Long-term Electrocardiography Research Articles

Diagnostic capability of long-term electrocardiographic telemonitoring for detection of heart rhythm disorders

prevalence of cardiac rhythm and conduction disorders is increasing and associated with the growing amount of people of older age groups. The extended use of traditional electrocardiography (ECG) recorders is currently limited due to duration of the investigation, possible skin irritation and withdrawal of consent by the patients. The aim: evaluation of the frequency of arrythmia incidents in adult patients of different ages by assessing the patch monitoring results, and the impact of the monitoring duration on the possibility of diagnosis. Materials and methods: study included 1123 patients aged from 18 to 99 (599 women and 524 men), data about whom was collected in 2021-2022. ECG was recorded by the «Mawi Heart patch» – a single-use dual-channel device for continuous ECG recording for up to2 7 days. Results: the average ECG monitoring time was 81,03±18,5 hours or 3,37±0,7 days. The majority of the participants (74,35%) received the continuous 3-day ECG monitoring. Most rhythm disorders were recorded in the first 24 hours, but over the next 2 days, atrial fibrillation (AF) was detected in 7,66%, more often in men (48 participants, p=0,048) and with some differences in age groups. Starting above 50 years of age, the frequency of AF diagnosing on the first monitoring days is decreased and it is significantly statistically lower (61,11%, p<0,05). Three-day ECG monitoring is associated with a higher chance of detecting AF (OR=2,03; 1,53-3,02, p=0,031) regardless of gender, while the 5-day monitoring wasn’t as effective. The prolonging of the monitoring time increased the number of diagnosed supraventricular tachycardia (SVT) cases by 5,53% in men and 8,35% in women. The 3 days of monitoring are associated with a higher chance of SVT detection (OR = 4,18; 3,32–4,95, p=0,009). There is a direct correlation between SVT detection and the older age groups (r=0,53, p<0,05). SVT detection is higher in patients of 71-80 years old (OR = 2,71; 2,35–3,75, p=0,0041), and of 81-99 years (OR = 2,92; 2,56–3,49, p=0,0039). Conclusion: The frequency of arrhythmia detection, in patients referred to long-term patch monitoring, is high both for AF and SVT. The sex and age patterns for the prevalence of the described arrhythmias were statistically confirmed. The use of long-term ECG patch monitoring increases the chance of arrhythmia detection, primarily for older patients.

Diagnostics and treatment of syncope

The 2018 guidelines of the European Society of Cardiology (ESC) provide improved algorithms for the diagnostics and treatment of syncope. New guidelines on ventricular tachycardia, on the prevention of sudden cardiac death and on cardiomyopathies and pacemakers have refined the recommendations. The detailed medical history and examination are crucial for differentiating between cardiac and noncardiac causes and determining the appropriate treatment. High-risk patients need urgent and comprehensive diagnostics. The basic diagnostics include medical history, physical examination and a12-lead electrocardiography (ECG). Further tests, such as long-term ECG monitoring, implantable loop recorders and electrophysiological investigations are helpful in unclear cases. The treatment depends on the cause, with pacemaker implantation and implantable cardioverter defibrillators (ICD) being important for cardiac causes, while behavioral measures and medication management have priority for noncardiac syncope.

Leveraging electrocardiography signals for deep learning-driven cardiovascular disease classification model

