Real-time Electrocardiogram Monitoring Research Articles

Advancing athlete safety through real-time ECG monitoring for enhanced cardiovascular health in sports performance

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.

Real-time analysis of conduction disturbances during TAVR with the CARA monitor

BackgroundThe occurrence of conduction disturbances (CDs) remains the most frequent complication of transcatheter aortic valve replacement (TAVR). However, little is known about the timing of electrocardiogram (ECG) changes and CDs during the TAVR procedure. ObjectiveThe objective of this study was to describe ECG changes throughout the TAVR procedure using the CARA monitor. MethodsThis was a multicenter study including 196 prospectively enrolled patients with no CDs undergoing TAVR. All patients were monitored with the CARA system, which uses a 12-lead ECG to measure PQ and QRS intervals, QRS axis, and variations with each heartbeat at every step: baseline, wire insertion, pre-dilatation, valve deployment, post-dilatation, and end of procedure. ResultsPQ and QRS intervals progressively increased throughout the procedure, with a cumulative increase from 169.0 ± 20.0 ms to 186.0 ± 31.6 ms (P < .001) for the PQ interval and from 101.3 ± 10.5 ms to 126.0 ± 25.4 ms (P < .001) for the QRS interval, from baseline to the end of the procedure. A significant increase in the number of patients with left axis deviation was observed (8% at baseline vs 32% at end of procedure; P < .001). A total of 161 (82.4%) patients exhibited at least 1 CD episode (PQ >200 ms, QRS ≥120 ms, advanced heart block) during the procedure, with most episodes occurring during pre-dilatation and valve implantation maneuvers. ConclusionThe CARA system facilitated real-time ECG monitoring, detecting subtle and progressive changes during TAVR. ECG changes occurred at each step, with most patients experiencing CDs, especially during pre-dilatation and valve implantation. The potential of ECG dynamics and timing for early detection of severe CDs should be explored in future studies.

Carbon Quantum Dot-Functionalized Dermis-Derived Transparent Electronic Skin for Multimodal Human Motion Signal Monitoring and Construction of Self-Powered Triboelectric Nanogenerator.

Electronic skin (e-skin) is considered as a highly promising interface for human-computer interaction systems and wearable electronic devices. Through elaborate design and assembly of various materials, it possesses multiple characteristics similar to human skin, including remarkable flexibility, stretchability, sensitivity to temperature and humidity, biocompatibility, and efficient interfacial ion/electron transport capabilities. Here, we innovatively integrate multifunctional carbon quantum dots (CQDs), which exhibit conductivity, antibacterial properties, ultraviolet absorption, and fluorescence emission, with poly(acrylic acid) and glycerin (Gly) into a three-dimensional network structure of natural goatskin collagen fibers. Through a top-down design strategy enhanced by hydrogen bond reconstruction, we successfully fabricated a novel transparent e-skin (PAC-eSkin). This e-skin exhibited significant tensile properties (4.94 MPa of tensile strength and 263.42% of a maximum breaking elongation), while also possessing Young's modulus similar to human skin (2.32 MPa). It is noteworthy that the functionalized CQDs used was derived from discarded goat hair, and the addition of Gly gave PAC-eSkin excellent antifreezing and moisturizing properties. Due to the presence of ultrasmall CQDs, which creates efficient ion/electron transport channels within PAC-eSkin, it could rapidly sense human motion and physiological signals (with a gauge factor (GF) of 1.88). Furthermore, PAC-eSkin had the potential to replace traditional electrode patches for real-time monitoring of electrocardiogram, electromyogram, and electrooculogram signals, with a higher SNR (signal-to-noise ratio) of 25.1 dB. Additionally, the customizable size and shape of PAC-eSkin offer vast possibilities for the construction of single-electrode triboelectric nanogenerator systems. We have reason to believe that the design and development of this transparent e-skin based on CQDs-functionalized dermal collagen matrices can pave a new way for innovations in human-computer interaction interfaces and their sensing application in diverse scenarios.

