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Keywords = electro-mechanical delay (EMD)

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26 pages, 15661 KiB  
Article
Highly Responsive Robotic Prosthetic Hand Control Considering Electrodynamic Delay
by Jiwoong Won and Masami Iwase
Sensors 2025, 25(1), 113; https://doi.org/10.3390/s25010113 - 27 Dec 2024
Viewed by 355
Abstract
As robots become increasingly integrated into human society, the importance of human–machine interfaces continues to grow. This study proposes a faster and more accurate control system for myoelectric prostheses by considering the Electromechanical Delay (EMD), a key characteristic of Electromyography (EMG) signals. Previous [...] Read more.
As robots become increasingly integrated into human society, the importance of human–machine interfaces continues to grow. This study proposes a faster and more accurate control system for myoelectric prostheses by considering the Electromechanical Delay (EMD), a key characteristic of Electromyography (EMG) signals. Previous studies have focused on systems designed for wrist movements without attempting implementation. To overcome this, we expanded the system’s capability to handle more complex movements, such as those of fingers, by replacing the existing four-channel wired EMG sensor with an eight-channel wireless EMG sensor. This replacement improved the number of channels and user convenience. Additionally, we analyzed the communication delay introduced by this change and validated the feasibility of utilizing EMD. Furthermore, to address the limitations of the SISO-NARX model, we proposed a MISO-NARX model. To resolve issues related to model complexity and reduced accuracy due to the increased number of EMG channels, we introduced ridge regression, improving the system identification accuracy. Finally, we applied the ZPETC+PID controller to an actual servo motor and verified its performance. The results showed that the system reached the target value approximately 0.240 s faster than the response time of 0.428 s without the controller. This study significantly enhances the responsiveness and accuracy of myoelectric prostheses and is expected to contribute to the development of practical devices in the future. Full article
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<p>Robot hand.</p>
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<p>GWS Micro 2BBMG—micro servo manufactured by Grand Wing Servo-Tech Co., Ltd. (GWS), a company based in Taipei, Taiwan.</p>
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<p>Block diagram of the control system used for the robotic hand. The system processes EMG signals obtained from the wrist through a PC-based NARX model with a low-pass filter to estimate the wrist angle. The estimated angle serves as input for two control strategies: the feedforward control (ZPETC) and the feedback control (PID controller). ZPETC compensates for phase delay by leveraging the EMD time, while the PID controller minimizes the error between the motor and wrist angles. The combined outputs of these controllers enable precise and responsive control of the robotic hand.</p>
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<p>(<b>a</b>) Myo armband from Thalmic Labs. (<b>b</b>) Wearing position of Myo armband.</p>
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<p>EMG measurement side view.</p>
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<p>(<b>a</b>) Dorsi flexion. (<b>b</b>) Palmar flexion.</p>
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<p>(<b>a</b>) SG65 Goniometer from Biometrics Ltd., a company based in Newport, United Kingdom. (<b>b</b>) Set up position of SG65.</p>
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<p>K800 Amplifier from Biometrics Ltd. (Newport, UK).</p>
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<p>Step response of discretized transfer function and output data.</p>
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<p>(<b>a</b>) Measured EMG signal by Myo armband. (<b>b</b>) low-pass filtered EMG signal.</p>
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<p>Wrist angle by goniometer.</p>
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<p>(<b>a</b>) Raw EMG signals and wrist angles. (<b>b</b>) Low-pass filtered EMG signals and wrist angles.</p>
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<p>(<b>a</b>) Enlarged view of raw EMG signals and wrist angles. (<b>b</b>) Enlarged view of low-pass filtered EMG signals and wrist angles.</p>
