Electrocardiograph Monitoring System Research Articles

Advanced ECG Monitoring: Machine Learning And Robotics For Real-Time Health Management

Cardiovascular diseases are becoming increasingly prevalent due to lifestyle factors such as poor diet, lack of exercise, and conditions like diabetes and hypertension. Electrocardiography (ECG) is a widely used diagnostic tool for detecting various heart conditions, including arrhythmias and myocardial infarction. However, manual analysis of ECG signals is often subjective, time-consuming, and prone to variability. To address these challenges, this paper proposes a comprehensive system that integrates an Arduino-based ECG acquisition module with neural networks for automatic analysis and a robotic system for autonomous sensor placement and intervention. The main objective is to create a smart, real-time ECG monitoring and Computer-Aided Diagnosis (CAD) system capable of early detection and management of heart diseases. The system utilizes machine learning and deep learning techniques to enhance diagnostic accuracy, focusing on implementing a neural network model for ECG classification. A robotic arm is also integrated into the system to ensure precise sensor placement and emergency response

Anti-Motion Interference Electrocardiograph Monitoring System: A Review

Anti-Motion Interference Electrocardiograph Monitoring System: A Review

Performance comparison of 6 in-hospital patient monitoring systems in the detection and alarm of ventricular cardiac arrhythmias

BackgroundPatient monitoring devices are critical in alerting potential cardiac arrhythmias during hospitalization; however, there are concerns of alarm fatigue due to high false alarm rates. ObjectiveTo evaluate sensitivity and false alarm rate of hospital-based continuous electrocardiographic (ECG) monitoring technologies. MethodsSix commonly-used multiparameter bedside monitoring systems available in the United States were evaluated: B125M (GE HealthCare), ePM10 and iPM12 (Mindray), Efficia and IntelliVue (Philips), and Life Scope (Nihon Kohden). Sensitivity was tested using ECG recordings containing 57 true ventricular tachycardia (VT) events. False positive rate testing used 205 patient-hours of ECG recordings containing no cardiac arrhythmias. Signals from ECG recordings were fed to devices simultaneously; high-severity arrhythmia alarms were tracked. Sensitivity to true VT events and false positive rates were determined. Differences were assessed using Fisher’s exact tests (sensitivity) and Z-tests (false positive rates). ResultsB125M raised 56 total alarms for 57 annotated VT events and had the highest sensitivity (98%, P<0.05), followed by iPM12 (84%), Life Scope (81%), Efficia (79%), ePM10 (77%), and IntelliVue (75%). B125M raised 20 false alarms, significantly lower (P<0.0001) than iPM12 (284), Life Scope (292), IntelliVue (304), ePM10 (324), and Efficia (493). The most common false alarm was VT, followed by non-sustained VT. ConclusionWe found significant performance differences among multiparameter bedside ECG monitoring systems using previously-collected recordings. B125M had the highest sensitivity in detecting true VT events and lowest false alarm rate. These results can assist in minimizing alarm fatigue and optimizing patient safety by careful selection of in-hospital continuous monitoring technology.

Remote ECG Monitoring System via Internet

Electrocardiography (ECG) is a non-invasive diagnostic tool that records the electrical activity of the heart. It is a valuable tool for diagnosing a variety of heart conditions, including arrhythmias, heart failure, and myocardial infarction. This review paper explores the landscape of Real-time Electrocardiogram (ECG) Monitoring Systems via the Internet, analyzing advancements, challenges, and future prospects in this domain. The study investigates the integration of ECG monitoring systems with internet connectivity, enabling real- time data transmission and analysis for remote healthcare applications. Examining the utilization of IoT frameworks, particularly Raspberry Pi and Arduino, the review elucidates their role in establishing connectivity and data transmission protocols. It assesses the impact of IoT-enabled ECG systems on enhancing patient care, enabling telemedicine, and improving healthcare accessibility. Keywords— Internet of Things (IoT), Raspberry Pi R3, Arduino UNO, Remote Monitoring, Electrocardigraphy (ECG), Internet connected Medical Devices, Remote Healthcare

The effectiveness of atrial fibrillation identification using noninvasive long-term electrocardiographic monitoring system (NOMED-AF TECH).

