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Domingues R, Batista P, Pintado M, Oliveira-Silva P, Rodrigues PM. Evaluation of the responsiveness pattern to caffeine through a smart data-driven ECG non-linear multi-band analysis. Heliyon 2024; 10:e31721. [PMID: 38867964 PMCID: PMC11167299 DOI: 10.1016/j.heliyon.2024.e31721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 05/06/2024] [Accepted: 05/21/2024] [Indexed: 06/14/2024] Open
Abstract
This study aimed to explore more efficient ways of administering caffeine to the body by investigating the impact of caffeine on the modulation of the nervous system's activity through the analysis of electrocardiographic signals (ECG). An ECG non-linear multi-band analysis using Discrete Wavelet Transform (DWT) was employed to extract various features from healthy individuals exposed to different caffeine consumption methods: expresso coffee (EC), decaffeinated coffee (ED), Caffeine Oral Films (OF_caffeine), and placebo OF (OF_placebo). Non-linear feature distributions representing every ECG minute time series have been selected by PCA with different variance percentages to serve as inputs for 23 machine learning models in a leave-one-out cross-validation process for analyzing the behavior differences between ED/EC and OF_placebo/OF_caffeine groups, respectively, over time. The study generated 50-point accuracy curves per model, representing the discrimination power between groups throughout the 50 min. The best model accuracies for ED/EC varied between 30 and 70 %, (using the decision tree classifier) and OF_placebo/OF_caffeine ranged from 62 to 84 % (using Fine Gaussian). Notably, caffeine delivery through OFs demonstrated effective capacity compared to its placebo counterpart, as evidenced by significant differences in accuracy curves between OF_placebo/OF_caffeine. Caffeine delivery via OFs also exhibited rapid dissolution efficiency and controlled release rate over time, distinguishing it from EC. The study supports the potential of caffeine delivery through Caffeine OFs as a superior technology compared to traditional methods by means of ECG analysis. It highlights the efficiency of OFs in controlling the release of caffeine and underscores their promise for future caffeine delivery systems.
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Affiliation(s)
- Rita Domingues
- Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina – Laboratório Associado, Escola Superior de Biotecnologia, Rua Diogo Botelho 1327, 4169-005, Porto, Portugal
| | - Patrícia Batista
- Universidade Católica Portuguesa, Faculty of Education and Psychology, Research Centre for Human Development, Human Neurobehavioral Laboratory, Rua de Diogo Botelho 1327, 4169-005, Porto, Portugal
| | - Manuela Pintado
- Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina – Laboratório Associado, Escola Superior de Biotecnologia, Rua Diogo Botelho 1327, 4169-005, Porto, Portugal
| | - Patrícia Oliveira-Silva
- Universidade Católica Portuguesa, Faculty of Education and Psychology, Research Centre for Human Development, Human Neurobehavioral Laboratory, Rua de Diogo Botelho 1327, 4169-005, Porto, Portugal
| | - Pedro Miguel Rodrigues
- Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina – Laboratório Associado, Escola Superior de Biotecnologia, Rua Diogo Botelho 1327, 4169-005, Porto, Portugal
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Georgieva-Tsaneva G, Gospodinova E, Cheshmedzhiev K. Examination of Cardiac Activity with ECG Monitoring Using Heart Rate Variability Methods. Diagnostics (Basel) 2024; 14:926. [PMID: 38732339 PMCID: PMC11083608 DOI: 10.3390/diagnostics14090926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 04/24/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024] Open
Abstract
The paper presents a system for analyzing cardiac activity with the possibility of continuous and remote monitoring. The created sensor mobile device monitors heart activity by means of the convenient and imperceptible registration of cardiac signals. At the same time, the behavior of the human body is also monitored through the accelerometer and gyroscope built into the device, thanks to which it is possible to signal in the event of loss of consciousness or fall (in patients with syncope). Conducting real-time cardio monitoring and the analysis of recordings using various mathematical methods (linear, non-linear, and graphical) enables the research, accurate diagnosis, timely assistance, and correct treatment of cardiovascular diseases. The paper examines the recordings of patients diagnosed with arrhythmia and syncope recorded by electrocardiography (ECG) sensors in real conditions. The obtained results are subjected to statistical analysis to determine the accuracy and significance of the obtained results. The studies show significant deviations in the patients with arrhythmia and syncope regarding the obtained values of the studied parameters of heart rate variability (HRV) from the accepted normal values (for example, the root mean square of successive differences between normal heartbeats (RMSSD) in healthy individuals is 24.02 ms, while, in patients with arrhythmia (6.09 ms) and syncope (5.21 ms), it is much lower). The obtained quantitative and graphic results identify some possible abnormalities and demonstrate disorders regarding the activity of the autonomic nervous system, which is directly related to the work of the heart.
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Bokov P, Koehl B, Dudoignon B, Benkerrou M, Delclaux C. Case-control study of heart rate variability and sleep apnea in childhood sickle cell disease. J Sleep Res 2024:e14209. [PMID: 38590226 DOI: 10.1111/jsr.14209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 03/07/2024] [Accepted: 03/28/2024] [Indexed: 04/10/2024]
Abstract
Obstructive sleep apnea (OSA) is common in sickle cell disease (SCD) despite the absence of overweight, suggesting a specific pathophysiology. We previously showed that otherwise healthy children with increased pharyngeal compliance, a main endotype of OSA, exhibited decreased sympathetic modulation. Our objective was to assess whether modifications of heart rate variability (HRV) and compliance are associated in SCD. Cases (children with SCD, African or Caribbean ethnicity) and controls (otherwise healthy children, same ethnicity), aged 4-18 years, were selected from our database of children referred for OSA and matched for sex, age, and obstructive apnea-hypopnoea index (OAHI) score. The children underwent polysomnography and acoustic pharyngometry (to compute compliance). HRV analyses were performed from 5 min ECG recordings in wakeful, NREM, and REM sleep states and from the whole night. Twenty-one pairs were analysed (median age 10.5 years, 24 girls). Children with SCD had lower BMI z-scores and more tonsil hypertrophy than control children. Children with SCD and OSA (OAHI ≥2/hour) were characterised by lower compliance than children with SCD without OSA. An inverse relationship between compliance and SD2 (HRV from whole night, inversely related to sympathetic modulation) was evidenced (negative relationship in SCD: R = -0.63, p = 0.002 vs. positive relationship in controls R = 0.59, p = 0.006). In conclusion, while the decrease in sympathetic modulation in control children may contribute to increasing pharyngeal compliance, its decrease seems protective in children with sickle cell disease, which underlines the specificity of OSAS pathophysiology in SCD that could be due to sickle cell disease related smooth muscle dystonia.
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Affiliation(s)
- Plamen Bokov
- AP-HP, Robert Debré Hospital, Department of Physiology, INSERM NeuroDiderot, Paris University, Paris, France
| | - Bérengère Koehl
- AP-HP, Robert Debré Hospital, Sickle Cell Disease Center, Hematology Unit, Paris University, Paris, France
- UMR_S1134, BIGR, INSERM, Institut National de la Transfusion Sanguine, Laboratoire d'Excellence GR-Ex, Paris, France
| | - Benjamin Dudoignon
- AP-HP, Robert Debré Hospital, Department of Physiology, INSERM NeuroDiderot, Paris University, Paris, France
| | - Malika Benkerrou
- Sickle Cell Disease Center, Hematology Unit, Robert Debré Hospital, Paris, France
- INSERM UMR 1123, ECEVE, Paris, France
| | - Christophe Delclaux
- AP-HP, Robert Debré Hospital, Department of Physiology, INSERM NeuroDiderot, Paris University, Paris, France
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Guan W, Wang Y, Zhao H, Lu H, Zhang S, Liu J, Shi B. Prediction models for lymph node metastasis in cervical cancer based on preoperative heart rate variability. Front Neurosci 2024; 18:1275487. [PMID: 38410157 PMCID: PMC10894972 DOI: 10.3389/fnins.2024.1275487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 01/15/2024] [Indexed: 02/28/2024] Open
Abstract
Background The occurrence of lymph node metastasis (LNM) is one of the critical factors in determining the staging, treatment and prognosis of cervical cancer (CC). Heart rate variability (HRV) is associated with LNM in patients with CC. The purpose of this study was to validate the feasibility of machine learning (ML) models constructed with preoperative HRV as a feature of CC patients in predicting CC LNM. Methods A total of 292 patients with pathologically confirmed CC admitted to the Department of Gynecological Oncology of the First Affiliated Hospital of Bengbu Medical University from November 2020 to September 2023 were included in the study. The patient' preoperative 5-min electrocardiogram data were collected, and HRV time-domain, frequency-domain and non-linear analyses were subsequently performed, and six ML models were constructed based on 32 parameters. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results Among the 6 ML models, the random forest (RF) model showed the best predictive performance, as specified by the following metrics on the test set: AUC (0.852), accuracy (0.744), sensitivity (0.783), and specificity (0.785). Conclusion The RF model built with preoperative HRV parameters showed superior performance in CC LNM prediction, but multicenter studies with larger datasets are needed to validate our findings, and the physiopathological mechanisms between HRV and CC LNM need to be further explored.