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<p>(<b>a</b>) The AIC results for Dataset 1. (<b>b</b>) The AIC results for Dataset 2. (<b>c</b>) The AIC results for Dataset 3. (<b>d</b>) The AIC results for Dataset 4.</p>
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<p>(<b>a</b>) The ARX estimation results for Dataset 1 with an order of 1 are presented below. (<b>b</b>) The ARX estimation results for Dataset 2 with an order of 1 are presented below. (<b>c</b>) The ARX estimation results for Dataset 1 with an order of 2 are presented below. (<b>d</b>) The ARX estimation results for Dataset 2 with an order of 2 are presented below.</p>
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<p>(<b>a</b>) The estimation result of wrist angle using EMG, 4ch SISO-NARX model. (<b>b</b>) The estimation result of wrist angle using EMG, 4ch MISO-NARX model.</p>
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<p>The estimation result of wrist angle using EMG, 8ch MISO-NARX model.</p>
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<p>Wrist angle estimated using a 8ch MISO-NARX model with ridge regression.</p>
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<p>Wrist angle estimated using a 4ch MISO-NARX model with ridge regression.</p>
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<p>Pole-zero map of the closed-loop system.</p>
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<p>Simulation results when step input is applied to the system transfer function and the system transfer function with ZPETC+PID controller.</p>
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<p>Output results when the ZPETC+PID controller is applied to the robotic hand system.</p>
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16 pages, 19215 KiB  
Article
Decision-Making Time Analysis for Assessing Processing Speed in Athletes during Motor Reaction Tasks
by Leonardo Ariel Cano, Gonzalo Daniel Gerez, María Soledad García, Ana Lía Albarracín, Fernando Daniel Farfán and Eduardo Fernández-Jover
Sports 2024, 12(6), 151; https://doi.org/10.3390/sports12060151 - 29 May 2024
Viewed by 1809
Abstract
Reaction time (RT) is a widely used measure for testing physical performance in motor tasks. This study focused on assessing the processing speed in athletes. Twenty-five healthy volunteers were assigned to the control (n = 16) or athletes groups (n = 9). They [...] Read more.
Reaction time (RT) is a widely used measure for testing physical performance in motor tasks. This study focused on assessing the processing speed in athletes. Twenty-five healthy volunteers were assigned to the control (n = 16) or athletes groups (n = 9). They were evaluated during motor reaction tasks based on visual stimuli and three difficulty conditions. Physiological measures were obtained from motion capture and electromyography recordings of several muscles. Two RT phases, decision-making (DMK) and electromechanical delay (EMD), were used to analyze the processing speed. The results show significant RT differences between groups. The athletes were ~30% faster compared to the control group. Despite the fact that all participants were right-handed, RT did not show any differences between hands performances in any group. However, DMK time revealed significant differences between the hands. Controls showed a longer DMK time for the right-hand election, ~20% more than the left, while athletes showed no such disparity. These findings reveal that quantifying the decision-making component of reaction time is crucial to assessing processing speed in sport. This approach could facilitate the monitoring of adaptations in both motor–cognitive and neuromuscular processes. The theoretical implications presented in this study offer perspectives on handedness research. Full article
(This article belongs to the Special Issue Sport Physiology and Physical Performance)
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<p>Experimental setup. (<b>A</b>) Photograph depicting the initial position of the participant. The tapes are attached to both forearms for detecting either hand movement. The participant was instructed to move their right hand towards the device when the green light turns on, and the red light indicated movement of the left hand. The subfigures below illustrate the motor tasks of three experimental conditions. (<b>B</b>) The EMG sensors’ locations for collecting muscle activity from <span class="html-italic">Pectoralis Major</span> (<span class="html-italic">pars clavicularis</span>), <span class="html-italic">Deltoideus Anterior</span>, <span class="html-italic">Trapezius</span> (<span class="html-italic">upper fibers</span>), and <span class="html-italic">Deltoideus Medius</span>.</p>