Although it is well‑known that longer electrocardiographic (ECG) monitoring allows for detection of paroxysmal silent atrial fibrillation (SAF), it is still unknown how long the ECG monitoring should last to increase the probability of SAF diagnosis. Our aim was to analyze ECG acquisition parameters and timing to detect SAF during the Noninvasive Monitoring for Early Detection of Atrial Fibrillation study. The protocol assumed up to 30 days of ECG telemonitoring of each participant in order to reveal AF / atrial flutter (AFL) episodes lasting at least 30 seconds. SAF was defined as AF detected and confirmed by cardiologists in asymptomatic individuals. The ECG signal analysis was based on the results of 2974 participants (98.67%). AF/AFL episodes were registered and confirmed by cardiologists in 515 individuals, that is, 75.7% of all patients (n = 680) in whom AF/AFL diagnosis was established. The median monitoring time to detect the first SAF episode was 6 days (interquartile range [IQR], 1-13). Fifty percent of the patients with this type of arrhythmia were identified by 6th day (IQR, 1-13) of the monitoring, and 75% by the 13th day of the study. Paroxysmal AF was registered on average on 4th day (IQR, 1-10). The ECG monitoring time to detect the first episode of SAF in at least 75% of patients at risk of this arrhythmia was 14 days. Detection of 1 patient with de novo AF, SAF, or de novo SAF, required monitoring of, respectively, 17, 11, and 23 patients.

Multifunctional, breathable MXene-PU mesh electronic skin for wearable intelligent 12-lead ECG monitoring system

With the advent of an aging society and increasing concern for cardiovascular health, comfortable long-term electrocardiographic (ECG) monitoring systems in daily life are much in demand. If the system can give real-time feedback on cardiovascular status and offer timely personalized advice like doctors, it will be beneficial to promote cardiovascular health and alleviate the imbalance of medical resources. Herein, we report a multifunctional, conformal, and air-permeable electronic skin (e-skin) based on MXene-Polyurethane mesh (MPM). When used as ECG electrodes, the e-skin shows ultra-low electrode–skin contact impedance (4.68 kΩ at 1 kHz), high signal-to-noise ratio (16.5 dB), and high breathability (2.1838 kg/m2/day), and can be continuously worn for at least 7 days. As a strain sensor, the e-skin exhibits a large measurement range (>40 %) and unique segmental sensitivity (GF = 172.54 in 0 ∼ 18.64 % and 79.38 in 18.64 ∼ 50 %), which is suitable for detecting both subtle and large physiological signals. More significantly, by combining the MPM e-skin with a flexible printed circuit board (FPCB) and a merged convolutional neural network & long-short-term memory (CNN & LSTM) intelligent algorithm, the acquired wearable intelligent e-skin system can realize daily 12-hour 12-lead ECG monitoring with diagnostic accuracy over 99 % for four arrhythmias. These results show that the MPM e-skin has huge potential in multifunctional intelligent healthcare systems. In particular, an intelligent ECG system similar to a “daily doctor” is proposed for daily long-term cardiovascular health monitoring.

Detection of hidden atrial fibrillation in primary care using a long-term band-based ECG registration system: The DESCUUBRE-FA study

ObjectiveTo assess whether the use of a band-based electrocardiographic (ECG) monitoring system improves the diagnostic accuracy of traditional diagnostic methods for the detection of atrial fibrillation (AF). MethodsMulticenter and observational study of primary care patients at risk of AF. To be included, patients had to be aged ≥70 years, with no known AF, and have at least 1 major criterion (obesity, hyperthyroidism, heart failure) or 2 minor criteria (hypertension, diabetes, female sex, ischemic stroke, transient ischemic attack or systemic embolism, COPD, dyslipidemia, ischemic heart disease, peripheral artery disease). All patients were monitored using the Nuubo™ system for a single 2-week period. ResultsA total of 600 patients were included (median age 77 years; 70% women; 84.3% hypertension, 27.2% diabetes, 9.2% prior stroke). The global rate of diagnosis of new AF in the overall population was 2.83%. All patients with AF were anticoagulated. In numerical terms, patients with AF (vs no AF) had a higher number of supraventricular extrasystoles and episodes of supraventricular tachycardia, as well as longer P wave duration; however, these differences did not reach statistical significance. Overall, participants were very satisfied with the device, and no relevant limitations in daily activities were observed during the 2-week study period. ConclusionsIn an elderly population at risk of AF, a band-based ECG monitoring approach applied for only 2 weeks detected approximately 3% of new cases of AF, leading to a change in antithrombotic therapy. Most participants considered the device easy to use and comfortable.