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Affiliation(s)
- Weizheng Guan
- School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, China
| | - Yuling Wang
- Department of Gynecologic Oncology, The First Affiliated Hospital, Bengbu Medical University, Bengbu, Anhui, China
| | - Huan Zhao
- School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, China
| | - Hui Lu
- School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, China
| | - Sai Zhang
- School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, China
| | - Jian Liu
- Department of Gynecologic Oncology, The First Affiliated Hospital, Bengbu Medical University, Bengbu, Anhui, China
| | - Bo Shi
- School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, China
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Riganello F, Vatrano M, Cortese MD, Tonin P, Soddu A. Central autonomic network and early prognosis in patients with disorders of consciousness. Sci Rep 2024; 14:1610. [PMID: 38238457 PMCID: PMC10796939 DOI: 10.1038/s41598-024-51457-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/05/2024] [Indexed: 01/22/2024] Open
Abstract
The central autonomic network (CAN) plays a crucial role in modulating the autonomic nervous system. Heart rate variability (HRV) is a valuable marker for assessing CAN function in disorders of consciousness (DOC) patients. We used HRV analysis for early prognosis in 58 DOC patients enrolled within ten days of hospitalization. They underwent a five-minute electrocardiogram during baseline and acoustic/visual stimulation. The coma recovery scale-revised (CRS-R) was used to define the patient's consciousness level and categorize the good/bad outcome at three months. The high-frequency Power Spectrum Density and the standard deviation of normal-to-normal peaks in baseline, the sample entropy during the stimulation, and the time from injury features were used in the support vector machine analysis (SVM) for outcome prediction. The SVM predicted the patients' outcome with an accuracy of 96% in the training test and 100% in the validation test, underscoring its potential to provide crucial clinical information about prognosis.
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Affiliation(s)
- Francesco Riganello
- Reseach in Advanced Neurorehabilitation, S. Anna Institute, 88900, Crotone, Italy.
| | - Martina Vatrano
- Reseach in Advanced Neurorehabilitation, S. Anna Institute, 88900, Crotone, Italy
| | | | - Paolo Tonin
- Reseach in Advanced Neurorehabilitation, S. Anna Institute, 88900, Crotone, Italy
| | - Andrea Soddu
- Physics & Astronomy Department and Western Institute for Neuroscience, University of Western Ontario, London, ON, Canada
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Sharma K, Sunkaria RK. Cardiac arrhythmia detection using cross-sample entropy measure based on short and long RR interval series. J Arrhythm 2023; 39:412-421. [PMID: 37324769 PMCID: PMC10264752 DOI: 10.1002/joa3.12839] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 01/30/2023] [Accepted: 02/23/2023] [Indexed: 06/17/2023] Open
Abstract
Background Accurate arrhythmia (atrial fibrillation (AF) and congestive heart failure (CHF)) detection is still a challenge in the biomedical signal-processing field. Different linear and nonlinear measures of the electrocardiogram (ECG) signal analysis are used to fix this problem. Methods Sample entropy (SampEn) is used as a nonlinear measure based on single series to detect healthy and arrhythmia subjects. To follow this measure, the proposed work presents a nonlinear technique, namely, the cross-sample entropy (CrossSampEn) based on two series to quantify healthy and arrhythmia subjects. Results The research work consists of 10 records of normal sinus rhythm, 20 records of Fantasia (old group), 10 records of AF, and 10 records of CHF. The method of CrossSampEn has been proposed to obtain the irregularity between two same and different R-R (R peak to peak) interval series of different data lengths. Unlike the SampEn technique, the CrossSampEn technique never awards a 'not defined' value for very short data lengths and was found to be more consistent than SampEn. One-way ANOVA test has validated the proposed algorithm by providing a large F value and p < .0001. The proposed algorithm is also verified by simulated data. Conclusions It is concluded that different RR interval series of approximate 1500 data points and same RR interval series of approximate 1000 data points are required for health-status detection with embedded dimensions, M = 2 and threshold, r = .2. Also, CrossSampEn has been found more consistent than Sample entropy algorithm.
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Affiliation(s)
- Kanchan Sharma
- Department of Electronics and Communication EngineeringDr B R Ambedkar National Institute of TechnologyJalandharPunjabIndia
| | - Ramesh Kumar Sunkaria
- Department of Electronics and Communication EngineeringDr B R Ambedkar National Institute of TechnologyJalandharPunjabIndia
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Xu Z, Karwowski W, Çakıt E, Reineman-Jones L, Murata A, Aljuaid A, Sapkota N, Hancock P. Nonlinear dynamics of EEG responses to unmanned vehicle visual detection with different levels of task difficulty. APPLIED ERGONOMICS 2023; 111:104045. [PMID: 37178489 DOI: 10.1016/j.apergo.2023.104045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 05/05/2023] [Accepted: 05/08/2023] [Indexed: 05/15/2023]
Abstract
The main objective of this study was to examine the presence of chaos in the EEG recordings of brain activity under simulated unmanned ground vehicle visual detection scenarios with different levels of task difficulty. One hundred and fifty people participated in the experiment and completed four visual detection task scenarios: (1) change detection, (2) a threat detection task, (3) a dual-task with different change detection task rates, and (4) a dual-task with different threat detection task rates. We used the largest Lyapunov exponent and correlation dimension of the EEG data and performed 0-1 tests on the EEG data. The results revealed a change in the level of nonlinearity in the EEG data corresponding to different levels of cognitive task difficulty. The differences in EEG nonlinearity measures among the studied levels of task difficulty, as well as between a single task scenario and a dual-task scenario, have also been assessed. The results increase our understanding of the nature of unmanned systems' operational requirements.
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Affiliation(s)
- Ziqing Xu
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, 32816-2993, USA
| | - Waldemar Karwowski
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, 32816-2993, USA
| | - Erman Çakıt
- Department of Industrial Engineering, Gazi University, 06570, Ankara, Turkey.
| | - Lauren Reineman-Jones
- Autonomous Mobility Simulation and Training Lab, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - Atsuo Murata
- Department of Intelligent Mechanical Systems, Graduate School of Natural Science and Technology, Okayama University, Okayama, 700-8530, Japan
| | - Awad Aljuaid
- Department of Industrial Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
| | - Nabin Sapkota
- Department of Engineering Technology, Northwestern State University of Louisiana, Natchitoches, 71497, USA
| | - Peter Hancock
- Department of Psychology, University of Central Florida, Orlando, FL, 32816-2993, USA
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Błażkiewicz M. Evaluation of Geometric Attractor Structure and Recurrence Analysis in Professional Dancers. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1310. [PMID: 36141196 PMCID: PMC9497806 DOI: 10.3390/e24091310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 09/08/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Human motor systems contain nonlinear features. The purpose of this study was to evaluate the geometric structure of attractors and analyze recurrence in two different pirouettes (jazz and classic) performed by 15 professional dancers. METHODS The kinematics of the body's center of mass (CoM) and knee of the supporting leg (LKNE) during the pirouette were measured using the Vicon system. A time series of selected points were resampled, normalized, and randomly reordered. Then, every second time series was flipped to be combined with other time series and make a long time series out of the repetitions of a single task. The attractors were reconstructed, and the convex hull volumes (CHV) were counted for the CoM and LKNE for each pirouette in each direction. Recurrence quantification analysis (RQA) was used to extract additional information. RESULTS The CHVs calculated for the LKNE were significantly lower for the jazz pirouette. All RQA measures had the highest values for LKNE along the mediolateral axis for the jazz pirouette. This result underscores the high determinism, high motion recurrence, and complexity of this maneuver. CONCLUSIONS The findings offer new insight into the evaluation of the approximation of homogeneity in motion control. A high determinism indicates a highly stable and predictive motion trajectory.