A hydrophobic graft-modified PVA hydrogel by michael addition joint with gelation strategy for underwater cardiac sensing

The rapid development of global big health and AI technology has made electrocardiogram (ECG) monitoring in all scenarios more important. While real-time ECG monitoring in the air is easy to realize, underwater ECG monitoring is still challenging for general hydrogels. Herein, we prepared a hydrogel with underwater viscosity, biocompatibility, and excellent mechanical properties by hydrophobic graft-modified of PVA (POV) hydrogel and used it to prepare electrodes for underwater ECG monitoring. The POV hydrogel is very flexible and co-deforms easily with the skin, with a Young's modulus as low as 0.03 MPa and underwater adhesion strength up to 11 KPa. The hydrophobic chain segments within the POV hydrogel give the hydrophobic gel excellent stability, adhesion, and ionic conductivity given by the free ionic groups within the POV hydrogel enable the POV hydrogel to detect and transmit physiological electrical signals. Compared to commercial gel electrodes, this POV electrode has better adhesion, conductivity and stability underwater. The POV gel electrodes can effectively collect real-time ECG signals in the air and underwater. Therefore, this study effectively broaden the application of POV hydrogels in ECG biosensors for underwater cardiac sensing.

Superimposed Semantic Communication for IoT-Based Real-Time ECG Monitoring.

Real-time electrocardiogram (ECG) monitoring and diagnosis through Internet of Things (IoT) are crucial for addressing the severity and timely treatment of cardiovascular diseases, enabling timely intervention and preventing life-threatening complications. However, current ECG monitoring research predominantly focuses on individual aspects such as signal compression, diagnostic analysis, or secure transmission, lacking joint optimization of various modules in IoT scenarios. To address this gap, this work proposes a novel framework based on superimposed semantic communication for real-time ECG monitoring in IoT. The framework comprises three hierarchical levels: the edge level for data collection and processing, the relay level for signal compression and coding, and the cloud level for data analysis and reconstruction. The proposed framework offers several unique advantages. By employing semantic encoding guided by ECG classification tasks, it selectively extracts crucial features within and between signals, improving compression ratio and adaptability to channel noise. The superimposed semantic encoding achieves content encryption without requiring any additional operations. Moreover, the framework utilizes lightweight anomaly detection neural networks, reducing edge device power consumption and conserving communication resources. Simulation and real experimental results demonstrate that the proposed method achieves real-time encoding and transmission of ECG signals with a compression ratio of 0.019 on the MIT-BIH dataset. Furthermore, it attains a heartbeat classification accuracy of 0.988 and a reconstruction error of 0.061.

Implantable conductive polymer bioelectrode with enzymatic antioxidant activity for enhanced tissue responses and in vivo performance

Implantable bioelectrodes enable precise diagnosis and treatment through direct transmission of electrical signals in the living tissues. However, a fundamental challenge arises as implanted bioelectrodes frequently lose performance due to adverse tissue reactions, primarily induced by high oxidative stress and subsequent inflammatory activation of macrophages. In this study, we investigated a novel strategy to address this challenge in implantable bioelectrodes by introducing reactive oxygen species (ROS)-scavenging capabilities to the bioelectrodes. Specifically, a polypyrrole (PPy) electrode doped with hemin-conjugated heparin (HepH) was fabricated to impart robust enzyme-like antioxidant properties. The HepH-doped PPy electrodes exhibited a catalase-mimicking activity, as evidenced by hydrogen peroxide scavenging and oxygen gas generation in the presence of hydrogen peroxide. In vitro primary macrophage culture and in vivo subcutaneous implantation studies revealed that HepH-incorporated PPy electrodes reduced intracellular ROS levels and directed macrophage polarization toward an anti-inflammatory phenotype, mitigating collagenous scar tissue formation around the implanted electrodes. Finally, real-time electrocardiogram monitoring for 20 days demonstrated the extended in vivo signal sensitivity of the HepH-doped PPy electrodes as reliable implantable bioelectrodes.

Hemodialysis Efficiency Predictor in End-Stage Kidney Disease Using Real-Time Heart Rate Variability.

