Real-time Electrocardiogram Monitoring Research Articles

An Open-Access Long-Term Wearable ECG Database for Premature Ventricular Contractions and Supraventricular Premature Beat Detection

Wearable electrocardiogram (ECG) devices can provide real-time, long-term, non-invasive and comfortable ECG monitoring for premature beats (PB) assessment (typically presenting as premature ventricular contractions (PVC) and supraventricular premature beat (SPB)), which may foreshadow stroke or sudden cardiac death. However, the poor quality, introduced by the dry electrode in wearable ECG monitoring system, leads to the inefficient recognition of the existing PB detection technologies. Although many methods can achieve high recognition rate on current widely-used open-access clinical ECG databases, they still fail to work properly on dynamic ECG signals. This study presents an open-access ECG database comprises of 24-hour wearable ECG recordings. The database is used for the 3rd China Physiological Signal Challenge (CPSC 2020), where participants are expected to recognize PVC and SPB from these recordings. All the approved algorithms are evaluated by scoring standards and regulations defined in terms of PVC detection and SPB detection, respectively.

An Open-Access Long-Term Wearable ECG Database for Premature Ventricular Contractions and Supraventricular Premature Beat Detection

Wearable electrocardiogram (ECG) devices can provide real-time, long-term, non-invasive and comfortable ECG monitoring for premature beats (PB) assessment (typically presenting as premature ventricular contractions (PVC) and supraventricular premature beat (SPB)), which may foreshadow stroke or sudden cardiac death. However, the poor quality, introduced by the dry electrode in wearable ECG monitoring system, leads to the inefficient recognition of the existing PB detection technologies. Although many methods can achieve high recognition rate on current widely-used open-access clinical ECG databases, they still fail to work properly on dynamic ECG signals. This study presents an open-access ECG database comprises of 24-hour wearable ECG recordings. The database is used for the 3rd China Physiological Signal Challenge (CPSC 2020), where participants are expected to recognize PVC and SPB from these recordings. All the approved algorithms are evaluated by scoring standards and regulations defined in terms of PVC detection and SPB detection, respectively.

Multifunctional and high-performance electronic skin based on silver nanowires bridging graphene

Graphene has great potential in the electronic skin to monitor physiological signals. However, many cracks are generated in graphene during the deformation of human body, which would form the open circuit and decrease the measure range, sensitivity, and stability of devices. To solve the problem, we find the answer in higher plant, whose symplasm consists of cytoplasm and plasmodesmata. Cytoplasm is wrapped in hard cell wall like the open circuit of graphene crack. The intercellular communication can only be achieved through plasmodesmata, a nanochannel connecting adjacent cytoplasm. In this article, by imitating the plasmodesmata, a silver nanowires (AgNWs) bridging laser scribed graphene oxide electronic skin (GES) have been proposed with a customized pattern. Compared with the pure GES, the plasmodesmata-like AgNWs bridging GES shows much better performance of mechanical sensitivity and measuring range. Mechanical signals such as pulse, respiration, and joint movement can be detected. In addition, the impedance between GES and skin decreases drastically. More importantly, the excellent matching of the AgNWs bridging GES electrode with the current commercial module facilitates the real-time monitoring of electrocardiogram (ECG) and electroencephalogram (EEG) signals. This work has great potential in the real-time, wearable, and multifunctional physiological monitoring system.

A Battery-Less Portable ECG Monitoring System With Wired Audio Transmission.

This paper proposes a batteryless electrocardiogram (ECG) monitoring chip and intelligent system with wired audio transmission, which can be applied to long-term and real-time ECG monitoring. The ECG signal is modulated and amplified in the front-end chip and then is transmitted through the microphone channel of the 3.5-mm headphone cable. The sinusoidal signals from the left and right channels are converted into a dc supply and a precise local oscillator signal in the front-end chip, respectively. The proposed system samples the signal through the high-precision audio analog-to-digital converter in smart devices and processes it through the internal software. Therefore, no external battery, local oscillator, and complicate modules are required in this system. A chopper/amplitude modulation (AM) reused mixer amplifier is proposed, which combines chopping with AM and reuses the local oscillator signal. The closed-loop gain of the proposed amplifier varies between 20 and 47 dB and can be automatically adjusted according to the amplitude of the output signal. The proposed chip was implemented in a standard 0.18-μm CMOS process and occupies 1.07 × 0.95 mm2 of core area. The power management module outputs a 1.5-V dc voltage to power the system. When connected with the headphone cable load, the chip consumes a total power of 156 μW. The proposed monitoring system has been verified by the audio device and MATLAB in the PC. The test results show that the input reference noise of the demodulated signal is 2.12 μVrms (0.1-200Hz) and the total harmonic distortion is 0.56%@3 mV input.

