Continuous Electrocardiography Research Articles

Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert–Huang and wavelet transforms with explainable vision transformer and CNN models

Background and Objective:Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. However, current methods predict SCD only a few minutes before its onset, limiting intervention time. This study aims to develop a deep learning-based model for the early prediction of SCD using electrocardiography (ECG) signals. Methods:A multimodal explainable deep learning-based model is developed to analyze ECG signals at discrete intervals ranging from 5 to 30 min before SCD onset. The raw ECG signals, 2D scalograms generated through wavelet transform and 2D Hilbert spectrum generated through Hilbert–Huang transform (HHT) of ECG signals were applied to multiple deep learning algorithms. For raw ECG, a combination of 1D-convolutional neural networks (1D-CNN) and long short-term memory networks were employed for feature extraction and temporal pattern recognition. Besides, to extract and analyze features from scalograms and Hilbert spectra, Vision Transformer (ViT) and 2D-CNN have been used. Results:The developed model achieved high performance, with accuracy, precision, recall and F1-score of 98.81%, 98.83%, 98.81%, and 98.81% respectively to predict SCD onset 30 min in advance. Further, the proposed model can accurately classify SCD patients and normal controls with 100% accuracy. Thus, the proposed method outperforms the existing state-of-the-art methods. Conclusions:The developed model is capable of capturing diverse patterns on ECG signals recorded at multiple discrete time intervals (at 5-minute increments from 5 min to 30 min) prior to SCD onset that could discriminate for SCD. The proposed model significantly improves early SCD prediction, providing a valuable tool for continuous ECG monitoring in high-risk patients.

Effects of intubation and hypoxemia on intraventricular hemorrhage in preterm infants during the first week: An observational study

BackgroundPreterm infants with intraventricular hemorrhage (IVH) are at a risk of developing neurodevelopmental disabilities. Few studies have examined the effects of oxygen saturation (SpO2) changes and intubation procedures on the risk of IVH. ObjectivesWe examined the effects of intubation and the rates of three thresholds of hypoxemia on the occurrence of IVH in preterm infants during their first week in the neonatal intensive care unit (NICU). MethodsIn this prospective observational cohort study, preterm infants with a gestational age (GA) of <37 weeks were included from two Level III NICUs in Taiwan. Continuous electrocardiography was used to monitor SpO2 changes, and cranial ultrasonography was used to monitor IVH. Thresholds of hypoxemia (SpO2 levels of <80 %, <85 %, and <90 %) were screened by digitally sampling data at 10-s intervals. Generalized estimating equations were used with logistic regression to analyze the effects of intubation and the rates of the three thresholds of hypoxemia on the risk of IVH during the first week after birth. ResultsIn all preterm infants (N = 73), the mean GA was 31.55 weeks, and the mean birth weight was 1508.86 g. Intubation within 3 days of birth, duration of mechanical ventilation and oxygen use, hypoxemia rate, and maternal use of magnesium sulfate before and during delivery were significantly associated with IVH. A multivariate analysis revealed that intubation was a key factor associated with the occurrence of IVH across different thresholds of hypoxemia (p = 0.004). ConclusionsAlthough the rate of hypoxemia, duration of mechanical ventilation and oxygen use, and maternal use of magnesium sulfate were significantly associated with IVH, intubation within 3 days of birth was the key factor responsible for increased IVH risk.

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&lt;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&lt;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.

Low-intensity focused ultrasound to the insula differentially modulates the heartbeat-evoked potential: A proof-of-concept study

ObjectiveThe heartbeat evoked potential (HEP) is a brain response time-locked to the heartbeat and a potential marker of interoceptive processing that may be generated in the insula and dorsal anterior cingulate cortex (dACC). Low-intensity focused ultrasound (LIFU) can selectively modulate sub-regions of the insula and dACC to better understand their contributions to the HEP. MethodsHealthy participants (n = 16) received stereotaxically targeted LIFU to the anterior insula (AI), posterior insula (PI), dACC, or Sham at rest during continuous electroencephalography (EEG) and electrocardiography (ECG) recording on separate days. Primary outcome was HEP amplitudes. Relationships between LIFU pressure and HEP changes and effects of LIFU on heart rate and heart rate variability (HRV) were also explored. ResultsRelative to sham, LIFU to the PI, but not AI or dACC, decreased HEP amplitudes; PI effects were partially explained by increased LIFU pressure. LIFU did not affect heart rate or HRV. ConclusionsThese results demonstrate the ability to modulate HEP amplitudes via non-invasive targeting of key interoceptive brain regions. SignificanceOur findings have implications for the causal role of these areas in bottom-up heart-brain communication that could guide future work investigating the HEP as a marker of interoceptive processing in healthy and clinical populations.