Electrocardiography (ECG) is the most non-invasive diagnostic tool for cardiovascular diseases (CVDs). Automatic analysis of ECG signals assists in accurately and rapidly detecting life-threatening arrhythmias like atrioventricular blockage, atrial fibrillation, ventricular tachycardia, etc. The ECG recognition models need to utilize algorithms to detect various kinds of waveforms in the ECG and identify complicated relationships over time. However, the high variability of wave morphology among patients and noise are challenging issues. Physicians frequently utilize automated ECG abnormality recognition models to classify long-term ECG signals. Recently, deep learning (DL) models can be used to achieve enhanced ECG recognition accuracy in the healthcare decision making system. In this aspect, this study introduces an automated DL enabled ECG signal recognition (ADL-ECGSR) technique for CVD detection and classification. The ADL-ECGSR technique employs three most important subprocesses: pre-processed, feature extraction, parameter tuning, and classification. Besides, the ADL-ECGSR technique involves the design of a bidirectional long short-term memory (BiLSTM) based feature extractor, and the Adamax optimizer is utilized to optimize the trained method of the BiLSTM model. Finally, the dragonfly algorithm (DFA) with a stacked sparse autoencoder (SSAE) module is applied to recognize and classify EEG signals. An extensive range of simulations occur on benchmark PTB-XL datasets to validate the enhanced ECG recognition efficiency. The comparative analysis of the ADL-ECGSR methodology showed a remarkable performance of 91.24 % on the existing methods.

Hydrogels and Carbon Nanotubes: Composite Electrode Materials for Long-Term Electrocardiography Monitoring.

This paper presents methods for developing high-performance interface electrode materials designed to enhance signal collection efficacy during long-term (over 24 h) electrocardiography (ECG) monitoring. The electrode materials are fabricated by integrating commercial ECG liquid hydrogels with carbon nanotubes (CNTs), which are widely utilized in dry-electrode technologies and extensively discussed in the current scientific literature. The composite materials are either prepared by dispersing CNTs within the commercial liquid hydrogel matrix or by encasing the hydrogels in macroscopic CNT films. Both approaches ensure the optimal wetting of the epidermis via the hydrogels, while the CNTs reduce material impedance and stabilize the drying process. The resulting electrode materials maintain their softness, allowing for micro-conformal skin attachment, and are biocompatible. Empirical testing confirms that the ECG electrodes employing these hybrid hydrogels adhere to relevant standards for durations exceeding 24 h. These innovative hybrid solutions merge the benefits of both wet and dry ECG electrode technologies, potentially facilitating the extended monitoring of ECG signals and thus advancing the diagnosis and treatment of various cardiac conditions.

Prolonged mHealth-Based Arrhythmia Monitoring in Patients With Hypertrophic Cardiomyopathy (HCM-PATCH): Protocol for a Single-Center Cohort Study.

Patients with hypertrophic cardiomyopathy (HCM) are at increased risk of sudden cardiac death (SCD) due to ventricular arrhythmias and other arrhythmias. Screening for arrhythmias is mandatory to assess the individual SCD risk, but long-term electrocardiography (ECG) is rarely performed in routine clinical practice. Intensified monitoring may increase the detection rate of ventricular arrhythmias and identify more patients with an increased SCD risk who are potential candidates for the primary prophylactic implantation of an implantable cardioverter-defibrillator. To date, reliable data on the clinical benefit of prolonged arrhythmia monitoring in patients with HCM are rare. This prospective study aims to measure the prevalence of ventricular arrhythmias in patients with HCM observed by mobile health (mHealth)-based continuous rhythm monitoring over 14 days compared to standard practice (a 24- and 48-h long-term ECG). The frequency of ventricular arrhythmias in this 14-day period is compared with the frequency in the first 24 or 48 hours for the same patient (intraindividual comparison). Following the sample size calculation, 34 patients with a low or intermediate risk for SCD, assessed by the HCM Risk-SCD calculator, will need to be recruited in this single-center cohort study between June 2023 and February 2024. All patients will receive an ECG patch that records their heart activity over 14 days. In addition, cardiac magnetic resonance imaging and genetic testing data will be integrated into risk stratification. All patients will be asked to complete questionnaires about their symptoms; previous therapy; family history; and, at the end of the study, their experience with the ECG patch-based monitoring. The Hypertrophic Cardiomyopathy: Clinical Impact of a Prolonged mHealth-Based Arrhythmia Monitoring by Single-Channel ECG (HCM-PATCH) study investigates the prevalence of nonsustained ventricular tachycardia (ie, ≥3 consecutive ventricular beats at a rate of 120 beats per minute, lasting for <30 seconds) in low- to intermediate-risk patients with HCM (according to the HCM Risk-SCD calculator) with additional mHealth-based prolonged rhythm monitoring. The study was funded by third-party funding from the Department of Cardiology and Intensive Care Medicine, University Hospital Ostwestfalen-Lippe of Bielefeld University in June 2023 and approved by the institutional review board in May 2023. Data collection began in June 2023, and we plan to end the study in February 2024. Of the 34 patients, 26 have been recruited. Data analysis has not yet taken place. Publication of the results is planned for the fall of 2024. Prolonged mHealth-based rhythm monitoring could lead to differences in the prevalence of arrhythmias compared to 24- and 48-hour long-term ECGs. This may lead to improved identification of patients at high risk and trigger therapeutic interventions that may provide better protection from SCD or atrial fibrillation-related complications such as embolic stroke. Deutsches Register Klinischer Studien DRKS00032144; https://tinyurl.com/498bkrx8. DERR1-10.2196/52035.