Wearable Fetal ECG Monitoring System from Abdominal Electrocardiography Recording.

Fetal electrocardiography (ECG) monitoring during pregnancy can provide crucial information for assessing the fetus’s health status and making timely decisions. This paper proposes a portable ECG monitoring system to record the abdominal ECG (AECG) of the pregnant woman, comprising both maternal ECG (MECG) and fetal ECG (FECG), which could be applied to fetal heart rate (FHR) monitoring at the home setting. The ECG monitoring system is based on data acquisition circuits, data transmission module, and signal analysis platform, which consists of low input-referred noise, high input impedance, and high resolution. The combination of the adaptive dual threshold (ADT) and the independent component analysis (ICA) algorithm is employed to extract the FECG from the AECG signals. To validate the performance of the proposed system, AECG is recorded and analyzed of pregnant women in three different postures (supine, seated, and standing). The result shows that the proposed system can record the AECG in different postures with good signal quality and high accuracy in fetal ECG and heart rate information. Sensitivity (Se), positive predictive accuracy (PPV), accuracy (ACC), and their harmonic mean (F1) are utilized as the metrics to evaluate the performance of the fetal QRS (fQRS) complexes extraction. The average Se, PPV, ACC, and F1 score are 99.62%, 97.90%, 97.40%, and 98.66% for the fQRS complexes extraction,, respectively. This paper shows the proposed system has a promising application in fetal health monitoring.

A Review on IoT Based Health Monitoring System

Abstract: Today, heart disease is considered one of the leading causes of untimely death. Thus, engineers have developed a variety of therapeutic devices to diagnose and evaluate various diseases. Health care has become one of the most important issues for both individuals and governments due to rapid population growth and medical costs. Many patients suffer from cardiovascular complications that cause serious threats to their health, so they need constant monitoring with a traditional monitoring system such as Electrocardiographic (ECG) which is a very important method used to measure cardiac output, this procedure is available only for the most expensive and remote hospital for remote patients. The development of wireless technology enables the construction of a network of Internet-connected devices. The proposed ECG monitoring system consists of the AD8382 ECG sensor for reading patient data, the ESP8266 Wi-Fi control module, and the IoT Blynk application. The implementation of the proposed ECG health care system enables the physician to monitor the patient remotely using the IoT Blynk program installed on his smartphone to monitor and visualize the patient's ECG signal. The monitoring procedure can be performed anytime and anywhere without the need for a hospital. Keywords: ECG, IoT, AD8232, Arduino Uno, ESP8266, Blynk.

An IoT based ECG system to diagnose cardiac pathologies for healthcare applications in smart cities

The present paper aims to describe the project and development of an ECG monitoring system which is able to diagnose specific cardiac pathologies by adapting the processing algorithm to the monitored patient. The system can work standalone by providing the final diagnosis to the patient by a LEDs set. In addition, an IoT based architecture allows the system to share data and diagnosis with a remote cardiologist in real-time or to store data in a FTP folder, as an Holter monitor, for post-processing and further analysis.The system consists of two sections: the digital one, based on the National Instruments MyRIO, digitalises the signal and processes it in real-time to make a diagnosis on possible occurring cardiac pathologies (bradycardia, tachycardia, infarction, ischemia); the analog section, which performs the acquisition, amplification, and preliminary filtering of a 3-leads electrocardiographic signal. The processing algorithm has been developed in NI LabVIEW environment. The main contribution to the state of the art is due to two removable and updatable memory devices. A first memory has been used to store clinical and personal data of patient so to configure the computing algorithm and adapt it to the monitored patient. A second memory has been used to store accuracy and uncertainty specifications of the ECG system in order to improve the reliability of the final diagnosis.The system aims to provide an electrocardiographic monitoring system for healthcare applications in the smart city context. The possibility to monitor constantly the patient health state at home is an important challenge of the next future smart cities. IoT and automation are the main aspects of the proposed system. Patients having chronic heart diseases need frequent hospitalizations to check their heart health. In this scenario, the proposed system is an Ambient Assisted Living solution developed to encourage the independent life of cardiac subjects by supporting his/her care and wellness during the daily life.

