Cardiac Monitoring Research Articles

No Detectable Differences in microRNA Plasma Levels between Diabetic Hypertensive Patients with and without Incident Subclinical Atrial Fibrillation.

Background: Long-term rhythm monitoring (LTRM) can detect undiagnosed atrial fibrillation (AF) in patients at risk of AF and stroke. Circulating microRNAs (miRNAs), which have been shown to play a role in atrial electrical and structural remodelling, could help to select patients who would benefit most from LTRM. The aim of this study was to investigate whether patients with diabetes mellitus (DM) and hypertension and screen-detected subclinical AF (SCAF) using an insertable cardiac monitor (ICM) have significantly different plasma baseline levels of five selected miRNAs playing a role in the modulation of atrial electrical and structural remodelling (miR-21-5p, miR-29b-3p, miR-150-5p, miR-328-3p, and miR-432-5p) compared to those without SCAF. Methods: This study was performed at the outpatient clinic of a secondary academic teaching hospital between December 2013 and November 2015. Eligible patients were ≥65 years of age with DM and hypertension but without known heart diseases. All patients received an ICM. On the day of ICM implantation, blood samples for the measurement of plasma levels of the five miRNAs were drawn. In this post hoc analysis, we investigated their expression by reverse transcription-quantitative polymerase chain reaction. MiRNA plasma levels in patients with and without newly detected SCAF were compared. Results: We included 82 consecutive patients (median age of 71.3 years (IQR 67.4-75.1)), who were followed for a median of 588 days (IQR: 453-712 days). Seventeen patients (20.7%) had ICM-detected SCAF. Plasma levels of miR-328-3p, miR-29b-3p, miR-21-5p, miR-432-5p, and miR-150-5p were slightly but not significantly different in patients with incident SCAF compared with patients without. Conclusions: In patients with hypertension and DM, newly detected SCAF was not significantly associated with changes in expression levels of miR-21-5p, miR-29b-3p, miR-150-5p, miR-328-3p, and miR-432-5p.

Contaminations in water distribution systems: a critical review of detection and response methods

ABSTRACT Water distribution networks play a crucial role in delivering safe water to communities. However, their extensive reach and complex structure make them susceptible to contamination. The development of efficient contamination warning systems (CWSs) can enable the monitoring and control of abnormal events. In an efficient CWS, several key aspects must be addressed: identifying potential contaminations that can occur, determining the most effective water parameters to monitor, and defining where these parameters can be strategically monitored. In the present study, literature articles will be analyzed to explore different parameters for detecting anomalies, assess the information they provide, and highlight the benefits of combining various parameters. Moreover, attention will be given to the definition of sensor placement, emphasizing the lack of attention in the literature for defining sensors’ detection thresholds. Finally, the study underscores that ensuring human safety requires not only prompt intrusion detection but also the implementation of corrective and preventive actions capable of mitigating contaminant spread through WDNs.

Early Cardiac Evaluation, Abnormal Test Results, and Associations with Outcomes in Patients with Acute Brain Injury Admitted to a Neurocritical Care Unit.