Design and Implementation of Real-time Electrocardiogram Monitoring System for Telemedicine Services

Design and Implementation of Real-time Electrocardiogram Monitoring System for Telemedicine Services

Design and Implementation of Real-time Electrocardiogram Monitoring System for Telemedicine Services

Background/Objectives: In this study, a real-time cost effective, light weight and portable Electrocardiogram (ECG) based telemedicine system for the monitoring of cardiac patients is designed and assembled. Methods/Statistical Analysis: The proposed system is realized with the aid of instrumentation amplifier to improve the signal strength and a notch filter that eliminates the noise from the ECG signal. The output interface of the system is a simple computer/laptop that receives an ECG signal from an Atmega-32 microcontroller using RS-232 serial module. Findings: The ECG waveform parameters such as heartbeat rate, amplitude level, and period of the PQRST wave are acquired and displayed on the GUI interface of LabVIEW. Applications/Improvements: The proposed system can be best implemented in a non-clinical environment such as home, offices and remote areas etc. Furthermore, improvements can be done by incorporating the designed system with the multimedia mobile service for the comfort of the patient and doctor so that they can send and receive ECG signal from any place at any time. Keywords: Atmega-32, Cardiovascular Disease, GUI Interface, Notch Filter, Telemedicine, ECG, RS-232

A Fetal ECG Monitoring System Based on the Android Smartphone.

In this paper, a fetal electrocardiogram (ECG) monitoring system based on the Android smartphone was proposed. We designed a portable low-power fetal ECG collector, which collected maternal abdominal ECG signals in real time. The ECG data were sent to a smartphone client via Bluetooth. Smartphone app software was developed based on the Android system. The app integrated the fast fixed-point algorithm for independent component analysis (FastICA) and the sample entropy algorithm, for the sake of real-time extraction of fetal ECG signals from the maternal abdominal ECG signals. The fetal heart rate was computed using the extracted fetal ECG signals. Experimental results showed that the FastICA algorithm can extract a clear fetal ECG, and the sample entropy can correctly determine the channel where the fetal ECG is located. The proposed fetal ECG monitoring system may be feasible for non-invasive, real-time monitoring of fetal ECGs.

Accepting telemedicine in a circulatory medicine ward in major hospitals in South Korea: patients\u2019 and health professionals\u2019 perception of real-time electrocardiogram monitoring

BackgroundSouth Korean government is currently in progress of expanding the coverage of telemedicine projects as part of an attempt to vitalize service industry, but is facing fierce opposition from KMA. Practice of telemedicine requires sufficient discussions among related parties. Although the participation of medical specialists is important, agreement from the public is essential.MethodsThree main tertiary care centers in Seoul were selected for data collection. A total of 224 patients (patients n = 180, patient guardian n = 44) and medical professionals (n = 41) were selected using simple random sampling. Mixed method of quantitative survey and qualitative semi-interview was used.ResultsThis study analyzed patients’ and medical professionals’ perception about the application of telemedicine in cardiology ward in tertiary care centers to provide baseline data when developing and applying telemedicine services. Results implied high need for encouraging telemedicine projects in order to appeal needs among population by providing experience (p < 0.001) and knowledge (p < 0.001). Other results showed that the need for electrocardiography monitoring was high among not only in remote areas but also in areas close to the capital. 64.52% of all participants thought that telemedicine was needed, and 73.21% of participants were willing to use telemedicine service if provided. Semi-interviews revealed that participants expected more cost and time saving services through remote treatment, by not having to visit long distance hospitals frequently.ConclusionsResearch results oppose Korean Medical Association’s opinion that the population is against enforcing telemedicine related laws. The findings in this study reflect an up-to-date perception of telemedicine among patients and medical professionals in a tertiary care centers’ cardiology ward. Moreover, the study provides a baseline that is needed in order to overcome past failures and to successfully implement telemedicine in South Korea.

ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform

BackgroundSince the electrocardiogram (ECG) signal has a low frequency and a weak amplitude, it is sensitive to miscellaneous mixed noises, which may reduce the diagnostic accuracy and hinder the physician’s correct decision on patients.MethodsThe dual tree wavelet transform (DT-WT) is one of the most recent enhanced versions of discrete wavelet transform. However, threshold tuning on this method for noise removal from ECG signal has not been investigated yet. In this work, we shall provide a comprehensive study on the impact of the choice of threshold algorithm, threshold value, and the appropriate wavelet decomposition level to evaluate the ECG signal de-noising performance.ResultsA set of simulations is performed on both synthetic and real ECG signals to achieve the promised results. First, the synthetic ECG signal is used to observe the algorithm response. The evaluation results of synthetic ECG signal corrupted by various types of noise has showed that the modified unified threshold and wavelet hyperbolic threshold de-noising method is better in realistic and colored noises. The tuned threshold is then used on real ECG signals from the MIT-BIH database. The results has shown that the proposed method achieves higher performance than the ordinary dual tree wavelet transform into all kinds of noise removal from ECG signal.ConclusionThe simulation results indicate that the algorithm is robust for all kinds of noises with varying degrees of input noise, providing a high quality clean signal. Moreover, the algorithm is quite simple and can be used in real time ECG monitoring.

