An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal

The development of automatic monitoring and diagnosis systems for cardiac patients over the internet has been facilitated by recent advancements in wearable sensor devices from electrocardiographs (ECGs), which need the use of patient-specific approaches. Premature ventricular contraction (PVC) is a...

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Vydané v:	Diagnostics (Basel) Ročník 13; číslo 1; s. 87
Hlavní autori:	Ullah, Hadaate, Heyat, Md Belal Bin, Akhtar, Faijan, Muaad, Abdullah Y., Ukwuoma, Chiagoziem C., Bilal, Muhammad, Miraz, Mahdi H., Bhuiyan, Mohammad Arif Sobhan, Wu, Kaishun, Damaševičius, Robertas, Pan, Taisong, Gao, Min, Lin, Yuan, Lai, Dakun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 28.12.2022 MDPI
Predmet:	Algorithms Cardiac arrhythmia Datasets Deep learning Diagnosis Discriminant analysis Electrocardiogram Electrocardiography Health aspects Heart Heart diseases imbalanced datasets Machine learning Morphology Neural networks patient-specific Patients premature ventricular contraction recognition Support vector machines Technology application transfer learning Visual perception Wavelet transforms China patient-specific premature ventricular contraction imbalanced datasets transfer learning electrocardiogram pre-trained recognition residual network
ISSN:	2075-4418, 2075-4418
On-line prístup:	Získať plný text
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