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Affiliation(s)
- Michalina Błażkiewicz
- Faculty of Rehabilitation, Józef Piłsudski University of Physical Education in Warsaw, 00-809 Warszawa, Poland
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Mughal NE, Khan MJ, Khalil K, Javed K, Sajid H, Naseer N, Ghafoor U, Hong KS. EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM. Front Neurorobot 2022; 16:873239. [PMID: 36119719 PMCID: PMC9472125 DOI: 10.3389/fnbot.2022.873239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 07/20/2022] [Indexed: 11/13/2022] Open
Abstract
The constantly evolving human–machine interaction and advancement in sociotechnical systems have made it essential to analyze vital human factors such as mental workload, vigilance, fatigue, and stress by monitoring brain states for optimum performance and human safety. Similarly, brain signals have become paramount for rehabilitation and assistive purposes in fields such as brain–computer interface (BCI) and closed-loop neuromodulation for neurological disorders and motor disabilities. The complexity, non-stationary nature, and low signal-to-noise ratio of brain signals pose significant challenges for researchers to design robust and reliable BCI systems to accurately detect meaningful changes in brain states outside the laboratory environment. Different neuroimaging modalities are used in hybrid settings to enhance accuracy, increase control commands, and decrease the time required for brain activity detection. Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) measure the hemodynamic and electrical activity of the brain with a good spatial and temporal resolution, respectively. However, in hybrid settings, where both modalities enhance the output performance of BCI, their data compatibility due to the huge discrepancy between their sampling rate and the number of channels remains a challenge for real-time BCI applications. Traditional methods, such as downsampling and channel selection, result in important information loss while making both modalities compatible. In this study, we present a novel recurrence plot (RP)-based time-distributed convolutional neural network and long short-term memory (CNN-LSTM) algorithm for the integrated classification of fNIRS EEG for hybrid BCI applications. The acquired brain signals are first projected into a non-linear dimension with RPs and fed into the CNN to extract essential features without performing any downsampling. Then, LSTM is used to learn the chronological features and time-dependence relation to detect brain activity. The average accuracies achieved with the proposed model were 78.44% for fNIRS, 86.24% for EEG, and 88.41% for hybrid EEG-fNIRS BCI. Moreover, the maximum accuracies achieved were 85.9, 88.1, and 92.4%, respectively. The results confirm the viability of the RP-based deep-learning algorithm for successful BCI systems.
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Affiliation(s)
- Nabeeha Ehsan Mughal
- School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Muhammad Jawad Khan
- School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
- National Center of Artificial Intelligence (NCAI) – NUST, Islamabad, Pakistan
| | - Khurram Khalil
- School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Kashif Javed
- School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Hasan Sajid
- School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
- National Center of Artificial Intelligence (NCAI) – NUST, Islamabad, Pakistan
| | - Noman Naseer
- Department of Mechatronics and Biomedical Engineering, Air University, Islamabad, Pakistan
| | - Usman Ghafoor
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
- *Correspondence: Keum-Shik Hong
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Xiang Y, Liu H, Yang W, Xu Z, Wu Y, Tang Z, Zhu Z, Zeng Z, Wang D, Wang T, Hu N, Zhang D. A biosensing system employing nanowell microelectrode arrays to record the intracellular potential of a single cardiomyocyte. MICROSYSTEMS & NANOENGINEERING 2022; 8:70. [PMID: 35774495 PMCID: PMC9237042 DOI: 10.1038/s41378-022-00408-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 04/24/2022] [Accepted: 05/22/2022] [Indexed: 06/15/2023]
Abstract
Electrophysiological recording is a widely used method to investigate cardiovascular pathology, pharmacology and developmental biology. Microelectrode arrays record the electrical potential of cells in a minimally invasive and high-throughput way. However, commonly used microelectrode arrays primarily employ planar microelectrodes and cannot work in applications that require a recording of the intracellular action potential of a single cell. In this study, we proposed a novel measuring method that is able to record the intracellular action potential of a single cardiomyocyte by using a nanowell patterned microelectrode array (NWMEA). The NWMEA consists of five nanoscale wells at the center of each circular planar microelectrode. Biphasic pulse electroporation was applied to the NWMEA to penetrate the cardiomyocyte membrane, and the intracellular action potential was continuously recorded. The intracellular potential recording of cardiomyocytes by the NWMEA measured a potential signal with a higher quality (213.76 ± 25.85%), reduced noise root-mean-square (~33%), and higher signal-to-noise ratio (254.36 ± 12.61%) when compared to those of the extracellular recording. Compared to previously reported nanopillar microelectrodes, the NWMEA could ensure single cell electroporation and acquire high-quality action potential of cardiomyocytes with reduced fabrication processes. This NWMEA-based biosensing system is a promising tool to record the intracellular action potential of a single cell to broaden the usage of microelectrode arrays in electrophysiological investigation.
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Affiliation(s)
- Yuting Xiang
- Department of Obstetrics and Gynecology, Affiliated Dongguan People’s Hospital, Southern Medical University, Dongguan, 523058 China
| | - Haitao Liu
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311100 China
- Research Center for Humanoid Sensing, Zhejiang Laboratory, Hangzhou, 311100 China
| | - Wenjian Yang
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311100 China
| | - Zhongyuan Xu
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311100 China
| | - Yue Wu
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311100 China
| | - Zhaojian Tang
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311100 China
| | - Zhijing Zhu
- Key Laboratory of Novel Target and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, School of Computer & Computing Science, Zhejiang University City College, Hangzhou, 310015 China
- School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, 310058 China
| | - Zhiyong Zeng
- School of Automation, Nanjing University of Science and Technology, Nanjing, 210094 China
| | - Depeng Wang
- College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016 China
| | - Tianxing Wang
- E-LinkCare Meditech Co., Ltd, Hangzhou, 310011 China
| | - Ning Hu
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Department of Chemistry, Zhejiang University, Hangzhou, 310058 China
| | - Diming Zhang
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311100 China
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Wang H, Wu Y, Zou Q, Yang W, Xu Z, Dong H, Zhu Z, Wang D, Wang T, Hu N, Zhang D. A biosensing system using a multiparameter nonlinear dynamic analysis of cardiomyocyte beating for drug-induced arrhythmia recognition. MICROSYSTEMS & NANOENGINEERING 2022; 8:49. [PMID: 35547605 PMCID: PMC9081091 DOI: 10.1038/s41378-022-00383-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 03/14/2022] [Accepted: 04/07/2022] [Indexed: 06/15/2023]
Abstract
Cardiovascular disease is the number one cause of death in humans. Therefore, cardiotoxicity is one of the most important adverse effects assessed by arrhythmia recognition in drug development. Recently, cell-based techniques developed for arrhythmia recognition primarily employ linear methods such as time-domain analysis that detect and compare individual waveforms and thus fall short in some applications that require automated and efficient arrhythmia recognition from large datasets. We carried out the first report to develop a biosensing system that integrated impedance measurement and multiparameter nonlinear dynamic algorithm (MNDA) analysis for drug-induced arrhythmia recognition and classification. The biosensing system cultured cardiomyocytes as physiologically relevant models, used interdigitated electrodes to detect the mechanical beating of the cardiomyocytes, and employed MNDA analysis to recognize drug-induced arrhythmia from the cardiomyocyte beating recording. The best performing MNDA parameter, approximate entropy, enabled the system to recognize the appearance of sertindole- and norepinephrine-induced arrhythmia in the recording. The MNDA reconstruction in phase space enabled the system to classify the different arrhythmias and quantify the severity of arrhythmia. This new biosensing system utilizing MNDA provides a promising and alternative method for drug-induced arrhythmia recognition and classification in cardiological and pharmaceutical applications.