Cardiac autonomic responses in relation to cognitive workload during simulated military flight

Understanding the operator's cognitive workload is crucial for efficiency and safety in human–machine systems. This study investigated how cognitive workload modulates cardiac autonomic regulation during a standardized military simulator flight. Military student pilots completed simulated flight tasks in a Hawk flight simulator. Continuous electrocardiography was recorded to analyze time and frequency domain heart rate variability (HRV). After the simulation, a flight instructor used a standardized method to evaluate student pilot's individual cognitive workload from video-recorded flight simulator data. Results indicated that HRV was able to differentiate flight phases that induced varying levels of cognitive workload; an increasing level of cognitive workload caused significant decreases in many HRV variables, mainly reflecting parasympathetic deactivation of cardiac autonomic regulation. In conclusion, autonomic physiological responses can be used to examine reactions to increased cognitive workload during simulated military flights. HRV could be beneficial in assessing individual responses to cognitive workload and pilot performance during simulator training.

Technical article: Overview of hospital-based data capture systems that acquire continuous ECG and physiologic data

Data capture systems that acquire continuous hospital-based electrocardiographic (ECG) and physiologic (vital signs) data can foster robust research (i.e., large sample sizes from consecutive patients). However, the application of these systems and the data generated are complex and requires careful human oversight to ensure that accurate and high quality data are procured. This technical article will describe two different data capture systems created by our research group designed to examine false alarms associated with alarm fatigue in nurses. The following aspects regarding these data capture systems will be discussed: (1) history of development; (2) summary of advantages, challenges, and important considerations; (3) their use in research; (4) their use in clinical care; and (5) future developments.

Fabric and Fiber-Integrated, Washable Hydrogel Electrodes for Clinically Accurate Sleep Monitoring in-Home

Longitudinal tracking of sleep metrics is important for detecting and managing various diseases, spanning cardiorespiratory disorders to dementia. However, at present, sleep monitoring primarily occurs in specialized medical facilities that are not conducive to longterm studies. In-home solutions either compromise user comfort or signal accuracy in tracking sleep variables and have not yet provided reliable longitudinal data. We believe that human-centered design of multimodal, low-form-factor, comfortable sensing systems is needed for this increasingly-important area of preventative health monitoring. Our lab developed a suite of fabric-based and/or garment-integrated sensors and sensing modalities that accurately and reliably extract important sleep metrics, such as posture, cardiorespiratory signals, eye movement and brain activity, over the course of an eight-hour sleep session. Our garment-integrated sensing arrays do not require a medical professional for on-body positioning, maintain their function independent of sizing and fit on individual users, and can be laundered or cleaned with antibacterial wipes without compromising its varied sensing elements. Here, we describe one of these two key sensors, a fabric or fiber-based electrochemical hydrogel electrode that enables wearable biopotential monitoring, including electrocardiography (ECG), eletrooculography (EOG) and electroencephalography (EEG), from which the aforementioned sleep metrics can be recorded and quantified with clinical precision and accuracy. The hydrogel is fabricated as a coating on either commodity fabrics or threads using a low-temperature reactive deposition process, termed photoinitiated chemical vapor deposition (piCVD), during which hydrogel monomers react with a photoinitator in the vapor phase to create a polymeric hydrogel coating directly on the surface of fabrics or threads that have been temporarily surface modified with a skin-safe electrolyte gel. The resulting composite hydrogel coating isolated from this operation is ionically conductive, can be reversibly swelled in water and dried without losing ionic conductivity, and transduces the ionic signals (fluxes) experienced by the body into an electrical signal that can be captured by any commercial ECG, EOG or EEG monitoring instrument. An array of these fabric or thread-based hydrogel electrodes can be integrated into readily-available garments using simple cut-sew techniques and these garments can then be worn throughout the night to effect continuous ECG, EOG and EEG monitoring at home. Design elements of a custom-made circuit board that allows simultaneous capture of these signals will be described. Results from an ongoing user study of >50 adults will be presented to validate the clinical precision and accuracy of these garment sensor for sleep quality monitoring.