CS-based multi-task learning network for arrhythmia reconstruction and classification using ECG signals

Objective. Although deep learning-based current methods have achieved impressive results in electrocardiograph (ECG) arrhythmia classification issues, they rely on using the original data to identify arrhythmia categories. However, a large amount of data generated by long-term ECG monitoring pose a significant challenge to the limited-bandwidth and real-time systems, which limits the application of deep learning in ECG monitoring. Approach. This paper, therefore, proposed a novel multi-task network that combined compressed sensing and convolutional neural networks, namely CSML-Net. According to the proposed model, the ECG signals were compressed by utilizing a learning measurement matrix and then recovered and classified simultaneously via shared layers and two task branches. Among them, the multi-scale feature module was designed to improve model performance. Main results. Experimental results on the MIT-BIH arrhythmia dataset demonstrate that our proposed method is superior to all the approaches that have been compared in terms of reconstruction quality and classification performance. Significance. Consequently, the proposed model achieving the reconstruction and classification in the compressed domain can be an improvement and become a promising approach for ECG arrhythmia reconstruction and classification.

Automatic electrocardiograph diagnosis of myocardial ischemia with support vector machine

ABSTRACT Background: Myocardial ischemia is a severe cardiac disease and it happens when the heart's blood flow is insufficient, which impairs its capacity to operate correctly and causes several systemic issues. A standard auxiliary for the clinical diagnosis of myocardial ischemia is an electrocardiograph (ECG). However, the typical static ECG cannot record the myocardial ischemia paroxysmal fragments quickly and accurately. The information contained in long-term ECG recordings is more abundant, but the large volume of data makes manual processing costly. Our aim is to propose an automated method for handling ECG signals and diagnosing myocardial ischemia. Methods: To get evidence of myocardial ischemia, dynamic ECG is frequently employed. This paper suggests a machine learning technique to create a classification model using support vector machine (SVM), automatically identifying the existence of myocardial ischemia fragments. This study comprises waveform identification, feature extraction, creation of data sets, model training, and classification. The Long Term ST Database, made available by PhysioNet, is used as the database for this research. Result: According to the final test results, the classification accuracy is 97.98%. The ECG signals can be automatically segmented, and the automated diagnosis of myocardial ischemia can achieve an accuracy rate of 97.98%. Conclusion: The results suggest that the method proposed in this paper can more precisely and practically identify myocardial ischemia pieces.

