Electrocardiographic System Research Articles

Usefulness of Prehospital 12-Lead Electrocardiography System in ST-Segment Elevation Myocardial Infarction Patients in Oita - Comparison Between Urban and Rural Areas, Weekday Daytime and Weekday Nighttime/Holidays.

We have reported that a prehospital 12-lead electrocardiography system (P-ECG) contributed to transport of suspected acute coronary syndrome (ACS) patients to appropriate institutes and in this study, we compared its usefulness between urban and rural areas, and between weekday daytime and weekday nighttime/holiday.Methods and Results: Consecutive STEMI patients who underwent successful primary percutaneous coronary intervention after using P-ECG were assigned to the P-ECG group (n=123; 29 female, 70±13 years), and comparable STEMI patients without using P-ECG were assigned to the conventional group (n=117; 33 females, mean age 70±13 years). There was no significant difference in door-to-reperfusion times between the rural and urban cases (70±32 vs. 69±29 min, P=0.73). Door-to-reperfusion times in the urban P-ECG group were shorter than those in the urban conventional group for weekday nighttime/holiday (65±21 vs. 83±32 min, P=0.0005). However, there was no significance different between groups for weekday daytime. First medical contact to reperfusion time (90±22 vs. 105±37 min, P=0.0091) in the urban P-ECG group were significantly shorter than in the urban conventional groups for weekday nighttime/holiday, but were not significantly different between the groups for weekday daytime. P-ECG is useful even in urban areas, especially for patients who develop STEMI during weekday nighttime or while on a holiday.

Early detection of cardiac autonomic dysfunction using the respiratory sinus arrhythmia accentuation maneuver in patients with stage B1 of Chagas Disease

Introduction: The maneuver to accentuate respiratory sinus arrhythmia (RSA) demonstrates to be a convenient instrument to evaluate the autonomic modulation of individuals with chronic Chagas disease cardiomyopathy (CCC), even with medication influence, as T-cruzi causes an inflammatory process creating lesions in the cardiac nerve endings of patients affected by Chagas disease. Objective: To evaluate the autonomic modulation of heart rate (HR) at rest and during the respiratory sinus arrhythmia accentuation maneuver (RSA-M) in patients with CCC in stage B1. Methods: thirty-six individuals were evaluated, divided into the CCC group, composed of 18 patients with Chagas heart disease, and the control group, formed by 18 healthy individuals. Heart rate variability (HRV) was collected beat by beat using the electrocardiography system Wincardio USB, supine for 10 minutes, and during the RSA-M for 6 minutes. Results: We observed higher values of statistical significance in Control group for mean HR (p = 0.005), IRR (p = 0.007), and worse autonomic adjustments related to SD2 / SD1 (p = 0.001) concerning CCC group in a supine position. However, evaluating the RSA-M, we observed a statistical difference in the delta I-E (p = 0,039) and the E / I rate (p = 0.045) of the Control group in relation to the CCC group. Conclusion: Patients with CCC showed better cardiovascular condition and HR control compared to the group of healthy individuals at rest. However, when subjected to RSA-M, the results showed a reduction in vagal balance in the autonomic control of individuals with CCC. Keywords: respiration; heart rate; chagas cardiomyopathy; autonomic nervous system diseases.

Detection of myocardial infarction using Shannon energy envelope, FA-MVEMD and deterministic learning

Myocardial infarction (MI) poses a significant clinical challenge, necessitating expeditious and precise detection to mitigate potentially fatal outcomes. Current MI diagnosis predominantly relies on electrocardiography (ECG); however, it is fraught with limitations, including inter-observer variability and a reliance on expert interpretation. This study introduces an automated MI detection framework that capitalizes on hybrid signal processing methodologies and deterministic learning theory. The initial step involves the extraction of the Shannon energy envelope (SEE) and its derivative from a single-lead ECG. Integration of the SEE into the ECG’s phase portrait provides a means to capture the underlying nonlinear system dynamics. Subsequently, the application of fast and adaptive multivariate empirical mode decomposition (FA-MVEMD) yields discriminative features originating from the most energetically dominant intrinsic mode components (IMFs) within the SEE. Profound dissimilarities are discernible between ECG signals recorded from healthy subjects and those afflicted with MI. In the subsequent phase, deterministic learning theory, implemented through neural networks, is employed to facilitate the classification of ECG signals into two distinct groups. The method’s efficacy is meticulously evaluated using the PTB diagnostic ECG database, resulting in a noteworthy average classification accuracy of 99.21%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} within a tenfold cross-validation framework. In summation, the findings affirm that the proposed features not only complement conventional ECG attributes but also align closely with the underlying dynamics of the ECG system, ultimately fortifying the automatic detection of MI. The imperative requirement for early and accurate MI diagnosis is addressed through our approach, offering a robust and dependable means to fulfill this pivotal clinical need.

