Why do ecg monitoring

It measures the electrical current that runs through your heart. Everybody has a unique ECG trace but there are patterns of an ECG that indicate various heart problems such as arrhythmias. So what does an electrocardiogram show? Do you want to learn more about ECG tests and arrhythmias? Share Your Story. A convenient and reliable touch-free alternative is radar cardiography.

Radar systems enable touchless, contactless, and continuous monitoring of heart rate through clothes, blankets, or other isolators. Plenty of the research work investigated the possibility of using radar systems for continuous heartbeat monitoring and detection of vital signs [ , , , , , ]. Alternatively, some researchers investigated implanted sensor technology for durable and long-term continuous monitoring [ , ].

For instance, Giancaterino et al. Sunnet et al. Implants offer a practical solution for long-term monitoring, given that continuous external monitoring for such a long period of time can be unfeasible. On the other hand, advancements in robotics introduced new opportunities for cardiac healthcare, especially with the great challenge of limited medical resources. Some researchers investigated the dimensions of the practical use of robotic-assisted heart surgeries [ , ].

Others studied the possibility of having a robot assistant providing medical feedback, diagnosis, and notifications [ , ]. ECG signal can be used to evaluate psychological state, emotions, and stress levels to aid mental healthcare for people living in stressful environments [ 59 , , ]. Advancement in deep learning technologies and algorithms can provide interesting opportunities for adaptation and personalization, overcoming individual differences by periodical retraining [ , ]. Techniques used can be categorized into two main classes.

The first class of security techniques are focused on encryption and cryptographic algorithms [ , ]. These techniques are accused of having a large computational overhead, which makes them unsuitable in a resource-constrained mobile environment.

The second class of security techniques is focused on concealing sensitive information inside another set of insensitive host data, with no increase in the host data size or computational overhead.

These techniques are called steganography techniques. As discussed in this paper, ECG monitoring systems involve many components, variable contexts, and various stakeholders and encompasses diverse technologies.

This diversity and variability of ECG monitoring system contexts and components impose a number of challenges that have been highlighted by several researchers. In the subsequent sections, we discuss ECG monitoring challenges related to the use of monitoring devices, signal quality, sensor design, durability, the size of the data, visualization, and integration.

Hsieh and Len [ 32 ] highlighted that patients may forget to carry out monitoring tasks in ad hoc monitoring in home settings, reducing the gains sought from regular monitoring.

Hence, alarm and screening reminder needs to be considered when designing home manual monitoring systems. In real-time monitoring, patients can enjoy real-life activities, including physical exercise and running, which usually result in motion artifacts, signal noise, and deterioration.

Lee and Chung [ 5 ] highlighted in their research the importance of combining efficient filtering methods for real-time monitoring setups with motion artifacts removal during running or physical exercise of a person.

They proposed the use of an accelerometer as a source of noise reference. High precision is very important in continuous ICU monitoring. However, the ECG signal is noisy and measured in millivolts, which accentuates the need for good filtering and amplifying techniques.

This challenge was highlighted and addressed in [ , 17 ]. For real-time monitoring, it is important to use energy-efficient devices and communication technologies to allow for long-term monitoring.

This challenge was highlighted by many researchers [ , , ]. Sampling algorithms were used by the authors of [ , ] to help conserving energy. Also, the authors of [ , ] used Bluetooth as a low-energy communication protocol.

Furthermore, data dimensionality reduction techniques can be used to reduce data size, thus alleviating its processing, which will support monitoring durability. Real-time screening is usually conducted for a relatively longer period of time compared to traditional screening e. As a result, the amount of generated ECG signal data is usually large and sometimes massive.

Subsequently, the process of signal analysis and interpretation turns into rather a challenging task. This accentuates the need for automatic analysis and interpretation of signal data for these monitoring setups in order to generate useful notifications for patients as well as health caregivers.

In addition to challenges related to the size and quality of ECG signal data, some challenges are related to electrode design, the number of leads, and the type of conductor used. For example, Fensli et al. It is, therefore, necessary to further explore the suitability of this recording principle for disease diagnostic purposes. Different diseases require different types of recordings, which should be supported by the selected electrode.

Concerns around the adverse effect of electrodes on the human body were addressed in [ 97 ]. Alternatively, the authors of [ , ] proposed a chemical formulation appropriate for the dipping of various textile fabrics e. These textile washable electrodes also solved the common challenge known in gel electrodes related to the low signal quality when electrodes dry out. Nevertheless, challenges regarding the design of wearable devices require further research. Gusev et al.

Mainly, the problems were related to handling different refreshing rate requirements via multiple platforms installed on displaying devices with low processing requirements. Long-term ECG data analysis can be challenging as it tends to oversimplify the visualized information, which results in losing significant components.

