Filtered By:
Education: Training
Procedure: Electrocardiogram

This page shows you your search results in order of date.

Order by Relevance | Date

Total 35 results found since Jan 2013.

A Multidimensional Visible Evaluation Model for Stroke Rehabilitation: A Pilot Study
In this study, we propose a visible evaluation model which can combine the FCMC indicators with a Ueda score to comprehensively evaluate the motor function. In this model, we first calculated the FCMC indicators based on our previous study, including transfer spectral entropy (TSE), wavelet package transfer entropy (WPTE) and multiscale transfer entropy (MSTE). We then apply Pearson correlation analysis to determine which FCMC indicators are significantly correlated with the Ueda score. Then, we simultaneously introduced a radar map to present the selected FCMC indicators and the Ueda score, and described the relation betw...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 27, 2023 Category: Neuroscience Source Type: research

Safety of different types of loaded breathing in adult Fontan patients
Conclusion: Short RMT activities are safe in asymptomatic adult Fontan patients. Physiological responses depend on type of RMT and do not reach maximum CPET levels. First trainings should be monitored specifically when applying high positive intrathoracic pressures.
Source: European Respiratory Journal - December 1, 2022 Category: Respiratory Medicine Authors: Schön, P., Boyle, K. G., Greutmann, M., Spengler, C. M. Tags: 09.01 - Respiratory function technologists/scientists Source Type: research

Evaluating the Risk of Paroxysmal Atrial Fibrillation in Noncardioembolic Ischemic Stroke Using Artificial Intelligence-Enabled ECG Algorithm
CONCLUSION: Using AI-ECG-AF, we found that noncardioembolic IS patients had a higher PAF risk relative to the general patient population. The results from our study imply the need for more vigorous cardiac monitoring in noncardioembolic IS patients. AI-ECG-AF can be a cost-effective screening tool to identify high-risk noncardioembolic IS patients of PAF on-the-spot to be candidates for receiving additional prolonged cardiac monitoring. Our study highlights the potential of AI in clinical practice.PMID:35463788 | PMC:PMC9024295 | DOI:10.3389/fcvm.2022.865852
Source: Atherosclerosis - April 25, 2022 Category: Cardiology Authors: Changho Han Oyeon Kwon Mineok Chang Sunghoon Joo Yeha Lee Jin Soo Lee Ji Man Hong Seong-Joon Lee Dukyong Yoon Source Type: research

Patient-Led Mass Screening for Atrial Fibrillation in the Older Population Using Handheld Electrocardiographic Devices Integrated With a Clinician-Coordinated Remote Central Monitoring System: Protocol for a Randomized Controlled Trial and Process Evaluation
CONCLUSIONS: By incorporating an integrated health care approach involving patient empowerment, centralized clinician-coordinated ECG monitoring, and facilitation of primary care and specialist services, it is possible to diagnose and treat AF early to reduce stroke risk. This study will provide new information on how to implement AF screening using digital health technology practicably and feasibly for older and frail populations residing in the community.TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12621000184875; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380877.INTERNAT...
Source: Rural Remote Health - February 1, 2022 Category: Rural Health Authors: Kam Cheong Wong Tu N Nguyen Simone Marschner Samual Turnbull Mason Jenner Burns Jia Yi Anna Ne Vishal Gopal Anupama Balasuriya Indrawansa Steven A Trankle Tim Usherwood Saurabh Kumar Richard I Lindley Clara K Chow Source Type: research

Data-driven clustering supports adaptive remodeling of athlete's hearts: An echocardiographic study from the Taipei Summer Universiade
CONCLUSION: Hierarchical clustering could analyze complicated multiparametric interactions among numerous echocardiography-derived phenotypes to discern the adaptive propensity of the athlete's heart. The endorsement or generation of hypotheses by a data-driven approach can be applied to various domains.PMID:34740491 | DOI:10.1016/j.jfma.2021.10.017
Source: J Formos Med Assoc - November 6, 2021 Category: General Medicine Authors: Kuan-Chih Huang Chang-En Lin Lian-Yu Lin Juey-Jen Hwang Lung-Chun Lin Source Type: research