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Affiliation(s)
- Hao Wang
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510006 China
| | - Yue Wu
- Research Center for Intelligent Sensing Systems, Zhejiang Lab, Hangzhou, 311121 China
| | - Quchao Zou
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Department of Chemistry, The Second Affiliated Hospital Zhejiang University School of Medicine, Department of Clinical Medical Engineering, Zhejiang University, Hangzhou, 310058 China
| | - Wenjian Yang
- Research Center for Intelligent Sensing Systems, Zhejiang Lab, Hangzhou, 311121 China
| | - Zhongyuan Xu
- Research Center for Intelligent Sensing Systems, Zhejiang Lab, Hangzhou, 311121 China
| | - Hao Dong
- Research Center for Intelligent Sensing Systems, Zhejiang Lab, Hangzhou, 311121 China
| | - Zhijing Zhu
- Key Laboratory of Novel Target and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, School of Computer & Computing Science, Zhejiang University City College, Hangzhou, 310015 China
- School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, 310058 China
| | - Depeng Wang
- College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016 China
| | - Tianxing Wang
- E-LinkCare Meditech Co., Ltd., Hangzhou, 310011 China
| | - Ning Hu
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510006 China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Department of Chemistry, The Second Affiliated Hospital Zhejiang University School of Medicine, Department of Clinical Medical Engineering, Zhejiang University, Hangzhou, 310058 China
- State Key Laboratory of Transducer Technology, Chinese Academy of Sciences, Shanghai, 200050 China
| | - Diming Zhang
- Research Center for Intelligent Sensing Systems, Zhejiang Lab, Hangzhou, 311121 China
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Flanders WH, Moïse NS, Pariaut R, Sargent J. The next heartbeat: creating dynamic and histographic Poincaré plots for the assessment of cardiac rhythms. J Vet Cardiol 2022; 42:1-13. [DOI: 10.1016/j.jvc.2022.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 04/19/2022] [Accepted: 04/28/2022] [Indexed: 10/18/2022]
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Preprocessing Methods for Ambulatory HRV Analysis Based on HRV Distribution, Variability and Characteristics (DVC). SENSORS 2022; 22:s22051984. [PMID: 35271128 PMCID: PMC8914897 DOI: 10.3390/s22051984] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 02/24/2022] [Accepted: 03/01/2022] [Indexed: 01/27/2023]
Abstract
Thanks to wearable devices joint with AI algorithms, it is possible to record and analyse physiological parameters such as heart rate variability (HRV) in ambulatory environments. The main downside to such setups is the bad quality of recorded data due to movement, noises, and data losses. These errors may considerably alter HRV analysis and should therefore be addressed beforehand, especially if used for medical diagnosis. One widely used method to handle such problems is interpolation, but this approach does not preserve the time dependence of the signal. In this study, we propose a new method for HRV processing including filtering and iterative data imputation using a Gaussian distribution. The particularity of the method is that many physiological aspects are taken into consideration, such as HRV distribution, RR variability, and normal boundaries, as well as time series characteristics. We study the effect of this method on classification using a random forest classifier (RF) and compare it to other data imputation methods including linear, shape-preserving piecewise cubic Hermite (pchip), and spline interpolation in a case study on stress. Features from reconstructed HRV signals of 67 healthy subjects using all four methods were analysed and separately classified by a random forest algorithm to detect stress against relaxation. The proposed method reached a stable F1 score of 61% even with a high percentage of missing data, whereas other interpolation methods reached approximately 54% F1 score for a low percentage of missing data, and the performance drops to about 44% when the percentage is increased. This suggests that our method gives better results for stress classification, especially on signals with a high percentage of missing data.
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Comparing the HRV Time-Series Signals Acquired from Cannabis Consuming and Non-Consuming Indian Paddy-Field Workers by Recurrence Quantification Analysis. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2020.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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15
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Contoyiannis Y, Diakonos FK, Kampitakis M, Potirakis SM. Can high-frequency ECG fluctuations differentiate between healthy and myocardial infarction cases? BIOMEDICAL ENGINEERING ADVANCES 2021. [DOI: 10.1016/j.bea.2021.100011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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Ortiz A, Bradler K, Mowete M, MacLean S, Garnham J, Slaney C, Mulsant BH, Alda M. The futility of long-term predictions in bipolar disorder: mood fluctuations are the result of deterministic chaotic processes. Int J Bipolar Disord 2021; 9:30. [PMID: 34596784 PMCID: PMC8486895 DOI: 10.1186/s40345-021-00235-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 08/17/2021] [Indexed: 03/06/2023] Open
Abstract
BACKGROUND Understanding the underlying architecture of mood regulation in bipolar disorder (BD) is important, as we are starting to conceptualize BD as a more complex disorder than one of recurring manic or depressive episodes. Nonlinear techniques are employed to understand and model the behavior of complex systems. Our aim was to assess the underlying nonlinear properties that account for mood and energy fluctuations in patients with BD; and to compare whether these processes were different in healthy controls (HC) and unaffected first-degree relatives (FDR). We used three different nonlinear techniques: Lyapunov exponent, detrended fluctuation analysis and fractal dimension to assess the underlying behavior of mood and energy fluctuations in all groups; and subsequently to assess whether these arise from different processes in each of these groups. RESULTS There was a positive, short-term autocorrelation for both mood and energy series in all three groups. In the mood series, the largest Lyapunov exponent was found in HC (1.84), compared to BD (1.63) and FDR (1.71) groups [F (2, 87) = 8.42, p < 0.005]. A post-hoc Tukey test showed that Lyapunov exponent in HC was significantly higher than both the BD (p = 0.003) and FDR groups (p = 0.03). Similarly, in the energy series, the largest Lyapunov exponent was found in HC (1.85), compared to BD (1.76) and FDR (1.67) [F (2, 87) = 11.02; p < 0.005]. There were no significant differences between groups for the detrended fluctuation analysis or fractal dimension. CONCLUSIONS The underlying nature of mood variability is in keeping with that of a chaotic system, which means that fluctuations are generated by deterministic nonlinear process(es) in HC, BD, and FDR. The value of this complex modeling lies in analyzing the nature of the processes involved in mood regulation. It also suggests that the window for episode prediction in BD will be inevitably short.
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Affiliation(s)
- Abigail Ortiz
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Centre for Addiction & Mental Health, CAMH 100 Stokes St., Rm 4229, Toronto, ON, M6J 1H4, Canada.
| | | | - Maxine Mowete
- Department of Electrical Engineering, University of Ottawa, Ottawa, ON, Canada
| | - Stephane MacLean
- Institute for Mental Health Research, The Royal Ottawa Hospital, Ottawa, ON, Canada
| | | | | | - Benoit H Mulsant
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Centre for Addiction & Mental Health, CAMH 100 Stokes St., Rm 4229, Toronto, ON, M6J 1H4, Canada
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
- National Institute of Mental Health, Klecany, Czech Republic
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Burton S, Vicinanza D, Exell T, Newell KM, Irwin G, Williams GKR. Attractor dynamics of elite performance: the high bar longswing. Sports Biomech 2021:1-14. [PMID: 34309483 DOI: 10.1080/14763141.2021.1954236] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 07/06/2021] [Indexed: 10/20/2022]
Abstract
Combining biomechanics and motor control, the aim of this study was to investigate the limit cycle dynamics during the high bar longswing across the UK elite gymnastics pathway age groupings. Senior, junior and development gymnasts (N = 30) performed three sets of eight consecutive longswings on the high bar. The centre of mass motion was examined through Poincaré plots and recurrence quantification analysis exploring the limit cycle dynamics of the longswing. Close to one-dimensional limit cycles were displayed for the senior (correlation dimension (CD) = 1.17 ± .08), junior (CD = 1.26 ± .08) and development gymnasts (CD = 1.33 ± .14). Senior elite gymnasts displayed increased recurrence characteristics in addition to longer longswing duration (p < .01) and lower radial angular velocity of the mass centre (p < .01). All groups of gymnasts had highly recurrent and predictable limit cycle characteristics. The findings of this research support the postulation that the further practice, experience and individual development associated with the senior gymnasts contribute to the refinement of the longswing from a nonlinear dynamics perspective. These findings support the idea of functional task decomposition informing the understanding of skill and influencing coaches' decisions around skill development and physical preparation.