Background: to examine factors associated with cardiac evaluation and associations between cardiac test abnormalities and clinical outcomes in patients with acute brain injury (ABI) due to acute ischemic stroke (AIS), spontaneous subarachnoid hemorrhage (SAH), spontaneous intracerebral hemorrhage (sICH), and traumatic brain injury (TBI) requiring neurocritical care. Methods: In a cohort of patients ≥18 years, we examined the utilization of electrocardiography (ECG), beta-natriuretic peptide (BNP), cardiac troponin (cTnI), and transthoracic echocardiography (TTE). We investigated the association between cTnI, BNP, sex-adjusted prolonged QTc interval, low ejection fraction (EF < 40%), all-cause mortality, death by neurologic criteria (DNC), transition to comfort measures only (CMO), and hospital discharge to home using univariable and multivariable analysis (adjusted for age, sex, race/ethnicity, insurance carrier, pre-admission cardiac disorder, ABI type, admission Glasgow Coma Scale Score, mechanical ventilation, and intracranial pressure [ICP] monitoring). Results: The final sample comprised 11,822 patients: AIS (46.7%), sICH (18.5%), SAH (14.8%), and TBI (20.0%). A total of 63% (n = 7472) received cardiac workup, which increased over nine years (p < 0.001). A cardiac investigation was associated with increased age, male sex (aOR 1.16 [1.07, 1.27]), non-white ethnicity (aOR), non-commercial insurance (aOR 1.21 [1.09, 1.33]), pre-admission cardiac disorder (aOR 1.21 [1.09, 1.34]), mechanical ventilation (aOR1.78 [1.57, 2.02]) and ICP monitoring (aOR1.68 [1.49, 1.89]). Compared to AIS, sICH (aOR 0.25 [0.22, 0.29]), SAH (aOR 0.36 [0.30, 0.43]), and TBI (aOR 0.19 [0.17, 0.24]) patients were less likely to receive cardiac investigation. Patients with troponin 25th-50th quartile (aOR 1.65 [1.10-2.47]), troponin 50th-75th quartile (aOR 1.79 [1.22-2.63]), troponin >75th quartile (aOR 2.18 [1.49-3.17]), BNP 50th-75th quartile (aOR 2.86 [1.28-6.40]), BNP >75th quartile (aOR 4.54 [2.09-9.85]), prolonged QTc (aOR 3.41 [2.28; 5.30]), and EF < 40% (aOR 2.47 [1.07; 5.14]) were more likely to be DNC. Patients with troponin 50th-75th quartile (aOR 1.77 [1.14-2.73]), troponin >75th quartile (aOR 1.81 [1.18-2.78]), and prolonged QTc (aOR 1.71 [1.39; 2.12]) were more likely to be associated with a transition to CMO. Patients with prolonged QTc (aOR 0.66 [0.58; 0.76]) were less likely to be discharged home. Conclusions: This large, single-center study demonstrates low rates of cardiac evaluations in TBI, SAH, and sICH compared to AIS. However, there are strong associations between electrocardiography, biomarkers of cardiac injury and heart failure, and echocardiography findings on clinical outcomes in patients with ABI. Findings need validation in a multicenter cohort.

Cardiac Toxicity of Trastuzumab in Patients Treated for Breast Cancer HER2 3+ An Algerian Study

Breast cancer is the most common cancer in women worldwide, its incidence is increasing but mortality has decreased in a huge way due to the growth of various cancer treatments. Patients with a contraindication for trastuzumab, an incomplete cardiographic echo record, and patients with already heart disease were excluded from the history. The means of cardiac monitoring was cardiac ultrasound or isotopic ventriculography, the toxicity criteria were those of the New York Heart Association (NYHA), a detailed operating sheet was made for each of our patients. The main objective of our study was to assess the incidence of cardiotoxicity in patients treated for breast cancer HER 2 3+ by trastuzumab.06% of our patients had had trastuzumab cardiotoxicity (i.e. 3 cases); two patients had an asymptomatic decrease in reversible LVEF and one patient had clinically significant heart failure requiring appropriate treatment for ICS and permanent discontinuation of trastuzumab. The protocols of targeted therapy with trastuzumab are an essential progress in oncology and cardiac toxicity is the major limiting factor for its use. Our work focuses on a retrospective study, conducted at the Department of Oncology and the Hospital Ahmida Ben adjila discussing cardiotoxicity of tarstuzumab in 50 patients with breast cancer HER2 3+ collected from january 2014 to december 2019.we have included in this study all patient with breast cancer overexpressing HER2 3+ treated by trastuzumab with an initial heart function and preserved LVEF ≥5 5%. We excluded patients contre to trastuzumab, incomplete echocardiographic folder, patients who received neoadjuvant trastuzumab and patients with metastatic breast cancer. Cardiotoxicity with trastuzumab is not common enough in our series but it does exist, which requires several careful preventive measures including monitoring by echocardiography.

A case report on digoxin toxicity

Digoxin is an inotropic drug that is commonly prescribed in patient with heart related diseases. The effective dose of digoxin is 0.8-2.0 ng/ml beyond which digoxin causes toxic effects like visual dysfunction, irregular heartbeat, and cardiovascular collapse. Digoxin toxicity is caused due to increased automaticity and inotropy due to intracellular calcium and decreased dromotropy due to poisoning of sodium potassium transporter and AV nodal blockade. Digoxin toxicity should be assessed by continuous hemodynamic and cardiac monitoring including 12-lead electrocardiogram. There should be timely and immediate evaluation of electrolyte levels like potassium, calcium, serum creatinine and digoxin levels and prompt intensive care unit admission, if necessary. In this case study we studied a 43 old female patient prescribed with digoxin 0.25 mg, who had a medical history of congestive cardiac failure presented with symptoms of digoxin toxicity. The patient was assessed with electrocardiography (ECG) and managed by keeping digoxin on hold and effective patient counselling.