Cardiac monitoring for patients with palpitations.

Palpitations are one of the most common reasons for medical consultation. They tend to worry patients and can affect their quality of life. They are often a symptom associated with cardiac rhythm disorders, although there are other etiologies. For diagnosis, it is essential to be able to reliably correlate the symptoms with an electrocardiographic record allowing the identification or ruling out of a possible rhythm disorder. However, reaching a diagnosis is not always simple, given that they tend to be transitory symptoms and the patient is frequently asymptomatic at the time of assessment. In recent years, electrocardiographic monitoring systems have incorporated many technical improvements that solve several of the 24-h Holter monitor limitations. The objective of this review is to provide an update on the different monitoring methods currently available, remarking their indications and limitations, to help healthcare professionals to appropriately select and use them in the work-up of patients with palpitations.

Detection of ST changes by a smartphone-based electrocardiographic monitoring system in patients with ST elevated myocardial infarction

Abstract Background Recent technological advances have led to several novel electrocardiographic monitoring devices appearing on the market. The pocket sized Kardia Mobile is a monitoring device that requires the patient to trigger the ECG recording through a smartphone-based application. This system is generally aimed to detect alterations in cardiac rhythm and its potential for detection of ST changes remains largely unknown. Purpose This study sought to assess the diagnostic sensitivity of the Kardia Mobile system for detection of ST changes in patients with ST elevated myocardial infarction (STEMI). Methods Patients admitted with diagnosis of STEMI according to current universal standard definition were consecutively included. Recordings with Kardia were obtained in leads I, II and anterior precordial (prec). All anonymized recordings were submitted for review to a single cardiologist blinded to the 12 leads ECG findings and to all clinical data. A Kardia recording was considered positive if any significant elevation or depression of ST segment was observed. Results A total of 102 consecutive patients were included (age 60.6±11.4 years, 27% females, weight 78.6±17.4 kg) admitted with diagnosis of STEMI after a median of 165 (IQR 120–367) minutes of chest pain. STEMI was anterior-lateral in 48% and inferior-posterior in 52%. Values of sensitivity for each lead and for combination of leads are shown in Table for all STEMI and separately for anterior and inferior locations. ST changes in all leads consisted mostly in depression (70–78%). Poor quality recording rates were 8% for lead I, 1% for lead II and 3% for lead prec. Conclusions The Kardia Mobile system with single lead use shows a modest sensitivity for detection of ST changes (mostly depression) in STEMI patients. However, the combination of two leads (particularly I+prec or II+prec) or three leads shows a very high sensitivity for both anterior and inferior STEMI. Funding Acknowledgement Type of funding source: None

Diagnostic sensitivity of a smartphone-based electrocardiographic monitoring system in patients with ST elevated myocardial infarction

Introduction and objectivesThe pocket sized KardiaMobile (KM) is a monitoring device for ECG recording through a smartphone-based application. This system is generally aimed as screening tool to detect alterations in cardiac rhythm but its diagnostic potential for detection of ST changes remains largely unknown. This study sought to assess the diagnostic sensitivity of the KM system for detection of ST changes in patients with ST elevated myocardial infarction (STEMI). MethodsPatients admitted with diagnosis of STEMI were consecutively included. Recordings with KM were obtained in leads I, II and anterior precordial. KM recordings were obtained as well from healthy individuals. All recordings were submitted for blinded assessment. ResultsA total of 112 patients with STEMI and 25 healthy individuals were included. Among those showing ST changes, these consisted mostly in depression (75%). Sensitivity was modest for individual leads (57-58%). The combination of 2-leads, when including the precordial, and the combination of 3-leads markedly increased sensitivity (88-98% and 100% respectively) for both inferior and anterior STEMI. Specificity was very high (96-100%) and the Youden's J statistic was excellent for the 2-leads combinations that included the precordial lead (0.84-0.94) and for the 3-leads combination (0.96). ConclusionsThe Kardia Mobile system with the combination of 2-leads (I+precordial or II+precordial) or 3-leads shows a very high sensitivity for both anterior and inferior STEMI.