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Affiliation(s)
- Sophie Burton
- School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, UK
| | - Domenico Vicinanza
- Department of Computing and Technology, Anglia Ruskin University, Cambridge, UK
| | - Timothy Exell
- School of Sport, Health and Exercise Science, University of Portsmouth, Portsmouth UK
| | - Karl M Newell
- Department of Kinesiology, University of Georgia, Athens, GA, USA
| | - Gareth Irwin
- School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, UK
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Tse G, Hao G, Lee S, Zhou J, Zhang Q, Du Y, Liu T, Cheng SH, Wong WT. Measures of repolarization variability predict ventricular arrhythmogenesis in heptanol-treated Langendorff-perfused mouse hearts. Curr Res Physiol 2021; 4:125-134. [PMID: 34746832 PMCID: PMC8562203 DOI: 10.1016/j.crphys.2021.04.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 04/07/2021] [Accepted: 04/12/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Time-domain and non-linear methods can be used to quantify beat-to-beat repolarization variability but whether measures of repolarization variability can predict ventricular arrhythmogenesis in mice have never been explored. METHODS Left ventricular monophasic action potentials (MAPs) were recorded during constant right ventricular 8 Hz pacing in Langendorff-perfused mouse hearts, in the presence or absence of the gap junction and sodium channel inhibitor heptanol (0.1, 0.5, 1 or 2 mM). RESULTS Under control conditions, mean action potential duration (APD) was 39.4 ± 8.1 ms. Standard deviation (SD) of APDs was 0.3 ± 0.2 ms, coefficient of variation was 0.9 ± 0.8% and the root mean square (RMS) of successive differences in APDs was 0.15 ± 0.14 ms. Poincaré plots of APDn+1 against APDn revealed ellipsoid morphologies with a SD along the line-of-identity (SD2) to SD perpendicular to the line-of-identity (SD1) ratio of 4.6 ± 2.1. Approximate and sample entropy were 0.61 ± 0.12 and 0.76 ± 0.26, respectively. Detrended fluctuation analysis revealed short- and long-term fluctuation slopes of 1.49 ± 0.27 and 0.81 ± 0.36, respectively. Heptanol at 2 mM induced ventricular tachycardia in five out of six hearts. None of the above parameters were altered by heptanol during which reproducible electrical activity was observed (KW-ANOVA, P > 0.05). Contrastingly, SD2/SD1 decreased to 2.03 ± 0.41, approximate and sample entropy increased to 0.82 ± 0.12 and 1.45 ± 0.34, and short-term fluctuation slope decreased to 0.82 ± 0.19 during the 20-s period preceding spontaneous ventricular tachy-arrhythmias (n = 6, KW-ANOVA, P < 0.05). CONCLUSION Measures of repolarization variability, such as SD2/SD1, entropy, and fluctuation slope are altered preceding the occurrence of ventricular arrhythmogenesis in mouse hearts. Changes in these variables may allow detection of impending arrhythmias for early intervention.
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Affiliation(s)
- Gary Tse
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, 300211, China
- Cardiovascular Analytics Group, Laboratory of Cardiovascular Physiology, Hong Kong, China
| | - Guoliang Hao
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
| | - Sharen Lee
- Cardiovascular Analytics Group, Laboratory of Cardiovascular Physiology, Hong Kong, China
| | - Jiandong Zhou
- School of Data Science, City University of Hong Kong, Hong Kong, China
| | - Qingpeng Zhang
- School of Data Science, City University of Hong Kong, Hong Kong, China
| | - Yimei Du
- Research Center of Ion Channelopathy, Institute of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tong Liu
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, 300211, China
| | - Shuk Han Cheng
- Department of Biomedical Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Hong Kong
| | - Wing Tak Wong
- School of Life Sciences, Chinese University of Hong Kong, Hong Kong, China
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Novel Approach for EKG Signals Analysis Based on Markovian and Non-Markovian Fractalization Type in Scale Relativity Theory. Symmetry (Basel) 2021. [DOI: 10.3390/sym13030456] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Two distinct operational procedures are proposed for diagnosis and tracking of heart disease evolution (in particular atrial fibrillations). The first procedure, based on the application of non-linear dynamic methods (strange attractors, skewness, kurtosis, histograms, Lyapunov exponent, etc.) analyzes the electrical activity of the heart (electrocardiogram signals). The second procedure, based on multifractalization through Markovian and non-Markovian-type stochasticizations in the framework of the scale relativity theory, reconstructs any type of EKG signal by means of harmonic mappings from the usual space to the hyperbolic one. These mappings mime various scale transitions by differential geometries, in Riemann spaces with symmetries of SL(2R)-type. Then, the two operational procedures are not mutually exclusive, but rather become complementary, through their finality, which is gaining valuable information concerning fibrillation crises. As such, the author’s proposed method could be used for developing new models for medical diagnosis and evolution tracking of heart diseases (patterns dynamics, signal reconstruction, etc.).
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Ilarraza-Lomelí H, Rius-Suárez MD. Complexus cordis. ARCHIVOS DE CARDIOLOGIA DE MEXICO 2020; 91:327-336. [PMID: 33232968 PMCID: PMC8351662 DOI: 10.24875/acm.200000391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 07/18/2020] [Indexed: 11/17/2022] Open
Abstract
The science-based study of the heart has allowed us to know its structure and function deeply, through the fragmentation and analysis of its parts, following the guidelines that so many achievements have given to us. However, at the time of reassembling those analyzed fragments, we realize that something is missing; the simply sum of the parts is not equal to everything. Thus, for decades, numerous scientists have studied novel strategies that allow us understanding, every natural phenomena from a more inclusive, open and integrative models, which closely address to interactions rather than components. In this way, we can observe how, the behavior of many variables usually transgress the conventional plane and moves towards non-linearity and fractality, making a complex tissue that will maintain its structure while thermodynamically viable. Thus, this document shows the way how, the non-linear study of complex cardiovascular dynamics, begins to give us answers to many questions that the clinical cardiologist poses every day.
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Affiliation(s)
- Hermes Ilarraza-Lomelí
- Servicio de Rehabilitación Cardiaca y Medicina Física, Instituto Nacional de Cardiología Ignacio Chávez, Ciudad de México, México
| | - María D. Rius-Suárez
- Servicio de Rehabilitación Cardiaca y Medicina Física, Instituto Nacional de Cardiología Ignacio Chávez, Ciudad de México, México
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21
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DeProspero DJ, Adin DB. Visual representations of canine cardiac arrhythmias with Lorenz (Poincaré) plots. Am J Vet Res 2020; 81:720-731. [PMID: 33112172 DOI: 10.2460/ajvr.81.9.720] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
OBJECTIVE To characterize the patterns associated with Lorenz plots (LPs) or Poincaré plots derived from the Holter recordings of dogs with various cardiac rhythms. ANIMALS 77 dogs with 24-hour Holter recordings. PROCEDURES A 1-hour period from the Holter recordings from each of 20 dogs without arrhythmias and from each of 57 dogs with arrhythmias (10 each with supraventricular premature complexes, complex supraventricular ectopy, ventricular premature complexes, complex ventricular ectopy, and atrial fibrillation, and 7 with high-grade second-degree atrioventricular block) were used to generate the LPs. Patterns depicted in the LPs were described. RESULTS Arrhythmia-free Holter recordings yielded LPs with a Y-shaped pattern and variable silent zones. Recordings with single premature complexes yielded LPs with double side and triple side lobes. Complex ectopy was denoted by dots clustered in the lower left corner of the LPs. The LPs of recordings with atrial fibrillation had fan patterns consistent with a nonlinear relationship between atrial electrical impulses and atrioventricular nodal conduction. The recordings with atrioventricular block yielded LPs with island patterns consistent with variable atrioventricular nodal conduction. CONCLUSIONS AND CLINICAL RELEVANCE Distinct LP patterns were identified for common cardiac rhythms of dogs, supportive of nonrandom mechanisms as the cause of most rhythms. Visual interpretation of an LP generated from a Holter recording may aid in determining the arrhythmia type and understanding the arrhythmia's mechanism in dogs and other species.
