Zobrazeno 1 - 10
of 637
pro vyhledávání: '"MIT-BIH"'
Autor:
Amin Abbaszadeh, Mahdi Bazargani
Publikováno v:
Heliyon, Vol 10, Iss 23, Pp e40537- (2024)
Annually, the proportion of individuals suffering from cardiovascular disease rises significantly. Heart attacks are the most prevalent and unpleasant illness among them. Heart disease (HD) diagnosis can be complicated when there are multiple symptom
Externí odkaz:
https://doaj.org/article/ef6b47673d3a4b6ab4d23b238093d2a7
Autor:
Mohammadjavad Hosseinpoor
Publikováno v:
Journal of Advanced Biomedical Sciences, Vol 14, Iss 1, Pp 47-55 (2024)
Background & Objectives: Cardiovascular disease is a leading cause of death worldwide. ECG signals are used to diagnose it. This study aims to eliminate signal noise by converting available wavelets and extracting existing waves. The location-related
Externí odkaz:
https://doaj.org/article/c9ffb2dece164b8f89cf490af31a9b37
Autor:
Rizwana Naz Asif, Allah Ditta, Hani Alquhayz, Sagheer Abbas, Muhammad Adnan Khan, Taher M. Ghazal, Sang-Woong Lee
Publikováno v:
IEEE Access, Vol 12, Pp 1909-1926 (2024)
In this study, a weighted federated learning approach is proposed for electrocardiogram (ECG) arrhythmia classification. The proposed approach considers the heterogeneity of data distribution among multiple clients in federated learning settings. The
Externí odkaz:
https://doaj.org/article/0961db5c1e6645c18ff05e1ac6b6e427
Publikováno v:
Applied Sciences, Vol 14, Iss 21, p 10078 (2024)
Electrocardiographic (ECG) R-peak detection is essential for every sensor-based cardiovascular health monitoring system. To validate R-peak detectors, comparing the predicted results with reference annotations is crucial. This comparison is typically
Externí odkaz:
https://doaj.org/article/6ac73602c7b644aa8208e4b740ccae2f
Autor:
Barbara Mika, Dariusz Komorowski
Publikováno v:
Sensors, Vol 24, Iss 13, p 4171 (2024)
The global burden of atrial fibrillation (AFIB) is constantly increasing, and its early detection is still a challenge for public health and motivates researchers to improve methods for automatic AFIB prediction and management. This work proposes hig
Externí odkaz:
https://doaj.org/article/585f1aef1ae74767a04739c9494efb19
Publikováno v:
IEEE Access, Vol 11, Pp 34808-34820 (2023)
Electrocardiograms (ECG) are the primary basis for the diagnosis of cardiovascular diseases. However, due to the large volume of patients’ ECG data, manual diagnosis is time-consuming and laborious. Therefore, intelligent automatic ECG signal class
Externí odkaz:
https://doaj.org/article/9156484eb2ba4f0aa17b2b2ca32f6800
Autor:
Velagapudi Swapna Sindhu, Kavuri Jaya Lakshmi, Ameya Sanjanita Tangellamudi, K. Ghousiya Begum
Publikováno v:
International Journal of Intelligent Networks, Vol 4, Iss , Pp 1-10 (2023)
The electrocardiogram (ECG) is a very useful diagnostic tool to examine the functioning of the heart and to detect myocardial infarction (MI) and arrhythmias. It contains the records of the electrical signal of the heart and it is an investigation to
Externí odkaz:
https://doaj.org/article/99c7668abaef4b15b1dd213d774a4a1c
Autor:
Chenchen Zhou, Xiangkui Li, Fan Feng, Jian Zhang, He Lyu, Weixuan Wu, Xuezhi Tang, Bin Luo, Dong Li, Wei Xiang, Dengju Yao
Publikováno v:
Frontiers in Physiology, Vol 14 (2023)
Objective: The objective of this research is to construct a method to alleviate the problem of sample imbalance in classification, especially for arrhythmia classification. This approach can improve the performance of the model without using data enh
Externí odkaz:
https://doaj.org/article/dc9b75e90e5a4d4f9fcced117ea97f9f
Publikováno v:
Sensors, Vol 24, Iss 5, p 1698 (2024)
Algorithms for QRS detection are fundamental in the ECG interpretive processing chain. They must meet several challenges, such as high reliability, high temporal accuracy, high immunity to noise, and low computational complexity. Unfortunately, the a
Externí odkaz:
https://doaj.org/article/9a5f463f74bc4a2084e62b58c8c60632