A Collaborative Platform for Advancing Automatic Interpretation in ECG Signals.

Numerous papers report the efficiency of the automatic interpretation capabilities of commercial algorithms. Unfortunately, these algorithms are proprietary, and academia has no means of directly contributing to these results. In fact, nothing at the same stage of development exists in academia. Despite the extensive research in ECG signal processing, from signal conditioning to expert systems, a cohesive single application for clinical use is not ready yet. This is due to a serious lack of coordination in the academic efforts, which involve not only algorithms for signal processing, but also the signal acquisition equipment itself. For instance, the different sampling rates and the different noise levels frequently found in the available signal databases can cause severe incompatibility problems when the integration of different algorithms is desired. Therefore, this work aims to solve this incompatibility problem by providing the academic community with a diagnostic-grade electrocardiograph. The intention is to create a new standardized ECG signals database in order to address the automatic interpretation problem and create an electrocardiography system that can fully assist clinical practitioners, as the proprietary systems do. Achieving this objective is expected through an open and coordinated collaboration platform for which a webpage has already been created.

Remote ECG Monitoring System via Internet

Electrocardiography (ECG) is a non-invasive diagnostic tool that records the electrical activity of the heart. It is a valuable tool for diagnosing a variety of heart conditions, including arrhythmias, heart failure, and myocardial infarction. This review paper explores the landscape of Real-time Electrocardiogram (ECG) Monitoring Systems via the Internet, analyzing advancements, challenges, and future prospects in this domain. The study investigates the integration of ECG monitoring systems with internet connectivity, enabling real- time data transmission and analysis for remote healthcare applications. Examining the utilization of IoT frameworks, particularly Raspberry Pi and Arduino, the review elucidates their role in establishing connectivity and data transmission protocols. It assesses the impact of IoT-enabled ECG systems on enhancing patient care, enabling telemedicine, and improving healthcare accessibility. Keywords— Internet of Things (IoT), Raspberry Pi R3, Arduino UNO, Remote Monitoring, Electrocardigraphy (ECG), Internet connected Medical Devices, Remote Healthcare

Abstract 15139: Applying an Artificial Intelligence-Enabled Electrocardiographic System for Reducing Mortality: A Pragmatic Randomized Clinical Trial

Introduction: The early identification of high-risk patients for sequent intensive care is a significant challenge. Hypothesis: We aimed to evaluate the outcome of an artificial intelligence (AI)-enabled electrocardiogram (ECG) in identifying patients at high risk of mortality. Methods: In this single-blind, patient-level randomized controlled trial (NCT05118035), we recruited 39 attending physicians and their patients from emergency and inpatient departments. The AI-ECG intervention involved an AI report in electronic health records and an active warning message to physicians for high-risk patients. Primary endpoint was 90-day all-cause mortality. Secondary analyses included medical behavior changes and causes of death. Results: Data from 15,965 patients (8,001 intervention; 7,964 control) with a mean age of 61±18 years were included. The intervention group had a significantly reduced cumulative proportion of death (HR: 0.83, 95% CI: 0.70-0.99) compared to the control group (3.6% vs. 4.3%). High-risk cases (709 intervention; 688 control) identified by AI-ECG showed a 31% reduction in mortality (16.0% in intervention arm vs. 23.0% in control arm, HR: 0.69, 95% CI: 0.53-0.90; p for interaction = 0.026). The intervention group with high-risk ECGs received more intensive care, arrhythmia interventions, echocardiographic and electrolyte examinations, contributing to a significant reduction in cardiac death (0.2% in intervention arm vs. 2.4% in control arm, HR: 0.07, 95% CI: 0.01-0.56). Conclusions: AI-ECG effectively identifies high-risk patients, facilitating intensive care and resulting in reduced all-cause mortality.

ECG Matching: An Approach to Synchronize ECG Datasets for Data Quality Comparisons.