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22
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Wright SE, Palmer C. Physiological and Behavioral Factors in Musicians' Performance Tempo. Front Hum Neurosci 2020; 14:311. [PMID: 33192375 PMCID: PMC7478117 DOI: 10.3389/fnhum.2020.00311] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 07/14/2020] [Indexed: 11/13/2022] Open
Abstract
Musicians display individual differences in their spontaneous performance rates (tempo) for simple melodies, but the factors responsible are unknown. Previous research suggests that musical tempo modulates listeners' cardiovascular activity. We report an investigation of musicians' melody performances measured over a 12-h day and subsequent changes in the musicians' physiological activity. Skilled pianists completed four testing sessions in a single day as cardiac activity was recorded during an initial 5 min of baseline rest and during performances of familiar and unfamiliar melodies. Results indicated slower tempi for familiar and unfamiliar melodies at early testing times. Performance rates at 09 h were predicted by differences in participants' alertness and musical training; these differences were not explained by sleep patterns, chronotype, or cardiac activity. Individual differences in pianists' performance tempo were consistent across testing sessions: participants with a faster tempo at 09 h maintained a faster tempo at later testing sessions. Cardiac measures at early testing times indicated increased heart rates and more predictable cardiac dynamics during music performance than baseline rest, and during performances of unfamiliar melodies than familiar melodies. These findings provide the first evidence of cardiac dynamics that are unique to music performance contexts.
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Affiliation(s)
- Shannon E Wright
- Department of Psychology, McGill University, Montreal, QC, Canada
| | - Caroline Palmer
- Department of Psychology, McGill University, Montreal, QC, Canada
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23
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Non-linear analysis of heart rate variability for evaluating the acute effects of caffeinated beverages in young adults. Cardiol Young 2020; 30:1018-1023. [PMID: 32513326 DOI: 10.1017/s1047951120001481] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Caffeinated beverages are the most consumed substances in the world. High rate of uptake of these beverages leads to various cardiovascular disorders ranging from palpitations to coronary failure. The objective of the study is to ascertain how the complexity parameters of heart rate variability are affected by acute consumption of caffeinated beverages in young adults.Electrocardiogram measurements were performed before consuming drinks. After consuming the drinks, measurements were done again at 30 minutes and 60 minutes. Heart rate variability signals were acquired from electrocardiogram signals. Also, the signals were reconstructed in the phase space and largest Lyapunov exponent, correlation dimension, approximate entropy, and detrended fluctuation analysis values were calculated.Heart rate increased for energy drink and cola groups but not in coffee group. Non-linear parameter values of energy drink, coffee, and cola group are increased within 60 minutes after drink consumption. This change is statistically significant just for energy drink group.Energy drink consumption increases the complexity of the cardiovascular system in young adults significantly. Coffee and cola consumption have no significant effect on the non-linear parameters of heart rate variability.
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Alizadeh Meghrazi M, Tian Y, Mahnam A, Bhattachan P, Eskandarian L, Taghizadeh Kakhki S, Popovic MR, Lankarany M. Multichannel ECG recording from waist using textile sensors. Biomed Eng Online 2020; 19:48. [PMID: 32546233 PMCID: PMC7296680 DOI: 10.1186/s12938-020-00788-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 05/28/2020] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The development of wearable health monitoring systems is garnering tremendous interest in research, technology and commercial applications. Their ability of providing unique capabilities in continuous, real-time, and non-invasive tracking of the physiological markers of users can provide insights into the performance and health of individuals. Electrocardiogram (ECG) signals are of particular interest, as cardiovascular disease is the leading cause of death globally. Monitoring heart health and its conditions such as ventricular disturbances and arrhythmias can be achieved through evaluating various features of ECG such as R-peaks, QRS complex, T-wave, and P-wave. Despite recent advances in biosensors for wearable applications, most of the currently available solutions rely solely on a single system attached to the body, limiting the ability to obtain reliable and multi-location biosignals. However, in engineering systems, sensor fusion, which is the optimal integration and processing of data from multiple sensors, has been a common theme and should be considered for wearables. In recent years, due to an increase in the availability and variety of different types of sensors, the possibility of achieving sensor fusion in wearable systems has become more attainable. Sensor fusion in multi-sensing systems results in significant enhancements of information inferences compared to those from systems with a sole sensor. One step towards the development of sensor fusion for wearable health monitoring systems is the accessibility to multiple reliable electrophysiological signals, which can be recorded continuously. RESULTS In this paper, we develop a textile-based multichannel ECG band that has the ability to measure ECG from multiple locations on the waist. As a proof of concept, we demonstrate that ECG signals can be reliably obtained from different locations on the waist where the shape of the QRS complex is nearly comparable with recordings from the chest using traditional gel electrodes. In addition, we develop a probabilistic approach-based on prediction and update strategies-to detect R-peaks from noisy textile data in different statuses, including sitting, standing, and jogging. In this approach, an optimal search method is utilized to detect R-peaks based on the history of the intervals between previously detected R-peaks. We show that the performance of our probabilistic approach in R-peak detection is significantly better than that based on Pan-Tompkins and optimal-threshold methods. CONCLUSION A textile-based multichannel ECG band was developed to track the heart rate changes from multiple locations on the waist. We demonstrated that (i) the ECG signal can be detected from different locations on the waist, and (ii) the accuracy of the detected R-peaks from textile sensors was improved by using our proposed probabilistic approach. Despite the limitations of the textile sensors that might compromise the quality of ECG signals, we anticipate that the textile-based multichannel ECG band can be considered as an effective wearable system to facilitate the development of sensor fusion methodology for pervasive and non-invasive health monitoring through continuous tracking of heart rate variability (HRV) from the waist. In addition, from the commercialization point of view, we anticipate that the developed band has the potential to be integrated into garments such as underwear, bras or pants so that individuals can use it on a daily basis.
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Affiliation(s)
- Milad Alizadeh Meghrazi
- Institute of Biomaterials & Biomedical Engineering (IBBME), University of Toronto, Toronto, ON, Canada.,Department of Materials Science& Engineering, University of Toronto, Toronto, ON, Canada
| | - Yupeng Tian
- Clinical and Computational Neuroscience, Krembil Research Institute, University Health Network, 60 Leonard Ave, Toronto, ON, M5T 0S8, Canada.,Institute of Biomaterials & Biomedical Engineering (IBBME), University of Toronto, Toronto, ON, Canada
| | | | - Presish Bhattachan
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Ladan Eskandarian
- Department of Materials Science& Engineering, University of Toronto, Toronto, ON, Canada
| | - Sara Taghizadeh Kakhki
- Clinical and Computational Neuroscience, Krembil Research Institute, University Health Network, 60 Leonard Ave, Toronto, ON, M5T 0S8, Canada
| | - Milos R Popovic
- Institute of Biomaterials & Biomedical Engineering (IBBME), University of Toronto, Toronto, ON, Canada.,KITE Research Institute, Toronto Rehabilitation Institute-University Health Network (UHN), Toronto, ON, Canada
| | - Milad Lankarany
- Clinical and Computational Neuroscience, Krembil Research Institute, University Health Network, 60 Leonard Ave, Toronto, ON, M5T 0S8, Canada. .,Institute of Biomaterials & Biomedical Engineering (IBBME), University of Toronto, Toronto, ON, Canada. .,KITE Research Institute, Toronto Rehabilitation Institute-University Health Network (UHN), Toronto, ON, Canada.
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Georgieva-Tsaneva G, Gospodinova E, Gospodinov M, Cheshmedzhiev K. Cardio-Diagnostic Assisting Computer System. Diagnostics (Basel) 2020; 10:diagnostics10050322. [PMID: 32438753 PMCID: PMC7277997 DOI: 10.3390/diagnostics10050322] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 05/07/2020] [Accepted: 05/13/2020] [Indexed: 12/29/2022] Open
Abstract
The mathematical analysis and the assessment of heart rate variability (HRV) based on computer systems can assist the diagnostic process with determining the cardiac status of patients. The new cardio-diagnostic assisting computer system created uses the classic Time-Domain, Frequency-Domain, and Time-Frequency analysis indices, as well as the nonlinear methods (Poincaré plot, Recurrence plot, Hurst R/S method, Detrended Fluctuation Analysis (DFA), Multi-Fractal DFA, Approximate Entropy and Sample Entropy). To test the feasibility of the software developed, 24-hour Holter recordings of four groups of people were analysed: healthy subjects and patients with arrhythmia, heart failure and syncope. Time-Domain (SDNN < 50 ms, SDANN < 100 ms, RMSSD < 17 ms) and Frequency-Domain (the spectrum of HRV in the LF < 550 ms2, and HF < 540 ms2) parameter values decreased in the cardiovascular disease groups compared to the control group as a result of lower HRV due to decreased parasympathetic and increased sympathetic activity. The results of the nonlinear analysis showed low values of (SD1 < 56 ms, SD2 < 110 ms) at Poincaré plot (Alpha < 90 ms) at DFA in patients with diseases. Significantly reducing these parameters are markers of cardiac dysfunction. The examined groups of patients showed an increase in the parameters (DET% > 95, REC% > 38, ENTR > 3.2) at the Recurrence plot. This is evidence of a pathological change in the regulation of heart rhythm. The system created can be useful in making the diagnosis by the cardiologist and in bringing greater accuracy and objectivity to the treatment.