Autonomic dysfunction as a long-term complication may occur in end-stage kidney disease (ESKD) patients and can be diagnosed using heart rate variability (HRV) analyzed from electrocardiogram (ECG) recordings. There is limited data about HRV using real-time ECG to predict hemodialysis (HD) efficiency in patients with ESKD who are routinely doing HD in the real world. A total of 50 patients (62.1 ± 10.7 years) with ESKD underwent continuous real-time ECG monitoring (237.4 ± 15.3 min) during HD for HRV using remote monitoring system. Their electrolyte levels were checked before and after HD. We compared HRV according to electrolyte levels. During the monitor, we checked the ECG and electrolyte levels simultaneously a total of 2374 times for all of the patients. Both time and frequency domain HRV were higher when the patients had lower K+ level (<0.5 mEq/L) and P+ level change (<2 mEq/L) before and after HD as compared to those with a higher K+ level (≥0.5 mEq/L) and P+ level change (≥2 mEq/L). Additionally, patients with lower K+ and P+ level change groups had higher incidences of arrhythmic events including atrial/ventricular premature complexes, despite no difference of mean heart rate (p < 0.001). Higher HRV was independently associated with a poorly controlled K+ and P+ level during HD in patients with ESKD. This is consistently evidenced by the independent association between higher HRV, K+ and P+ levels in real time, suggesting that low electrolyte changes before and after HD alone may cause cardiac autonomic dysfunction.

Application of remote electrocardiogram monitoring systems in chest pain centers for patients with high-risk chest pain.

Chinese chest pain centers (CPCs) have been expanding and maturing for the past decade, but patient wait times for pre-hospital care remain long. To demonstrate that the remote electrocardiogram (ECG) monitoring system can ensure more efficient treatment for patients with ST-elevation myocardial infarction (STEMI) in CPCs, we compared patients with high-risk chest pain who used remote ECG monitoring systems to those who used conventional ECGs in retrospective cohort study. Based on the inclusion and exclusion criteria, 290 patients who visited our CPC between June 2019 and March 2022 with acute chest pain and a diagnosis of STEMI as well as patients who had undergone an emergency primary percutaneous coronary intervention were selected. Among them, 73 patients with STEMI had employed remote real-time dynamic 12-lead ECG monitoring devices, while 217 patients with STEMI (i.e., the controls) had used conventional ECG monitoring. The effectiveness of treatment procedures for the two groups was investigated. As statistical measures, the symptom onset-to-wire times, first medical contact (FMC)-to-wire times, door-to-wire times, major adverse cardiac events in hospital, and the troponin T levels were analyzed. Compared with the control group, the patients with remote real-time dynamic 12-lead ECG monitoring devices showed shorter times for both symptom onset-to-wire (234.8 ± 95.8 min vs. 317.6 ± 129.6 min, P= 0.0321) and from symptom onset-to-FMC (170.5 ± 86.3 min vs. 245.3 ± 115.6min, P= 0.0287); this group also had a lower 30-day mortality rate (2.73% vs. 4.14%, P= 0.003). The differences between the two groups were statistically significant (P< 0.05). With remote real-time dynamic 12-lead ECG monitoring equipment, myocardial ischemia can be treated more quickly, leading to fewer possible cardiac events and a better prognosis.

Real-time heart rate variability according to ambulatory glucose profile in patients with diabetes mellitus.

Autonomic neuropathy commonly occurs as a long-term complication of diabetes mellitus (DM) and can be diagnosed based on heart rate variability (HRV), calculated from electrocardiogram (ECG) recordings. There are limited data on HRV using real-time ECG and ambulatory glucose monitoring in patients with DM. The aim of this study was to investigate real-time HRV according to ambulatory glucose levels in patients with DM. A total of 43 patients (66.3 ± 7.5 years) with DM underwent continuous real-time ECG monitoring (225.7 ± 107.3 h) for HRV and ambulatory glucose monitoring using a remote monitoring system. We compared the HRV according to the ambulatory glucose profile. Data were analyzed according to the target in glucose range (TIR). There were no significant differences in the baseline characteristics of the patients according to the TIR. During monitoring, we checked ECG and ambulatory glucose levels (a total of 15,090 times) simultaneously for all patients. Both time- and frequency-domain HRVs were lower when the patients had poorly controlled glucose levels (TIR < 70%) compared with well controlled glucose levels (TIR > 70%). In addition, heart and respiratory rates increased with real-time glucose levels (P < 0.001). Poorly controlled glucose levels were independently associated with lower HRV in patients with DM. This was further substantiated by the independent continuous association between real-time measurements of hyperglycemia and lower HRV. These data strongly suggest that cardiac autonomic dysfunction is caused by elevated blood sugar levels.

