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ABSTRACT Background It is unclear to what extent silent atrial fibrillation (AF) is present in subjects previously undiagnosed with AF. The recently popular 7‐day patch electrocardiography (ECG) monitoring may help answer this question. Methods In the Kitsuki and Usuki cities in Oita Prefecture, Japan, a study was conducted among subjects who underwent 7‐day patch ECG monitoring (Heartnote) for silent AF screening during the national insurance health checkup between June and November 2023. Subjects were (1) 65–74 years old and have ≥ 1 of the following risk factors: hypertension, diabetes mellitus, stroke, transient ischemic attack, and underlying heart disease (heart failure and/or previous myocardial infarction) and (2) 75 years and older. Results A total of 571 subjects (307 females and 264 males, mean age 75.3 ± 5.4 years) were analyzed. Silent AF was detected in 16 out of 571 subjects (2.8%). Among those aged 75 years or older, silent AF was detected in 15 out of 291 subjects (5.2%). In multivariate analysis, among age, body mass index (BMI), hypertension, diabetes, stroke, and underlying heart disease, only age was the independent predictor of silent AF detection (odds ratio: 1.16, 95% confidence interval: 1.06–1.28, p < 0.01). Conclusions Seven‐day patch ECG monitoring during the national insurance health checkup efficiently detected silent AF in individuals aged 75 years and older.

This research paper explores the implementation and efficacy of real-time electrocardiogram (ECG) monitoring systems for athletes, emphasizing their potential to significantly enhance safety and performance in sports settings. By utilizing advanced ECG technology, the study investigates how continuous, real-time heart rate and rhythm monitoring can aid in the immediate detection of cardiovascular anomalies during high-intensity activities. The research methodology incorporates the deployment of portable ECG devices in a controlled experimental setup, analyzing data from athletes during training sessions and competitive events. Results from the study highlight the system's ability to provide swift and accurate cardiac assessments, thereby enabling timely medical interventions. Moreover, the paper discusses the technical challenges associated with real-time ECG monitoring, such as signal interference and data accuracy, and addresses privacy and ethical considerations concerning the continuous collection of health data. The discussion extends to the implications of integrating such technology within sports medicine, suggesting that while the systems offer substantial benefits in monitoring and preventing cardiac issues, they also necessitate rigorous standards for data security and ethical oversight. The conclusion advocates for a balanced approach to the adoption of these technologies, proposing future research directions that focus on enhancing system reliability and integrating artificial intelligence to predict potential health risks proactively. This study contributes to the ongoing discourse in sports health technology by providing a comprehensive analysis of real-time ECG monitoring as a transformative tool for athlete healthcare management.

Background Sleep apnea is common in patients with cardiovascular disease and is a factor that worsens prognosis. Holter 24‐h ECG screening for sleep apnea is beneficial in the care of these patients, but due to high night‐to‐night variability of sleep apnea, it can lead to misdiagnosis and misclassification of disease severity. Methods To investigate the long‐term dynamic behavior of sleep apnea, seven‐day ECGs recorded with a patch ECG recorder in 120 patients were analyzed for the cyclic variation of heart rate (CVHR) during sleep periods as determined by a built‐in three‐axis accelerometer. Results The frequency of CVHR (Fcv) showed considerable night‐to‐night variability (coefficient of variance, 66 ± 35%), which was consistent with the night‐to‐night variability in apnea‐hypopnea index and oxygen desaturation index reported in earlier studies. In patients with presumed moderate‐to‐severe sleep apnea (Fcv > 15 cph at least one night), it was missed on 62% of nights, and on at least one night in 88% of patients. The CV of Fcv was negatively correlated with the average of Fcv, suggesting that patients with mild sleep apnea show greater night‐to‐night variability and would benefit from long‐term assessment. The average Fcv was higher in the supine position, but the night‐to‐night variability was not explained by the night‐to‐night variability of time spent in the supine position. Conclusions CVHR analysis of long‐term ambulatory ECG recordings is useful for improving the reliability of screening for sleep apnea without placing an extra burden on patients with cardiovascular disease and their care.

Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

With cardiovascular diseases (CVD) remaining a leading cause of mortality, wearable devices for monitoring cardiac activity have gained significant, renewed interest among the medical community. This paper introduces an innovative ECG monitoring system based on a single-lead ECG machine, enhanced using machine learning methods. The system only processes and analyzes ECG data, but it can also be used to predict potential heart disease at an early stage. The wearable device was built on the ADS1298 and a microcontroller STM32L151xD. A server module based on the architecture style of the REST API was designed to facilitate interaction with the web-based segment of the system. The module is responsible for receiving data in real time from the microcontroller and delivering this data to the web-based segment of the module. Algorithms for analyzing ECG signals have been developed, including band filter artifact removal, K-means clustering for signal segmentation, and PQRST analysis. Machine learning methods, such as isolation forests, have been employed for ECG anomaly detection. Moreover, a comparative analysis with various machine learning methods, including logistic regression, random forest, SVM, XGBoost, decision forest, and CNNs, was conducted to predict the incidence of cardiovascular diseases. Convoluted neural networks (CNN) showed an accuracy of 0.926, proving their high effectiveness for ECG data processing.