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Towards the Development of Nonlinear Approaches to Discriminate AF from NSR Using a Single-Lead ECG. ENTROPY 2020; 22:e22050531. [PMID: 33286303 PMCID: PMC7517025 DOI: 10.3390/e22050531] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 05/04/2020] [Accepted: 05/05/2020] [Indexed: 12/30/2022]
Abstract
Paroxysmal atrial fibrillation (Paro. AF) is challenging to identify at the right moment. This disease is often undiagnosed using currently existing methods. Nonlinear analysis is gaining importance due to its capability to provide more insight into complex heart dynamics. The aim of this study is to use several recently developed nonlinear techniques to discriminate persistent AF (Pers. AF) from normal sinus rhythm (NSR), and more importantly, Paro. AF from NSR, using short-term single-lead electrocardiogram (ECG) signals. Specifically, we adapted and modified the time-delayed embedding method to minimize incorrect embedding parameter selection and further support to reconstruct proper phase plots of NSR and AF heart dynamics, from MIT-BIH databases. We also examine information-based methods, such as multiscale entropy (MSE) and kurtosis (Kt) for the same purposes. Our results demonstrate that embedding parameter time delay (τ), as well as MSE and Kt values can be successfully used to discriminate between Pers. AF and NSR. Moreover, we demonstrate that τ and Kt can successfully discriminate Paro. AF from NSR. Our results suggest that nonlinear time-delayed embedding method and information-based methods provide robust discriminating features to distinguish both Pers. AF and Paro. AF from NSR, thus offering effective treatment before suffering chaotic Pers. AF.
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Henriques T, Ribeiro M, Teixeira A, Castro L, Antunes L, Costa-Santos C. Nonlinear Methods Most Applied to Heart-Rate Time Series: A Review. ENTROPY 2020; 22:e22030309. [PMID: 33286083 PMCID: PMC7516766 DOI: 10.3390/e22030309] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 03/05/2020] [Accepted: 03/06/2020] [Indexed: 12/29/2022]
Abstract
The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions. Notwithstanding, they are still far from clinical practice. In this paper, we aim to review the nonlinear methods most used to assess heart-rate dynamics. We focused on methods based on concepts of chaos, fractality, and complexity: Poincaré plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, Hurst exponent, Lyapunov exponent entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy), and symbolic dynamics. We present the description of the methods along with their most notable applications.
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Affiliation(s)
- Teresa Henriques
- Centre for Health Technology and Services Research (CINTESIS), Faculty of Medicine University of Porto, 4200-450 Porto, Portugal; (A.T.); (L.C.); (C.C.-S.)
- Health Information and Decision Sciences Department-MEDCIDS, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
- Correspondence: ; Tel.: +351-225-513-622
| | - Maria Ribeiro
- Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), 4200-465 Porto, Portugal; (M.R.); (L.A.)
- Computer Science Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Andreia Teixeira
- Centre for Health Technology and Services Research (CINTESIS), Faculty of Medicine University of Porto, 4200-450 Porto, Portugal; (A.T.); (L.C.); (C.C.-S.)
- Health Information and Decision Sciences Department-MEDCIDS, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
| | - Luísa Castro
- Centre for Health Technology and Services Research (CINTESIS), Faculty of Medicine University of Porto, 4200-450 Porto, Portugal; (A.T.); (L.C.); (C.C.-S.)
- Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), 4200-465 Porto, Portugal; (M.R.); (L.A.)
| | - Luís Antunes
- Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), 4200-465 Porto, Portugal; (M.R.); (L.A.)
- Computer Science Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Cristina Costa-Santos
- Centre for Health Technology and Services Research (CINTESIS), Faculty of Medicine University of Porto, 4200-450 Porto, Portugal; (A.T.); (L.C.); (C.C.-S.)
- Health Information and Decision Sciences Department-MEDCIDS, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
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Vemishetty N, Gunukula RL, Acharyya A, Puddu PE, Das S, Maharatna K. Phase Space Reconstruction Based CVD Classifier Using Localized Features. Sci Rep 2019; 9:14593. [PMID: 31601877 PMCID: PMC6787214 DOI: 10.1038/s41598-019-51061-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 08/23/2019] [Indexed: 01/06/2023] Open
Abstract
This paper proposes a generalized Phase Space Reconstruction (PSR) based Cardiovascular Diseases (CVD) classification methodology by exploiting the localized features of the ECG. The proposed methodology first extracts the ECG localized features including PR interval, QRS complex, and QT interval from the continuous ECG waveform using features extraction logic, then the PSR technique is applied to get the phase portraits of all the localized features. Based on the cleanliness and contour of the phase portraits CVD classification will be done. This is first of its kind approach where the localized features of ECG are being taken into considerations unlike the state-of-art approaches, where the entire ECG beats have been considered. The proposed methodology is generic and can be extended to most of the CVD cases. It is verified on the PTBDB and IAFDB databases by taking the CVD including Atrial Fibrillation, Myocardial Infarction, Bundle Branch Block, Cardiomyopathy, Dysrhythmia, and Hypertrophy. The methodology has been tested on 65 patients’ data for the classification of abnormalities in PR interval, QRS complex, and QT interval. Based on the obtained statistical results, to detect the abnormality in PR interval, QRS complex and QT interval the Coefficient Variation (CV) should be greater than or equal to 0.1012, 0.083, 0.082 respectively with individual accuracy levels of 95.3%, 96.9%, and 98.5% respectively. To justify the clinical significance of the proposed methodology, the Confidence Interval (CI), the p-value using ANOVA have been computed. The p-value obtained is less than 0.05, and greater F-statistic values reveal the robust classification of CVD using localized features.
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Affiliation(s)
- Naresh Vemishetty
- Department of Electrical Engineering, IIT Hyderabad, Hyderabad, 502285, India
| | | | - Amit Acharyya
- Department of Electrical Engineering, IIT Hyderabad, Hyderabad, 502285, India.
| | - Paolo Emilio Puddu
- Department of Cardiovascular Sciences, Sapienza University of Rome, Viale del Policlinico 155, I-00161, Rome, Italy
| | - Saptarshi Das
- Department of Mathematics, University of Exeter, Cornwall, TR10 9FE, UK
| | - Koushik Maharatna
- School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK
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Safarbali B, Hashemi Golpayegani SMR. Nonlinear dynamic approaches to identify atrial fibrillation progression based on topological methods. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.101563] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Kaniusas E, Kampusch S, Tittgemeyer M, Panetsos F, Gines RF, Papa M, Kiss A, Podesser B, Cassara AM, Tanghe E, Samoudi AM, Tarnaud T, Joseph W, Marozas V, Lukosevicius A, Ištuk N, Lechner S, Klonowski W, Varoneckas G, Széles JC, Šarolić A. Current Directions in the Auricular Vagus Nerve Stimulation II - An Engineering Perspective. Front Neurosci 2019; 13:772. [PMID: 31396044 PMCID: PMC6667675 DOI: 10.3389/fnins.2019.00772] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 07/09/2019] [Indexed: 01/05/2023] Open
Abstract
Electrical stimulation of the auricular vagus nerve (aVNS) is an emerging electroceutical technology in the field of bioelectronic medicine with applications in therapy. Artificial modulation of the afferent vagus nerve - a powerful entrance to the brain - affects a large number of physiological processes implicating interactions between the brain and body. Engineering aspects of aVNS determine its efficiency in application. The relevant safety and regulatory issues need to be appropriately addressed. In particular, in silico modeling acts as a tool for aVNS optimization. The evolution of personalized electroceuticals using novel architectures of the closed-loop aVNS paradigms with biofeedback can be expected to optimally meet therapy needs. For the first time, two international workshops on aVNS have been held in Warsaw and Vienna in 2017 within the scope of EU COST Action "European network for innovative uses of EMFs in biomedical applications (BM1309)." Both workshops focused critically on the driving physiological mechanisms of aVNS, its experimental and clinical studies in animals and humans, in silico aVNS studies, technological advancements, and regulatory barriers. The results of the workshops are covered in two reviews, covering physiological and engineering aspects. The present review summarizes on engineering aspects - a discussion of physiological aspects is provided by our accompanying article (Kaniusas et al., 2019). Both reviews build a reasonable bridge from the rationale of aVNS as a therapeutic tool to current research lines, all of them being highly relevant for the promising aVNS technology to reach the patient.