P146 A phase II, randomised, double-blind, placebo-controlled trial exploring pharmacokinetics, safety and efficacy of intravenous factor XIII treatment in adults with systemic sclerosis

Abstract Background/Aims Systemic sclerosis (SSc) is a multi-system autoimmune disease with significant morbidity and mortality, and an unmet therapeutic need. The pathogenesis of SSc involves dysfunctional immune signalling, fibroblast behaviour and vascular development. Factor XIII (FXIII) is an enzyme involved in coagulation, and in wound healing where it promotes angiogenesis via inhibition of thrombospondin-1 (TSP-1). Therapeutic administration of FXIII in SSc is proposed to increase angiogenesis via suppression of TSP-1, which is known to be dysregulated in SSc. Previous preclinical and clinical studies suggest that therapeutic administration of factor XIII could improve vascular function in SSc. Methods Two interlinked clinical trials were conducted at a single centre. First, a single-dose open-label study was performed to assess the safety and pharmacokinetics of FXIII treatment in SSc. Eight adult patients with limited or diffuse SSc received a single dose of intravenous purified human FXIII, followed by a 6-week monitoring period. Second, a phase II, double-blind, randomised, placebo-controlled study was performed to investigate the safety and efficacy of factor XIII treatment in SSc. 18 adult patients with limited or diffuse SSc were randomised 2:1 to receive study intervention (intravenous purified human FXIII) or placebo (intravenous 0.9% sodium chloride solution). Safety endpoints were assessment of the safety and tolerability of FXIII treatment via review of adverse events, physical examination and monitoring of physiological function. Efficacy was explored as change in skin severity by modified Rodnan skin score (mRSS) at week 24 compared to baseline, and change in severity of Raynaud’s phenomenon, assessed by review of the Raynaud’s Condition Score (RCS) at week 24, compared to baseline. The study is registered with ClinicalTrials.gov (number NCT02551042). Results The main objective of the clinical trial was met by demonstrating that FXIII is safe and well tolerated in SSc. Pharmacokinetic analysis showed a predictable rise and fall in FXIII level following therapeutic administration, with the mean time taken for the FXIII level to return to within 10 IU/dL of the endogenous level being 16.6 days (SD 8.7). Adverse event and safety monitoring did not reveal any concerning pattern. Study intervention-related adverse events were self-resolving and tolerable. There was no statistically significant change in mRSS or RCS during the trial, but there was a trend towards improvement in RCS in the FXIII group (mean change in RCS of -1.2 (95% CI -2.26 to -0.14) in the FXIII group versus -0.83 (95% CI -3.44 to 1.77) in the placebo group). Conclusion Our results suggest that therapeutic strategies to modulate links between coagulation and tissue repair using FXIII are safe and feasible. Further studies with different patient populations and FXIII doses could investigate the role of FXIII in SSc further. Disclosure A. Leslie: Other; A.L. is an employee of GSK. S. Vigneswaran: None. K. Khan: None. V. Ong: None. C.P. Denton: Consultancies; C. D. has received consulting fees from Roche, Janssen, GlaxoSmithKline, Sanofi, Galapagos, Boehringer Ingelheim, and CSL Behring, and Acceleron. Grants/research support; C. D. has received research funding from CSL Behring, Horizon, Abbvie, and GlaxoSmithKline.

