Cardiac Monitoring in the Emergency Department

Real-time arrhythmia detection using convolutional neural networks

Cardiovascular diseases, such as heart attack and congestive heart failure, are the leading cause of death both in the United States and worldwide. The current medical practice for diagnosing cardiovascular diseases is not suitable for long-term, out-of-hospital use. A key to long-term monitoring is the ability to detect abnormal cardiac rhythms, i.e., arrhythmia, in real-time. Most existing studies only focus on the accuracy of arrhythmia classification, instead of runtime performance of the workflow. In this paper, we present our work on supporting real-time arrhythmic detection using convolutional neural networks, which take images of electrocardiogram (ECG) segments as input, and classify the arrhythmia conditions. To support real-time processing, we have carried out extensive experiments and evaluated the computational cost of each step of the classification workflow. Our results show that it is feasible to achieve real-time arrhythmic detection using convolutional neural networks. To further demonstrate the generalizability of this approach, we used the trained model with processed data collected by a customized wearable sensor from a lab setting, and the results shown that our approach is highly accurate and efficient. This research provides the potentials to enable in-home real-time heart monitoring based on 2D image data, which opens up opportunities for integrating both machine learning and traditional diagnostic approaches.

Identification of Heart Arrhythmias by Utilizing a Deep Learning Approach of the ECG Signals on Edge Devices

Accurate and timely detection of cardiac arrhythmias is crucial in reducing treatment times and, ultimately, preventing serious life-threatening complications, such as the incidence of a stroke. This becomes of major importance, especially during the diagnostic process, where there is limited access to cardiologists, such as in hospital emergency departments. The proposed lightweight solution uses a novel classifier, consistently designed and implemented, based on a 2D convolutional neural network (CNN) and properly optimized in terms of storage and computational complexity, thus making it suitable for deployment on edge devices capable of operating in hospital emergency departments, providing privacy, portability, and constant operation. The experiments on the MIT-BIH arrhythmia database, show that the proposed 2D-CNN obtains an overall accuracy of 95.3%, mean sensitivity of 95.27%, mean specificity of 98.82%, and a One-vs-Rest ROC-AUC score of 0.9934. Moreover, the results and metrics based on the NVIDIA® Jetson Nano™ platform show that the proposed method achieved excellent performance and speed, and would be particularly useful in the clinical practice for continuous real-time (RT) monitoring scenarios.

Effects of Electrocardiographic Monitoring Education on Nurses' Confidence and Psychological Stress: An Online Cross-Sectional Survey in Japan

We aimed to investigate the association between nurses’ electrocardiographic (ECG) monitoring education and their confidence and psychological stress regarding ECG monitoring. In 2019, a web-based cross-sectional study was conducted among Japanese nurses. A multivariable logistic regression analysis was performed to evaluate the effects of education on nurses’ confidence and psychological stress regarding ECG monitoring. In total, 1652 nurses were included in the study. Factors significantly associated with nurses’ confidence were post-graduate education experience (odds ratio [OR], 2.4; 95% confidence interval [CI], 1.6–3.6), ≥11 post-graduate years (OR, 2.2; 95% CI, 1.5–3.1), male gender (OR, 4.4; 95% CI, 2.9–6.6), ≥5 helpful experiences with ECG monitoring (OR, 10.7; 95% CI, 6.0–19.1), work experience in an intensive care unit (OR, 2.3; 95% CI, 1.5–3.7), and work experience in a cardiology department (OR, 1.7; 95% CI, 1.2–2.4). Factors significantly associated with nurses’ psychological stress were male gender (OR, 1.9; 95% CI, 1.2–2.9), ≥5 helpful experiences with ECG monitoring (OR, 1.9; 95% CI, 1.2–2.9), and work experience in an emergency room (OR, 2.4; 95% CI, 1.3–4.8). These results suggest that nurses’ post-graduate ECG monitoring education enhanced their confidence, but did not reduce psychological stress regarding ECG monitoring.

The prevalence, management, and thirty-day outcomes of symptomatic atrial fibrillation in a Tanzanian emergency department

Introduction

Data describing atrial fibrillation (AF) care in emergency centres (ECs) in sub-Saharan Africa is lacking. We sought to describe the prevalence and outcomes of AF in a Tanzanian EC.

Methods

In a prospective, observational study, adults presenting with chest pain or shortness of breath to a Tanzanian EC were enrolled from January through October 2019. Participants underwent electrocardiogram testing which were reviewed by two independent physician judges to determine presence of AF. Participants were asked about their medical history and medication use at enrollment, and a follow-up questionnaire was administered via telephone thirty days later to assess mortality, interim stroke, and medication use.

Results

Of 681 enrolled patients, 53 (7.8%) had AF. The mean age of participants with AF was 68.1, with a standard deviation (sd) of 21.1 years, and 23 of the 53 (43.4%) being male. On presentation, none of the participants found to have AF reported a previous history of AF. The median CHADS-VASC score among participants was 4 with an interquartile range (IQR) of 2-4. No participants were taking an anticoagulant at baseline. On index presentation, 49 (92.5%) participants with AF were hospitalised with 52 (98.1%) participants completing 30-day follow-up. 18 (34%) participants died, and 5 (9.6%) suffered a stroke. Of the surviving 31 participants with AF and a CHADS-VASC score ≥ 2, none were taking other anti-coagulants at 30 days. Compared to participants without AF, participants with AF were more likely to be hospitalised (OR 5.25, 95% CI 2.10-17.95, p < 0.001), more likely to die within thirty days (OR 1.93, 95% CI 1.03-3.50, p = 0.031), and more likely to suffer a stroke within thirty days (OR 5.91, 95% CI 1.76-17.28, p < 0.001).

Discussion

AF is common in a Tanzanian EC, with thirty-day mortality being high, but use of evidence-based therapies is rare. There is an opportunity to improve AF care and outcomes in Tanzania.

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In a Tanzanian emergency centre, atrial fibrillation was common but patient awareness of their condition was low

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Use of anticoagulants among patients with atrial fibrillation was rare both before and after hospital presentation

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Following hospital presentation, thirty-day mortality and stroke rates among patients with atrial fibrillation were high

Clifford Wavelet Entropy for Fetal ECG Extraction

Analysis of the fetal heart rate during pregnancy is essential for monitoring the proper development of the fetus. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The challenge lies in the extraction of the fetal ECG from the mother ECG during pregnancy. This approach has the advantage of being a reliable and non-invasive technique. In the present paper, a wavelet/multiwavelet method is proposed to perfectly extract the fetal ECG parameters from the abdominal mother ECG. In a first step, due to the wavelet/mutiwavelet processing, a denoising procedure is applied to separate the noised parts from the denoised ones. The denoised signal is assumed to be a mixture of both the MECG and the FECG. One of the well-known measures of accuracy in information processing is the concept of entropy. In the present work, a wavelet/multiwavelet Shannon-type entropy is constructed and applied to evaluate the order/disorder of the extracted FECG signal. The experimental results apply to a recent class of Clifford wavelets constructed in Arfaoui, et al. J. Math. Imaging Vis. 2020, 62, 73-97, and Arfaoui, et al.Acta Appl. Math.2020, 170, 1-35.. Additionally, classical Haar-Faber-Schauder wavelets are applied for the purpose of comparison. Two main well-known databases have been applied, the DAISY database and the CinC Challenge 2013 database. The achieved accuracy over the test databases resulted in Se=100%, PPV=100% for FECG extraction and peak detection.