Design of a PC-Based Electrocardiogram (ECG) Recorder as - Internet Appliance

Focal atrial tachycardia (AT) is a relatively uncommon cause of supraventricular tachycardia, but when present is frequently difficult to treat medically. Atrial tachycardias tend to originate from anatomically determined atrial sites. The P-wave morphology on surface electrocardiogram (ECG) together with more sophisticated contemporary mapping techniques facilitates precise localisation and ablation of these ectopic foci. Catheter ablation of focal AT is associated with high long-term success and may be viewed as a primary treatment strategy in symptomatic patients.

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Prevalence of abnormalities in electrocardiogram conduction in dialysis patients: a comparative study

Brazilian Journal of Nephrology ◽

10.1590/10.1590/2175-8239-jbn-2020-0018 ◽

2020 ◽

Author(s):

Firas Ajam ◽

Arda Akoluk ◽

Anas Alrefaee ◽

Natasha Campbell ◽

Avais Masud ◽

...

Keyword(s):

Peritoneal Dialysis ◽

Cardiac Conduction ◽

Future Research ◽

Dialysis Patients ◽

Bundle Branch Block ◽

Conduction Abnormality ◽

Ckd Stage ◽

Stage 1 ◽

Electrocardiogram Ecg ◽

Ecg Abnormalities

ABSTRACT Background: The electrocardiogram (ECG) can aid in identification of chronic kidney disease (CKD) patients at high risk for cardiovascular diseases. Cohort studies describe ECG abnormalities in patients on hemodialysis (HD), but we did not find data comparing ECG abnormalities among patients with normal kidney function or peritoneal dialysis (PD) to those on hemodialysis. We hypothesized that ECG conduction abnormalities would be more common, and cardiac conduction interval times longer, among patients on hemodialysis vs. those on peritoneal dialysis and CKD 1 or 2. Methods: Retrospective review of adult inpatients’ charts, comparing those with billing codes for “Hemodialysis” vs. inpatients without those charges, and an outpatient peritoneal dialysis cohort. Patients with CKD 3 or 4 were excluded. Results: One hundred and sixty-seven charts were reviewed. ECG conduction intervals were consistently and statistically longer among hemodialysis patients (n=88) vs. peritoneal dialysis (n=22) and CKD stage 1 and 2 (n=57): PR (175±35 vs 160±44 vs 157±22 msec) (p=0.009), QRS (115±32 vs. 111±31 vs 91±18 msec) (p=0.001), QT (411±71 vs. 403±46 vs 374±55 msec) (p=0.006), QTc (487±49 vs. 464±38 vs 452±52 msec) (p=0.0001). The only significantly different conduction abnormality was prevalence of left bundle branch block: 13.6% among HD patients, 5% in PD, and 2% in CKD 1 and 2 (p=0.03). Conclusion: To our knowledge, this is the first study to report that ECG conduction intervals are significantly longer as one progresses from CKD Stage 1 and 2, to PD, to HD. These and other data support the need for future research to utilize ECG conduction times to identify dialysis patients who could potentially benefit from proactive cardiac evaluations and risk reduction.

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Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

npj Digital Medicine ◽

10.1038/s41746-021-00447-y ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Elisa Mejía-Mejía ◽

James M. May ◽

Mohamed Elgendi ◽

Panayiotis A. Kyriacou

Keyword(s):

Blood Pressure ◽

Heart Rate ◽

Heart Rate Variability ◽

Pulse Rate ◽

Pulse Transit Time ◽

Pulse Rate Variability ◽

Physiological Factors ◽

Ecg Signals ◽

Non Invasive ◽

Electrocardiogram Ecg

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

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Time Course of ECG Depolarization and Repolarization Changes during Ischemia in PTCA Recordings

Methods of Information in Medicine ◽

10.1055/s-0038-1633832 ◽

2004 ◽

Vol 43 (01) ◽

pp. 43-46 ◽

Cited By ~ 9

Author(s):

J. García ◽

G. Wagner ◽

R. Bailón ◽

L. Sörnmo ◽

P. Laguna ◽

...