An Innovative Toolkit to Investigate the Complex Mechanisms of Cardiac Arrhythmias

ARTICLES DISCUSSED: Tomsits, P., Schuttler, D., Kaab, S., Clauss, S., Voigt, N. Isolation of high quality murine atrial and ventricular myocytes for simultaneous measurements of Ca2+ transients and L-Type calcium current. Journal of Visualized Experiments. (165), 10.3791/61964 (2020). Seibertz, F., Reynolds, M., Voigt, N. Single-Cell optical action potential measurement in human induced pluripotent stem cell-derived cardiomyocytes. Journal of Visualized Experiments. (166), 10.3791/61890 (2020). Xia, R. et al. Isolation and culture of resident cardiac macrophages from the murine sinoatrial and atrioventricular node. Journal of Visualized Experiments. (171), 10.3791/62236 (2021). Xia, R. et al. Whole-Mount immunofluorescence staining, confocal imaging and 3D reconstruction of the sinoatrial and atrioventricular node in the mouse. Journal of Visualized Experiments. (166), 10.3791/62058 (2020). Kumar, P., Si, M., Paulhus, K., Glasscock, E. Microelectrode array recording of sinoatrial node firing rate to identify intrinsic cardiac pacemaking defects in mice. Journal of Visualized Experiments. (173), 10.3791/62735 (2021). Scherschel, K. et al. Location, dissection, and analysis of the murine stellate ganglion. Journal of Visualized Experiments. (166), 10.3791/62026 (2020). Shimura, D., Hunter, J., Katsumata, M., Shaw, R. M. Removal of an internal translational start site from mRNA while retaining expression of the full-length protein. Journal of Visualized Experiments. (181), 10.3791/63405 (2022). Rotzer, R. D. et al. Implantation of combined telemetric ECG and blood pressure transmitters to determine spontaneous baroreflex sensitivity in conscious mice. Journal of Visualized Experiments. (168), 10.3791/62101 (2021). Tomsits, P. et al. Analyzing long-term electrocardiography recordings to detect arrhythmias in mice. Journal of Visualized Experiments. (171), 10.3791/62386 (2021). Grab, M. et al. Development and evaluation of 3D-printed cardiovascular phantoms for interventional planning and training. Journal of Visualized Experiments. (167), 10.3791/62063 (2021).

Embroidered textile electrodes for long-term ECG monitoring

Electrocardiography (ECG) monitoring is very important for the diagnosis and examination of heart-related diseases. Electrodes are the main components in monitoring and recording ECG signals. In this work, we have developed embroidered electrodes using two types of conductive yarns. ECG detection performance of the electrodes was measured at static and dynamic conditions and results were compared to standard Ag/AgCl electrodes. The electrodes were developed with three different dimensional areas to investigate the effect of the size on ECG acquisition performance. ECG signals collected from both the embroidered and standard Ag/AgCl electrodes have visible P, QRS, and T waveforms. Signals collected using large-size textile electrodes show better signal amplitude, which would reveal that the performance of the electrodes becomes improved with an increase in size. However, signals collect more artifacts dynamic conditions contain motion artifacts, indicating this aspect requires further improvement.

Parameters Of Myocardial Electrical Instability In Patients After Myocardial Infarction Comorbid With A Novel Coronavirus Infection (COVID-19)

Objective — This article aims to assess parameters of myocardial electrical instability and arrhythmic events in patients after myocardial infarction (MI), with and without ST-segment elevation, comorbid/noncomorbid with a novel coronavirus infection (COVID-19) using a long-term electrocardiographic (ECG) monitoring. Methods — The study included 64 subjects: 25 (39%) patients with MI comorbid with COVID-19 (MI+C group) and 39 (61%) patients with MI noncomorbid with a novel coronavirus infection (MI group). The mean age of patients was 54.3±6.8 years. A long-term ECG monitoring for 97.4 (95% CI 77.9-115.2) hours was performed with Astrocard®-Telemetry system (Meditek JSC, Russia), starting from the 4th day of MI. Rhythm and conduction disorders, along with ischemic episodes were recorded; an analysis of ventricular late potentials, heart rate turbulence, and QT dispersion was carried out. Results — There were no differences in the frequency of delayed afterdepolarizations in MI and MI+C groups: 15-28% and 18-33% of patients, respectively. An analysis of turbulence parameters did not reveal statistically significant differences between the groups. Such arrhythmic events as frequent supraventricular extrasystole and life-threatening arrhythmias (ventricular extrasystole of grade 4A and higher sensu B. Lown and M. Wolf) were recorded significantly more often in the MI+C group than in the MI group: 48% vs. 20.5% (p=0.021) and 24% vs. 5.1% (p=0.026), respectively. Conclusion — The novel coronavirus infection (COVID-19) exacerbates myocardial electrophysiological heterogeneity in the acute cardiovascular event and is associated with an increase in clinically significant arrhythmic events.