Post-operative atrial fibrillation after noncardiac surgery: diagnostic role of ambulatory rhythm monitoring for recurrent arrhythmia after hospital discharge

Abstract Introduction Postoperative atrial fibrillation (POAF) after noncardiac surgery constitutes 10-15% of all cases of incident atrial fibrillation (AF) in the community and has been associated with an increased risk of stroke. The risk of subsequent AF among patients with POAF is not well defined. Purpose To evaluate the role of systematic 30-day electrocardiographic (ECG) monitoring in the detection of AF recurrence after POAF. Methods In a prospective interventional study, patients who underwent major noncardiac surgeries and had new AF documented by ECG in the post-operative period between May 2022 and April 2023 were identified. For these patients, a 30-day ambulatory ECG monitor was recommended upon discharge, with final ordering deferred to the primary inpatient teams. Rates of recurrent AF of any duration detected by 30-day monitoring plus routine care versus routine care alone after the surgery were compared. Results Eighty-five patients with new-onset POAF were included (mean age 72 years, male 67%). Of these, 35 (41%) patients underwent up to 30 days [median 23 (IQR 6-27) days] of continuous ECG monitoring after hospital discharge. There were no significant differences in baseline characteristics between patients who did or did not undergo monitoring (Table 1). During median 281 (IQR 197-385) days of follow-up, recurrent AF was documented in 14/35 (40%) patients in the monitoring group as opposed to 7/50 (14%) patients in the usual care group (log-rank p=0.003, Figure 1). The median time from day of surgery until documented AF recurrence was 25 (IQR 6-53) days in the monitoring group and 66 (IQR 25-145) in the usual care group. Overall, 81% of recurrent AF events in both groups were detected in the first 3 months after surgery. The median burden of AF in the cardiac monitoring group was 7% (IQR 1-15), with the longest AF event lasting a median of 6 (IQR 2-18) hours. Anticoagulation for AF was prescribed in 24/64 (37%) patients with POAF without recurrence and in 16/21 (76%) patients with recurrent AF. Conclusion Recurrent AF following POAF is common and detected most frequently in the first 3 months after noncardiac surgery. Systematic ambulatory rhythm monitoring is associated with a higher rate of AF detection following hospital discharge and may identify a patient subgroup who might benefit from anticoagulation therapy.Table 1Figure 1

Continuous peripheral electrical nerve stimulation improves cardiac function via autonomic nerve regulation in MI rats

BackgroundPeripheral electrical nerve stimulation (PENS) reportedly improves cardiac function after myocardial ischemia (MI) by rebalancing the cardiac autonomic nervous system. The dynamic and continuous influence of PENS on autonomic and cardiac function based on cardiac self-repair is not well understood. ObjectivesThis study aimed to explore the relationship between autonomic nervous balance and functional cardiac repair after MI and to clarify the optimal acupoint selection and time course for PENS. MethodsThe activities of the superior cervical cardiac sympathetic nerve and vagus nerve were recorded to evaluate the autonomic tone directly. The pressure-volume loop system was used for left ventricular diastolic and systolic function. Noninvasive continuous electrocardiography and echocardiography were performed to analyze heart rate, heart rate variability, and left ventricular function. The effect of continuous PENS (cPENS) or instant PENS (iPENS) on autonomic and cardiac indications was tested. ResultsSympathetic nerve activity and vagus nerve activity increased as compensatory self-regulation on days 7 and 14 post-MI, followed by an imbalance of autonomic tone and cardiac dysfunction on day 28. cPENS at acupoint PC6 maintained autonomic hyperexcitability, improved myocardial systolic and diastolic abilities, and reduced myocardial fibrosis on day 28 post-MI, whereas cPENS at acupoint ST36 had a limited effect. Both iPENS at PC6 and ST36 improved the autonomic and cardiac function of rats in the cPENS groups. ConclusionRats showed autonomic fluctuations and cardiac dysfunction 28 days post-MI. cPENS produced sympathomimetic action to sustain cardiac self-compensation, but with acupoint specificity. On the basis of cPENS, iPENS evoked autonomic regulation and cardiac benefits without acupoint differentiation.