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<p>Diagram showing all synchronized time-series for measuring reaction time. (<b>A</b>) Partial data depicting four repetitions of complex reaction task over time. The upper line shows the reactimeter signal, binary on–off data are marked by green and red, depending on the light stimulus shown. The second line shows motion capture when either hand was moved. These data are an analog time-series scaled in millimeters derived from digital signals collected with a linear position transducer. The movement of either hand is detected when there is a change in the steady state, transitioning from a flat line to an increasing amplitude following the presentation of a visual stimulus. The next lines depict the normalized EMG data from eight muscles. (<b>B</b>) Zoom-in of the light gray rectangle in panel A. Reaction time (RT) is the sum of decision-making (DMK) and electromechanical delay (EMD). The crosses (×) indicate the onset of light, movement, and muscle contraction. The photographs represent four instants of the motor task: waiting for stimulus, device light turning on, starting right-hand movement after the green light turned on, and reaching the target with right hand, then the device light turned off.</p>
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<p>Violin plot of reaction time (RT)<b>.</b> Three panels for the three experimental conditions. The control (CON) and athletes (ATH) groups are on the horizontal axis. For the three panels, the following color codes are the same. The green color represents the RT for the right hand, while the red color represents the RT for the left hand. The violin-shaped plot represents the normal distribution of all the variables. The horizontal black lines represent the mean (M), while the colored rectangles are the standard deviation (std). The numerical values are M ± std. * <span class="html-italic">p</span> &lt; 0.001 ** <span class="html-italic">p</span> &lt; 0.005.</p>
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<p>Violin plot of decision-making time (DMK<sub>TIME</sub>)<b>.</b> Three panels for the three experimental conditions. The control (CON) and athlete (ATH) groups are on the horizontal axis. For the three panels, the following color codes are the same. The green color represents the DMK<sub>TIME</sub> for the right hand, while the red color represents the DMK<sub>TIME</sub> for the left hand. The violin-shaped plot represents the normal distribution of all the variables. The horizontal black lines represent the mean (M), while the colored rectangles are the standard deviation (std). The numerical values are M ± std. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.005.</p>
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<p>Violin plot of electromechanical delay (EMD<sub>TIME</sub>). Three panels for the three experimental conditions. The control (CON) and athletes (ATH) groups are on the horizontal axis. For the three panels, the following color codes are the same. The green color represents the EMD<sub>TIME</sub> for the right hand, while the red color represents the EMD<sub>TIME</sub> for the left hand. The violin-shaped plot represents the normal distribution of all the variables. The horizontal black lines represent the mean (M), while the colored rectangles are the standard deviation (std). The numerical values are M ± std. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.005.</p>
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19 pages, 3999 KiB  
Article
Excitation and Contraction of the Failing Human Heart In Situ and Effects of Cardiac Resynchronization Therapy: Application of Electrocardiographic Imaging and Speckle Tracking Echo-Cardiography
by Christopher M. Andrews, Gautam K. Singh and Yoram Rudy
Hearts 2021, 2(3), 331-349; https://doi.org/10.3390/hearts2030027 - 23 Jul 2021
Cited by 1 | Viewed by 3756
Abstract
Despite the success of cardiac resynchronization therapy (CRT) for treating heart failure (HF), the rate of nonresponders remains 30%. Improvements to CRT require understanding of reverse remodeling and the relationship between electrical and mechanical measures of synchrony. The objective was to utilize electrocardiographic [...] Read more.
