Cardiac autonomic regulation following a 246-km mountain ultra-marathon: An observational study.

Physical exercise requires integrated autonomic and cardiovascular adjustments to maintain homeostasis. We aimed to observe acute posture-related changes in blood pressure, and apply a portable noninvasive monitor to measure the heart index for detecting arrhythmia among elite participants of a 246-km mountain ultra-marathon. Nine experienced ultra-marathoners (8 males and 1 female) participating in the Run Across Taiwan Ultra-marathon in 2018 were enrolled. The runners' Heart Spectrum Blood Pressure Monitor measurements were obtained in the standing and supine positions before and immediately after the race. Their high-sensitivity troponin T and N-terminal proB-type natriuretic peptide levels were analyzed 1 week before and immediately after the event. Heart rate was differed significantly in the immediate postrace assessment compared to the prerace assessment, in both the standing (P = .011; d = 1.19) and supine positions (P = .008; d = 1.35). Postural hypotension occurred in 4 (44.4%) individuals immediately postrace. In 3 out of 9 (33.3%) recruited finishers, the occurrence of premature ventricular complex signals in the standing position was detected; premature ventricular complex signal effect was observed in the supine position postrace in only 1 participant (11.1%). Premature ventricular complex signal was positively correlated with running speed (P = .037). Of the 6 individuals who completed the biochemical tests postrace, 2 (33.3%) had high-sensitivity troponin T and 6 (100%) had N-terminal proB-type natriuretic peptide values above the reference interval. A statistically significant increase was observed in both the high-sensitivity troponin T (P = .028; d = 1.97), and N-terminal proB-type natriuretic peptide (P = .028; d = 2.91) levels postrace compared to prerace. In conclusion, significant alterations in blood pressure and heart rate were observed in the standing position, and postexercise (postural) hypotension occurred among ultra-marathoners. The incidence of premature ventricular complexes was higher after the race than before.

An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection.

Arrhythmia, a frequently encountered and life-threatening cardiac disorder, can manifest as a transient or isolated event. Traditional automatic arrhythmia detection methods have predominantly relied on QRS-wave signal detection. Contemporary research has focused on the utilization of wearable devices for continuous monitoring of heart rates and rhythms through single-lead electrocardiogram (ECG), which holds the potential to promptly detect arrhythmias. However, in this study, we employed a convolutional neural network (CNN) to classify distinct arrhythmias without QRS wave detection step. The ECG data utilized in this study were sourced from the publicly accessible PhysioNet databases. Taking into account the impact of the duration of ECG signal on accuracy, this study trained one-dimensional CNN models with 5-s and 10-s segments, respectively, and compared their results. In the results, the CNN model exhibited the capability to differentiate between Normal Sinus Rhythm (NSR) and various arrhythmias, including Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Wolff-Parkinson-White syndrome (WPW), Ventricular Fibrillation (VF), Ventricular Tachycardia (VT), Ventricular Flutter (VFL), Mobitz II AV Block (MII), and Sinus Bradycardia (SB). Both 10-s and 5-s ECG segments exhibited comparable results, with an average classification accuracy of 97.31%. It reveals the feasibility of utilizing even shorter 5-s recordings for detecting arrhythmias in everyday scenarios. Detecting arrhythmias with a single lead aligns well with the practicality of wearable devices for daily use, and shorter detection times also align with their clinical utility in emergency situations.

Fall Recognition Based on an IMU Wearable Device and Fall Verification through a Smart Speaker and the IoT.

A fall is one of the most devastating events that aging people can experience. Fall-related physical injuries, hospital admission, or even mortality among the elderly are all critical health issues. As the population continues to age worldwide, there is an imperative need to develop fall detection systems. We propose a system for the recognition and verification of falls based on a chest-worn wearable device, which can be used for elderly health institutions or home care. The wearable device utilizes a built-in three-axis accelerometer and gyroscope in the nine-axis inertial sensor to determine the user's postures, such as standing, sitting, and lying down. The resultant force was obtained by calculation with three-axis acceleration. Integration of three-axis acceleration and a three-axis gyroscope can obtain a pitch angle through the gradient descent algorithm. The height value was converted from a barometer. Integration of the pitch angle with the height value can determine the behavior state including sitting down, standing up, walking, lying down, and falling. In our study, we can clearly determine the direction of the fall. Acceleration changes during the fall can determine the force of the impact. Furthermore, with the IoT (Internet of Things) and smart speakers, we can verify whether the user has fallen by asking from smart speakers. In this study, posture determination is operated directly on the wearable device through the state machine. The ability to recognize and report a fall event in real-time can help to lessen the response time of a caregiver. The family members or care provider monitor, in real-time, the user's current posture via a mobile device app or internet webpage. All collected data supports subsequent medical evaluation and further intervention.

Comparison of the validity of checklist assessment in cardiac arrest simulations with an app in an academic hospital in Taiwan: a retrospective observational study.

INTRODUCTION: Robust assessment is a crucial component in Advanced Cardiac Life Support (ACLS) training to determine whether participants have achieved learning objectives with little or no variation in their overall outcomes. This study aimed to evaluate resuscitation performance by real-time logs. We hypothesised that instructors may not be able to evaluate time-sensitive parameters, namely, chest compression fraction, time to initiating chest compression and time to initiating defibrillation accurately in a subjective manner. METHODS: Video records and formal checklist-based test results of Megacode scenarios for the ACLS certification examination at several hospitals in Taipei were examined. For the study interest, three time-sensitive parameters were measured via video review assisted by a mobile phone application, and were used for evaluation. We evaluated if the pass/fail results made by instructors via checklists were correlated with these parameters. RESULTS: A total of 185 Megacode scenarios were eligible for the final analysis. Among the three parameters, good chest compression fraction was statistically significant with a higher OR of passing (OR=3.65; 95% CI 1.36 to 9.91; p=0.01). In 112 participants with one parameter that did not meet the criteria, 25 were graded as fail, making the specificity 22.3% (95% CI 15.0% to 31.2%). CONCLUSIONS: Visual observation of cardiopulmonary resuscitation performance is not accurate when evaluating time-sensitive parameters. Objective results should be offered for training outcome evaluation, and also for feedback to participants.

An injured man with acute altered mental status.

CLINICAL INTRODUCTION: A 58-year-old man sustained injuries due to accidental fall from a height of 5 m onto a solid floor while operating a crane. He was fully conscious without external evidence of head injury when presented to the ED. The patient had multiple fractures involving the fourth, fifth and seventh of the left ribs, left iliac wing and superior ramus of the pelvis, comminuted fracture of the left femur shaft, and an open fracture of the mandible. Two hours after ED presentation, the patient developed acute confusion and lethargy, which rapidly progressed to coma and respiratory distress, and was subsequently intubated with mechanical ventilation support. Emergent CT scan of the head is shown (figure 1).emermed;35/3/148/F1F1F1Figure 1CT scan of the head without contrast. QUESTION: What is the most likely diagnosis?A. Cerebral haemorrhageB. Brain metastasisC. Cerebral fat embolismD. Diffuse axonal injury.