indexed: 11/26/2022]
Abstract
Myoelectric prostheses help amputees to regain independence and a higher quality of life. These prostheses are controlled by state-of-the-art electromyography sensors, which use a conductive connection to the skin and are therefore sensitive to sweat. They are applied with some pressure to ensure a conductive connection, which may result in pressure marks and can be problematic for patients with circulatory disorders, who constitute a major group of amputees. Here, we present ultra-low-power digital signal processing algorithms for an insulated EMG sensor which couples the EMG signal capacitively. These sensors require neither conductive connection to the skin nor electrolytic paste or skin preparation. Capacitive sensors allow straightforward application. However, they make a sophisticated signal amplification and noise suppression necessary. A low-cost sensor has been developed for real-time myoelectric prostheses control. The major hurdles in measuring the EMG are movement artifacts and external noise. We designed various digital filters to attenuate this noise. Optimal system setup and filter parameters for the trade-off between attenuation of this noise and sufficient EMG signal power for high signal quality were investigated. Additionally, an algorithm for movement artifact suppression, enabling robust application in real-world environments, is presented. The algorithms, which require minimal calculation resources and memory, are implemented on an ultra-low-power microcontroller.
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Nabian M, Yin Y, Wormwood J, Quigley KS, Barrett LF, Ostadabbas S. An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data. IEEE J Transl Eng Health Med 2018; 6:2800711. [PMID: 30443441 PMCID: PMC6231905 DOI: 10.1109/jtehm.2018.2878000] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 09/05/2018] [Accepted: 10/22/2018] [Indexed: 11/09/2022]
Abstract
Electrocardiogram, electrodermal activity, electromyogram, continuous blood pressure, and impedance cardiography are among the most commonly used peripheral physiological signals (biosignals) in psychological studies and healthcare applications, including health tracking, sleep quality assessment, disease early-detection/diagnosis, and understanding human emotional and affective phenomena. This paper presents the development of a biosignal-specific processing toolbox (Bio-SP tool) for preprocessing and feature extraction of these physiological signals according to the state-of-the-art studies reported in the scientific literature and feedback received from the field experts. Our open-source Bio-SP tool is intended to assist researchers in affective computing, digital and mobile health, and telemedicine to extract relevant physiological patterns (i.e., features) from these biosignals semi-automatically and reliably. In this paper, we describe the successful algorithms used for signal-specific quality checking, artifact/noise filtering, and segmentation along with introducing features shown to be highly relevant to category discrimination in several healthcare applications (e.g., discriminating patterns associated with disease versus non-disease). Further, the Bio-SP tool is a publicly-available software written in MATLAB with a user-friendly graphical user interface (GUI), enabling future crowd-sourced modification to these tools. The GUI is compatible with MathWorks Classification Learner app for inference model development, such as model training, cross-validation scheme farming, and classification result computation.
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Affiliation(s)
- Mohsen Nabian
- Augmented Cognition LabElectrical and Computer Engineering DepartmentNortheastern UniversityBostonMA02115USA
- Harvard Medical SchoolBostonMA02115USA
| | - Yu Yin
- Augmented Cognition LabElectrical and Computer Engineering DepartmentNortheastern UniversityBostonMA02115USA
| | | | | | - Lisa F. Barrett
- Department of PsychologyNortheastern UniversityBostonMA02115USA
| | - Sarah Ostadabbas
- Augmented Cognition LabElectrical and Computer Engineering DepartmentNortheastern UniversityBostonMA02115USA
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Hoang P, Huebsch N, Bang SH, Siemons BA, Conklin BR, Healy KE, Ma Z, Jacquir S. Quantitatively characterizing drug-induced arrhythmic contractile motions of human stem cell-derived cardiomyocytes. Biotechnol Bioeng 2018; 115:1958-1970. [PMID: 29663322 PMCID: PMC6283051 DOI: 10.1002/bit.26709] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 03/07/2018] [Accepted: 04/06/2018] [Indexed: 12/31/2022]
Abstract
Quantification of abnormal contractile motions of cardiac tissue has been a noteworthy challenge and significant limitation in assessing and classifying the drug-induced arrhythmias (i.e., Torsades de pointes). To overcome these challenges, researchers have taken advantage of computational image processing tools to measure contractile motion from cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs). However, the amplitude and frequency analysis of contractile motion waveforms does not produce sufficient information to objectively classify the degree of variations between two or more sets of cardiac contractile motions. In this paper, we generated contractile motion data from beating hiPSC-CMs using motion tracking software based on optical flow analysis, and then implemented a computational algorithm, phase space reconstruction (PSR), to derive parameters (embedding, regularity, and fractal dimensions) to further characterize the dynamic nature of the cardiac contractile motions. Application of drugs known to cause cardiac arrhythmia induced significant changes to these resultant dimensional parameters calculated from PSR analysis. Integrating this new computational algorithm with the existing analytical toolbox of cardiac contractile motions will allow us to expand current assessments of cardiac tissue physiology into an automated, high-throughput, and quantifiable manner which will allow more objective assessments of drug-induced proarrhythmias.