Therefore, Jarchi et al. Display adaptation and customization are another challenging issues for the ECG monitoring system. Display customization of data reporting for each stakeholder, such as the doctor, the nurse, the caregiver, and the patient, is included. Each stakeholder requires a different reporting context. These challenges were addressed in [ ], in which special dashboard functionalities were integrated with visualization features, such as zoom-in and zoom-out, and filtering.

Baig et al. They suggested exploiting Cloud resources for real-time processing to handle the integration issue. Furthermore, Jovanov et al.

Service-Oriented Architecture SOA has also been considered a very promising solution for integrating heterogeneous systems. This is the case for ECG monitoring systems where various technologies, data sources, and devices are used to collect, process, analyze, and visualize data over various interfaces. The involvement of mobile devices in continuous ECG monitoring makes them less effective for computational, data-intensive processing. Though the mobile device improves some flexibility to the monitoring process, problems related to battery consumption and the limited processing capability of the device are still not resolved completely.

ECG monitoring systems have been studied thoroughly in the literature; however, the multi-dimensional aspects of these systems make it difficult for researchers, medical practitioners, and others to select, among these systems, those that fulfill their monitoring needs, match the context of their use, and support the required disease monitoring requirements.

In this paper, we carried out an extensive review of the literature related to ECG monitoring systems, focusing on different aspects including applicability, the technology used, architecture, lifecycle, classification, and challenges. We presented and discussed an expert-verified classification model. Current development in ECG monitoring systems leveraged new technologies, such as deep learning, AI, Big Data and IoT to provide efficient, cost-efficient, fully connected, and powerful monitoring system.

Enabling technologies provide huge opportunities for the advancement of ECG monitoring systems. IoT brings in remote, unconstrained connectivity and services that leverage data and facilitate timely, meaningful, and critical decisions for a better lifestyle. Furthermore, Fog processing and cloud processing contribute to an increased opportunity to improve efficiency and fulfill numerous in-demand scalable application services.

Furthermore, blockchain technology enables security over a distributed environment for various transactions throughout the different layers of the ECG monitoring system architecture. However, these processes are not distinctly defined in the literature; some overlap and others are better merged. None of the researches addressed the details of the complete lifecycle of an ECG monitoring system.

In this paper, to the best of our knowledge, we tried to generalize a complete lifecycle, including all main processes starting from data acquisition, preprocessing, feature extraction, processing and, finally, visualization.

We have also defined a set of supporting processes, such as signal selection, encryption, and compression, which are only required by specialized systems. As a future direction, exploring the field of robotics and healthcare automation has the potential to transform the next generation of ECG monitoring systems and to simplify robotic-assisted surgery procedures, elderly care, and remote and in-hospital continuous patient monitoring.

Robotic-assisted surgery should be performed with higher precision, control, and improved vision, paving the way for the revolutionary healthcare of tomorrow.

To that end, we endorse that this work, with a detailed discussion on many relevant research work, provides a comprehensive state-of-the-art review of ECG monitoring systems.

It can serve as reference for various researchers and stakeholders in the field to compare, understand, and value ECG monitoring system features.

It also highlights the main challenges these systems exhibit in terms of adaptability, integration, monitoring quality and durability. Finally, it outlines a future vision of the next-generation ECG monitoring systems for healthcare. S conceived the main conceptual ideas related to taxonomy, architecture, value chain, and proof outline, supervised the study and was in charge of overall direction and planning. All authors contributed to the revision and proofreading of the final version of the manuscript.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. National Center for Biotechnology Information , U. Journal List Sensors Basel v. Sensors Basel. Published online Mar Hadeel T.

El Kassabi. Author information Article notes Copyright and License information Disclaimer. Received Feb 29; Accepted Mar This article has been cited by other articles in PMC. Abstract Health monitoring and its related technologies is an attractive research area. Introduction The last decade has witnessed an increasing number of deaths caused by chronic and cardiovascular diseases CVDs in all countries across the world.

Open in a separate window. Figure 1. The overall architecture of electrocardiogram ECG monitoring systems. ECG Monitoring Value Chain: Comparative Study ECG monitoring value chain encompasses a set of common processes, including data acquisition, preprocessing, feature extraction, processing, analysis, and visualization.

Figure 2. Preprocessing Preprocessing is intended to enhance the accuracy of prediction; it improves the quality of the raw signal, removes the noise, and removes the baseline wander and powerline interference.

Feature Extraction One of the most important processes in the ECG monitoring lifecycle is the feature extraction process. Processing and Analysis Intensive research was devoted to improve the efficiency of processing and analysis of ECG signals to achieve high diagnostic accuracy. Visualization The visualization process typically includes all the functionalities that will allow users to inspect and interact with recorded or annotated ECG signals in real-time, as well as offline from a file [ 80 , 81 ].