Effects of bradycardia, hypoxemia and early intubation on bronchopulmonary dysplasia in very preterm infants: An observational study

BackgroundBronchopulmonary dysplasia (BPD) is the most common pulmonary complication in preterm infants. ObjectivesThe study aimed to explore the effects of bradycardia, hypoxemia, and early intubation on BPD in very preterm infants. MethodsThis is a prospective observational cohort study. Preterm infants with a mean gestational age of 28.67 weeks were recruited from two level III neonatal intensive care units (NICUs) in Taiwan. Continuous electrocardiography was used to monitor heart rates and oxygen saturation (SpO2). Infants were monitored for heart rates of <100 beats per minute and SpO2 levels of <90 % lasting for 30 s. Generalized estimating equations were used to analyze the effects of bradycardia, hypoxemia, and early intubation on BPD in very preterm infants. Model fit was visually assessed using receiver operating characteristic curve analysis. ResultsBradycardia, hypoxemia, and early intubation significantly increased the odds of BPD among the preterm infants (N = 39) during NICU stay; the odds ratios for bradycardia, hypoxemia, and early intubation for BPD versus non-BPD were 1.058, 1.013, and 29.631, respectively (all p < 0.05). A model combining bradycardia, hypoxemia, and early intubation accurately predicted BPD development (area under the curve = 0.919). ConclusionsBradycardia, hypoxemia, and early intubation significantly increased the odds of BPD among very preterm infants during NICU stay. The model combining bradycardia, hypoxemia, and early intubation accurately predicted BPD development.

Evaluation of an algorithm-guided photoplethysmography for atrial fibrillation burden using a smartwatch.

Wearable devices based on the PPG algorithm can detect atrial fibrillation (AF) effectively. However, further investigation of its application on long-term, continuous monitoring of AF burden is warranted. The performance of a smartwatch with continuous photoplethysmography (PPG) and PPG-based algorithms for AF burden estimation was evaluated in a prospective study enrolling AF patients admitted to Beijing Anzhen Hospital for catheter ablation from September to November 2022. A continuous Electrocardiograph patch (ECG) was used as the reference device to validate algorithm performance for AF detection in 30-s intervals. A total of 578669 non-overlapping 30-s intervals for PPG and ECG each from 245 eligible patients were generated. An interval-level sensitivity of PPG was 96.3% (95% CI 96.2%-96.4%), and specificity was 99.5% (95% CI 99.5%-99.6%) for the estimation of AF burden. AF burden estimation by PPG was highly correlated with AF burden calculated by ECG via Pearson correlation coefficient (R2 = 0.996) with a mean difference of -0.59 (95% limits of agreement, -7.9% to 6.7%). The subgroup study showed the robust performance of the algorithm in different subgroups, including heart rate and different hours of the day. Our results showed the smartwatch with an algorithm-based PPG monitor has good accuracy and stability in continuously monitoring AF burden compared with ECG patch monitors, indicating its potential for diagnosing and managing AF.

Continuous Atrial Fibrillation Monitoring From Photoplethysmography: Comparison Between Supervised Deep Learning and Heuristic Signal Processing

BackgroundContinuous monitoring for atrial fibrillation (AF) using photoplethysmography (PPG) from smartwatches or other wearables is challenging due to periods of poor signal quality during motion or suboptimal wearing. As a result, many consumer wearables sample infrequently and only analyze when the user is at rest, which limits the ability to perform continuous monitoring or to quantify AF. ObjectivesThis study aimed to compare 2 methods of continuous monitoring for AF in free-living patients: a well-validated signal processing (SP) heuristic and a convolutional deep neural network (DNN) trained on raw signal. MethodsWe collected 4 weeks of continuous PPG and electrocardiography signals in 204 free-living patients. Both SP and DNN models were developed and validated both on holdout patients and an external validation set. ResultsThe results show that the SP model demonstrated receiver-operating characteristic area under the curve (AUC) of 0.972 (sensitivity 99.6%, specificity: 94.4%), which was similar to the DNN receiver-operating characteristic AUC of 0.973 (sensitivity 92.2, specificity: 95.5%); however, the DNN classified significantly more data (95% vs 62%), revealing its superior tolerance of tracings prone to motion artifact. Explainability analysis revealed that the DNN automatically suppresses motion artifacts, evaluates irregularity, and learns natural AF interbeat variability. The DNN performed better and analyzed more signal in the external validation cohort using a different population and PPG sensor (AUC, 0.994; 97% analyzed vs AUC, 0.989; 88% analyzed). ConclusionsDNNs perform at least as well as SP models, classify more data, and thus may be better for continuous PPG monitoring.