A Single Blinded, Cross-Sectional, Single-Center Study for the Validation of Atrial Fibrillation Detection using Spandan Smartphone ECG

The atrial fibrillation prevalence is found between 0.1% and 4% in community-based research and between 2.8% and 14% in studies conducted in hospitals; nevertheless, a large portion of AF is still misdiagnosed. Smartphone applications for diagnosing AF have been proposed given that a big portion of Indians own smartphones, although it is unknown how accurate these programs would be. The study's objectives were as follows: a) To evaluate the diagnostic efficacy of the Spandan ECG in detecting AF in a single center trial that took place in SMIH Dehradun under the supervision of the cardiologist, and b) to investigate arrhythmia detection through a smartphone-based and monitoring. This single-blinded, cross-sectional, single-center study was conducted at Shri Mahant Indresh Hospital (SMIH), Dehradun, Uttarakhand, India from August 2022 to December 2022. Patients (n=32) undergoing the electrocardiogram (ECG) at the Department of Cardiology of the SMIH, Dehradun during the study period and diagnosed with atrial fibrillation. Mean age (SD) was 59.93±13.81 years. Males (n=17/32, 53.1%) show more frequency than females. Most patients had a medical history of coronary artery disease (21.8%). True positive cases derived from confusion matrix of atrial fibrillation detected using smartphone based ECG and 12 lead standard ECG along with the cardiologist’s diagnosis was 29/32 on smartphone-based as compared to 19/32 from 12 lead gold standard. Atrial fibrillation was detected correctly in 29/32 cases and 19/32 cases by smartphone ECG and 12 lead gold standard, respectively. The study hypotheses that Spandan's ability of real-time ECG monitoring will be useful in evaluating whether a patient's discomfort is caused by recurrent arrhythmia.

A Tiny Matched Filter-Based CNN for Inter-Patient ECG Classification and Arrhythmia Detection at the Edge.

Automated electrocardiogram (ECG) classification using machine learning (ML) is extensively utilized for arrhythmia detection. Contemporary ML algorithms are typically deployed on the cloud, which may not always meet the availability and privacy requirements of ECG monitoring. Edge inference is an emerging alternative that overcomes the concerns of cloud inference; however, it poses new challenges due to the demanding computational requirements of modern ML algorithms and the tight constraints of edge devices. In this work, we propose a tiny convolutional neural network (CNN) classifier for real-time monitoring of ECG at the edge with the aid of the matched filter (MF) theory. The MIT-BIH dataset with inter-patient division is used for model training and testing. The model generalization capability is validated on the INCART, QT, and PTB diagnostic databases, and the model performance in the presence of noise is experimentally analyzed. The proposed classifier can achieve average accuracy, sensitivity, and F1 scores of 98.18%, 91.90%, and 92.17%, respectively. The sensitivity of detecting supraventricular and ventricular ectopic beats (SVEB and VEB) is 85.3% and 96.34%, respectively. The model is 15 KB in size, with an average inference time of less than 1 ms. The proposed model achieves superior classification and real-time performance results compared to the state-of-the-art ECG classifiers while minimizing the model complexity. The proposed classifier can be readily deployed on a wide range of resource-constrained edge devices for arrhythmia monitoring, which can save millions of cardiovascular disease patients.

A Stochastic Resonance Electrocardiogram Enhancement Algorithm for Robust QRS Detection.

This study presents a new QRS detection algorithm making use of the background noise that is inevitably present in electrocardiogram (ECG) recordings. The algorithm suppresses noise, enhances the QRS-waves, and applies a threshold for QRS detection. Noise suppression and QRS enhancement are performed by a band-pass filter stage followed by a nonlinear stage based on the interaction of a particle inside an underdamped monostable potential well. The nonlinear stage maximizes the output when there is a QRS-wave and minimizes the output otherwise. One of the instruments that the nonlinear stage uses to enhance the QRS-waves is stochastic resonance, where the output is maximized for a non-zero intensity background noise. In terms of QRS-wave detection F1 score, which ranges from 98.87% to 99.99% on four major benchmarking databases (MIT-BIH Arrhythmia, QT, European ST-T, and MIT-BIH Noise Stress Test), the algorithm outperforms all existing ECG processing algorithms. The study, for the first time, demonstrates QRS-enhancement by facilitating stochastic resonance while suppressing in-band noise of ECG signals. Detecting QRS-waves as the ECG data streams, having a complexity of O(n), and not requiring any training data make the algorithm convenient for real-time ECG monitoring applications with limited computational resources.