Investigating Textile-Based Electrodes for ECG Monitoring in Veterinary Clinical Practice

Abstract There is an increasing interest in long-term electrocardiography (ECG) monitoring in veterinary clinical practice. ECG is the most essential physiological signal in diagnosing and managing heart diseases both in humans and animals. Electrodes are the main components that affect the quality of the acquired signal. This study focuses on the development of silver-coated textile electrodes for veterinary ECG testing (particularly for dogs). Silver printed polyester, embroidered, and silver-plated conductive hook textile electrodes were used for ECG measurement in dogs. This is an important validation for the use of textile ECG sensors in combination with hairy skin. ECG signals were collected while the animal was in a static position and walking on a smooth surface. The ECG signals collected from the dog using the silver printed polyester and embroidered textile electrodes with slight skin preparation have identifiable P, QRS, and T waveforms and were comparable with signals from standard silver/silver chloride (Ag/AgCl) electrodes. Results revealed that these textile electrodes can be used for ECG monitoring in a dog to avoid associated problems with commercially used crocodile clamps and standard Ag/AgCl electrodes. The hook electrodes show promising results when placed on the hairy regions of a dog without any skin preparation.

Improving public health using contactless ECG electrodes for long-term monitoring of arrhythmia

Cardiovascular disease (CVD) is the leading cause of death and disability worldwide, resulting in 17.3 million deaths annually. Thus, any CVD symptoms should be immediately diagnosed for effective treatments [1]. In this case, Electrocardiography (ECG) is widely considered for monitoring and diagnosing heart symptoms. The traditional wet Ag/AgCl electrodes can acquire high-quality ECG signals. However, they cause skin irritation and require careful skin preparation by a specialist. Therefore, they are not practical for efficient long-term ECG monitoring. To overcome these limitations, contactless capacitive electrodes are introduced as an alternative. But these electrodes suffer various shortfalls in collecting high-quality and clinically acceptable signals. As a coupling capacitor between the body and electrode is formed in these sensors and this capacitor changes due to the subject’s physiological or body movements leading to Motion Artifacts (MAs). In addition, the variable skin potential across the capacitor forms an additive voltage that may distort the ECG signal adversely. This presentation aims to illustrate a comprehensive literature review and methodology used in my study. Regarding the methodology, I intend to simulate the equivalent model of the measurement system and implement it in the real world. Moreover, two issues will be addressed in my research questions. Firstly, I will extract the variable impedance between the sensor and the subject’s skin, which has the highest correlation with MAs according to literature, by injecting a voltage with a known frequency into the circuit. Unlike many previous methods, the ECG signal, and impedance signal will be measured simultaneously. I will address the optimal frequency and impedance signal with maximum correlation with MAs, which are the gaps in previous works. Secondly, in past studies, the effects of skin potential on MAs have not been sufficiently investigated, which is aimed to be analyzed, estimated, and subtracted from the polluted ECG noise signal.

Probes of New Types of Electrodes of ECG

Long-term electrocardiography (ECG) monitoring can scrutinize human health, which is very essential for the early detection and treatment of cardiovascular disorders. For present, commercially used silver/silver chloride (Ag/AgCl) electrodes have drawbacks, and these would be more obvious in long-term monitoring. Therefore, people are developing new types of electrodes for ECG from different perspectives. At present, innovations are made: 1) Applying conductive thread into sewing to make textile electrodes for wearable ECG monitoring. 2) New material (Graphite Based Electrode) replacing the conventionally used Ag/AgCl.