Continuous Fetal Cardiac Monitoring during Fetoscopic Myelomeningocele Repair and Relationship to Spectral Doppler Changes

Introduction: No evidence-based protocols exist for fetal cardiac monitoring during fetoscopic myelomeningocele (fMMC) repair and intraprocedural spectral Doppler data are limited. We determined the feasibility of continuous fetal echocardiography during fMMC repair and correlated Doppler changes with qualitative fetal cardiac function during each phase of fMMC repair. Methods: Patients undergoing fMMC repair had continuous fetal echocardiography interpreted in real-time by pediatric cardiology. Fetal data included fetal heart rate (FHR), qualitative cardiac function, mitral and tricuspid valve inflow waveforms, and umbilical artery (UA), umbilical vein (UV), ductus arteriosus (DA), and ductus venosus (DV) Dopplers. Results: UA abnormalities were noted in 14/25 patients, UV abnormalities were observed in 2 patients, and DV and DA abnormalities were each noted in 4 patients. Qualitative cardiac function was normal for all patients with the exception of one with isolated left ventricular dysfunction during myofascial flap creation, concurrent with an abnormal UA flow pattern. All abnormalities resolved by the first postoperative day. Conclusions: Continuous fetal echocardiography was feasible during all fMMC repairs. Spectral Doppler changes in the UA were common during fMMC procedures but qualitative cardiac dysfunction was rare. Abnormalities in the UV, DV, and DA Dopplers, FHR, and cardiac function were less common findings.

BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.

Social media platforms serve as a valuable resource for users to share health-related information, aiding in the monitoring of adverse events linked to medications and treatments in drug safety surveillance. However, extracting drug-related adverse events accurately and efficiently from social media poses challenges in both natural language processing research and the pharmacovigilance domain. Recognizing the lack of detailed implementation and evaluation of Bidirectional Encoder Representations from Transformers (BERT)-based models for drug adverse event extraction on social media, we developed a BERT-based language model tailored to identifying drug adverse events in this context. Our model utilized publicly available labeled adverse event data from the ADE-Corpus-V2. Constructing the BERT-based model involved optimizing key hyperparameters, such as the number of training epochs, batch size, and learning rate. Through ten hold-out evaluations on ADE-Corpus-V2 data and external social media datasets, our model consistently demonstrated high accuracy in drug adverse event detection. The hold-out evaluations resulted in average F1 scores of 0.8575, 0.9049, and 0.9813 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. External validation using human-labeled adverse event tweets data from SMM4H further substantiated the effectiveness of our model, yielding F1 scores 0.8127, 0.8068, and 0.9790 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. This study not only showcases the effectiveness of BERT-based language models in accurately identifying drug-related adverse events in the dynamic landscape of social media data, but also addresses the need for the implementation of a comprehensive study design and evaluation. By doing so, we contribute to the advancement of pharmacovigilance practices and methodologies in the context of emerging information sources like social media.

Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms.

Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record. We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively. The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.

Incidence and prevalence of hypertension in 18-40-year-old patients referred for palpitations with normal cardiac monitor findings.

Sixteen percent of patients referred for cardiology evaluation are found to have no cause for palpitations. Studies show that hypertension intricately influences "heart rate" and "contractility,?" the key components of "palpitation." While the prevalence of hypertension is 22.4% in 18-39-year-olds, the relationship between palpitations and hypertension remains unknown in this age group. In our study, we assessed the incidence and prevalence of hypertension over 5 years in 18-40-year-olds referred for palpitations who had no known arrhythmic cause for palpitations between January 1, 206 and December 31, 2017. We found that over a period of 2.2 (0.7-4.1) years, an additional 56% patients were diagnosed with stage 1 (65/130) and stage 2 (28/130) hypertension, increasing the prevalence from 16% at the start of the study period to 72% at the end of the study period (p<.0001). Hypertensive patients were obese (BMI: 29 [24-36] kg/m2 vs. 25 [22-31] kg/m2; p=.03), used nonsteroidal anti-inflammatory drugs (NSAIDs) (62 vs. 35%; p=.04), had a stronger family history of hypertension (55 vs. 4%; p<.0001) and exhibited higher systolic (124[120-130] mmHg vs. 112[108-115] mmHg; p<.0001) and diastolic (80[76-83] mmHg vs. 72[69-75] mmHg; p<.0001) blood pressures. Hypertension is commonly diagnosed in 18-40-year-old predominantly white female patients referred for palpitations without a known arrhythmic cause. The possibility of untreated hypertension causing palpitations in this cohort needs further evaluation.