Clinical assessment of newly developed sensors is important for ensuring their validity. Comparing recordings of emerging electrocardiography (ECG) systems to a reference ECG system requires accurate synchronization of data from both devices. Current methods can be inefficient and prone to errors. To address this issue, three algorithms are presented to synchronize two ECG time series from different recording systems: Binned R-peak Correlation, R-R Interval Correlation, and Average R-peak Distance. These algorithms reduce ECG data to their cyclic features, mitigating inefficiencies and minimizing discrepancies between different recording systems. We evaluate the performance of these algorithms using high-quality data and then assess their robustness after manipulating the R-peaks. Our results show that R-R Interval Correlation was the most efficient, whereas the Average R-peak Distance and Binned R-peak Correlation were more robust against noisy data.

Coronary Artery Occlusion Detection Using 3-Lead ECG System Suitable for Credit Card-Size Personal Device Integration

BackgroundEarly coronary occlusion detection by portable personal device with limited number of electrocardiographic (ECG) leads might shorten symptom-to-balloon time in acute coronary syndromes. ObjectivesThe purpose of this study was to compare the accuracy of coronary occlusion detection using vectorcardgiographic analysis of a near-orthogonal 3-lead ECG configuration suitable for credit card-size personal device integration with automated and human 12 lead ECG interpretation. MethodsThe 12-lead ECGs with 3 additional leads (“abc”) using 2 arm and 2 left parasternal electrodes were recorded in 66 patients undergoing percutaneous coronary intervention prior to (“baseline”, n = 66), immediately before (“preinflation”, n = 66), and after 90-second balloon coronary occlusion (“inflation”, n = 120). Performance of computer-measured ST-segment shift on vectorcardgiographic loops constructed from “abc” and 12 leads, standard 12-lead ECG, and consensus human interpretation in coronary occlusion detection were compared in “comparative” and “spot” modes (with/without reference to “baseline”) using areas under ROC curves (AUC), reliability, and sensitivity/specificity analysis. ResultsComparative “abc”-derived ST-segment shift was similar to two 12-lead methods (vector/traditional) in detecting balloon coronary occlusion (AUC = 0.95, 0.96, and 0.97, respectively, P = NS). Spot “abc” and 12-lead measurements (AUC = 0.72, 0.77, 0.68, respectively, P = NS) demonstrated poorer performance (P < 0.01 vs comparative measurements). Reliability analysis demonstrated comparative automated measurements in “good” agreement with reference (preinflation/inflation), while comparative human interpretation was in “moderate” range. Spot automated and human reading showed “poor” agreement. ConclusionsVectorcardiographic ST-segment analysis using baseline comparison of 3-lead ECG system suitable for credit card-size personal device integration is similar to established 12-lead ECG methods in detecting balloon coronary occlusion.

A Capacitive Electrocardiography System With Dedicated Noise-Cancellation Algorithms for Morphological Analysis.

Capacitive electrocardiography (cECG) has been proposed for ambulatory cardiac health monitoring. However, due to the high sensitivity of capacitive electrodes to various noise sources, particularly the power-line interference (PLI) and motion artifacts (MA), the existing capacitive systems are only verified in terms of RR interval. The aim of the present study is to explore the feasibility of using cECG for morphological analysis, and thus to extract clinical meaningful parameters. A capacitive electrode with active guarding is realized. A phase-locked-loop (PLL) based adaptive canceller is employed to remove PLI from the cECG. Wavelet analysis is adopted to cancel other noises. The developed capacitive system and algorithms are evaluated by real ECG measurements on 7 volunteers using 3-lead configuration. The correlation coefficient (CC) between the processed cECG and the wet ECG is calculated in two different conditions: with and without the QRS complex. Several frequently used diagnostic parameters, i.e., RR interval, QRS interval, P segment, T segment, ST segment, are extracted and compared with that obtained from the wet ECG. High CCs are observed between the cECG and the wet ECG in both conditions, i.e., 0.97±0.03 with the QRS complex and 0.92±0.07 without the QRS complex. Besides, RR interval extracted from the two different ECG signals are identical for each subject. Other diagnostic parameters are quite similar. Our results suggest cECG to be reliable for ambulatory heart rate monitoring. The results also indicate the feasibility of using cECG for clinical diagnosis.