Armband Sensors Location Assessment for Left Arm-ECG Bipolar Leads Waveform Components Discovery Tendencies around the MUAC Line.

Sudden cardiac death (SCD) risk can be reduced by early detection of short-lived and transient cardiac arrhythmias using long-term electrocardiographic (ECG) monitoring. Early detection of ventricular arrhythmias can reduce the risk of SCD by allowing appropriate interventions. Long-term continuous ECG monitoring, using a non-invasive armband-based wearable device is an appealing solution for detecting early heart rhythm abnormalities. However, there is a paucity of understanding on the number and best bipolar ECG electrode pairs axial orientation around the left mid-upper arm circumference (MUAC) for such devices. This study addresses the question on the best axial orientation of ECG bipolar electrode pairs around the left MUAC in non-invasive armband-based wearable devices, for the early detection of heart rhythm abnormalities. A total of 18 subjects with almost same BMI values in the WASTCArD arm-ECG database were selected to assess arm-ECG bipolar leads quality using proposed metrics of relative (normalized) signal strength measurement, arm-ECG detection performance of the main ECG waveform event component (QRS) and heart-rate variability (HRV) in six derived bipolar arm ECG-lead sensor pairs around the armband circumference, having regularly spaced axis angles (at 30° steps) orientation. The analysis revealed that the angular range from −30° to +30°of arm-lead sensors pair axis orientation around the arm, including the 0° axis (which is co-planar to chest plane), provided the best orientation on the arm for reasonably good QRS detection; presenting the highest sensitivity (Se) median value of 93.3%, precision PPV median value at 99.6%; HRV RMS correlation (p) of 0.97 and coefficient of determination (R2) of 0.95 with HRV gold standard values measured in the standard Lead-I ECG.

Flexible Hybrid Electronics Nanofiber Electrodes with Excellent Stretchability and Highly Stable Electrical Conductivity for Smart Clothing.

In this paper, a side-by-side, dual-nozzle electrospinning process was used to prepare a flexible hybrid electronics (FHE) material with excellent stretchable properties. A highly stable electrical conductivity was also imparted to the resulting membrane electrodes using silver nanoparticles (AgNPs) and carbon-based nanomaterials of different structures. The AgNP/carbon-based nanomaterials were coated onto bicomponent polymer nanofibers (composed of polyurethane (PU) and polyvinylidene difluoride (PVDF)) on the nanofiber membrane. The FHE nanofiber electrodes were finally integrated into clothing designed to accurately measure human body sensing signals (e.g., electrocardiography (ECG) and electromyography (EMG) signals). To effectively increase the high electrical conductivity, a polymer-type dispersant (polyisobutylene-b-poly(oxyethylene)-b-polyisobutylene, a triblock copolymer) was used to effectively and stably disperse AgNPs with different particle sizes and carbon-based nanomaterials with different geometric dimensions (e.g., zero-dimensional carbon black, one-dimensional carbon nanotubes, and two-dimensional graphene) through non-covalent adsorption. Moreover, the bicomponent PVDF-PU nanofibers were immersed in a mixed dispersant of AgNPs and carbon-based nanomaterials at low concentrations, and thermal post-treatment was conducted to improve the electrical conductivity. The AgNP/graphene oxide (GO) nanofiber electrode exhibited a continuous phase with a stable material microstructure after 5000 repetitions of 50% tension-tension fatigue testing. The waveform pattern obtained from the proposed AgNP/GO nanofiber electrode was compared with those of traditional ECG and EMG electrodes. The nanofiber web electrode treated with organic/inorganic mixed dispersants and verified via tests of its electrical and fatigue properties was found to be suitable for long-term ECG and EMG monitoring, and it has excellent potential in wearable smart sensors.