Comparison of Gas Signature and Void Fraction in Water- and Oil-Based Muds Using Fiber-Optic Distributed Acoustic Sensor, Distributed Temperature Sensor, and Distributed Strain Sensor

Summary Accurate estimation and prediction of gas rise velocity, length of the gas influx region, and void fraction are important for optimal gas kick removal, riser gas management, and well control planning. These parameters are also essential in monitoring and characterization of multiphase flow. However, gas dynamics in non-Newtonian fluids, such as drilling mud, which is essential for gas influx control, are poorly understood due to the inability to create full-scale annular flow conditions that approximate the conditions observed in the field. This results in a lack of understanding and poor prediction of gas kick behavior in the field. To bridge this gap, we use distributed fiber-optic sensors (DFOS) for real-time estimation of gas rise velocity, void fraction, and influx length in water and oil-based mud (OBM) at the well scale. DFOS can overcome a major limitation of downhole gauges and logging tools by enabling the in-situ monitoring of dynamic events simultaneously across the entire wellbore. This study is the first well-scale deployment of distributed acoustic sensor (DAS), distributed temperature sensor (DTS), and distributed strain sensor (DSS) for investigation of gas behavior in water and OBM. Gas void fraction, migration velocities, and gas influx lengths were analyzed across a 5,163-ft-deep wellbore for multiphase experiments conducted with nitrogen in water and nitrogen in synthetic-based mud, at similar operating conditions. An improved transient drift flux–based numerical model was developed to simulate the experimental processes and understand the gas dynamics in different wellbore fluid environments. The gas velocities, void fractions, and gas influx lengths estimated independently using DAS, DTS, and DSS showed good agreement with the simulation results, as well as the downhole gauge analysis.

A Smartphone-Based M-Health Monitoring System for Arrhythmia Diagnosis.

Deep learning technology has been widely adopted in the research of automatic arrhythmia detection. However, there are several limitations in existing diagnostic models, e.g., difficulties in extracting temporal information from long-term ECG signals, a plethora of parameters, and sluggish operation speed. Additionally, the diagnosis performance of arrhythmia is prone to mistakes from signal noise. This paper proposes a smartphone-based m-health system for arrhythmia diagnosis. First, we design a cycle-GAN-based ECG denoising model which takes real-world noise signals as input and aims to produce clean ECG signals. In order to train its two generators and two discriminators simultaneously, we explore an unsupervised pre-training strategy to initialize the generator and accelerate the convergence speed during training. Second, we propose an arrhythmia diagnosis model based on the time convolution network (TCN). This model can identify 34 common arrhythmia events using eight-lead ECG signals, and we deploy such a model on the Android platform to develop an at-home ECG monitoring system. Experimental results have demonstrated that our approach outperforms the existing noise reduction methods and arrhythmia diagnosis models in terms of denoising effect, recognition accuracy, model size, and operation speed, making it more suitable for deployment on mobile devices for m-health monitoring services.

ECG signal quality in intermittent long-term dry electrode recordings with controlled motion artifacts.

Wearable long-term monitoring applications are becoming more and more popular in both the consumer and the medical market. In wearable ECG monitoring, the data quality depends on the properties of the electrodes and on how they interface with the skin. Dry electrodes do not require any action from the user. They usually do not irritate the skin, and they provide sufficiently high-quality data for ECG monitoring purposes during low-intensity user activity. We investigated prospective motion artifact-resistant dry electrode materials for wearable ECG monitoring. The tested materials were (1) porous: conductive polymer, conductive silver fabric; and (2) solid: stainless steel, silver, and platinum. ECG was acquired from test subjects in a 10-min continuous settling test and in a 48-h intermittent long-term test. In the settling test, the electrodes were stationary, whereas both stationary and controlled motion artifact tests were included in the long-term test. The signal-to-noise ratio (SNR) was used as the figure of merit to quantify the results. Skin-electrode interface impedance was measured to quantify its effect on the ECG, as well as to leverage the dry electrode ECG amplifier design. The SNR of all electrode types increased during the settling test. In the long-term test, the SNR was generally elevated further. The introduction of electrode movement reduced the SNR markedly. Solid electrodes had a higher SNR and lower skin-electrode impedance than porous electrodes. In the stationary testing, stainless steel showed the highest SNR, followed by platinum, silver, conductive polymer, and conductive fabric. In the movement testing, the order was platinum, stainless steel, silver, conductive polymer, and conductive fabric.

A practical guide to combination advanced therapy in inflammatory bowel disease.