Noninvasive neuromodulation of subregions of the human insula differentially affect pain processing and heart-rate variability: a within-subjects pseudo-randomized trial.

The insula is an intriguing target for pain modulation. Unfortunately, it lies deep to the cortex making spatially specific noninvasive access difficult. Here, we leverage the high spatial resolution and deep penetration depth of low-intensity focused ultrasound (LIFU) to nonsurgically modulate the anterior insula (AI) or posterior insula (PI) in humans for effect on subjective pain ratings, electroencephalographic (EEG) contact heat-evoked potentials, as well as autonomic measures including heart-rate variability (HRV). In a within-subjects, repeated-measures, pseudo-randomized trial design, 23 healthy volunteers received brief noxious heat pain stimuli to the dorsum of their right hand during continuous heart-rate, electrodermal, electrocardiography and EEG recording. Low-intensity focused ultrasound was delivered to the AI (anterior short gyrus), PI (posterior longus gyrus), or under an inert Sham condition. The primary outcome measure was pain rating. Low-intensity focused ultrasound to both AI and PI similarly reduced pain ratings but had differential effects on EEG activity. Low-intensity focused ultrasound to PI affected earlier EEG amplitudes, whereas LIFU to AI affected later EEG amplitudes. Only LIFU to the AI affected HRV as indexed by an increase in SD of N-N intervals and mean HRV low-frequency power. Taken together, LIFU is an effective noninvasive method to individually target subregions of the insula in humans for site-specific effects on brain biomarkers of pain processing and autonomic reactivity that translates to reduced perceived pain to a transient heat stimulus.

Exercise-Induced Electrocardiography Changes in Pulmonary Arterial Hypertension

Cardiopulmonary exercise testing (CPET) is an important tool in assessing the functional status of patients with pulmonary arterial hypertension (PAH). During CPET, continuous electrocardiography (ECG) is used as a marker of exercise-induced ischemia or arrhythmia. We hypothesize that ECG changes with exercise may be an early indicator of clinical worsening in PAH and could predict adverse outcomes. Clinical, hemodynamic, and CPET data of 155 children and young adult patients with PAH who underwent CPET between 2012 and 2019 in our pulmonary hypertension (PH) center were included in this retrospective analysis. ECGs were analyzed for ST depressions and T-wave inversions, along with coincident hemodynamic data. These data were correlated with adverse outcomes divided into 2 categories: severe worsening (death or receiving lung transplant) and mild to moderate worsening (PAH medication escalation, hospitalization, shunt creation, or listing for lung transplant). The median age was 19years (range 7 to 40years), 69% were female, and the average follow-up time was 5years (range 1 to 8years). A total of 63 patients (41%) had at least 1 adverse outcome. A total of 39 patients (25%) demonstrated significant ST-T-wave changes with exercise. Patients with ST-T-wave changes were 20% more likely to die or need lung transplant than those without. The multiple linear regression found that ST-T-wave changes were a predictor of elevated mean pulmonary arterial pressure (mPAP) found on catheterization (R=0.489, p=0.003), although not of pulmonary vascular resistance index (R=-0.112, p=0.484). An mPAP of 55mm Hg was the most sensitive and specific point in identifying when ST-T-wave changes with exercise begin to appear. In conclusion, ST-T-wave changes on exercise ECG are significantly associated with adverse outcomes in PH in a medium-term follow-up study, and the presence of ST-T-wave changes correlates with higher mPAP. These ECG changes with exercise may be used as early indicators of clinical worsening in PH and predictors of adverse outcomes.

Hyperacute autonomic and cortical function recovery following cardiac arrest resuscitation in a rodent model.