Machine learning-based signal quality assessment for cardiac volume monitoring in electrical impedance tomography

Owing to recent advances in thoracic electrical impedance tomography (EIT), a patient’s hemodynamic function can be noninvasively and continuously estimated in real-time by surveilling a cardiac volume signal (CVS) associated with stroke volume and cardiac output. In clinical applications, however, a CVS is often of low quality, mainly because of the patient’s deliberate movements or inevitable motions during clinical interventions. This study aims to develop a signal quality indexing method that assesses the influence of motion artifacts on transient CVSs. The assessment is performed on each cardiac cycle to take advantage of the periodicity and regularity in cardiac volume changes. Time intervals are identified using the synchronized electrocardiography system. We apply divergent machine-learning methods, which can be sorted into discriminative-model and manifold-learning approaches. The use of machine-learning could be suitable for our real-time monitoring application that requires fast inference and automation as well as high accuracy. In the clinical environment, the proposed method can be utilized to provide immediate warnings so that clinicians can minimize confusion regarding patients’ conditions, reduce clinical resource utilization, and improve the confidence level of the monitoring system. Numerous experiments using actual EIT data validate the capability of CVSs degraded by motion artifacts to be accurately and automatically assessed in real-time by machine learning. The best model achieved an accuracy of 0.95, positive and negative predictive values of 0.96 and 0.86, sensitivity of 0.98, specificity of 0.77, and AUC of 0.96.

Continuous heart rate variability monitoring of freely moving chicken through a wearable electrocardiography recording system

Identification and quantification of stress and stress inducing factors are important components of animal welfare assessment and essential parts of poultry management. Measurement of the autonomic nervous system's influence on cardiac function using heart rate and heart rate variability (HR/HRV) indices can provide a non-invasive assessment of the welfare status of an animal. This paper presents a preliminary study showing the feasibility of continuous long-term measurement of HR/HRV indices in freely moving chicken. We developed and evaluated an electrocardiography (ECG) based HR/HRV recording system that can be used as a poultry wearable backpack for research studies. The backpack system was first validated against a commercial ECG amplifier, and the corresponding estimations of HR values matched well with each other. Then, an in vivo proof-of-concept experiment was conducted on floor-reared chickens to collect ECG data for 2 weeks. The extracted HR/HRV values show strong alignment with circadian patterns and well-defined sleep cycles. Wearable devices, like the backpack ECG system used in this study, may be best suited for application in freely moving poultry to get an insight into circadian abnormalities and sleep quality for stress and welfare management.

Multifunctional, breathable MXene-PU mesh electronic skin for wearable intelligent 12-lead ECG monitoring system

With the advent of an aging society and increasing concern for cardiovascular health, comfortable long-term electrocardiographic (ECG) monitoring systems in daily life are much in demand. If the system can give real-time feedback on cardiovascular status and offer timely personalized advice like doctors, it will be beneficial to promote cardiovascular health and alleviate the imbalance of medical resources. Herein, we report a multifunctional, conformal, and air-permeable electronic skin (e-skin) based on MXene-Polyurethane mesh (MPM). When used as ECG electrodes, the e-skin shows ultra-low electrode–skin contact impedance (4.68 kΩ at 1 kHz), high signal-to-noise ratio (16.5 dB), and high breathability (2.1838 kg/m2/day), and can be continuously worn for at least 7 days. As a strain sensor, the e-skin exhibits a large measurement range (>40 %) and unique segmental sensitivity (GF = 172.54 in 0 ∼ 18.64 % and 79.38 in 18.64 ∼ 50 %), which is suitable for detecting both subtle and large physiological signals. More significantly, by combining the MPM e-skin with a flexible printed circuit board (FPCB) and a merged convolutional neural network & long-short-term memory (CNN & LSTM) intelligent algorithm, the acquired wearable intelligent e-skin system can realize daily 12-hour 12-lead ECG monitoring with diagnostic accuracy over 99 % for four arrhythmias. These results show that the MPM e-skin has huge potential in multifunctional intelligent healthcare systems. In particular, an intelligent ECG system similar to a “daily doctor” is proposed for daily long-term cardiovascular health monitoring.

Clinical Validation of a Smartphone-based Handheld ECG Device: A Validation Study.