Real-Time Electrocardiogram Monitoring during Treadmill Training in Mice.

Regular physical exercise is a major contributor to cardiovascular health, influencing various metabolic as well as electrophysiological processes. However, in certain cardiac diseases such as inherited arrhythmia syndromes, e.g., arrhythmogenic cardiomyopathy (ACM) or myocarditis, physical exercise may have negative effects on the heart leading to a proarrhythmogenic substrate production. Currently, the underlying molecular mechanisms of exercise-related proarrhythmogenic remodeling are largely unknown, thus it remains unclear which frequency, duration, and intensity of exercise can be considered safe in the context of disease(s). The proposed method allows to study proarrhythmic/antiarrhythmic effects of physical exercise by combining treadmill training with real-time monitoring of the ECG. Implantable telemetry devices are used to continuously record the ECG of freely moving mice over a period of up to 3 months both at rest and during treadmill training. Data acquisition software with its analysis modules is used to analyze basic ECG parameters such as heart rate, P wave duration, PR interval, QRS interval, or QT duration at rest, during and after training. Furthermore, heart rate variability (HRV) parameters and occurrence of arrhythmias are evaluated. In brief, this manuscript describes a step-by-step approach to experimentally explore exercise induced effects on cardiac electrophysiology, including potential proarrhythmogenic remodeling in mouse models.

Real-Time Electrocardiogram Monitoring during Treadmill Training in Mice

Real-Time Electrocardiogram Monitoring during Treadmill Training in Mice

XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System

This work presents XBeats, a novel platform for real-time electrocardiogram monitoring and analysis that uses edge computing and machine learning for early anomaly detection. The platform encompasses a data acquisition ECG patch with 12 leads to collect heart signals, perform on-chip processing, and transmit the data to healthcare providers in real-time for further analysis. The ECG patch provides a dynamically configurable selection of the active ECG leads that could be transmitted to the backend monitoring system. The selection ranges from a single ECG lead to a complete 12-lead ECG testing configuration. XBeats implements a lightweight binary classifier for early anomaly detection to reduce the time to action should abnormal heart conditions occur. This initial detection phase is performed on the edge (i.e., the device paired with the patch) and alerts can be configured to notify designated healthcare providers. Further deep analysis can be performed on the full fidelity 12-lead data sent to the backend. A fully functional prototype of the XBeats has been implemented to demonstrate the feasibly and usability of the proposed system. Performance evaluation shows that XBeats can achieve up to 95.30% detection accuracy for abnormal conditions, while maintaining a high data acquisition rate of up to 441 samples per second. Moreover, the analytical results of the energy consumption profile show that the ECG patch provides up to 37 h of continuous 12-lead ECG streaming.

Real-time electrocardiogram monitoring for heart diseases with secured internet of thing protocol

Real-time electrocardiogram monitoring for heart diseases with secured internet of thing protocol

Self-adhesive, stretchable, and dry silver nanorods embedded polydimethylsiloxane biopotential electrodes for electrocardiography

Biopotential signals are extremely useful in assessing organ function and diagnosing diseases. This study proposes a highly flexible, conductive, antibacterial, and self-adhesive silver nanorods (AgNRs) embedded polydimethylsiloxane (PDMS) dry electrode for long-term electrocardiogram (ECG) monitoring with portable instrumentation. We use a unique glancing angle deposition method to fabricate AgNRs and embedded them in a biocompatible PDMS matrix. These electrodes do not cause skin irritation even after several hours of use and work efficiently without skin preparation. These AgNRs-PDMS electrodes have an electrical resistivity of 10−7 Ω m and a skin contact impedance of 93.9 ± 0.7 kΩ to 6.2 ± 3.7 kΩ for frequencies ranging from 40 Hz to 1 kHz, which is around 18% less than most conventional Ag/AgCl wet electrodes. The fabricated electrodes have a signal-to-noise ratio of 12 dB, comparable to that of a conventional Ag/AgCl electrode. A signal acquisition circuit is designed to detect ECG signals combined with proposed dry electrodes and a wireless monitoring device. Finally, real-time ECG signals are displayed on a mobile phone via an Android application. These AgNRs-PDMS dry electrodes, in combination with the portable wireless device, may be used in future clinical studies that require real-time and long-term ECG monitoring.