Despite the success of cardiac resynchronization therapy (CRT) for treating heart failure (HF), the rate of nonresponders remains 30%. Improvements to CRT require understanding of reverse remodeling and the relationship between electrical and mechanical measures of synchrony. The objective was to utilize electrocardiographic imaging (ECGI, a method for noninvasive cardiac electrophysiology mapping) and speckle tracking echocardiography (STE) to study the physiology of HF and reverse remodeling induced by CRT. We imaged 30 patients (63% male, mean age 63.7 years) longitudinally using ECGI and STE. We quantified CRT-induced remodeling of electromechanical parameters and evaluated a novel index, the electromechanical delay (EMD, the delay from activation to peak contraction). We also measured dyssynchrony using ECGI and STE and compared their effectiveness for predicting response to CRT. EMD values were elevated in HF patients compared to controls. However, the EMD values were dependent on the activation sequence (CRT-paced vs. un-paced), indicating that the EMD is not intrinsic to the local tissue, but is influenced by factors such as opposing wall contractions. After 6 months of CRT, patients had increased contraction in native rhythm compared to baseline pre-CRT (baseline: −8.55%, 6 months: −10.14%, p = 0.008). They also had prolonged repolarization at the location of the LV pacing lead. The pre-CRT delay between mean lateral LV and RV electrical activation time was the best predictor of beneficial reduction in LV end systolic volume by CRT (Spearman’s Rho: −0.722, p < 0.001); it outperformed mechanical indices and 12-lead ECG criteria. HF patients have abnormal EMD. The EMD depends upon the activation sequence and is not predictive of response to CRT. ECGI-measured LV activation delay is an effective index for CRT patient selection. CRT causes persistent improvements in contractile function. Full article
(This article belongs to the Special Issue The Application of Computer Techniques to ECG Interpretation)
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<p>Schematic of the ECGI procedure. Body-surface potentials are recorded from the torso surface using a portable recording system (top). The heart-torso geometry is obtained using a computed tomography (CT) or magnetic resonance imaging (MRI) scan (bottom). The heart-torso geometry and torso potentials are combined and the inverse problem is solved to reconstruct unipolar epicardial electrograms. Electrograms are processed to determine local electrical parameters of interest (right frame).</p>
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<p>The LV was segmented using a modified version of the American Heart Association 17-Segment Model. Apical segments were modified from the standard model because ECGI images the epicardium which does not include any septal segments. The apical LV segments from the ECGI maps were divided into Apical Anterolateral and Apical Inferolateral segments.</p>
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<p>Healthy adult activation and contraction. (<b>A</b>) Activation isochrones. Atria and left anterior descending coronary artery are shown in gray. Right ventricular outflow tract is shown in blue. Left ventricular outflow tract is shown in pink. Asterisk indicates epicardial breakthrough site. (<b>B</b>) Speckle tracking echocardiography (STE) strain curves plotted below the ECG. Electrical activation times are indicated in the plot with vertical lines (dashed lines indicate right ventricular activation as an approximation of septal activation time). Dotted line indicates aortic valve closure. The timing of peak strain within anatomical segments (top bullseye plot) was homogeneous within the LV. Regional electromechanical delay (EMD) values (bottom bullseye plot) were computed by subtracting the electrical activation time from the time of peak strain within regions. EMD values were not computed for septal regions (shown in gray) because ECGI does not image the septum. RV: right ventricle; LV: left ventricle; RA: right atrium; LA: left atrium.</p>
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<p>Activation isochrone maps in HF-CRT patients in native rhythm prior to CRT pacing (left) and at pacing onset (right). Pacing lead locations are indicated with black spheres. CRT pacing decreases LV activation delay absolute value (“Improvement”). Echocardiographic responders (top 2 rows) generally had high levels of dyssynchrony at baseline which was substantially improved by CRT pacing. Nonresponders often had less baseline dyssynchrony (row 3) or ineffective lead placement (row 4). RV: right ventricle; LV: left ventricle.</p>
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<p>Native rhythm strains in HF-CRT patients (top) were dyssynchronous and lower in amplitude than controls. Lateral regions often stretched prior to contraction (arrow) and reached peak strain after aortic valve closure (dotted line). Many regions reached peak strain later than controls (top bullseye). The mean EMD in HF patients was the same as in controls, but values within the LV showed greater dispersion (bottom bullseye). The acute onset of CRT (bottom) decreased pre-systolic lateral wall stretch. Peak strain timing values did not capture synchrony improvements effectively. Regional EMDs were different for each activation sequence (native rhythm vs. CRT pacing), indicating that EMD is not a purely intrinsic property.</p>
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<p>Left ventricular views of electrogram fractionation maps (first column), native rhythm activation (middle column), and CRT-paced activation (right column). Representative fractionated and un-fractionated electrograms are provided to the left of the maps. Numbers indicate electrogram locations. Pacing electrodes are indicated with black or white spheres. Pacing within regions of fractionation was less effective at activating nearby regions outside the scar (top row). Patients with large regions of fractionation could still be resynchronized effectively when paced outside of the fractionated region (bottom row). NICM: Nonischemic cardiomyopathy.</p>
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<p>Peak contraction magnitudes improved during the course of CRT. Values at each visit were determined from un-paced native rhythm beats, indicating persistent improvements in contraction as a result of chronic CRT pacing. Global longitudinal strain values (in percent) are indicated below each bullseye plot.</p>
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<p>Native rhythm epicardial activation-recovery interval (ARI) maps in heart failure (<b>left</b>) and after 6 months of CRT pacing (<b>right</b>). After 6 months of CRT pacing, ARI values were prolonged at and around the location of the left ventricle (LV) pacing lead.</p>
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24 pages, 551 KiB  
Review
The Influence of Growth, Maturation and Resistance Training on Muscle-Tendon and Neuromuscular Adaptations: A Narrative Review
by Nakul Tumkur Anil Kumar, Jon L. Oliver, Rhodri S. Lloyd, Jason S. Pedley and John M. Radnor
Sports 2021, 9(5), 59; https://doi.org/10.3390/sports9050059 - 8 May 2021
Cited by 32 | Viewed by 10002
Abstract
The purpose of this article is to provide an overview of the growth, maturation and resistance training-related changes in muscle-tendon and neuromuscular mechanisms in youth, and the subsequent effect on performance. Sprinting, jumping, kicking, and throwing are common movements in sport that have [...] Read more.
