Temporal change in repolarization parameters after surgical correction of valvular heart diseases.

BACKGROUND: Ventricular tachyarrhythmia is a potentially fatal outcome of cardiac surgery. Abrupt changes in the hemodynamics after surgical correction of valvular heart disease (VHD) can lead to alterations in ventricular repolarization. We compared the difference between temporal changes in repolarization parameters after correction of left-sided VHD. METHODS: We retrospectively analyzed the electrograms of patients who underwent surgical correction of isolated VHD between 2006 and 2015 at Asan Medical Center, including mitral stenosis (MS), mitral regurgitation (MR), aortic stenosis (AS), and aortic regurgitation (AR). Ventricular repolarization parameters were measured at pre-specified time intervals after index surgery using a custom-made ECG analysis program. We compared repolarization parameters, including QT and corrected QT intervals, T peak-to-end interval, and corrected T peak-to-end interval. RESULTS: Analysis of 8265 ECGs from 2110 patients (266 MS, 1059 MR, 421 AS, and 364 AR) was performed. Patients with AS were characterized by older age and more comorbidities than other VHDs. The corrected QT interval showed a peak value immediately after surgery and decreased thereafter in the AS groups. However, a gradual increase over 1 month after surgery in AR, MS, and MR groups was observed. The corrected T peak-to-end interval increased in the MS and MR groups and was unchanged in the AS and AR groups. CONCLUSIONS: The repolarization parameters of surgery changed dynamically after left-sided valvular surgery. Understanding differential temporal change of repolarization parameters according to the type of VHD would help clinicians avoid fatal arrhythmias related to the repolarization changes.

A simple apparatus for safety assessment of magnetically induced torque on active implantable medical devices (AIMDs) under 1.5 T and 3.0 T MRI.

OBJECTIVE: Our goal is to design and validate a simple apparatus for the safety assessments of magnetically induced torques by four active implantable medical devices (AIMDs) in 1.5 T and 3.0 T magnetic resonance imaging (MRI) environments. MATERIALS AND METHODS: A simple apparatus was designed to measure the magnetically induced torque. Before testing the apparatus, its effectiveness was first evaluated with a commercially available weights. The torque values of the four AIMDs, namely implantable cardioverter defibrillator (ICD), pacemaker, bone conduction implant, and cochlear implant, under 1.5 T and 3.0 T MRI environments were measured and compared with established international standard. RESULTS: The ICD, pacemaker, bone conduction implant, and cochlear implant had average torques of 1.405, 0.255, 9.460, and 5.490 N·cm under 1.5 T MRI and 2.950, 0.668, 14.618, and 11.484 N·cm under 3.0 T MRI, respectively. DISCUSSION: The apparatus developed in this study was verified to be feasible for measuring magnetically induced torques, with tests on four types of AIMDs under 1.5 T and 3.0 T MRI environments. Although the designed structure is simple, the apparatus was shown to measure torque values of AIMDs under MRI accurately.

Tele-monitoring system for intensive care ventilators in isolation rooms.

The COVID-19 pandemic and discovery of new mutant strains have a devastating impact worldwide. Patients with severe COVID-19 require various equipment, such as ventilators, infusion pumps, and patient monitors, and a dedicated medical team to operate and monitor the equipment in isolated intensive care units (ICUs). Medical staff must wear personal protective equipment to reduce the risk of infection. This study proposes a tele-monitoring system for isolation ICUs to assist in the monitoring of COVID-19 patients. The tele-monitoring system consists of three parts: medical-device panel image processing, transmission, and tele-monitoring. This system can monitor the ventilator screen with obstacles, receive and store data, and provide real-time monitoring and data analysis. The proposed tele-monitoring system is compared with previous studies, and the image combination algorithm for reconstruction is evaluated using structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The system achieves an SSIM score of 0.948 in the left side and a PSNR of 23.414 dB in the right side with no obstacles. It also reduces blind spots, with an SSIM score of 0.901 and a PSNR score of 18.13 dB. The proposed tele-monitoring system is compatible with both wired and wireless communication, making it accessible in various situations. It uses camera and performs live data monitoring, and the two monitoring systems complement each other. The system also includes a comprehensive database and an analysis tool, allowing medical staff to collect and analyze data on ventilator use, providing them a quick, at-a-glance view of the patient's condition. With the implementation of this system, patient outcomes may be improved and the burden on medical professionals may be reduced during the COVID-19 pandemic-like situations.

Multiple electrocardiogram generator with single-lead electrocardiogram.

BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) is measured in various ways. The three main ECG measurement methods include resting ECG, Holter monitoring, and treadmill method. In standard ECG measurement methods, multiple electrodes are attached to the limb and chest. Limb and chest leads measure the frontal and sagittal planes of the heart, respectively. In this case, ECG signals are measured briefly up to 10 seconds. To measure ECG signals based on a single lead, wearable devices have been developed that could measure long-term ECG signals daily. ECG signals are vectors in the heart, which is a three-dimensional structure. Therefore, a single-lead measurement lacks detailed information. The objective of this study was to synthesize multiple ECGs from a single-lead ECG using a generative adversarial network (GAN). METHODS: We trained our model with two independent datasets and one combined dataset. For experiment 1, the PTB-XL dataset was used as the training set, and the China dataset was used as the test set. For experiment 2, the China dataset was used as the training set, and the PTB-XL was used as the test set. Optimized GAN models were obtained for each experiment and evaluated. RESULTS: The Fréchet distance (FD) score and mean squared error (MSE) were used for evaluation. The FD and MSE scores for experiments 1 and 2 were 7.237 and 0.024, and 8.055 and 0.011, respectively. CONCLUSION: We proposed a method to overcome the limitations of modern ECG measurement methods. Low FD and MSE scores not only indicate the possibility but also the similarity between synthesized ECG and reference ECG when compared in ECG paper format. This indicates that the proposed method can be applied to wearable devices that measure single-lead ECG.