An IoT Assisted Real-Time High CMRR Wireless Ambulatory ECG Monitoring System with Arrhythmia Detection

The absence of cardiovascular disease (CVD) diagnostic and management solutions cause significant morbidity among populations in rural areas and the coronavirus disease of 2019 (COVID-19) emergency. To tackle this problem, in this paper, the development of an Internet of things (IoT) assisted ambulatory electrocardiogram (ECG) monitoring system is presented. The system’s wearable single-channel data acquisition device supports 25 h of continuous operation. A right leg drive (RLD) circuit supported analog frontend (AFE) with a high common mode rejection ratio (CMRR) of 121 dB and a digitally implemented notch filter is used to suppress power-line frequency interference. The wearable device continuously sends the collected ECG data via Bluetooth to the user’s smartphone. An application on the user’s smartphone renders real-time ECG trace and heart rate and detects abnormal heart rhythms. This data are then shared in real-time with the user’s doctor via a real-time cloud database. An application on the doctor’s smartphone allows real-time visualization of this data and detection of arrhythmias. Simulations and experimental results demonstrate that reliable ECG signals can be captured with low latency and the heart rate computation is comparable to a commercial application. Low cost, scalability, low latency, real-time ECG monitoring, and improved performance of the system make the system highly suitable for the real-time remote identification and management of CVDs in users of rural areas and in the COVID-19 pandemic.

IoT-enabled cloud-based real-time remote ECG monitoring system

Statistical reports all around the world have deemed cardiovascular diseases (CVDs) as the largest contributor to the death count. The electrocardiogram (ECG) is a widely accepted technology employed for investigation of CVDs of the person. The proposed solution deals with an efficient internet of things (IoT) enabled real-time ECG monitoring system using cloud computing technologies. The article presents a cloud-centric solution to provide remote monitoring of CVD. Sensed ECG data are transmitted to S3 bucket provided by Amazon web service (AWS) through a mobile gateway. AWS cloud uses HTTP and MQTT servers to provide data visualisation, quick response and long-live connection to device and user. Bluetooth low energy (BLE 4.0) is used as a communication protocol for low-power data transmission between device and mobile gateway. The proposed system is implemented with filtering algorithms to ignore distractions, environmental noise and motion artefacts. It offers an analysis of ECG signals to detect various parameters such as heartbeat, PQRST wave and QRS complex intervals along with respiration rate. The proposed system prototype has been tested and validated for reliable ECG monitoring remotely in real-time.

Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling

Cardiovascular diseases have been reported to be the leading cause of mortality across the globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of main interest among researchers, as its early diagnosis can prevent life threatening cardiac conditions and potentially save human lives. Analyzing the Electrocardiogram (ECG) can provide valuable diagnostic information to detect different types of cardiac arrhythmia. Real-time ECG monitoring systems with advanced machine learning methods provide information about the health status in real-time and have improved user’s experience. However, advanced machine learning methods have put a burden on portable and wearable devices due to their high computing requirements. We present an improved, less complex Convolutional Neural Network (CNN)-based classifier model that identifies multiple arrhythmia types using the two-dimensional image of the ECG wave in real-time. The proposed model is presented as a three-layer ECG signal analysis model that can potentially be adopted in real-time portable and wearable monitoring devices. We have designed, implemented, and simulated the proposed CNN network using Matlab. We also present the hardware implementation of the proposed method to validate its adaptability in real-time wearable systems. The European ST-T database recorded with single lead L3 is used to validate the CNN classifier and achieved an accuracy of 99.23%, outperforming most existing solutions.