Keyword(s):

Time Course ◽

Wave Amplitude ◽

Temporal Evolution ◽

Delayed Response ◽

S Wave ◽

Ecg Changes ◽

T Complex ◽

Karhunen Loeve Transform ◽

Low Amplitude ◽

Electrocardiogram Ecg

Summary Objectives: In this work we studied the temporal evolution of changes in the electrocardiogram (ECG) as a consequence of the induced ischemia during prolonged coronary angioplasty, comparing the time course of indexes reflecting depolarization and those reflecting repolarization. Methods: We considered both local (measured at specific points of the ECG) and global (obtained from the Karhunen-Loève transform) indexes. In particular, the evolution of Q, R and S wave amplitudes during ischemia was analyzed with respect to classical indexes such as ST level. As a measurement of sensitivity we used an Ischemic Changes Sensor (ICS), which reflects the capacity of an index to detect changes in the ECG. Results: The results showed that, in leads with low-amplitude ST-T complexes, the S wave amplitude was more sensitive in detecting ischemia than was the commonly used index ST60. It was found that in such leads the S wave amplitude initially exhibited a delayed response to ischemia when compared to ST60, but its performance was better from the second minute of occlusion. The global indexes describing the ST-T complex were, in terms of the ICS, superior to the S wave amplitude for ischemia detection. Conclusions: Ischemic ECG changes occur both at repolarization and depolarization, with alterations in the depolarization period appearing later in time. Local indexes are less sensitive to ischemia than global ones.

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Analysing electrocardiographic traits and predicting cardiac risk in UK biobank

JRSM Cardiovascular Disease ◽

10.1177/20480040211023664 ◽

2021 ◽

Vol 10 ◽

pp. 204800402110236

Author(s):

Julia Ramírez ◽

Stefan van Duijvenboden ◽

William J Young ◽

Michele Orini ◽

Aled R Jones ◽

...

Keyword(s):

Genetic Information ◽

Association Studies ◽

Cardiac Risk ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Biological Mechanisms ◽

Clinical Tool ◽

Genome Wide ◽

Response To Exercise ◽

Electrocardiogram Ecg

The electrocardiogram (ECG) is a commonly used clinical tool that reflects cardiac excitability and disease. Many parameters are can be measured and with the improvement of methodology can now be quantified in an automated fashion, with accuracy and at scale. Furthermore, these measurements can be heritable and thus genome wide association studies inform the underpinning biological mechanisms. In this review we describe how we have used the resources in UK Biobank to undertake such work. In particular, we focus on a substudy uniquely describing the response to exercise performed at scale with accompanying genetic information.

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Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate

Sensors ◽

10.3390/s21051906 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1906

Author(s):

Jia-Zheng Jian ◽

Tzong-Rong Ger ◽

Han-Hua Lai ◽

Chi-Ming Ku ◽

Chiung-An Chen ◽

...

Keyword(s):

Myocardial Infarction ◽

Network Structure ◽

Class Imbalance ◽

Class Imbalance Problem ◽

Multi Scale ◽

Imbalance Problem ◽

Average Accuracy ◽

Significant Difference ◽

Electrocardiogram Ecg

Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, it is unclear whether the network structure is too simple or complex. Toward this end, the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally, multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result, the N-Net reached a 95.76% accuracy in the MI detection task, whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of p < 0.001 evaluated by the U test compared with the state-of-the-art. The models are also smaller in size thus are suitable to fit in wearable devices for offline monitoring. In conclusion, testing throughout the simple and complex network structure is indispensable. However, the way of dealing with the class imbalance problem and the quality of the extracted features are yet to be discussed.