To provide an overview of the current literature regarding the use of advanced combination therapy (ACT) in patients with inflammatory bowel disease (IBD). Although the treatment of IBD has come a long way, many patients do not respond or will lose response to currently available treatments over time. ACT has been proposed as a model to create sustained remission in difficult-to-treat IBD patient populations. This review discusses the available literature supporting the use of ACT, followed by practical tips for applying this model of treatment to clinical practice. Both observational and controlled evidence have demonstrated that there may be an increased benefit of ACT in specific IBD patient populations compared to advanced targeted immunomodulator (TIM) monotherapy. Additional data is required to understand how to best use combination TIMs and the long-term risks associated with this strategy. While the literature has demonstrated the potential for benefit in both Crohn's disease and ulcerative colitis, the use of ACT is currently off-label and long-term controlled data is needed. The successful application of ACT requires careful consideration of both patient and disease profiles as well as close monitoring of treatment response and adverse events.

Edge Computing Based Cardiac Monitoring and Detection System in the Internet of Medical Things

Currently, cardiac disease becomes one of the major health issues in the world. The death due to cardiac arrests has been increasing day by day. The survival rate for the sudden cardiac arrest is low. It is possible to save 60% human life by continuously monitoring the fitness of the person. In recent times, the Internet of Things is emerging promising technologies. If the doctor cannot monitor the patient face to face, there Internet of Things plays important role in remotely monitoring the patient’s condition. With the rise in the use of smart wearable gadgets, the contribution of IoT will be more. In this paper, about five predictor model are analyzed for a particular dataset and chosen the best as Random forest algorithm. The proposed system integrates oxygen saturation level and heart rate sensor data from MAX30100 sensor for monitoring the patient’s vital signs. The remaining parametric data of the patient (AGE, GENDER, BLOOD SUGAR LEVEL, BLOOD PRESSURE LEVEL, GENETIC ISSUE and CHLOESTEROL LEVEL) are fed through the matrix keypad. The designed system monitors the online patient’s vitals continuously. The patient’s vitals will be stored in the edge server and also analyzed. The chosen predictor model checks the vital data for any abnormality and based on abnormality data detection in the heart rate, the warning message is conveyed to the nearby health care center via Thingspeak cloud. By tracking the patient’s GPS location, the server cloud sends alert notification along with the GPS location to the nearby health center and to the registered mobile numbers.

Stratospheric Gravity Waves Impact on Infrasound Transmission Losses Across the International Monitoring System

AbstractThe international monitoring system (IMS) has been put in place to monitor compliance with the comprehensive nuclear-test-ban treaty (CTBT). Its infrasound component, dedicated to the monitoring of atmospheric events, gives also room to civil applications (e.g. monitoring of volcanic eruptions, meteorites, severe weather). Infrasound detection capabilities are largely determined by the state of the middle atmosphere. This requires an accurate knowledge of the atmospheric processes at play. More particularly internal gravity waves (GW) pose a challenge to atmospheric modelling because of unresolved processes. Using high-resolution simulation outputs over winter 2020 (20 January–1 March) we present a method to assess the impact of GW on infrasound surface transmission losses across the IMS. We validate the method by comparing simulated GW perturbations to GW lidar observations at Observatoire de Haute-Provence in France, and satellite-based GW energy estimations globally. We perform propagation simulations using atmospheric specifications where GW are filtered out and kept in, respectively. We demonstrate that the largest impact of GW across the IMS is not where GW activity is the largest, but rather where GW activity combines with infrasound waveguides not firmly set in a given direction. In northern winter, the largest variations of transmission losses at 1 Hz due to GW occur in the southern (summer) hemisphere in the direction of the main guide (westward propagation), with average values ranging between 10 and 25 dB in the first shadow zone. It corresponds to an average signal amplification of at least a factor 5 to 15, while this amplification is around 2 to 5 for the main guide in the northern winter hemisphere (eastward propagation).