There is a complex interaction between nervous and cardiovascular systems, but sparse data exist on brain-heart electrophysiological responses to cardiac arrest resuscitation. Our aim was to investigate dynamic changes in autonomic and cortical function during hyperacute stage post-resuscitation. Ten rats were resuscitated from 7-min cardiac arrest, as indicators of autonomic response, heart rate (HR), and its variability (HRV) were measured. HR was monitored through continuous electrocardiography, while HRV was assessed via spectral analysis, whereby the ratio of low-/high-frequency (LF/HF) power indicates the balance between sympathetic/parasympathetic activities. Cortical response was evaluated by continuous electroencephalography and quantitative analysis. Parameters were quantified at 5-min intervals over the first-hour post-resuscitation. Neurological outcome was assessed by Neurological Deficit Score (NDS, range 0-80, higher = better outcomes) at 4-h post-resuscitation. A significant increase in HR was noted over 15-30 min post-resuscitation (p < 0.01 vs.15-min, respectively) and correlated with higher NDS (rs = 0.56, p < 0.01). LF/HF ratio over 15-20 min was positively correlated with NDS (rs = 0.75, p < 0.05). Gamma band power surged over 15-30 min post-resuscitation (p < 0.05 vs. 0-15 min, respectively), and gamma band fraction during this period was associated with NDS (rs ≥0.70, p < 0.05, respectively). Significant correlations were identified between increased HR and gamma band power during 15-30 min (rs ≥0.83, p < 0.01, respectively) and between gamma band fraction and LF/HF ratio over 15-20 min post-resuscitation (rs = 0.85, p < 0.01). Hyperacute recovery of autonomic and cortical function is associated with favorable functional outcomes. While this observation needs further validation, it presents a translational opportunity for better autonomic and neurologic monitoring during early periods post-resuscitation to develop novel interventions.

157 Budget impact analysis of Holter monitors provision in an outpatient clinic

Abstract Background Outpatient clinic attendees (aged &amp;gt;64) commonly present with falls, dizziness, syncope and palpitations. The most common non-invasive investigation ordered includes continuous ambulatory electrocardiography monitoring, namely the Holter monitor. The clinic is outsourcing Holter monitor requests to the Cardiology Department, with a long waiting list. If the patient wishes the test to be done sooner in a private hospital, it costs between 160 to 295 euros, covered by the patient’s private healthcare insurance. Methods A budget analysis was carried out to further evaluate by provisioning 5 Holter monitors in the outpatient clinic to quicken patient turnover and reduce the cardiology department waiting list. The intervention scope was limited to the designated clinic of approximately 450 patients a month. The intervention is not for generating revenue for the clinic. The budget analysis doesn’t include sensitivity analysis from the best-case towards worst-case scenarios of Holter frequency usage and duration of Holter monitoring. The objectives of the proposed change are to lessen the turnaround time by 20% for the patients from being assessed until discharge from the clinic without having delayed investigation of the Holter monitor, to have a timely and accurate diagnosis of suspected arrhythmia and the appropriate management within the new intervention. Results The decision tree, utilising the Decision analytic modelling, resource identification, cost implication, estimated changes including 2-hour staff training, capital, year one to year five costs, potential cost savings year-on-year, cost-per-wear comparison against the private hospital provider, pre and post-intervention scenario analysis were constructed. Conclusion The budget impact analysis designed a simple cost calculator to add additional tests of Holter monitors that rely on the decision model approach. It shows a potential cost saving within a five-year horizon and increases the number of patients’ attendance by optimising patient turnover. Apart from the cost and organisation, there are other positive impacts towards financial and clinical areas, ensuring high-quality service for the population.

SRECG: ECG Signal Super-Resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification

A combination of cloud-based deep learning (DL) al-gorithms with portable/wearable (P/W) devices has been devel-oped as a smart heath care system to support automatic cardiac arrhythmias (CAs) classification using electrocardiography (ECG). However, long-term and continuous ECG monitoring is challenging because of limitations of batteries and transmission bandwidth of P/W devices while incorporated with consumer elec-tronics (CE). A feasible approach to address this challenge is to decrease sampling rates. However, low sampling rates lead to low-resolution signals that hinder the CAs classification performance. In this study, we propose a DL-based ECG signal super-resolution framework (called SRECG) to enhance low-resolution ECG sig-nals by jointly considering the accuracies when applied to the DL-based high-resolution multiclass classifier (HMC) of CAs. In our experiments, we downsampled the ECG signals from the CPSC2018 dataset and evaluated their HMC accuracies with and without the SRECG. Experimental results show that SRECG can well improve the HMC accuracies as compared to traditional in-terpolation methods. Moreover, approximately half of the CAs classification accuracies of HMC were maintained within the en-hanced ECG signals by SRECG. The promising results confirm that SRECG can be suitably used to enhance low-resolution ECG signals from P/W devices with CE to improve their cloud-based HMC performances.

A comprehensive wireless neurological and cardiopulmonary monitoring platform for pediatrics.

