Electrocardiogram Telemonitoring Research Articles

Establishment of a digital electrocardiogram telemonitoring system and remote consultation service.

The use of telemonitoring electrocardiogram (ECG) improves the detection rate of various arrhythmias as it offers an extended recording time and does not impose activity restrictions. However, many community hospitals lack the resources to conduct this test due to a lack of cardiac function specialists and antiquated screening equipment. To establish a digital ECG telemonitoring system and remote consultation service between tertiary hospitals and community medical service stations to improve diagnosis and treatment effectiveness. We used the PI ECG telemonitoring data acquisition system, with a personal digital assistant (PDA) or computer serving as the platform for ECG acquisition, storage, display, printing, and transmission. We introduced this system to standardize the storage and transmission of ECG telemonitoring data, and ensure accurate transmission, reproducibility, and preservation of data. The implementation of the PI ECG telemonitoring data acquisition system enabled the sharing of remote ambulatory electrocardiography data between Beijing Shijitan Hospital and its affiliated community hospitals. This has resulted in reduced waiting times for patients to receive reports (from 0.3 to 1 hour, to 0.1 or less), shortened consultation times (from 2 hours to 0.1 hour), and improved patient satisfaction with consultations (> 20% of the patients reported they were highly satisfied). The system successfully combined the goals of intelligence, real-time synchronization, and efficiency, revolutionizing ECG telemonitoring diagnosis in the network era. Sharing digital telematics information between tertiary and community hospitals enables a tiered approach to diagnosis and treatment and allows for more efficient, accurate, and convenient remote diagnostics.

Comparison of Novel Telemonitoring System Using the Single-lead Electrocardiogram Patch With Conventional Telemetry System.

Although a single-lead electrocardiogram (ECG) patch may provide advantages for detecting arrhythmias in outpatient settings owing to user convenience, its comparative effectiveness for real-time telemonitoring in inpatient settings remains unclear. We aimed to compare a novel telemonitoring system using a single-lead ECG patch with a conventional telemonitoring system in an inpatient setting. This was a single-center, prospective cohort study. Patients admitted to the cardiology unit for arrhythmia treatment who required a wireless ECG telemonitoring system were enrolled. A single-lead ECG patch and conventional telemetry were applied simultaneously in hospitalized patients for over 24 hours for real-time telemonitoring. The basic ECG parameters, arrhythmia episodes, and signal loss or noise were compared between the 2 systems. Eighty participants (mean age 62±10 years, 76.3% male) were enrolled. The three most common indications for ECG telemonitoring were atrial fibrillation (66.3%), sick sinus syndrome (12.5%), and atrioventricular block (10.0%). The intra-class correlation coefficients for detecting the number of total beats, atrial and ventricular premature complexes, maximal, average, and minimal heart rates, and pauses were all over 0.9 with p values for reliability <0.001. Compared to a conventional system, a novel system demonstrated significantly lower signal noise (median 0.3% [0.1-1.6%] vs. 2.4% [1.4-3.7%], p<0.001) and fewer episodes of signal loss (median 22 [2-53] vs. 64 [22-112] episodes, p=0.002). The novel telemonitoring system using a single-lead ECG patch offers performance comparable to that of a conventional system while significantly reducing signal loss and noise. Clinical Research Information Service Identifier: KCT0008176.

Smart Healthcare for ECG Telemonitoring System

Cardiovascular disorders are one of the major causes of sad death among older and middle-aged people. Over the past two decades, health monitoring services have evolved quickly and had the ability to change the way health care is currently provided. However, the most challenging aspect of the mobile and wearable sensor-based human activity recognition pipeline is the extraction of the related features. Feature extraction decreases both computational complexity and time. Deep learning techniques are used for automatic feature learning in a variety of fields, including health, image classification, and, most recently, for the extraction and classification of complex and straightforward human activity recognition in smart health care. This paper reviews the recent state of the art in electrocardiogram (ECG) smart health monitoring systems based on the Internet of things with the machine and deep learning techniques. Moreover, the paper provided possible research and challenges that can help researchers advance state of art in future work.