Sensors, Vol. 21, Pages 5302: Automatic Detection of Short-Term Atrial Fibrillation Segments Based on Frequency Slice Wavelet Transform and Machine Learning Techniques
uang Zhou Atrial fibrillation (AF) is the most frequently encountered cardiac arrhythmia and is often associated with other cardiovascular and cerebrovascular diseases, such as ischemic heart disease, chronic heart failure, and stroke. Automatic detection of AF by analyzing electrocardiogram (ECG) signals has an important application value. Using the contaminated and actual ECG signals, it is not enough to only analyze the atrial activity of disappeared P wave and appeared F wave in the TQ segment. Moreover, the best analysis method is to combine nonlinear features analyzing ventricular activity based on the detection ...
Source: Sensors - August 5, 2021 Category: Biotechnology Authors: Yaru Yue Chengdong Chen Pengkun Liu Ying Xing Xiaoguang Zhou Tags: Article Source Type: research

Comparative study of cardiorespiratory adaptations in elite basketball players of different age groups.
CONCLUSIONS: The increased LVM is attributed to concentric hypertrophy. This is further supported by the finding that there was no relationship between V̇ O2max and echo parameters. The results of the present study indicate that the cardiorespiratory adaptations caused by basketball training are mainly formed in the early age stages without further increase throughout the years of action. PMID: 33269884 [PubMed - as supplied by publisher]
Source: Journal of Sports Medicine and Physical Fitness - December 5, 2020 Category: Sports Medicine Tags: J Sports Med Phys Fitness Source Type: research

Sensors, Vol. 20, Pages 2136: Detection of Atrial Fibrillation Using 1D Convolutional Neural Network
a Hsiao The automatic detection of atrial fibrillation (AF) is crucial for its association with the risk of embolic stroke. Most of the existing AF detection methods usually convert 1D time-series electrocardiogram (ECG) signal into 2D spectrogram to train a complex AF detection system, which results in heavy training computation and high implementation cost. This paper proposes an AF detection method based on an end-to-end 1D convolutional neural network (CNN) architecture to raise the detection accuracy and reduce network complexity. By investigating the impact of major components of a convolutional block on detectio...
Source: Sensors - April 9, 2020 Category: Biotechnology Authors: Chaur-Heh Hsieh Yan-Shuo Li Bor-Jiunn Hwang Ching-Hua Hsiao Tags: Article Source Type: research

Sensors, Vol. 20, Pages 765: Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine
Jezewski Leski Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficien...
Source: Sensors - January 29, 2020 Category: Biotechnology Authors: Czabanski Horoba Wrobel Matonia Martinek Kupka Jezewski Kahankova Jezewski Leski Tags: Article Source Type: research

A Unique Signature of Cardiac-Induced Cranial Forces During Acute Large Vessel Stroke and Development of a Predictive Model
ConclusionsHeadpulse recordings performed on patients with suspected acute stroke significantly identify those with LVO. The lack of temporal correlation of the headpulse with cardiac contraction and resolution to normal may reflect changes in cerebral blood flow and may provide a useful technique to triage stroke patients for thrombectomy using a noninvasive device.
Source: Neurocritical Care - October 6, 2019 Category: Neurology Source Type: research

An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction
Publication date: Available online 1 August 2019Source: The LancetAuthor(s): Zachi I Attia, Peter A Noseworthy, Francisco Lopez-Jimenez, Samuel J Asirvatham, Abhishek J Deshmukh, Bernard J Gersh, Rickey E Carter, Xiaoxi Yao, Alejandro A Rabinstein, Brad J Erickson, Suraj Kapa, Paul A FriedmanSummaryBackgroundAtrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low yield. We aimed to develop a rapid, inexpensive, point-of-care means of identifying patients with atrial ...
Source: The Lancet - August 2, 2019 Category: General Medicine Source Type: research

An Efficient Cardiac Arrhythmia Onset Detection Technique Using a Novel Feature Rank Score Algorithm
AbstractThe interpretation of various cardiovascular blood flow abnormalities can be identified using Electrocardiogram (ECG). The predominant anomaly due to the blood flow dynamics leads to the occurrence of cardiac arrhythmias in the cardiac system. In this work, estimation of cardiac output (CO) parameter using blood flow rate analysis is carried out, which is a vital parameter to identify the subjects with left- ventricular arrhythmias (LVA). In particular, LVA is a resultant component of characteristic changes in blood rheology (blood flow rate). The CO is an intrinsic parameter derived from the stroke volume (SV) cha...
Source: Journal of Medical Systems - May 5, 2019 Category: Information Technology Source Type: research