Multipoint ECG Signal Extraction on Palms and Figures for Human Identification System

The ECG signal contain vital information for cardiac disease which is one of main cause of catastrophic mortality. In conventional technique, the signals are extracted through different points located on the limbs and surface of the chest. The biological impedance of human body eliminates any practical means of making fake copies of the relevant physiological traits. Moreover, this method needs the complex data acquisition system and apparatus as well as inconvenience way of acquisition of signal at the chest. Hence, there is a demand to find out the promising alternative technique to extract the ECG signals in more convenient way with high accuracy. In this paper, the real-time ECG monitor has been developed to extract the signals from the multipoint on the hands and fingers. Placement of the electrodes are done based on the acupressure point on palms and fingers. A total of 120 volunteers were investigated to develop the ECG database as the development dataset. This work uses the template matching algorithm and distance classification method to analyze and to find the best similarities between the developed dataset of ECG biometric signals to improve the identification rate. In order to lessen the noise that was recorded with the ECG signals, signal averaging was used to create ECG databases and templates. The recognition rate rose to 98% accuracy on the development dataset when the prescreening procedure was introduced to create a combined system model. ECG biometric model was created by combining the two models and using the development dataset's results. The algorithm was applied on the entire developed ECG dataset and 96% of accuracy rate was achieved in identification.

Rift obliquity in the Northern Volcanic Zone in Iceland using UAV-based structural data

Rift obliquity is known to be very frequent at divergent plate boundaries and shear components in the opening of fissures have been detected through seismicity in the monitoring of recent rifting events. However, the field structural evidence of obliquity in active rifts for Holocene events is still poorly studied, despite its relevance at both the lithospheric and upper-crust scales.We performed extensive high resolution UAV surveys on Holocene terrains in four areas along the active rift in the Northern Volcanic Zone of Iceland, to map structures and morphotectonic features in detail. We did a kinematic analysis and found an increase of the obliquity from N to S.We further discuss the origins of this obliquity and suggest that the combination of inherited structures reactivation, magmatic intrusions, and topography influence on the propagation of both fractures and magma intrusions can have an impact on the mismatch between rift opening direction and the far-field strain.We conclude that obliquity can be inferred for Holocene rifting events from surface structural data. It is thus fundamental to take into account the existing structural pattern and its (re)activation history in active rift zones to avoid missing out on possible preferential structure reactivation, magma propagation ways, and eruption sites during the monitoring of ongoing rifting events.

Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets.

ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. This research aims at digitizing a dataset of images of ECG records into time series signals and then applying deep learning (DL) techniques on the digitized dataset. State-of-the-art DL techniques are proposed for the classification of the ECG signals into different cardiac classes. Multiple DL models, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a self-supervised learning (SSL)-based model using autoencoders are explored and compared in this study. The models are trained on the dataset generated from ECG plots of patients from various healthcare institutes in Pakistan. First, the ECG images are digitized, segmenting the lead II heartbeats, and then the digitized signals are passed to the proposed deep learning models for classification. Among the different DL models used in this study, the proposed CNN model achieves the highest accuracy of ∼92%. The proposed model is highly accurate and provides fast inference for real-time and direct monitoring of ECG signals that are captured from the electrodes (sensors) placed on different parts of the body. Using the digitized form of ECG signals instead of images for the classification of cardiac arrhythmia allows cardiologists to utilize DL models directly on ECG signals from an ECG machine for the real-time and accurate monitoring of ECGs.

Electrocardiographic approach strategies in patients with Parkinson disease treated with deep brain stimulation.

Deep brain stimulation (DBS) is an interdisciplinary and reversible therapy that uses high-frequency electrical stimulation to correct aberrant neural pathways in motor and cognitive neurological disorders. However, the high frequency of the waves used in DBS can interfere with electrical recording devices (e.g., electrocardiogram, electroencephalogram, cardiac monitor), creating artifacts that hinder their interpretation. The compatibility of DBS with these devices varies and depends on factors such as the underlying disease and the configuration of the neurostimulator. In emergencies where obtaining an electrocardiogram is crucial, the need for more consensus on reducing electrical artifacts in patients with DBS becomes a significant challenge. Various strategies have been proposed to attenuate the artifact generated by DBS, such as changing the DBS configuration from monopolar to bipolar, temporarily deactivating DBS during electrocardiographic recording, applying frequency filters both lower and higher than those used by DBS, and using non-standard leads. However, the inexperience of medical personnel, variability in DBS models, or the lack of a controller at the time of approach limit the application of these strategies. Current evidence on their reproducibility and efficacy is limited. Due to the growing elderly population and the rising utilization of DBS, it is imperative to create electrocardiographic methods that are easily accessible and reproducible for general physicians and emergency services.