Increasing accuracy of pulse arrival time estimation in low frequency recordings

Objective. Wearable devices that measure vital signals using photoplethysmography are becoming more commonplace. To reduce battery consumption, computational complexity, memory footprint or transmission bandwidth, companies of commercial wearable technologies are often looking to minimize the sampling frequency of the measured vital signals. One such vital signal of interest is the pulse arrival time (PAT), which is an indicator of blood pressure. To leverage this non-invasive and non-intrusive measurement data for use in clinical decision making, the accuracy of obtained PAT-parameters needs to increase in lower sampling frequency recordings. The aim of this paper is to develop a new strategy to estimate PAT at sampling frequencies up to 25 Hertz. Approach. The method applies template matching to leverage the random nature of sampling time and expected change in the PAT. Main results. The algorithm was tested on a publicly available dataset from 22 healthy volunteers, under sitting, walking and running conditions. The method significantly reduces both the mean and the standard deviation of the error when going to lower sampling frequencies by an average of 16.6% and 20.2%, respectively. Looking only at the sitting position, this reduction is even larger, increasing to an average of 22.2% and 48.8%, respectively. Significance. This new method shows promise in allowing more accurate estimation of PAT even in lower frequency recordings.</p

Improving the efficiency of the cardiac catheterization laboratories through understanding the stochastic behavior of the scheduled procedures

  Background: In this study, we sought to analyze the stochastic behavior of Catherization Labora­tories (Cath Labs) procedures in our institution. Statistical models may help to improve estimated case durations to support management in the cost-effective use of expensive surgical resources. Methods: We retrospectively analyzed all the procedures performed in the Cath Labs in 2012. The duration of procedures is strictly positive (larger than zero) and has mostly a large mini­mum duration. Because of the strictly positive character of the Cath Lab procedures, a fit of a lognormal model may be desirable. Having a minimum duration requires an estimate of the threshold (shift) parameter of the lognormal model. Therefore, the 3-parameter lognormal model is interesting. To avoid heterogeneous groups of observations, we tested every group-car­diologist-procedure combination for the normal, 2- and 3-parameter lognormal distribution. Results: The total number of elective and emergency procedures performed was 6,393 (8,186 h). The final analysis included 6,135 procedures (7,779 h). Electrophysiology (intervention) pro­cedures fit the 3-parameter lognormal model 86.1% (80.1%). Using Friedman test statistics, we conclude that the 3-parameter lognormal model is superior to the 2-parameter lognormal model. Furthermore, the 2-parameter lognormal is superior to the normal model. Conclusions: Cath Lab procedures are well-modelled by lognormal models. This information helps to improve and to refine Cath Lab schedules and hence their efficient use.

Prehospital risk assessment in patients suspected of non-ST-segment elevation acute coronary syndrome:a systematic review and meta-analysis

OBJECTIVE: To review, inventory and compare available diagnostic tools and investigate which tool has the best performance for prehospital risk assessment in patients suspected of non-ST-segment elevation acute coronary syndrome (NSTE-ACS). METHODS: Systematic review and meta-analysis. Medline and Embase were searched up till 1 April 2021. Prospective studies with patients, suspected of NSTE-ACS, presenting in the primary care setting or by emergency medical services (EMS) were included. The most important exclusion criteria were studies including only patients with ST-elevation myocardial infarction and studies before 1995, the pretroponin era. The primary end point was the final hospital discharge diagnosis of NSTE-ACS or major adverse cardiac events (MACE) within 6 weeks. Risk of bias was evaluated by the Quality Assessment of Diagnostic Accuracy Studies Criteria. MAIN OUTCOME AND MEASURES: Sensitivity, specificity and likelihood ratio of findings for risk stratification in patients suspected of NSTE-ACS. RESULTS: In total, 15 prospective studies were included; these studies reflected in total 26 083 patients. No specific variables related to symptoms, physical examination or risk factors were useful in risk stratification for NSTE-ACS diagnosis. The most useful electrocardiographic finding was ST-segment depression (LR+3.85 (95% CI 2.58 to 5.76)). Point-of-care troponin was found to be a strong predictor for NSTE-ACS in primary care (LR+14.16 (95% CI 4.28 to 46.90) and EMS setting (LR+6.16 (95% CI 5.02 to 7.57)). Combined risk scores were the best for risk assessment in an NSTE-ACS. From the combined risk scores that can be used immediately in a prehospital setting, the PreHEART score, a validated combined risk score for prehospital use, derived from the HEART score (History, ECG, Age, Risk factors, Troponin), was most useful for risk stratification in patients with NSTE-ACS (LR+8.19 (95% CI 5.47 to 12.26)) and for identifying patients without ACS (LR-0.05 (95% CI 0.02 to 0.15)). DISCUSSION: Important study limitations were verification bias and heterogeneity between studies. In the prehospital setting, several diagnostic tools have been reported which could improve risk stratification, triage and early treatment in patients suspected for NSTE-ACS. On-site assessment of troponin and combined risk scores derived from the HEART score are strong predictors. These results support further studies to investigate the impact of these new tools on logistics and clinical outcome. FUNDING: This study is funded by ZonMw, the Dutch Organisation for Health Research and Development. TRIAL REGISTRATION NUMBER: This meta-analysis was published for registration in PROSPERO prior to starting (CRD York, CRD42021254122).</p