Resuscitation outcomes of a wireless ECG telemonitoring system for cardiovascular ward patients experiencing in-hospital cardiac arrest

In-hospital cardiac arrest is a serious issue for hospitalized patients. The documented initial rhythm and detected medical events have been reported to influence the survival of cardiopulmonary resuscitation. This study aimed to identify the effect of continuous real-time electrocardiogram (ECG) monitoring on the prognosis of resuscitated patients in a general cardiac ward. We conducted this retrospective study using medical records of hospitalized patients in a cardiovascular ward who experienced an in-hospital cardiac arrest and received cardiopulmonary resuscitation from February 2015 to December 2018. The patients who were considered to be at high risk of cardiac events such as ventricular arrhythmia would receive continuous ECG monitoring. A wireless ECG telemonitoring system was introduced to replace traditional bedside ECG monitors. The outcome measures were the initial success of resuscitation, 24-h survival after resuscitation, and survival to discharge. We enrolled 115 patients with a cardiac arrest during hospitalization, of whom 73 (63%) patients received wireless ECG telemonitoring. Patients receiving continuous ECG monitoring were associated with higher opportunities of initial success of resuscitation and 24-h survival after resuscitation (67.1% vs. 40.5%, p=0.005; and 49.3% vs. 26.2%, p=0.015, respectively) when comparing to the non-monitoring group; but no significant difference in survival to discharge (21.9% vs. 16.7%, p=0.498) was observed. With adjustment of the covariates, the monitoring group was associated with a higher likelihood to reach the initial success of resuscitation (odds ratios [ORs], 3.21; 95% confidence interval [CI], 1.03-9.98). However, the effect of monitoring on 24-h survival and survival to discharge was close to null after adjusting for covariates. A wireless ECG telemonitoring system were beneficial to the initial success of resuscitation for patients at high risk of cardiovascular events suffering an in-hospital cardiac arrest; but had less impact on 24-h survival and survival to discharge.

A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection.

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.

A Fast and Robust Non-Sparse Signal Recovery Algorithm for Wearable ECG Telemonitoring Using ADMM-Based Block Sparse Bayesian Learning.

Wearable telemonitoring of electrocardiogram (ECG) based on wireless body Area networks (WBAN) is a promising approach in next-generation patient-centric telecardiology solutions. In order to guarantee long-term effective operation of monitoring systems, the power consumption of the sensors must be strictly limited. Compressed sensing (CS) is an effective method to alleviate this problem. However, ECG signals in WBAN are usually non-sparse, and most traditional compressed sensing recovery algorithms have difficulty recovering non-sparse signals. In this paper, we proposed a fast and robust non-sparse signal recovery algorithm for wearable ECG telemonitoring. In the proposed algorithm, the alternating direction method of multipliers (ADMM) is used to accelerate the speed of block sparse Bayesian learning (BSBL) framework. We used the famous MIT-BIH Arrhythmia Database, MIT-BIH Long-Term ECG Database and ECG datasets collected in our practical wearable ECG telemonitoring system to verify the performance of the proposed algorithm. The experimental results show that the proposed algorithm can directly recover ECG signals with a satisfactory accuracy in a time domain without a dictionary matrix. Due to acceleration by ADMM, the proposed algorithm has a fast speed, and also it is robust for different ECG datasets. These results suggest that the proposed algorithm is very promising for wearable ECG telemonitoring.

Pit-a-Pat: A Smart Electrocardiogram System for Detecting Arrhythmia.

Electrocardiogram (ECG) telemonitoring is one of the most promising applications of medical telemetry. However, previous approaches to ECG telemonitoring have largely relied on public databases of ECG results. In this article we propose a smart ECG system called Pit-a-Pat, which extracts features from ECG signals and detects arrhythmia. It is designed to run on an Android™ (Google, Mountain View, CA) device, without requiring modifications to other software. We implemented the Pit-a-Pat system using a commercial ECG device, and the experimental results demonstrate the effectiveness and accuracy of Pit-a-Pat for monitoring the ECG signal and analyzing the cardiac activity of a mobile patient. The proposed system allows monitoring of cardiac activity with automatic analysis, thereby providing a convenient, inexpensive, and ubiquitous adjunct to personal healthcare.

Real-time ECG telemonitoring system design with mobile phone platform

In this paper, we propose an ECG (electrocardiogram) telemonitoring system based on a mobile phone platform. It transmits abnormal heartbeats, which are identified in the patient-worn unit (Holter), in real time by using MMS (multimedia messaging service) on GPRS (general packet radio service) and transfers all ECG data acquired and stored in the Holter by the Internet. By this way, the Holter can be used outdoors, and the communication cost can be reduced without losing important information of patients. The GPS (global positioning system) information provided by the Holter can be used to locate the patient for emergency help. To identify abnormal beats, we develop a real-time ECG classification algorithm that can be executed by the dual-core processor in the Holter. Experimental results show that the proposed system achieves an ECG classification accuracy of 98.98%.

A portable ECG and blood pressure telemonitoring system.

This article discusses a cost-effective portable home unit for electrocardiogram (ECG) and blood pressure (BP) monitoring. Based on feedback from our previous work concerning a PC-based ECG/BP telemonitoring system, the system described helps to overcome past problems by simplifying home installation, operation, and maintenance, and by reducing cost. The strategy and methodology used in the design and development of the system, as well as the results, are presented.