ECG data dependency for atrial fibrillation detection based on residual networks.

Atrial fibrillation (AF) is an arrhythmia that can cause blood clot and may lead to stroke and heart failure. To detect AF, deep learning-based detection algorithms have recently been developed. However, deep learning models were often trained with limited datasets and were evaluated within the same datasets, which makes their performance generally drops on the external datasets, known as data dependency. For this study, three different databases from PhysioNet were used to investigate the data dependency of deep learning-based AF detection algorithm using the residual neural network (Resnet). Resnet 18, 34, 50 and 152 model were trained with raw electrocardiogram (ECG) signal extracted from independent database. The highest accuracy was about 98-99% which is evaluation results of test dataset from the own database. On the other hand, the lowest accuracy was about 53-92% which was evaluation results of the external dataset extracted from different source. There are data dependency according to the train dataset and the test dataset. However, the data dependency decreased as a large amount of train data.

Development of a Portable Respiratory Gas Analyzer for Measuring Indirect Resting Energy Expenditure (REE).

OBJECTIVE: A rapidly growing home healthcare market has resulted in the development of many portable or wearable products. Most of these products measure, estimate, or calculate physiologic signals or parameters, such as step counts, blood pressure, or electrocardiogram. One of the most important applications in home healthcare is monitoring one's metabolic state since the change of metabolic state could reveal minor or major changes in one's health condition. A simple and noninvasive way to measure metabolism is through breath monitoring. With breath monitoring by breath gas analysis, two important indicators like the respiratory quotient (RQ) and resting energy exposure (REE) can be calculated. Therefore, we developed a portable respiratory gas analyzer for breath monitoring to monitor metabolic state, and the performance of the developed device was tested in a clinical trial. Approach. The subjects consisted of 40 healthy men and women. Subjects begin to measure exhalation gas using Vmax 29 for 15 minutes. After that, subjects begin to measure exhalation gas via the developed respiratory gas analyzer. Finally, the recorded data on the volume of oxygen (VO2), volume of carbon dioxide (VCO2), RQ, and REE were used to validate correlations between Vmax 29 and the developed respiratory gas analyzer. RESULTS: The results showed that the root-mean-square errors (RMSE) values of VCO2, VO2, RQ, and REE are 0.0315, 0.0417, 0.504, and 0.127. Bland-Altman plots showed that most of the VCO2, VO2, RQ, and REE values are within 95% of the significance level. CONCLUSIONS: We have successfully developed and tested a portable respiratory gas analyzer for home healthcare. However, there are limitations of the clinical trial; the number of subjects is small in size, and the age and race of subjects are confined. The developed portable respiratory gas analyzer is a cost-efficient method for measuring metabolic state and a new application of home healthcare.

Impact of a custom-made 3D printed ergonomic grip for direct laryngoscopy on novice intubation performance in a simulated easy and difficult airway scenario-A manikin study.

Direct laryngoscopy using a Macintosh laryngoscope is the most widely used approach; however, this skill is not easy for novices and trainees. We evaluated the performance of novices using a laryngoscope with a three-dimensional (3D)-printed ergonomic grip on an airway manikin. Forty second-year medical students were enrolled. Endotracheal intubation was attempted using a conventional Macintosh laryngoscope with or without a 3D-printed ergonomic support grip. Primary outcomes were intubation time and overall success rate. Secondary outcomes were number of unsuccessful attempts, first-attempt success rate, airway Cormack-Lehane (CL) grade, and difficulty score. In the easy airway scenario, intubation time, and the overall success rate were similar between two group. CL grade and ease-of-use scores were significantly better for those using the ergonomic support grip (P < 0.05). In the difficult airway scenario, intubation time (49.7±37.5 vs. 35.5±29.2, P = 0.013), the first-attempt success rate (67.5% vs. 90%, P = 0.029), number of attempts (1.4±0.6 vs. 1.1±0.4, P = 0.006), CL grade (2 [2, 2] vs. 2 [1, 1], P = 0.012), and ease-of-use scores (3.5 [2, 4] vs. 4 [3, 5], P = 0.008) were significantly better for those using the ergonomic support grip. Linear mixed model analysis showed that the ergonomic support grip had a favorable effect on CL grade (P<0.001), ease-of-use scores (P<0.001), intubation time (P = 0.015), and number of intubation attempts (P = 0.029). Our custom 3D-printed ergonomic laryngoscope support grip improved several indicators related to the successful endotracheal intubation in the easy and difficult scenario simulated on an airway manikin. This grip may be useful for intubation training and practice.