Remote cardiac monitoring and screening have already become an integral telemedicine component. The wide usage of several different wireless electrocardiography (ECG) devices warrants a validation study on their accuracy and reliability. Totally, 300 inpatients with the Nabz Hooshmand-1 handheld ECG device and the GE MAC 1200 ECG system (as the reference) were studied to check the accuracy of the devices in 1 and 6-limb lead performance. Simultaneous 10-second resting ECGs were assessed for the most common ECG parameters in lead I. Afterward, 6-lead ECGs (limb leads), were performed immediately and studied for their morphologies. Of the 300 patients, 297 had acceptable ECG quality in both devices for simultaneous lead I ECGs. The ECGs were inspected on-screen by a cardiologist for their rhythms, rates, axes, numbers, morphologies of premature atrial and ventricular beats, morphologies and amplitudes of PQRST waves, P-wave durations, QRS-wave durations, P-R intervals, and QT intervals. No significant differences were detected between the devices, and no major abnormalities were missed. Six-limb lead ECGs were obtained in 284 patients, of whom 281 had acceptable quality in ECGs by both devices. The morphology matching evaluation of the ECGs demonstrated an overall 98% compatibility rate, with the highest compatibility in lead I and the lowest in lead augmented vector foot. The diagnosis of critical pathological rhythms, including atrial fibrillation and high-grade atrioventricular node block, was not missed by the Nabz Hooshmand-1 and GE MAC 1200 ECG devices. Accordingly, rhythm detection as the primary purpose of handheld ECG devices was highly accurate. Both devices had acceptable sensitivity to diagnose long P-R and long and short QT intervals. Although the modern technology of smartphones and the physical inability for the 6-limb mode might cause old patients difficulty in utilizing such devices, their use for screening and follow-up is safe.

Real time system on chip based wearable cardiac activity monitoring sensor

In this research, a real time low power wearable cardiac activity monitoring device is introduced to display real time electrocardiography (ECG) signal and heart rate (HR). ECG records heart’s electrical activity and heart rate measures the contraction of the heart. The developed device consumes very less power as SoC used in this research has power consumption of 85 μW at 1.1V supply voltage. To measure heart rate and ECG waveform, the pre-gelled ECG electrodes are attached on the RA, LA and LL positon of the human’s body. These pre-gelled ECG electrodes attached to the ECG lead which is further connected to the ECG device. The ECG system is validated and the measured average HR of the subject was 71 to 82 beats per minute (BPM) and ECG graph was also plotted.

Automated Atrial Fibrillation Detection with ECG.

An electrocardiography system records electrical activities of the heart, and it is used to assist doctors in the diagnosis of cardiac arrhythmia such as atrial fibrillation. This study presents a fast, automated deep-learning algorithm that predicts atrial fibrillation with excellent performance (F-1 score 88.2% and accuracy 97.3%). Our approach involves the pre-processing of ECG signals, followed by an alternative representation of the signals using a spectrogram, which is then fed to a fine-tuned EfficientNet B0, a pre-trained convolution neural network model, for the classification task. Using the transfer learning approach and with fine-tuning of the EfficientNet, we optimize the model to achieve highly efficient and effective classification of the atrial fibrillation.

Prehospital 12-Lead Electrocardiography System in Oita Assisted Transport of "True" Acute Coronary Syndrome Patients to Optimal Institutes.

Mobile cloud electrocardiography (C-ECG) can reduce the door-to-balloon time of acute coronary syndrome (ACS) patients, so we hypothesized it would also assist in transporting ACS-suspected patients to the optimal institutes.Methods and Results: Initially, 10 fire departments in Oita had 10 ambulances equipped with C-ECG. Ambulance crews recorded a 12-lead ECG from the patient at the first point of contact and transmitted them to 18 hospitals (13 institutions (PCII) with 24-h availability for percutaneous coronary intervention (PCI) and 5 regional core hospitals (RCH) without 24-h PCI) for analysis by a cardiologist. During 41 months, 476 ECGs suspected to be ACS were transmitted and analyzed. Of these, 24 ECGs transmitted to PCII were judged as not requiring PCI, and the patients were directly transported to a RCH (PCII-RCH); 35 ECGs sent to a RCH were judged as requiring PCI, and the patients were directly transported to a PCII (RCH-PCII). The prevalence of cardiovascular disease was significantly higher in the RCH-PCII group than in the PCII-RCH group (P<0.01). There was no significant difference in the door-to-balloon time between the RCH-PCII and the group in which the C-ECG was sent to a PCII and the patients were transported directly to PCII (PCII-PCII) (49±14 vs. 59±20 min, P=0.14). Prehospital 12-lead ECG can assist in transporting ACS-suspect patients to the optimal treatment facility.