For the long-time monitoring of electrocardiograms, electrodes must be skin-friendly and non-irritating, but in addition they must deliver leads without artifacts even if the skin is dry and the body is moving. Today’s adhesive conducting gel electrodes are not suitable for such applications. We have developed an embroidered textile electrode from polyethylene terephthalate yarn which is plasma-coated with silver for electrical conductivity and with an ultra-thin titanium layer on top for passivation. Two of these electrodes are embedded into a breast belt. They are moisturized with a very low amount of water vapor from an integrated reservoir. The combination of silver, titanium and water vapor results in an excellent electrode chemistry. With this belt the long-time monitoring of electrocardiography (ECG) is possible at rest as well as when the patient is moving.

Wearable health monitoring is an emerging technology for continuous monitoring of vital signs including the electrocardiogram (ECG). This signal is widely adopted to diagnose and assess major health risks and chronic cardiac diseases. This paper focuses on reviewing wearable ECG monitoring systems in the form of wireless, mobile and remote technologies related to older adults. Furthermore, the efficiency, user acceptability, strategies and recommendations on improving current ECG monitoring systems with an overview of the design and modelling are presented. In this paper, over 120 ECG monitoring systems were reviewed and classified into smart wearable, wireless, mobile ECG monitoring systems with related signal processing algorithms. The results of the review suggest that most research in wearable ECG monitoring systems focus on the older adults and this technology has been adopted in aged care facilitates. Moreover, it is shown that how mobile telemedicine systems have evolved and how advances in wearable wireless textile-based systems could ensure better quality of healthcare delivery. The main drawbacks of deployed ECG monitoring systems including imposed limitations on patients, short battery life, lack of user acceptability and medical professional’s feedback, and lack of security and privacy of essential data have been also discussed.

Implantable medical devices (IMDs) enable patients to monitor their health anytime and receive treatment anywhere. However, due to the limited capacity of a battery, their functionalities are restricted, and the devices may not achieve their intended potential fully. The most promising way to solve this limited capacity problem is wireless power transfer (WPT) technology. In this study, a WPT based implantable electrocardiogram (ECG) monitoring device that continuously records ECG data has been proposed, and its effectiveness is verified through an animal experiment using a rat model. Our proposed device is designed to be of size 24 × 27 × 8 mm, and it is small enough to be implanted in the rat. The device transmits data continuously using a low power Bluetooth Low Energy (BLE) communication technology. To charge the battery wirelessly, transmitting (Tx) and receiving (Rx) antennas were designed and fabricated. The animal experiment results clearly showed that our WPT system enables the device to monitor the ECG of a heart in various conditions continuously, while transmitting all ECG data in real-time.

In this paper we proposed a wearable electrocardiogram (ECG) telemonitoring system for atrial fibrillation (AF) detection based on a smartphone and cloud computing. A wearable ECG patch was designed to collect ECG signals and send the signals to an Android smartphone via Bluetooth. An Android APP was developed to display the ECG waveforms in real time and transmit every 30 s ECG data to a remote cloud server. A machine learning (CatBoost)-based ECG classification method was proposed to detect AF in the cloud server. In case of detected AF, the cloud server pushed the ECG data and classification results to the web browser of a doctor. Finally, the Android APP displayed the doctor’s diagnosis for the ECG signals. Experimental results showed the proposed CatBoost classifier trained with 17 selected features achieved an overall F1 score of 0.92 on the test set (n = 7270). The proposed wearable ECG monitoring system may potentially be useful for long-term ECG telemonitoring for AF detection.

Research of novel biosignal modalities with application to remote patient monitoring is a subject of state-of-the-art developments. This study is focused on sonified ECG modality, which can be transmitted as an acoustic wave and received by GSM (Global System for Mobile Communications) microphones. Thus, the wireless connection between the patient module and the cloud server can be provided over an audio channel, such as a standard telephone call or audio message. Patients, especially the elderly or visually impaired, can benefit from ECG sonification because the wireless interface is readily available, facilitating the communication and transmission of secure ECG data from the patient monitoring device to the remote server. The aim of this study is to develop an AI-driven algorithm for 12-lead ECG sonification to support diagnostic reliability in the signal processing chain of the audio ECG stream. Our methods present the design of two algorithms: (1) a transformer (ECG-to-Audio) based on the frequency modulation (FM) of eight independent ECG leads in the very low frequency band (300–2700 Hz); and (2) a transformer (Audio-to-ECG) based on a four-layer 1D convolutional neural network (CNN) to decode the audio ECG stream (10 s @ 11 kHz) to the original eight-lead ECG (10 s @ 250 Hz). The CNN model is trained in unsupervised regression mode, searching for the minimum error between the transformed and original ECG signals. The results are reported using the PTB-XL 12-lead ECG database (21,837 recordings), split 50:50 for training and test. The quality of FM-modulated ECG audio is monitored by short-time Fourier transform, and examples are illustrated in this paper and supplementary audio files. The errors of the reconstructed ECG are estimated by a popular ECG diagnostic toolbox. They are substantially low in all ECG leads: amplitude error (quartile range RMSE = 3–7 μV, PRD = 2–5.2%), QRS detector (Se, PPV > 99.7%), P-QRS-T fiducial points’ time deviation (<2 ms). Low errors generalized across diverse patients and arrhythmias are a testament to the efficacy of the developments. They support 12-lead ECG sonification as a wireless interface to provide reliable data for diagnostic measurements by automated tools or medical experts.