Mapping for acute transvenous phrenic nerve stimulation study (MAPS Study)

Background: Central sleep apnea syndrome, correlated with the occurrence of heart failure, is characterized by periods of insufficient ventilation during sleep. This acute study in 15 patients aims to map the venous system and determine if diaphragmatic movement can be achieved by phrenic nerve stimulation at various locations within the venous system. Methods: Subjects underwent a scheduled catheter ablation procedure. During the procedural waiting time, one multielectrode electrophysiology catheter was subsequently placed at the superior and inferior vena cava and the junctions of the left jugular and left brachiocephalic vein and right jugular and right brachiocephalic vein, for phrenic nerve stimulation (1–2 seconds ON/2–3 seconds OFF, 40 Hz, pulse width 210 μs). Diaphragmatic movement was assessed manually and by a breathing mask. During a follow-up assessment between 2 and 4 weeks postprocedure, occurrence of adverse events was assessed. Results: In all patients diaphragmatic movement was induced at one or more locations using a median threshold of at least 2 V and maximally 7.5 V (i.e., e 3.3 mA, 14.2 mA). The lowest median current to obtain diaphragmatic stimulation without discomfort was found for the right brachiocephalic vein (4.7 mA). In 12/15 patients diaphragmatic movement could be induced without any discomfort, but in three patients hiccups occurred. Conclusion: Diaphragmatic stimulation from the brachiocephalic and caval veins is feasible. Potential side effects should be eliminated by adapting the stimulation pattern. This information could be used to design a catheter, combining cardiac pacing with enhancing diaphragm movement during a sleep apnea episode

Atrial fibrillation monitoring with wrist-worn photoplethysmography-based wearables: State-of-the-art review

Early detection and diagnosis of atrial fibrillation (AF) is essential in order to prevent stroke and other severe health consequences. The challenges in diagnosing AF arise from its intermittent and asymptomatic nature. Wrist-worn devices that use monitoring based on photoplethysmography have been proposed recently as a possible solution because of their ability to monitor heart rate and rhythm for long periods of time at low cost. Long-term continuous monitoring with implantable devices has been shown to increase the percentage of detected AF episodes, but the additional value of wrist-worn devices has yet to be determined. In this review, we present the state of the art in AF detection with wrist-worn devices, discuss the potential of the technology and current knowledge gaps, and propose directions for future research. The state-of-the-art methods show excellent accuracy for AF detection. However, most of the studies were conducted in hospital settings, and more studies showing the accuracy of the technology for ambulatory long-term monitoring are needed. Objective comparison of results and methodologies among different studies currently is difficult due to the lack of adequate public datasets

Continuous cardiac monitoring around Atrial fibrillation ablation: insights on clinical classifications and end points

Background: Atrial fibrillation (AF) is an arrhythmia that can be difficult to identify and classify with short-term monitoring. However, current standard of practice requires only short-term monitoring to determine AF classifications and identify symptom-arrhythmia correlations prior to AF ablation procedures. Insertable cardiac monitors (ICMs) offer continuous arrhythmia monitoring, which could lead to a more accurate measurement of AF burden than standard of practice. Methods: This analysis focused on 121 patients enrolled in the LINQ Usability Study indicated for an AF ablation. Patients were followed for up to 1 year after ICM insertion. Clinical AF classifications were made by physicians prior to ICM implantation based on available clinical information. Device-detected AF burden and maximum daily burden were collected from device interrogations and remote transmissions. Device AF classifications were determined by categorizing the AF burden based on guidelines. Results: Agreement between clinical and device AF classifications preablation was poor (48.3%, N = 58). The strongest agreement was in the paroxysmal AF group but still was only 61.8%. Furthermore, device-detected preablation AF burden led to the decision to defer AF ablation procedures in 16 (13.2%) patients. The median AF burden in patients with ≥6 months follow-up postablation (n = 71) was reduced from 7.8% (interquartile range [IQR]: 0–32.1%) to 0% (IQR: 0–0.7%). Conclusions: ICM monitoring to determine AF burden pre- and post-AF ablation may have clinical utility for management of ablation candidates through more accurate AF classification and guiding treatment decisions

Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning

Medical instrument segmentation in 3D ultrasound is essential for image-guided intervention. However, to train a successful deep neural network for instrument segmentation, a large number of labeled images are required, which is expensive and time-consuming to obtain. In this article, we propose a semi-supervised learning (SSL) framework for instrument segmentation in 3D US, which requires much less annotation effort than the existing methods. To achieve the SSL learning, a Dual-UNet is proposed to segment the instrument. The Dual-UNet leverages unlabeled data using a novel hybrid loss function, consisting of uncertainty and contextual constraints. Specifically, the uncertainty constraints leverage the uncertainty estimation of the predictions of the UNet, and therefore improve the unlabeled information for SSL training. In addition, contextual constraints exploit the contextual information of the training images, which are used as the complementary information for voxel-wise uncertainty estimation. Extensive experiments on multiple ex-vivo and in-vivo datasets show that our proposed method achieves Dice score of about 68.6%-69.1% and the inference time of about 1 sec. per volume. These results are better than the state-of-the-art SSL methods and the inference time is comparable to the supervised approaches