The purpose of this article is to provide an overview of the growth, maturation and resistance training-related changes in muscle-tendon and neuromuscular mechanisms in youth, and the subsequent effect on performance. Sprinting, jumping, kicking, and throwing are common movements in sport that have been shown to develop naturally with age, with improvements in performance being attributed to growth and maturity-related changes in neuromuscular mechanisms. These changes include moderate to very large increases in muscle physiological cross-sectional area (CSA), muscle volume and thickness, tendon CSA and stiffness, fascicle length, muscle activation, pre-activation, stretch reflex control accompanied by large reductions in electro-mechanical delay and co-contraction. Furthermore, a limited number of training studies examining neuromuscular changes following four to 20 weeks of resistance training have reported trivial to moderate differences in tendon stiffness, muscle CSA, muscle thickness, and motor unit activation accompanied by reductions in electromechanical delay (EMD) in pre-pubertal children. However, the interaction of maturity- and training-related neuromuscular adaptions remains unclear. An understanding of how different neuromuscular mechanisms adapt in response to growth, maturation and training is important in order to optimise training responsiveness in youth populations. Additionally, the impact that these muscle-tendon and neuromuscular changes have on force producing capabilities underpinning performance is unclear. Full article
10 pages, 879 KiB  
Article
Acute Effects of a High Volume vs. High Intensity Bench Press Protocol on Electromechanical Delay and Muscle Morphology in Recreationally Trained Women
by Sandro Bartolomei, Federico Nigro, Ivan Malagoli Lanzoni, Anna Lisa Mangia, Matteo Cortesi, Simone Ciacci and Silvia Fantozzi
Int. J. Environ. Res. Public Health 2021, 18(9), 4874; https://doi.org/10.3390/ijerph18094874 - 3 May 2021
Cited by 4 | Viewed by 3021
Abstract
The purpose of the present investigation was to compare the acute responses on muscle architecture, electromechanical delay (EMD) and performance following a high volume (HV: 5 sets of 10 reps at 70% of 1 repetition maximum (1RM)) and a high intensity (HI: 5 [...] Read more.
