scholarly journals Patient Adaptive Pattern Matching Method for Premature Ventricular Contraction(PVC) Classification

Author(s):  
Ik-Sung Cho ◽  
Hyeog-Soong Kwon
2019 ◽  
Vol 5 (4) ◽  
pp. 528-529
Author(s):  
Pedro A. Sousa ◽  
Luís Elvas ◽  
Sérgio Barra ◽  
Natália António ◽  
Lino Gonçalves

2021 ◽  
Vol 77 (18) ◽  
pp. 579
Author(s):  
Yuichi Hori ◽  
Taro Temma ◽  
Christian Wooten ◽  
Christopher O Sobowale ◽  
Christopher Chan ◽  
...  

2021 ◽  
Vol 29 ◽  
pp. 115-124
Author(s):  
Xinlu Wang ◽  
Ahmed A.F. Saif ◽  
Dayou Liu ◽  
Yungang Zhu ◽  
Jon Atli Benediktsson

BACKGROUND: DNA sequence alignment is one of the most fundamental and important operation to identify which gene family may contain this sequence, pattern matching for DNA sequence has been a fundamental issue in biomedical engineering, biotechnology and health informatics. OBJECTIVE: To solve this problem, this study proposes an optimal multi pattern matching with wildcards for DNA sequence. METHODS: This proposed method packs the patterns and a sliding window of texts, and the window slides along the given packed text, matching against stored packed patterns. RESULTS: Three data sets are used to test the performance of the proposed algorithm, and the algorithm was seen to be more efficient than the competitors because its operation is close to machine language. CONCLUSIONS: Theoretical analysis and experimental results both demonstrate that the proposed method outperforms the state-of-the-art methods and is especially effective for the DNA sequence.


2021 ◽  
Vol 29 ◽  
pp. 475-486
Author(s):  
Bohdan Petryshak ◽  
Illia Kachko ◽  
Mykola Maksymenko ◽  
Oles Dobosevych

BACKGROUND: Premature ventricular contraction (PVC) is among the most frequently occurring types of arrhythmias. Existing approaches for automated PVC identification suffer from a range of disadvantages related to hand-crafted features and benchmarking on datasets with a tiny sample of PVC beats. OBJECTIVE: The main objective is to address the drawbacks described above in the proposed framework, which takes a raw ECG signal as an input and localizes R peaks of the PVC beats. METHODS: Our method consists of two neural networks. First, an encoder-decoder architecture trained on PVC-rich dataset localizes the R peak of both Normal and anomalous heartbeats. Provided R peaks positions, our CardioIncNet model does the delineation of healthy versus PVC beats. RESULTS: We have performed an extensive evaluation of our pipeline with both single- and cross-dataset paradigms on three public datasets. Our approach results in over 0.99 and 0.979 F1-measure on both single- and cross-dataset paradigms for R peaks localization task and above 0.96 and 0.85 F1 score for the PVC beats classification task. CONCLUSIONS: We have shown a method that provides robust performance beyond the beats of Normal nature and clearly outperforms classical algorithms both in the case of a single and cross-dataset evaluation. We provide a Github1 repository for the reproduction of the results.


Heart Rhythm ◽  
2021 ◽  
Vol 18 (8) ◽  
pp. S238-S239
Author(s):  
Kenichi Tokutake ◽  
Ikutaro Nakajima ◽  
Ansel P. Amaral ◽  
Jason Cook ◽  
Asad A. Aboud ◽  
...  

Author(s):  
Gurukripa N. Kowlgi ◽  
Arman Arghami ◽  
Juan A. Crestanello ◽  
Christopher J. François ◽  
Paul A. Friedman ◽  
...  

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