Thermoelectric Generators: Alternative Power Supply for Wearable Electrocardiographic Systems.

Research interest in the development of real‐time monitoring of personal health indicators using wearable electrocardiographic systems has intensified in recent years. New advanced thermoelectrics are potentially a key enabling technology that can be used to transform human body heat into power for use in wearable electrographic monitoring devices. This work provides a systematic review of the potential application of thermoelectric generators for use as power sources in wearable electrocardiographic monitoring systems. New strategies on miniaturized rigid thermoelectric modules combined with batteries or supercapacitors can provide adequate power supply for wearable electrocardiographic systems. Flexible thermoelectric generators can also support wearable electrocardiographic systems directly when a heat sink is incorporated into the design in order to enlarge and stabilize the temperature gradient. Recent advances in enhancing the performance of novel fiber/fabric based flexible thermoelectrics has opened up an exciting direction for the development of wearable electrocardiographic systems.

Designing ECG Monitoring Healthcare System Based on Internet of Things Blynk Application

Nowadays, heart diseases are considered to be the primary reasons for unexpected deaths. Thus, various medical devices have been developed by engineers to diagnose and scrutinize various diseases. Healthcare has become one of the most substantial issues for both individuals and government due to brisk growth in human population and medical expenditure. Many patients suffer from heart problems causing some critical threats to their life, therefore they need continuous monitoring by a traditional monitoring system such as Electrocardiographic (ECG) which is the most important technique used in measuring the electrical activity of the heart, this technique is available only in the hospital which is very costly and far for remote patients. The development of wireless technologies enables to build a network of connected devices via the internet. The proposed ECG monitoring system consists of AD8382 ECG sensor to read patient's data, Arduino Uno, ESP8266 Wi-Fi module, and IoT Blynk application. The implementation of the proposed ECG healthcare system enables the doctor to monitor the patient's remotely using IoT Blynk application installed on his smartphone for processing and visualizing the patient's ECG signal. The monitoring process can be done at any time and anywhere without the need for the hospital.

Inverse associations between parasympathetic activity and cognitive flexibility in African Americans: Preliminary findings

Previous research suggests that vagal activity and executive function (EF) are positively associated. However, existing data exploring the relationship between HRV and EF remains limited. Though Blacks may have higher HRV, they remain underrepresented in previous research examining HRV and EF. The current study aimed to determine the relationship between EF and HRV in a sample of 92 Black undergraduates (Mean age = 20.32, SD = 2.28). Participants wore an 6‑lead ambulatory electrocardiographic impedance monitoring system to obtain the root mean square of interbeat interval differences (rMSSD) and Cardiac Sympathetic Index. After baseline autonomic activity assessment, participants completed the Berg Card Sorting Test. Utilizing hierarchical regression analyses, HRV was negatively associated with correct responses (Beta = −0.40, SE = 0.04, p=0.01) and categories experienced (Beta = −0.37, SE = 0.01, p=0.01), and positively associated with total errors (Beta = 0.39, SE =0.04, p=0.01). To further elucidate these findings, participants were assigned to coinhibition (n = 12), parasympathetically dominant (n = 34), sympathetically dominant (n = 35), or coactivation (n = 11) autonomic space subgroups. Participants in the sympathetically dominant subgroup completed more categories (M = 6.86, SD = 2.13) and committed fewer errors (M = 30.63, SD = 11.53) than their parasympathetic counterparts (M = 5.74, SD = 2.44; M = 43.29, SD = 18.83, respectively). This study suggests that a state of sympathetic arousal immediately prior to the administration of an EF task, may aid in better task performance.

Optimal Lead Position in Patch-Type Monitoring Sensors for Reconstructing 12-Lead ECG Signals with Universal Transformation Coefficient.