Detection of hidden atrial fibrillation in primary care using a long-term band-based ECG registration system: The DESCUUBRE-FA study

ObjectiveTo assess whether the use of a band-based electrocardiographic (ECG) monitoring system improves the diagnostic accuracy of traditional diagnostic methods for the detection of atrial fibrillation (AF). MethodsMulticenter and observational study of primary care patients at risk of AF. To be included, patients had to be aged ≥70 years, with no known AF, and have at least 1 major criterion (obesity, hyperthyroidism, heart failure) or 2 minor criteria (hypertension, diabetes, female sex, ischemic stroke, transient ischemic attack or systemic embolism, COPD, dyslipidemia, ischemic heart disease, peripheral artery disease). All patients were monitored using the Nuubo™ system for a single 2-week period. ResultsA total of 600 patients were included (median age 77 years; 70% women; 84.3% hypertension, 27.2% diabetes, 9.2% prior stroke). The global rate of diagnosis of new AF in the overall population was 2.83%. All patients with AF were anticoagulated. In numerical terms, patients with AF (vs no AF) had a higher number of supraventricular extrasystoles and episodes of supraventricular tachycardia, as well as longer P wave duration; however, these differences did not reach statistical significance. Overall, participants were very satisfied with the device, and no relevant limitations in daily activities were observed during the 2-week study period. ConclusionsIn an elderly population at risk of AF, a band-based ECG monitoring approach applied for only 2 weeks detected approximately 3% of new cases of AF, leading to a change in antithrombotic therapy. Most participants considered the device easy to use and comfortable.

Compressed-Domain ECG-based Biometric User Identification Using Task-Driven Dictionary Learning

In recent years, user identification has become crucial for authorized machine access. Electrocardiography (ECG) is a new and rising biometrics signature. Rather than traditional biological traits, ECG cannot be easily imitated. In the long-term monitoring system, the wireless wearable ECG biomedical sensor nodes are resource-limited. Recently, compressive sensing (CS) technology is extensively applied to reduce the power of data transmission and acquisition. The prior CS-based reconstruction process aims at improving energy efficiency with different schemes, and they focus on the performance of reconstruction only. Therefore, we present a sparse coding-based classifier, trained by task-driven dictionary learning (TDDL), to realize low-complexity user identification in compressed-domain directly. TDDL is one of the dictionary learning and designed for classification tasks. It co-optimizes the dictionary and classifier weighting simultaneously, which gives better accuracy. In this article, we are proposing a TDDL-based compression learning algorithm for ECG biometric user identification as this directly identifies user identity (ID) without undergoing reconstruction process and conventional classifier. It can extract necessary information from the compressed-ECG signal directly to save the system power and computational complexity. The algorithm has 2%–10% accuracy improvements compared with state-of-the-art algorithms and maintains low complexity at the same time. Our proposed TDDL-CL will be the better choice in the long-term wearable ECG biometric devices.

Paroxysmal Atrial Fibrillation in Horses: Pathophysiology, Diagnostics and Clinical Aspects.

Simple SummaryAtrial fibrillation is the most common pathological cardiac arrhythmia affecting performance in horses. The sporadic form of atrial fibrillation, termed paroxysmal atrial fibrillation, spontaneously cardioverts to normal sinus rhythm usually within 7 days. The condition may go undetected, as episodes can occur intermittently at different frequencies and vary in duration from minutes to hours. However, paroxysmal atrial fibrillation may still result in poor performance, especially in racehorses, resulting in possible negative consequences for the horses and their owners. The epidemiology and pathophysiology of the disease are not well described. We investigate the current knowledge and present possible risk factors that may predispose horses to paroxysmal atrial fibrillation. Early diagnosis is crucial, which is why current and future diagnostic modalities are discussed.Atrial fibrillation (AF) is the most common arrhythmia in horses causing poor performance. As in humans, the condition can be intermittent in nature, known as paroxysmal atrial fibrillation (pAF). This review covers the literature relating to pAF in horses and includes references to the human literature to compare pathophysiology, clinical presentation, diagnostic tools and treatment. The arrhythmia is diagnosed by auscultation and electrocardiography (ECG), and clinical signs can vary from sudden loss of racing performance to reduced fitness or no signs at all. If left untreated, pAF may promote electrical, functional and structural remodeling of the myocardium, thus creating a substrate that is able to maintain the arrhythmia, which over time may progress into permanent AF. Long-term ECG monitoring is essential for diagnosing the condition and fully understanding the duration and frequency of pAF episodes. The potential to adapt human cardiac monitoring systems and computational ECG analysis is therefore of interest and may benefit future diagnostic tools in equine medicine.