Adaptive compressive engine for real‐time electrocardiogram monitoring under unreliable wireless channels

Traditional compressive sensing (CS) methods assume the data sparsity to be constant over time, which holds well in many long-term scenarios. However, the authors’ recent study on electrocardiogram (ECG) monitoring reveals that data sparsity varies dramatically for real-time monitoring systems where the data latency must be bounded, due to limited data collected within the delay bound. The variation of data sparsity makes the reconstruction error (RE) unstable. Furthermore, the variation of wireless channel quality also impacts the reconstruction quality. To accommodate both variations, this study proposes a novel adaptive feedback architecture for real-time wireless ECG monitoring based on the CS technique, which can bound the REs in the presence of the variations of data sparsity and wireless channel. An experiment testbed has been built to evaluate the performance of the proposed system. The results show that the data latency can be limited to <300 ms and the RE can be controlled to 9%.

Convolutional Neural Networks for patient-specific ECG classification.

We propose a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system using an adaptive implementation of 1D Convolutional Neural Networks (CNNs) that can fuse feature extraction and classification into a unified learner. In this way, a dedicated CNN will be trained for each patient by using relatively small common and patient-specific training data and thus it can also be used to classify long ECG records such as Holter registers in a fast and accurate manner. Alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. The experimental results demonstrate that the proposed system achieves a superior classification performance for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB).

Real-Time ECG Noise Reduction with QRS Complex Detection for Mobile Health Services

Cardiovascular disease is a serious threat to human life, especially when a sudden attack occurs, so real-time patient monitoring is crucial. Recent advances in health care and technology have led to equipment such as mobile micro-electro-mechanical systems, which can be used for more accessible public healthcare services. Electrocardiogram (ECG) data are traditionally used to investigate and monitor heart activities. However, the necessary electronic logic tags and (wireless) signal transmissions in a mobile healthcare device are susceptible to noise, which can result in false interpretations. Consequently, this study proposed a novel, low-complexity method for generating an optimized ECG wave suitable for mobile architecture. We first apply a bi-quad, high-pass filter to adjust baseline drifts. Then, a Savitzky–Golay filter smoothes the raw ECG, and moving variance and integral filters with thresholds are used to determine the QRS complex. We compared the results of the proposed technique to those from the moving average, Savitzky–Golay, PRASMMA, and Pan–Tompkins algorithms, using the well-known QT and MIT-BIH databases, and human subjects. The method was implemented on a mobile device integrating an open ECG platform as a prototype for real-time ECG monitoring systems.

Feasibility and efficacy of a remote real-time wireless ECG monitoring and stimulation system for management of ventricular arrhythmia in rabbits with myocardial infarction.

The purpose of this study was to explore the feasibility of continuous remote monitoring, and the induction and termination of malignant ventricular arrhythmias (VAs) by a novel implantable electronic cardiovascular device (IECD) system in rabbits with myocardial infarction (MI). The IECD was implanted and MI was induced by ligation of the left anterior descending coronary artery in 20 adult rabbits. Internet-based remote electrocardiogram (ECG) monitoring and ventricular stimulation were conducted in remote locations with internet access. The voltage amplitudes of the stimulation signals were recorded synchronously by remote and surface ECG. Programmed stimulation with regular stimuli and regular stimuli with an added extra stimulus were performed prior to and following the MI surgery to induce and terminate VAs. IECD implantation and MI surgery, as well as qualified remote and bidirectional signal communications between the implanted IECD and extracorporeal system, were successfully achieved in 18 rabbits. The voltage of the stimulation signals recorded by the remote and surface ECGs showed a good correlation with the stimulation current (remote ECG, r=0.972 and surface ECG, r=0.988; P<0.001). Sustained ventricular tachycardia (VT) was induced in five rabbits (5/20, 25%) prior to MI induction and in 12 rabbits (12/16, 75%) following MI induction. Of the 17 induced VTs, 16 were successfully terminated by remote ventricular stimulation. The novel IECD system provides qualified remote wireless ECG monitoring and possesses the potential to induce and terminate VAs by remote ventricular pacing in this rabbit model of MI. Thus, this model of MI may be used to test the efficacy of novel drugs and devices for the management of VAs.

An adaptive framework for real-time ECG transmission in mobile environments.