The aim of this study was to reconstruct a 12-lead electrocardiograph (ECG) with a universal transformation coefficient and find the appropriate electrode position and shape for designing a patch-type ECG sensor. A 35-channel ECG monitoring system was developed, and 14 subjects were recruited for the experiment. A feedforward neural network with one hidden layer was applied to train the transformation coefficient. Three electrode shapes (5 cm × 5 cm square, 10 cm × 10 cm square, and right-angled triangle) were considered for the patch-type ECG sensor. The mean correlation coefficient (CC) and minimum CC methods were applied to evaluate the reconstruction performance. The average CCs between the standard 12-lead ECG and reconstructed 12-lead ECG were 0.860, 0.893, and 0.893 for a 5 cm × 5 cm square, 10 cm × 10 cm square, and right-angled triangle shape. The right-angled triangle showed the highest performance among the considered shapes. The results also suggested that the bottom of the central area of the chest was the most suitable position for attaching the patch-type ECG sensor.

Design of Portable Electrocardiograph Monitoring System

Cardiovascular disease is one of the major diseases that threaten the human health. But the existing electrocardiograph (ECG) monitoring system has many limitations in practical application. In order to monitor ECG in real time, a portable ECG monitoring system based on the Android platform is developed to meet the needs of the public. The system uses BMD101 ECG chip to collect and process ECG signals in the Android system, where data storage and waveform display of ECG data can be realized. The Bluetooth HC-07 module is used for ECG data transmission. The abnormal ECG can be judged by P wave, QRS bandwidth, and RR interval. If abnormal ECG is found, an early warning mechanism will be activated to locate the user’s location in real time and send preset short messages, so that the user can get timely treatment, avoiding dangerous occurrence. The monitoring system is convenient and portable, which brings great convenie to the life of ordinary cardiovascular users.

Smartphone ECG Monitoring System Helps Lower Emergency Room and Clinic Visits in Post–Atrial Fibrillation Ablation Patients

Aim:To evaluate the effectiveness of using a smartphone-based electrocardiography (ECG) monitoring device (ECG Check) on the frequency of clinic or emergency room visits in patients who underwent ablation of atrial fibrillation (AF).Methods:Two groups of patients were identified and compared: The conventional monitoring group (CM group) included patients who were prescribed conventional event monitoring or Holter monitoring systems. The ECG Check group (EC group) included patients who were prescribed the ECG Check device for continuous monitoring in addition to conventional event monitoring. The primary outcome was the number of patient visits to clinic or emergency room. The feasibility, accuracy, and detection rate of mobile ECG Check were also evaluated.Results:Ninety patients were studied (mean age: 66.2 ± 11 years, 64 males, mean CHA2DS2-VASc score: 2.6 ± 2). In the EC group, forty-five patients sent an average of 52.8 ± 6 ECG records for either routine monitoring or symptoms of potential AF during the follow-up period. The rhythm strips identified sinus rhythm (84.7%), sinus tachycardia (8.4%), AF (4.2%), and atrial flutter (0.9%). Forty-two EC transmissions (1.8%) were uninterpretable. Six patients (13%) in the EC group were seen in the clinic or emergency room over a 100-day study period versus 16 (33%) in the standard care arm (P value < 0.001).Conclusions:Use of smartphone-based ECG monitoring led to a significant reduction in AF-related visits to clinic or emergency department in the postablation period.

Comparison of Active Electrode Materials for Non-Contact ECG Measurement.

For long-term and more convenience electrocardiograph (ECG) monitoring, an active- electrode-based ECG monitoring system, which can measure ECG through clothes, is proposed in this paper. The hardware of the system includes active electrodes, signal processing and data transmission modules and the software mainly includes a denoising algorithm based on empirical mode decomposition (EMD). Then the proposed system was verified using the comparison of the ECG signals measured synchronously by active electrodes and Ag/AgCl electrodes. In addition, three flexible materials, including conductive textile, copper foil tape and a flexible printed circuit (FPC) are developed for the sensing layer with active electrodes. To compare the performance of these three materials for the sensing layer, the ECG signals of 10 subjects were measured by different materials in three postures and several indexes for quality evaluation were calculated. Results show that effective and clear ECG waveforms can be measured by all three kinds of materials and the quality of ECG signals measured by FPC is the best by conducting a significant t-test for signal quality indexes of three materials.