Neurodevelopment in the first 10 years of life is a critical time window during which milestones that define an individual's functional potential are achieved. Comprehensive multimodal neurodevelopmental monitoring is particularly crucial for socioeconomically disadvantaged, marginalized, historically underserved and underrepresented communities as well as medically underserved areas. Solutions designed for use outside the traditional clinical environment represent an opportunity for addressing such health inequalities. In this work, we present an experimental platform, ANNE EEG, which adds 16-channel cerebral activity monitoring to the existing, USA FDA-cleared ANNE wireless monitoring platform which provides continuous electrocardiography, respiratory rate, pulse oximetry, motion, and temperature measurements. The system features low-cost consumables, real-time control and streaming with widely available mobile devices, and fully wearable operation to allow a child to remain in their naturalistic environment. This multi-center pilot study successfully collected ANNE EEG recordings from 91 neonatal and pediatric patients at academic quaternary pediatric care centers and in LMIC settings. We demonstrate the practicality and feasibility to conduct electroencephalography studies with high levels of accuracy, validated via both quantitative and qualitative metrics, compared against gold standard systems. An overwhelming majority of parents surveyed during studies indicated not only an overall preference for the wireless system, but also that its use would improve their children's physical and emotional health. Our findings demonstrate the potential for the ANNE system to perform multimodal monitoring to screen for a variety of neurologic diseases that have the potential to negatively impact neurodevelopment.

Skin‐Adherent Elastomer‐Hydrogel Patch for Continuous 12‐Lead Cardiac Ambulatory Monitoring during Physical Activities

AbstractContinuous electrocardiography (ECG) monitoring is a necessity to prevent the sudden occurrence of cardiac events and premature death. Wearable bioelectronics provides the opportunity to realize noninvasive 12‐lead ambulatory monitoring for cardiovascular disease (CVD) detection and diagnosis. This work introduces a 12‐lead ECG patch that eliminates the issues associated with compliance, skin damage, and user‐discomfort encountered in conventional ECG sensors. The patch is safe, stretchable, and skin‐adherent with an adaptable design that allows for user‐friendly ECG monitoring. These can be attributed to the unique 3‐bridge patch structure, embedded liquid metal circuitry, and the wet‐adhesive polyacrylamide‐polydopamine‐tannic acid (PAM‐PDA‐TA) hydrogel. The hydrogel displays improved wet adhesiveness and biosafety for long‐term usage on deforming skin. The patch can acquire clean and consistent ECG signals with a higher signal‐to‐noise (SNR) ratio compared to commercial electrodes during both static and dynamic monitoring. The outstanding performance of the patch ascertains its potential for daily continuous ECG monitoring and remote diagnosis.

Premature depolarisations in horses competing in United States Eventing Association and Fédération Equestre Internationale-sanctioned 3-day events.

Injuries and sudden death during the cross-country (XC) phase of eventing have raised interest in the frequency and types of cardiac arrhythmias occurring in these equine athletes. To characterise the frequency and types of rhythm disturbances and examine possible risk factors for premature depolarisations (PDs) occurring during the XC phase of United States Eventing Association (USEA) and Fédération Equestre Internationale (FEI)-sanctioned events. Prospective, cross-sectional study. Continuous electrocardiographic (ECG) recordings were obtained from horses prior to, during and immediately following the XC competition. Physiological rhythms pre-XC were included in the arrhythmia group for analysis. The frequency of PDs was calculated for all horses, as well as by division. PDs were categorised by complexity (singles, couplets, triplets, complex) and variably grouped for statistical analysis. Multiple logistic regression was used to identify risk factors for the presence of specific cardiac arrhythmia groupings during XC. PDs were identified in 42/75 horses (56% [45, 67]) during XC. Horses competing in the upper divisions of eventing had higher odds for having any PD during XC compared to the lower divisions (OR = 17.5 [4.3, 72.01], p = 0.006). The amount of time the heart rate (HR) was greater than 199 beats per minute (BPM) was associated with more complex arrhythmias (triplets, salvos and tachyarrhythmias) (OR = 1.01 [1.0, 1.02], p = 0.005). An arrhythmia at rest (physiological or PD) was associated with arrhythmia in the early recovery period (OR = 3.5 [1.1, 10.8], p = 0.03). Convenience sampling and technical challenges of continuous ECG recordings under competition settings limited the number of enrollments. PDs were seen in a high percentage of horses during the XC competition. Upper levels and time the HR was greater than 199 BPM were related to PD presence and complexity.