Wireless electrocardiogram (ECG) monitoring involves the measurement of ECG signals and their timely transmission over wireless networks to remote healthcare professionals. However, fluctuations in wireless channel conditions pose quality-of-service challenges for real-time ECG monitoring services in a mobile environment. We present an adaptive framework for layered coding and transmission of ECG data that can cope with a time-varying wireless channel. The ECG is segmented into layers with differing importance with respect to the quality of the reconstructed signal. According to this observation, we have devised a simple and efficient real-time scheduling algorithm based on the earliest deadline first (EDF) policy, which decides the order of transmitting or retransmitting packets that contain ECG data at any given time for the delivery of scalable ECG data over a lossy channel. The algorithm takes into account the differing priorities of packets in each layer, which prevents the perceived quality of the reconstructed ECG signal from degrading abruptly as channel conditions worsen, while using the available bandwidth efficiently. Extensive simulations demonstrate this improvement in perceived quality.

The tendency of changes in blood oxygen saturation and arrhythmia: a clinical report of 500 cases

To observe the correlation of duration of hypoxia and degree of arrhythmia in patients with and without past history of cardiac disease, in order to provide a warning value supplied by pulse oxygen saturation (SpO2) monitoring for clinical surveillance. A prospective study was conducted. Patients with arrhythmia induced solely by hypoxia were included, and they were grouped into cardiac diseases group (n=487) and non-cardiac diseases group (n=13) according to whether or not they had cardiac diseases. Both groups were undergoing mechanical ventilation, and the real time monitoring of electrocardiogram (ECG) and the SpO2 were monitored. The types of arrhythmia and duration and severity of hypoxia were observed. The method of logistic regression was conducted to find the affecting factors of arrhythmia. There were significant differences in SpO2 (0.82±0.12 vs. 0.50±0.10) and duration of arrhythmia (2.55±1.02 minutes vs. 17.13±1.45 minutes) between the cardiac disease group and the non-cardiac disease group (both P<0.01). The multifactor logistic regression analysis on the affecting factors in patients with cardiac arrhythmia indicated that cough during sputum suction [odds ratio (OR)=3.078, 95% confidence interval (95%CI) 1.756 - 6.235], long duration of tracheal intubation (OR=0.267, 95%CI 0.122 - 0.573), over anesthesia (OR=2.541, 95%CI 1.039 - 6.615), inadequate tidal volume (OR=4.360, 95%CI 2.187 - 9.443), low respiratory frequency (OR=0.297, 95%CI 0.069 - 1.113) were all risk factors of arrhythmia (P<0.05 or P<0.01). The hypoxemia (lowering of SpO2) is the independent risk factor leading to arrhythmia. Healthy heart can tolerate hypoxia better, and the SpO2 warning value is ≤0.60. Compared with the non-cardiac disease group, patients in cardiac disease group has significantly lower toleration ability against hypoxia, and its SpO2 warning value is lower than 0.95.

Integrated approach for smart implantable cardioverter defibrillator (ICD) device with real time ECG monitoring: use of flexible sensors for localized arrhythmia sensing and stimulation

Arrhythmias are the most common cause of death associated with sudden death and are common in US and worldwide. Cardiac resynchronization therapy (CRT), evolving from pacemakers and development of implantable cardioverter defibrillator (ICD), has been adopted for therapeutic use and demonstrated benefits in patients over the years due to its design and intricate functionality. Recent research has been focused on significant design improvement and efforts are dedicated toward device size reduction, weight and functionality in commercially available ICD's since its invention in the 1960's. Commercially available CRT-D has shown advancement on both clinical and technical side. However, improved focus is required on the device miniaturization, technologically supported and integrated wireless based system for real time heart monitoring electrocardiogram (ECG). In the present report a concise overview for the state-of-the art technology in ICDs and avenues for future development are presented. A unique perspective is also included for ICD device miniaturization and integration of flexible sensing array. Sensor array integration along with its capabilities for identifying localized arrhythmia detection and targeted stimulation for enhancing ICD device capabilities is reviewed.

A real-time cardiac arrhythmia classification system with wearable sensor networks.

Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. This paper presents the design of a real-time wearable ECG monitoring system with associated cardiac arrhythmia classification algorithms. One of the striking advantages is that ECG analog front-end and on-node digital processing are designed to remove most of the noise and bias. In addition, the wearable sensor node is able to monitor the patient's ECG and motion signal in an unobstructive way. To realize the real-time medical analysis, the ECG is digitalized and transmitted to a smart phone via Bluetooth. On the smart phone, the ECG waveform is visualized and a novel layered hidden Markov model is seamlessly integrated to classify multiple cardiac arrhythmias in real time. Experimental results demonstrate that the clean and reliable ECG waveform can be captured in multiple stressed conditions and the real-time classification on cardiac arrhythmia is competent to other workbenches.