A Comparative Study of Silver Microflakes in Digitally Printable Liquid Metal Embedded Elastomer Inks for Stretchable Electronics

AbstractPrinted soft conductive materials for stretchable electronics should have low electrical resistivity, high strain limit, and stable electrical properties when stretched. Previously, it has been shown that a bi‐phasic ink composed of silver (Ag) microflakes, eutectic gallium−indium (EGaIn) alloy, and styrene isoprene (SIS) block copolymer is a promising formulation for printed soft electronics and has the potential to satisfy the necessary criteria. In this study, further improvements to the ink formulation are explored, with a focus on how the choice of Ag microflakes affects the electrical and electromechanical properties of the composite. By using specific Ag microflakes, AgInGa‐SIS inks that have conductivity as high as 6.38 × 105 S m−1 and a strain limit of over 1000%, with low electromechanical coupling can be synthesized. More broadly, when comparing the composite with different silver flakes, there is a 176% relative difference in conductivity, &gt;600% difference in strain limit, and 277% relative difference in electromechanical coupling. To demonstrate the applicability of these inks for various use cases such as wearable bioelectronics, interconnects are printed for connecting electronic breakout boards with microcontrollers that provide a stable electrical connection when stretched, and the interconnects and electrodes of a wearable electrocardiography system that monitors the heart pulses in real‐time.

Analysis of the effect of low dye concentration for electrical system applications of reactive red 45 dye loaded hydrogels

Structural, optical and dielectric properties determining the conduction mechanism were investigated using appropriate measurement techniques to determine the suitability of low-concentrations (LC) (1000 μL, 500 μL, 250 μL and 125 μL) reactive red 45 (RR-45) dye loaded hydrogels for electrical circuit applications. The UV spectra of the all samples displayed a fluctuating behaviour depending on the concentration. It was found that the dielectric plane plots for all the samples are compatible with the Cole-Cole relaxation model and correspond to an equivalent electrical circuit (RC) representing with the impedance circuit in the Smith Chart. It was concluded that RR-45 dye loaded hydrogels could be a suitable material for electrodes used in electroencephalography and electrocardiography systems due to the decrease of the real part of complex dielectric constant and impedance values with increasing frequency. The s-parameter for the samples, which determines the conductivity mechanism, was calculated separately for different regions.

Relationship between QRS duration and resynchronization window for CRT optimization: Implications for CRT in narrow QRS patients

AimsCardiac resynchronization therapy (CRT) response is proportional to QRS duration (QRSd). We hypothesize that this is, in part, due to slower conduction velocity and hence wider range of programmed device settings that produce adequate electrical wavefront fusion and resynchronization in wider QRSd patients. MethodsCRT patients (n = 122) with left ventricular (LV) conduction delay, sinus rhythm and intact atrioventricular node conduction were studied. Patients were categorized by QRSd: narrow (<120 ms; n = 20); moderate (120–150 ms, n = 37); and prolonged (≥150 ms; n = 65). Electrocardiographic data was acquired during native rhythm and LV-only pacing at varying atrioventricular delays (AVDs). Electrical synchrony was quantified as cardiac resynchronization index (CRI) using multi‑lead electrocardiographic systems and a proprietary algorithm that quantified wavefront fusion. A Gaussian distribution equation was fitted to CRI response. ResultsPeak CRI was high (87.6 ± 6.3%) and similar (p = 0.716) across QRSd groups. The standard deviation of the Gaussian distribution significantly correlated with QRSd (R = 0.614, p < 0.001), and progressively and significantly (p < 0.001) increased as QRSd increased from narrow (34.8 ± 10.0 ms), to moderate (50.6 ± 8.4 ms), to prolonged (67.6 ± 18.3 ms). At AVDs 20 and 40 ms from optimal, CRI differed significantly (p < 0.001) between groups, with progressively higher CRI values as native QRSd increased. ConclusionElectrical resynchronization with optimally programmed LV-only pacing was similar between patients with varying QRSd, including patients with narrow QRSd. The resynchronization window that corresponded with optimal electrical resynchronization decreased as native QRSd decreased. This finding provides one potential explanation for the lack of significant benefit of CRT in narrow QRSd patients in previous studies.

A Portable Electrocardiograph System Design based on STM32 Chip

A Portable Electrocardiograph System Design based on STM32 Chip