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BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets

Applied Sciences ◽

10.3390/app11135880 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5880

Author(s):

Paloma Tirado-Martin ◽

Raul Sanchez-Reillo

Keyword(s):

Deep Learning ◽

Temporal Variations ◽

Physical Activities ◽

Learning Tools ◽

Biometric Recognition ◽

Learning Network ◽

Deep Learning Network ◽

Electrocardiogram Ecg

Nowadays, Deep Learning tools have been widely applied in biometrics. Electrocardiogram (ECG) biometrics is not the exception. However, the algorithm performances rely heavily on a representative dataset for training. ECGs suffer constant temporal variations, and it is even more relevant to collect databases that can represent these conditions. Nonetheless, the restriction in database publications obstructs further research on this topic. This work was developed with the help of a database that represents potential scenarios in biometric recognition as data was acquired in different days, physical activities and positions. The classification was implemented with a Deep Learning network, BioECG, avoiding complex and time-consuming signal transformations. An exhaustive tuning was completed including variations in enrollment length, improving ECG verification for more complex and realistic biometric conditions. Finally, this work studied one-day and two-days enrollments and their effects. Two-days enrollments resulted in huge general improvements even when verification was accomplished with more unstable signals. EER was improved in 63% when including a change of position, up to almost 99% when visits were in a different day and up to 91% if the user experienced a heartbeat increase after exercise.

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A new wearable multichannel magnetocardiogram system with a SERF atomic magnetometer array

Scientific Reports ◽

10.1038/s41598-021-84971-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yanfei Yang ◽

Mingzhu Xu ◽

Aimin Liang ◽

Yan Yin ◽

Xin Ma ◽

...

Keyword(s):

Independent Component Analysis ◽

Data Acquisition ◽

Current Distribution ◽

Spin Exchange ◽

Healthy Human ◽

Ecg Signals ◽

Mode Decomposition ◽

Magnetometer Array ◽

Data Acquisition And Processing ◽

Electrocardiogram Ecg

AbstractIn this study, a wearable multichannel human magnetocardiogram (MCG) system based on a spin exchange relaxation-free regime (SERF) magnetometer array is developed. The MCG system consists of a magnetically shielded device, a wearable SERF magnetometer array, and a computer for data acquisition and processing. Multichannel MCG signals from a healthy human are successfully recorded simultaneously. Independent component analysis (ICA) and empirical mode decomposition (EMD) are used to denoise MCG data. MCG imaging is realized to visualize the magnetic and current distribution around the heart. The validity of the MCG signals detected by the system is verified by electrocardiogram (ECG) signals obtained at the same position, and similar features and intervals of cardiac signal waveform appear on both MCG and ECG. Experiments show that our wearable MCG system is reliable for detecting MCG signals and can provide cardiac electromagnetic activity imaging.

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Advances of ECG Sensors from Hardware, Software and Format Interoperability Perspectives

Electronics ◽

10.3390/electronics10020105 ◽

2021 ◽

Vol 10 (2) ◽

pp. 105

Author(s):

Khaleel Husain ◽

Mohd Soperi Mohd Zahid ◽

Shahab Ul Hassan ◽

Sumayyah Hasbullah ◽

Satria Mandala

Keyword(s):

Machine Learning ◽

Heart Attack ◽

Privacy Preservation ◽

Causes Of Death ◽

Clinical Settings ◽

Data Format ◽

Medical Institutions ◽

Comprehensive Survey ◽

Electrocardiogram Ecg ◽

The Relationship

It is well-known that cardiovascular disease is one of the major causes of death worldwide nowadays. Electrocardiogram (ECG) sensor is one of the tools commonly used by cardiologists to diagnose and detect signs of heart disease with their patients. Since fast, prompt and accurate interpretation and decision is important in saving the life of patients from sudden heart attack or cardiac arrest, many innovations have been made to ECG sensors. However, the use of traditional ECG sensors is still prevalent in the clinical settings of many medical institutions. This article provides a comprehensive survey on ECG sensors from hardware, software and data format interoperability perspectives. The hardware perspective outlines a general hardware architecture of an ECG sensor along with the description of its hardware components. The software perspective describes various techniques (denoising, machine learning, deep learning, and privacy preservation) and other computer paradigms used in the software development and deployment for ECG sensors. Finally, the format interoperability perspective offers a detailed taxonomy of current ECG formats and the relationship among these formats. The intention is to help researchers towards the development of modern ECG sensors that are suitable and approved for adoption in real clinical settings.

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