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Affiliation(s)
- Eugenijus Kaniusas
- Institute of Electrodynamics, Microwave and Circuit Engineering, Vienna University of Technology, Vienna, Austria
| | - Stefan Kampusch
- Institute of Electrodynamics, Microwave and Circuit Engineering, Vienna University of Technology, Vienna, Austria
- SzeleSTIM GmbH, Vienna, Austria
| | - Marc Tittgemeyer
- Max Planck Institute for Metabolism Research, Cologne, Germany
- Cologne Cluster of Excellence in Cellular Stress and Aging Associated Disease (CECAD), Cologne, Germany
| | - Fivos Panetsos
- Neurocomputing & Neurorobotics Research Group, Complutense University of Madrid, Madrid, Spain
| | - Raquel Fernandez Gines
- Neurocomputing & Neurorobotics Research Group, Complutense University of Madrid, Madrid, Spain
| | - Michele Papa
- Laboratory of Neuronal Networks, Department of Mental and Physical Health and Preventive Medicine, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Attila Kiss
- Ludwig Boltzmann Cluster for Cardiovascular Research at Center for Biomedical Research, Medical University of Vienna, Vienna, Austria
| | - Bruno Podesser
- Ludwig Boltzmann Cluster for Cardiovascular Research at Center for Biomedical Research, Medical University of Vienna, Vienna, Austria
| | | | - Emmeric Tanghe
- Department of Information Technology, Ghent University/IMEC, Ghent, Belgium
| | | | - Thomas Tarnaud
- Department of Information Technology, Ghent University/IMEC, Ghent, Belgium
| | - Wout Joseph
- Department of Information Technology, Ghent University/IMEC, Ghent, Belgium
| | - Vaidotas Marozas
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Arunas Lukosevicius
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Niko Ištuk
- Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Split, Croatia
| | | | - Wlodzimierz Klonowski
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland
| | - Giedrius Varoneckas
- Sleep Medicine Centre, Klaipeda University Hospital, Klaipėda, Lithuania
- Institute of Neuroscience, Lithuanian University of Health Sciences, Palanga, Lithuania
| | | | - Antonio Šarolić
- Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Split, Croatia
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Using Wearable ECG/PPG Sensors for Driver Drowsiness Detection Based on Distinguishable Pattern of Recurrence Plots. ELECTRONICS 2019. [DOI: 10.3390/electronics8020192] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
This paper aims to investigate the robust and distinguishable pattern of heart rate variability (HRV) signals, acquired from wearable electrocardiogram (ECG) or photoplethysmogram (PPG) sensors, for driver drowsiness detection. As wearable sensors are so vulnerable to slight movement, they often produce more noise in signals. Thus, from noisy HRV signals, we need to find good traits that differentiate well between drowsy and awake states. To this end, we explored three types of recurrence plots (RPs) generated from the R–R intervals (RRIs) of heartbeats: Bin-RP, Cont-RP, and ReLU-RP. Here Bin-RP is a binary recurrence plot, Cont-RP is a continuous recurrence plot, and ReLU-RP is a thresholded recurrence plot obtained by filtering Cont-RP with a modified rectified linear unit (ReLU) function. By utilizing each of these RPs as input features to a convolutional neural network (CNN), we examined their usefulness for drowsy/awake classification. For experiments, we collected RRIs at drowsy and awake conditions with an ECG sensor of the Polar H7 strap and a PPG sensor of the Microsoft (MS) band 2 in a virtual driving environment. The results showed that ReLU-RP is the most distinct and reliable pattern for drowsiness detection, regardless of sensor types (i.e., ECG or PPG). In particular, the ReLU-RP based CNN models showed their superiority to other conventional models, providing approximately 6–17% better accuracy for ECG and 4–14% for PPG in drowsy/awake classification.
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Tse G, Du Y, Hao G, Li KHC, Chan FYW, Liu T, Li G, Bazoukis G, Letsas KP, Wu WKK, Cheng SH, Wong WT. Quantification of Beat-To-Beat Variability of Action Potential Durations in Langendorff-Perfused Mouse Hearts. Front Physiol 2018; 9:1578. [PMID: 30538638 PMCID: PMC6277547 DOI: 10.3389/fphys.2018.01578] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2018] [Accepted: 10/22/2018] [Indexed: 12/27/2022] Open
Abstract
Background: Beat-to-beat variability in action potential duration (APD) is an intrinsic property of cardiac tissue and is altered in pro-arrhythmic states. However, it has never been examined in mice. Methods: Left atrial or ventricular monophasic action potentials (MAPs) were recorded from Langendorff-perfused mouse hearts during regular 8 Hz pacing. Time-domain, frequency-domain and non-linear analyses were used to quantify APD variability. Results: Mean atrial APD (90% repolarization) was 23.5 ± 6.3 ms and standard deviation (SD) was 0.9 ± 0.5 ms (n = 6 hearts). Coefficient of variation (CoV) was 4.0 ± 1.9% and root mean square (RMS) of successive differences in APDs was 0.3 ± 0.2 ms. The peaks for low- and high-frequency were 0.7 ± 0.5 and 2.7 ± 0.9 Hz, respectively, with percentage powers of 39.0 ± 20.5 and 59.3 ± 22.9%. Poincaré plots of APDn+1 against APDn revealed ellipsoid shapes. The ratio of the SD along the line-of-identity (SD2) to the SD perpendicular to the line-of-identity (SD1) was 8.28 ± 4.78. Approximate and sample entropy were 0.57 ± 0.12 and 0.57 ± 0.15, respectively. Detrended fluctuation analysis revealed short- and long-term fluctuation slopes of 1.80 ± 0.15 and 0.85 ± 0.29, respectively. When compared to atrial APDs, ventricular APDs were longer (ANOVA, P < 0.05), showed lower mean SD and CoV but similar RMS of successive differences in APDs and showed lower SD2 (P < 0.05). No difference in the remaining parameters was observed. Conclusion: Beat-to-beat variability in APD is observed in mouse hearts during regular pacing. Atrial MAPs showed greater degree of variability than ventricular MAPs. Non-linear techniques offer further insights on short-term and long-term variability and signal complexity.
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Affiliation(s)
- Gary Tse
- Department of Medicine and Therapeutics, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China.,Li Ka Shing Institute of Health Sciences, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China.,Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
| | - Yimei Du
- Research Center of Ion Channelopathy, Institute of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guoliang Hao
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
| | | | - Fiona Yin Wah Chan
- School of Biological Sciences, University of Cambridge, Cambridge, United Kingdom
| | - Tong Liu
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - Guangping Li
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - George Bazoukis
- Laboratory of Cardiac Electrophysiology, Second Department of Cardiology, Evangelismos General Hospital of Athens, Athens, Greece
| | - Konstantinos P Letsas
- Laboratory of Cardiac Electrophysiology, Second Department of Cardiology, Evangelismos General Hospital of Athens, Athens, Greece
| | - William K K Wu
- State Key Laboratory of Digestive Disease, Department of anesthesia and Intensive Care, LKS Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Shuk Han Cheng
- Department of Biomedical Sciences, College of Veterinary Medicine and Life Science, City University of Hong Kong, Hong Kong, China.,State Key Laboratory of Marine Pollution at City University of Hong Kong, Hong Kong, China.,Department of Materials Science and Engineering, College of Science and Engineering, City University of Hong Kong, Hong Kong, China
| | - Wing Tak Wong
- State Key Laboratory of Agrobiotechnology, School of Life Sciences, Chinese University of Hong Kong, Hong Kong, China
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