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.

Implementation of a real-time qualitative app to evaluate resuscitation performance in an Advanced Cardiac Life Support course.

OBJECTIVE: In addition to high-quality chest compression, parameters of resuscitation efficiency such as early chest compression, early defibrillation, and decreased hands-off time are also vital in the Advanced Cardiac Life Support (ACLS) protocol. However, because of limited time and equipment in ACLS courses, efficiency of performance is difficult to evaluate. MATERIALS AND METHODS: A free, easy-to-use iOS and Android app (CodeTracer®) was developed for real-time recording of cardiopulmonary resuscitation (CPR) performance. Interventions performed during resuscitation were set up as buttons. When the simulated scenario in the ACLS course began, instructors recorded every intervention and the team performed by pushing the appropriate buttons. When the scenario ended, the CodeTracer® automatically computed parameters, including the percentage of no-flow time, time to initiating CPR, and time to initiating defibrillation and also generated a graphic log for later discussion. RESULTS: A total of 76 resuscitation episodes were recorded, 27 in the practice scenarios and 49 in the final Megacode simulations. After the course, the average percentage of no-flow time decreased 5.79%, time to initiating CPR decreased 3.05 s, and time to initiating defibrillation decreased up to 20.27 s. Of note, physicians as leaders seem to have better performance after the ACLS course than before, but the results were insignificant except for the percentage of no-flow time. CONCLUSIONS: CodeTracer® can record and calculate objective parameters for resuscitation performance in ACLS courses and can assist instructors in disseminating important concepts to participants. It can be a useful tool in ACLS courses.

Evaluation of the Accuracy of ECG Captured by CardioChip through Comparison of Lead I Recording to a Standard 12-Lead ECG Recording Device.

BACKGROUND: Remote cardiac rhythm monitoring and recording, using hand-carried electrocardiogram (ECG) device had been widely used in telemedicine. The feasibility and accuracy analysis on the data recorded by a new miniature ECG system-on-chip (SoC) system has not been explored before. METHODS: This study evaluated the accuracy of the ECG recordings captured by CardioChip - a single-channeled, low-powered, miniature ECG SoC designed for mobile applications; comparing against Philips Pagewriter Trim III - a Food and Drug Administration certified, widely-used standard 12-lead ECG recording device, within Mackay Memorial Hospital in Taiwan. RESULTS: Total of 111 participants, age ranging from 39 to 87years old [mean age: 61.2 ± 13.4, 57 male (51.3%)] were enrolled. Two experienced cardiologists rated and scored the ECG morphology to be the same between the two devices, while CardioChip ECG was more sensitive to baseline noise. R-peak amplitudes measured both devices using single lead information (CardioChip ECG vs. Lead 1 in standard 12-lead ECG) showed statistical consistency. Offline analysis of signal correlation coefficients and coherence showed good correlation with both over 0.94 in average (0.94 ± 0.04 and 0.95 ± 0.04, respectively), high agreement between raters (94% agreement) for detecting abnormal cardiac rhythm with excellent R-peak amplitude (r = 0.98, p < 0.001) and PR interval (r = 0.91, p < 0.001) correlations, indicating excellent correlation between ECG recordings derived from two different modalities. CONCLUSIONS: The results suggested that CardioChip ECG is comparable to medical industry standard ECG. The future implementation of wearable ECG device embedded with miniature ECG system-on-chip (SoC) system is ready for clinical use, which will potentially enhance efficacy on identifying subjects with suspected cardiac arrhythmias.

Self-assembling functionalized nanopeptides for immediate hemostasis and accelerative liver tissue regeneration.

Traumatic injury or surgery may trigger extensive bleeding. However, conventional hemostatic methods have limited efficacy and may cause surrounding tissue damage. In this study, we use self-assembling peptides (SAPs) and specifically extend fragments of functional motifs derived from fibronectin and laminin to evaluate the capability of these functionalized SAPs in the effect of hemostasis and liver tissue regeneration. From the results, these peptides can self-assemble into nanofibrous network structure and gelate into hydrogel with pH adjustment. In animal studies, the efficacy of hemostasis is achieved immediately within seconds in a rat liver model. The histological analyses by hematoxylin-eosin stain and immunohistochemistry reveal that SAPs with these functionalized motifs significantly enhance liver tissue regeneration. In brief, these SAPs may have potential as pharmacological tools to extensively advance clinical therapeutic applications in hemostasis and tissue regeneration in the field of regenerative medicine.