Evidence map›Paper›PMID 37308508›Full record

ArticleScientific reports2023

High accuracy distinction of shockable and non-shockable arrhythmias in abnormal classes through wavelet transform with pseudo differential like operators.

Md Masudur Rahman, Sergio Albeverio, Toshinao Kagawa, Shuji Kawasaki, Takayuki Okai, Hidetoshi Oya, Yumi Yahagi, Minoru W Yoshida

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Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Md Masudur RahmanGraduate School of Engineering, Kanagawa University, Yokohama, Japan. masudur_2006@yahoo.com.
Sergio AlbeverioInst. Angewandte Mathematik, and HCM, University of Bonn, Bonn, Germany.
Toshinao KagawaSchool of General Education and Management Studies, Suwa University of Science, Nagano, Japan.
Shuji KawasakiFaculty of Science and Engineering, Iwate University, Iwate, Japan.
Takayuki OkaiFaculty of Information Engineering, Tokyo City University, Tokyo, Japan.
Hidetoshi OyaFaculty of Information Engineering, Tokyo City University, Tokyo, Japan.
Yumi YahagiDepartment of Information Systems, Tokyo City University, Tokyo, Japan.
Minoru W YoshidaGraduate School of Engineering, Kanagawa University, Yokohama, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Arrhythmia is an abnormal rhythm of the heart which leads to sudden death. Among these arrhythmias, some are shockable, and some are non-shockable arrhythmias with external defibrillation. The automated external defibrillator (AED) is used as the automated arrhythmia diagnosis system and requires an accurate and rapid decision to increase the survival rate. Therefore, a precise and quick decision by the AED has become essential in improving the survival rate. This paper presents an arrhythmia diagnosis system for the AED by engineering methods and generalized function theories. In the arrhythmia diagnosis system, the proposed wavelet transform with pseudo-differential like operators-based method effectively generates a distinguishable scalogram for the shockable and non-shockable arrhythmia in the abnormal class signals, which leads to the decision algorithm getting the best distinction. Then, a new quality parameter is introduced to get more details by quantizing the statistical features on the scalogram. Finally, design a simple AED shock and non-shock advice method by following this information to improve the precision and rapid decision. Here, an adequate topology (metric function) is adopted to the space of the scatter plot, where we can give different scales to select the best area of the scatter plot for the test sample. As a consequence, the proposed decision method gives the highest accuracy and rapid decision between shockable and non-shockable arrhythmias. The proposed arrhythmia diagnosis system increases the accuracy to 97.98%, with a gain of 11.75% compared to the conventional approach in the abnormal class signals. Therefore, the proposed method contributes an additional 11.75% possibility for increasing the survival rate. The proposed arrhythmia diagnosis system is general and could be applied to distinguish different arrhythmia-based applications. Also, each contribution could be used independently in various applications.

Indexed as

Arrhythmias, CardiacWavelet AnalysisAlgorithmsDeath, SuddenHeartHumans

Identifiers

PMID37308508
PMCPMC10261052

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.