The purpose of the present investigation was to compare the acute responses on muscle architecture, electromechanical delay (EMD) and performance following a high volume (HV: 5 sets of 10 reps at 70% of 1 repetition maximum (1RM)) and a high intensity (HI: 5 sets of 3 reps at 90% of 1RM) bench press protocol in women. Eleven recreationally trained women (age = 23.3 ± 1.8 y; body weight = 59.7 ± 6.0 kg; height = 164.0 ± 6.3 cm) performed each protocol in a counterbalanced randomized order. Muscle thickness of pectoral (PEC MT) and triceps muscles (TR MT) were collected prior to and 15 min post each trial. In addition, EMD of pectoral (PEC EMD) and triceps (TR EMD) muscles were calculated during isometric bench press maximum force tests performed at the same timepoints (IBPF). Significantly greater increases in PEC MT (p < 0.001) and TR MT (p < 0.001) were detected following HV compared to HI. PEC EMD showed a significantly greater increase following HV compared to HI (p = 0.039). Results of the present study indicate that the HV bench press protocol results in greater acute morphological and neuromuscular changes compared to a HI protocol in women. Evaluations of muscle morphology and electromechanical delay appear more sensitive to fatigue than maximum isometric force assessments. Full article
(This article belongs to the Special Issue Effects of Resistance Training on Strength and Muscle Thickness)
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<p>Experimental protocol of the counterbalanced cross-over research design. HV = high volume protocol; HI = high intensity protocol; EMG = electromyography; IBP = isometric bench press assessment.</p>
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<p>Percentage changes from pre to post the high volume (HV) and the high intensity (HI) protocols. IBPF = isometric bench press force; pRFD20 = peak rate of force development; PEC MT = pectoral muscle thickness; TR MT = triceps muscle thickness; PEC EMD = pectoral electromechanical delay; TR EMD = triceps electromechanical delay. # indicates a significant (<span class="html-italic">p</span> ≤ 0.05) main effect of time. * indicates a significant (<span class="html-italic">p</span> ≤ 0.05) trial x time interaction.</p>
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10 pages, 2201 KiB  
Article
Evaluation of Atrial Electromechanical Delay in Children with Obesity
by Fatih Temiz, Hatice Güneş and Hakan Güneş
Medicina 2019, 55(6), 228; https://doi.org/10.3390/medicina55060228 - 30 May 2019
Cited by 10 | Viewed by 2335
Abstract
Background and Objective: Childhood obesity is one of the worldwide health problems with an increasing prevalence and accompanied by severe morbidity and mortality. It is a serious predisposing risk factor especially for the development of cardiovascular diseases and arrhythmias. Electromechanical delay (EMD) [...] Read more.
Background and Objective: Childhood obesity is one of the worldwide health problems with an increasing prevalence and accompanied by severe morbidity and mortality. It is a serious predisposing risk factor especially for the development of cardiovascular diseases and arrhythmias. Electromechanical delay (EMD) is known to be a predictor for the development of atrial fibrillation (AF). Our study aims to investigate whether EMD, which is a predictor of AF, prolongs in obese children or not. Material and Methods: The study included 59 obese patients aged between 8–18 years and 38 healthy patients as the control group with a similar age and gender. All the individuals underwent transthoracic echo and tissue Doppler echocardiography. Systolic and diastolic left ventricular (LV) functions, inter- and intra-atrial electromechanical delay were measured by tissue Doppler imaging (TDI) and conventional echocardiography. Results: Obese patients had significantly lengthened P-wave on surface ECG to the beginning of the late diastolic wave (PA) lateral, PA septum, intra- and inter-atrial electromechanical delays when compared with the control group (p < 0.001, p = 0.001, p < 0.001 and p < 0.001, respectively) Inter-atrial EMD and intra-atrial EMD correlated positively with body mass index (BMI) values (r = 0.484, p < 0.001 and r = 0.376, p = 0.001; respectively) BMI was significantly related with inter-atrial EMD (β = 0.473, p < 0.001) However, there was no relationship between inter-atrial EMD and serum glucose and platelet count. Conclusion: In our study, we declared that electromechanical delay was increased in obese children when compared to the control group and intra- and inter-atrial electromechanical delay was in correlation with body mass index. Furthermore, we discovered that BMI is an independent predictor of the inter-atrial EMD in obese children. Full article
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<p>Measurement of the time interval from the onset of the P wave on the surface electrocardiography (ECG) to the starting of late diastolic wave (Am wave) interval with tissue Doppler imaging (PA tricuspid, PA septal and PA lateral, respectively).</p>
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<p>Correlation between inter-atrial electromechanical delay (EMD) and body mass index.</p>
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<p>Correlation between intra-atrial EMD and body mass index.</p>
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<p>Correlation between intra-atrial EMD and P wave dispersion.</p>
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<p>Correlation between inter-atrial EMD and P wave dispersion.</p>
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