Continuous ECG monitoring versus mobile telemetry: A comparison of arrhythmia diagnostics in human- versus algorithmic-dependent systems.

BackgroundClinicians rarely scrutinize the full disclosure of a myriad of FDA-approved long-term rhythm monitors, and they rely on manufacturers to detect and report relevant rhythm abnormalities.ObjectiveThe objective of this study is to compare the diagnostic accuracy between mobile cardiac telemetry (MCT), which uses an algorithm-based detection strategy, and continuous long-term electrocardiography (LT-ECG) monitoring, which uses a human-based detection strategy.MethodsIn an outpatient arrhythmia clinic, we enrolled 50 sequential patients ordered to wear a 30-day MCT, to simultaneously wear a continuous LT-ECG monitor. Periods of concomitant wear of both devices were examined using the associated report, which was over-read by 2 electrophysiologists.ResultsForty-six of 50 patients wore both monitors simultaneously for an average of 10.3 ± 4.4 days (range: 1.2–14.8 days). During simultaneous recording, patients were more often diagnosed with arrhythmia by LT-ECG compared to MCT (23/46 vs 11/46), P = .018. Similarly, more arrhythmia episodes were detected during simultaneous recording with the LT-ECG compared to MCT (61 vs 19), P < .001. This trend remained consistent across arrhythmia subtypes, including ventricular tachycardia (13 patients by LT-ECG vs 7 by MCT), atrioventricular (AV) block (3 patients by LT-ECG vs 0 by MCT), and AV node reentrant tachycardia (2 patients by LT-ECG vs 0 by MCT). Atrial fibrillation (AF) was documented by both monitors in 2 patients; however, LT-ECG monitoring captured 4 additional AF episodes missed by MCT.ConclusionIn a time-controlled, paired analysis of 2 disparate rhythm monitors worn simultaneously, human-dependent LT-ECG arrhythmia detection significantly outperformed algorithm-based MCT arrhythmia detection.

Evaluation of dry textile electrodes for long-term electrocardiographic monitoring

BackgroundContinuous long-term electrocardiography monitoring has been increasingly recognized for early diagnosis and management of different types of cardiovascular diseases. To find an alternative to Ag/AgCl gel electrodes that are improper for this application scenario, many efforts have been undertaken to develop novel flexible dry textile electrodes integrated into the everyday garments. With significant progresses made to address the potential issues (e.g., low signal-to-noise ratio, high skin–electrode impedance, motion artifact, and low durability), the lack of standard evaluation procedure hinders the further development of dry electrodes (mainly the design and optimization).ResultsA standard testing procedure and framework for skin–electrode impedance measurement is demonstrated for the development of novel dry textile electrodes. Different representative electrode materials have been screen-printed on textile substrates. To verify the performance of dry textile electrodes, impedance measurements are conducted on an agar skin model using a universal setup with consistent frequency and pressure. In addition, they are demonstrated for ECG signals acquisition, in comparison to those obtained using conventional gel electrodes.ConclusionsDry textile electrodes demonstrated similar impedance when in raised or flat structures. The tested pressure variations had an insignificant impact on electrode impedance. Looking at the effect of impedance on ECG signals, a noticeable effect on ECG signal performance metrics was not observed. Therefore, it is suggested that impedance alone is possibly not the primary indicator of signal quality. As well, the developed methods can also serve as useful guidelines for future textile dry-electrode design and testing for practical ECG monitoring applications.