An Ontology driven model for detection and classification of cardiac arrhythmias using ECG data
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Intelligent Information Systems"
DOI: 10.1007/s10844-021-00685-2
Abstract: Cardiac arrhythmias are not life-threatening straight away but can cause serious heart-related complications if not medically handled appropriately. An electrocardiogram (ECG) captures the heart’s electric activity and has widespread usage due to its easy deployment… read more here.
Keywords: ontology driven; ecg data; ontology; classification ... See more keywords
Study on the use of standard 12-lead ECG data for rhythm-type ECG classification problems
Sign Up to like & getrecommendations! Published in 2022 at "Computer methods and programs in biomedicine"
DOI: 10.1016/j.cmpb.2021.106521
Abstract: BACKGROUND AND OBJECTIVES Most deep-learning-related methodologies for electrocardiogram (ECG) classification are focused on finding an optimal deep-learning architecture to improve classification performance. However, in this study, we proposed a methodology for fusion of various single-lead… read more here.
Keywords: ecg; lead; single lead; lead ecg ... See more keywords
Opportunities and Challenges in Deep Learning Methods on Electrocardiogram Data: A Systematic Review
Sign Up to like & getrecommendations! Published in 2020 at "Computers in biology and medicine"
DOI: 10.1016/j.compbiomed.2020.103801
Abstract: BACKGROUND The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. OBJECTIVE This paper presents… read more here.
Keywords: ecg data; learning; systematic review; deep learning ... See more keywords
Electrocardiogram Data Compression Techniques for Cardiac Healthcare Systems: A Methodological Review
Sign Up to like & getrecommendations! Published in 2021 at "IRBM"
DOI: 10.1016/j.irbm.2021.06.007
Abstract: Abstract Objective: Globally, cardiovascular diseases (CVDs) are one of the most leading causes of death. In medical screening and diagnostic procedures of CVDs, electrocardiogram (ECG) signals are widely used. Early detection of CVDs requires acquisition… read more here.
Keywords: compression; ecg data; compression techniques; healthcare systems ... See more keywords
Optimization of Sleep classification in Patients With Serious Mental Illness Using Accelerometer and ECG Data From a Wearable Patch
Sign Up to like & getrecommendations! Published in 2023 at "CNS Spectrums"
DOI: 10.1017/s1092852923001657
Abstract: Abstract Introduction Sleep is an important behavioral biomarker for patients with serious mental illness (SMI). The ability to accurately quantify sleep in a real-world setting could thus provide insight into patient well-being. In this study,… read more here.
Keywords: ecg data; classification; ecg; accelerometer ecg ... See more keywords
Multiplex recurrence networks from multi-lead ECG data.
Sign Up to like & getrecommendations! Published in 2020 at "Chaos"
DOI: 10.1063/5.0026954
Abstract: We present an integrated approach to analyze the multi-lead electrocardiogram (ECG) data using the framework of multiplex recurrence networks (MRNs). We explore how their intralayer and interlayer topological features can capture the subtle variations in… read more here.
Keywords: multi lead; ecg data; recurrence; multiplex recurrence ... See more keywords
Three-Heartbeat Multilead ECG Recognition Method for Arrhythmia Classification
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3169893
Abstract: Electrocardiogram (ECG) is the primary basis for the diagnosis of cardiovascular diseases. However, the amount of ECG data of patients makes manual interpretation time-consuming and onerous. Therefore, the intelligent ECG recognition technology is an important… read more here.
Keywords: classification; ecg data; ecg; method ... See more keywords
Warehouse LSTM-SVM-Based ECG Data Classification With Mitigated Device Heterogeneity
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Computational Social Systems"
DOI: 10.1109/tcss.2021.3116428
Abstract: Device heterogeneity is a social concern, especially in healthcare domain. In this work, we mitigate the problem of device heterogeneity and further classify the healthcare electrocardiogram (ECG) data with improved performance using a proposed variant… read more here.
Keywords: heterogeneity; lstm svm; classification; ecg data ... See more keywords
Digitizing the ECG Workflow - A State-of-the-Art Analysis.
Sign Up to like & getrecommendations! Published in 2022 at "Studies in health technology and informatics"
DOI: 10.3233/shti220327
Abstract: ECG is one of the most common examinations in hospitals, e.g. for diagnosing cardiovascular diseases - the most frequent cause of death worldwide. Goal of this paper was to identify and describe the typical digitized… read more here.
Keywords: workflow state; ecg data; ecg workflow; data management ... See more keywords