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Affiliation(s)
- Plansky Hoang
- Department of Biomedical & Chemical Engineering, Syracuse University, Syracuse, NY, USA
- Syracuse Biomaterials Institute, Syracuse University, NY, USA
| | - Nathaniel Huebsch
- Department of Bioengineering, University of California, Berkeley, CA, USA
- Department of Material Science & Engineering, University of California, Berkeley, CA, USA
| | - Shin Hyuk Bang
- Department of Biomedical & Chemical Engineering, Syracuse University, Syracuse, NY, USA
| | - Brian A. Siemons
- Department of Bioengineering, University of California, Berkeley, CA, USA
| | - Bruce R. Conklin
- Glastone Institute of Cardiovascular Diseases, San Francisco, CA, USA
- Department of Medicine, and Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
| | - Kevin E. Healy
- Department of Bioengineering, University of California, Berkeley, CA, USA
- Department of Material Science & Engineering, University of California, Berkeley, CA, USA
| | - Zhen Ma
- Department of Biomedical & Chemical Engineering, Syracuse University, Syracuse, NY, USA
- Syracuse Biomaterials Institute, Syracuse University, NY, USA
| | - Sabir Jacquir
- Laboratoire LE2I UMR CNRS 6306, Université de Bourgogne Franche-Comté, Dijon, France
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Pirog A, Bornat Y, Perrier R, Raoux M, Jaffredo M, Quotb A, Lang J, Lewis N, Renaud S. Multimed: An Integrated, Multi-Application Platform for the Real-Time Recording and Sub-Millisecond Processing of Biosignals. Sensors (Basel) 2018; 18:E2099. [PMID: 29966339 PMCID: PMC6069272 DOI: 10.3390/s18072099] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 06/23/2018] [Accepted: 06/27/2018] [Indexed: 12/30/2022]
Abstract
Enhanced understanding and control of electrophysiology mechanisms are increasingly being hailed as key knowledge in the fields of modern biology and medicine. As more and more excitable cell mechanics are being investigated and exploited, the need for flexible electrophysiology setups becomes apparent. With that aim, we designed Multimed, which is a versatile hardware platform for the real-time recording and processing of biosignals. Digital processing in Multimed is an arrangement of generic processing units from a custom library. These can freely be rearranged to match the needs of the application. Embedded onto a Field Programmable Gate Array (FPGA), these modules utilize full-hardware signal processing to lower processing latency. It achieves constant latency, and sub-millisecond processing and decision-making on 64 channels. The FPGA core processing unit makes Multimed suitable as either a reconfigurable electrophysiology system or a prototyping platform for VLSI implantable medical devices. It is specifically designed for open- and closed-loop experiments and provides consistent feedback rules, well within biological microseconds timeframes. This paper presents the specifications and architecture of the Multimed system, then details the biosignal processing algorithms and their digital implementation. Finally, three applications utilizing Multimed in neuroscience and diabetes research are described. They demonstrate the system’s configurability, its multi-channel, real-time processing, and its feedback control capabilities.
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Affiliation(s)
- Antoine Pirog
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, F-33400 Talence, France.
| | - Yannick Bornat
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, F-33400 Talence, France.
| | - Romain Perrier
- Signalisation et physiopathologie cardiovasculaire, INSERM S-1180, University of Paris Sud, F-92296 Châtenay-Malabry, France.
| | - Matthieu Raoux
- Institut de Chimie et Biologie des Membranes et des Nano-objets (CBMN), University of Bordeaux, CNRS UMR 5248, F-33600 Pessac, France.
| | - Manon Jaffredo
- Institut de Chimie et Biologie des Membranes et des Nano-objets (CBMN), University of Bordeaux, CNRS UMR 5248, F-33600 Pessac, France.
| | - Adam Quotb
- Laboratoire d'Analyse et d'Architecture des Systèmes (LAAS), Federal University of Toulouse Midi-Pyrénées, CNRS UMR 8001, F-31031 Toulouse, France.
| | - Jochen Lang
- Institut de Chimie et Biologie des Membranes et des Nano-objets (CBMN), University of Bordeaux, CNRS UMR 5248, F-33600 Pessac, France.
| | - Noëlle Lewis
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, F-33400 Talence, France.
| | - Sylvie Renaud
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, F-33400 Talence, France.
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Abstract
OBJECTIVES The diet and lifestyle affect our life. Inadequate nutrition can cause various diseases including cardiovascular diseases. The aim of this study was to show the correlations between the fruit and vegetable diet and high signal resolution pulse wave parameters. DESIGN This was an observational study. SETTINGS The study was done during two-weeks rehabilitation treatment. PARTICIPANTS In this study 154 people using the fruit and vegetable diet have been examined. MEASUREMENTS The participants were monitored using a new diagnostic method high signal resolution pulseoximetry (HSR-PW). They were examined two times: before starting the diet and after two weeks of using it. The high signal resolution pulse wave and its characteristic parameters have been compared. RESULTS Analyzing the research results at the beginning and after two weeks of using this diet, the improvement of selected parameters has been noticed. With the improvement in the pulse wave was observed weight loss, improved blood counts (e.g. cholesterol, triglycerides) as well as decreased blood pressure particularly in people with treated hypertension. CONCLUSIONS The study shows that applied fruit and vegetable diet influenced favorably the people using it and contributed to the improvement of the HSR-PW parameters which are the source of information about the state of the cardiovascular system.
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Affiliation(s)
- R Krzyminiewski
- Bernadeta Dobosz, PhD, Medical Physics Division, Faculty of Physics, Adam Mickiewicz University, Umultowska 85, 61-614 Poznań, Poland,
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Abstract
The authors present a tutorial description of adaptive fractal analysis (AFA). AFA utilizes an adaptive detrending algorithm to extract globally smooth trend signals from the data and then analyzes the scaling of the residuals to the fit as a function of the time scale at which the fit is computed. The authors present applications to synthetic mathematical signals to verify the accuracy of AFA and demonstrate the basic steps of the analysis. The authors then present results from applying AFA to time series from a cognitive psychology experiment on repeated estimation of durations of time to illustrate some of the complexities of real-world data. AFA shows promise in dealing with many types of signals, but like any fractal analysis method there are special challenges and considerations to take into account, such as determining the presence of linear scaling regions.
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Affiliation(s)
- Michael A Riley
- Department of Psychology, Center for Cognition, Action, and Perception, University of Cincinnati Cincinnati, OH, USA
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