Supporting Processes The supporting processes are the activities that provide extra functionalities to support the primary processes to realize an efficient monitoring system. Figure 3. Context-Aware ECG Monitoring Systems The first cluster of work incorporates the group of systems that are organized into monitoring contexts for which ECG monitoring systems were developed and consist of home-based, hospital-based, ambulatory-based, and remote-based ECG monitoring systems.

Table 2 Classification of selected context-aware ECG monitoring systems. Home ECG Monitoring Systems ECG Monitoring systems deployed in the home environment are generally classified into what is called telemonitoring, wearable continuous monitoring, and monitoring the elderly people in their homes.

Hospital ECG Monitoring Systems Hospital-based ECG monitoring systems are classified into systems developed either for an intensive care unit ICU clinical setting [ 16 , 17 ], non-ICU clinical setting [ 14 , 15 , , ], or a Holter monitoring setting [ 36 , , , , ]. Ambulatory ECG Monitoring Systems Considerable research and development is being undertaken for ambulatory ECG monitoring systems [ 8 , 21 , 22 , 23 , , , , ]; most of the researches support data collection, transmission, and analytics for ambulatory emergency situations.

Remote ECG Monitoring Systems The telemonitoring method proposed within the context of remote ECG monitoring system in [ 10 , , ] differs from the one described for the home monitoring context above.

Technology-Aware ECG Monitoring Systems The second cluster of researches encompasses a group of monitoring systems that emphasizes the use of emerging technologies to support ECG monitoring. Table 3 Classification of selected technology-aware ECG monitoring systems. Enabling Technologies ECG monitoring relies on key technologies to support various ECG processes, including preprocessing, processing, storage, analytics, and visualization of ECG signals.

Table 4 Classification of ECG monitoring systems based on scheme and frequency. Traditional ECG Monitoring A number of research work addressed traditional ECG monitoring setups in various contexts such as hospitals [ , , ], homes, or remote ambulatory settings [ 31 , 32 , , , ].

ECG Monitoring System Targets and Purposes Several ECG monitoring systems in the literature have been developed to serve a certain purpose or to target a specific functionality, which we grouped into the fourth cluster. Service-Based Monitoring Systems In this section, we classify the service-based ECG monitoring systems into three main categories: diagnoses, activities, and prognoses.

Table 5 Classification of selected service-based ECG monitoring systems. Performance-Based Monitoring Systems We define performance-based monitoring systems as those that address performance advances in different aspects and characteristics. Table 6 Classification of selected performance-based ECG monitoring systems. Performance-Based Category Selected papers Energy Transmission BLE [ , , , , , , , , , , , , , ] Other Wireless [ , ] Processing [ , , , , ] Compression [ , , , , , , ] Cost Device Phone [ , , , ] Circuit [ ] Wearable [ , ] Others [ 10 , , , ] Disease Prevention [ , ] Resources Storage [ ] Other resource allocation [ , , ].

Table 7 Classification of selected futuristic ECG monitoring systems. Futuristic Dimension Selected Papers Radar Cardiography [ , , , , , ] Implants [ , , , ] Robotics [ , , , ] AI [ 59 , , , , ] Steganography [ 48 , , , ]. Key Challenges of ECG Monitoring Systems As discussed in this paper, ECG monitoring systems involve many components, variable contexts, and various stakeholders and encompasses diverse technologies.

Challenges Related to Signal Quality In real-time monitoring, patients can enjoy real-life activities, including physical exercise and running, which usually result in motion artifacts, signal noise, and deterioration. Challenges Related to Monitoring Durability For real-time monitoring, it is important to use energy-efficient devices and communication technologies to allow for long-term monitoring. Challenges Related to Size of ECG Signal Data Real-time screening is usually conducted for a relatively longer period of time compared to traditional screening e.

Challenges Related to Visualization Gusev et al. Challenges Related to System Integration Baig et al. Discussion, Conclusion, and Future Direction ECG monitoring systems have been studied thoroughly in the literature; however, the multi-dimensional aspects of these systems make it difficult for researchers, medical practitioners, and others to select, among these systems, those that fulfill their monitoring needs, match the context of their use, and support the required disease monitoring requirements.

Author Contributions M. Conflicts of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Medical tests. Home Medical tests. ECG test. Actions for this page Listen Print. Summary Read the full fact sheet. On this page. About ECG test An electrocardiogram ECG is a medical test that detects cardiac heart abnormalities by measuring the electrical activity generated by the heart as it contracts. They may also recommend an ECG if a person is experiencing symptoms such as: chest pain shortness of breath dizziness fainting, or fast or irregular heartbeats palpitations.