High accuracy distinction of shockable and non-shockable arrhythmias in abnormal classes through wavelet transform with pseudo differential like operators
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...
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| Vydáno v: | Scientific Reports Ročník 13; číslo 1; s. 9513 - 23 |
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Springer Science and Business Media LLC
12.06.2023
Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
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| 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. |
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| AbstractList | 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. 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. 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.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. |
| ArticleNumber | 9513 |
| Author | Toshinao Kagawa Yumi Yahagi Hidetoshi Oya Md. Masudur Rahman Shuji Kawasaki Minoru W. Yoshida Sergio Albeverio Takayuki Okai |
| Author_xml | – sequence: 1 givenname: Md. Masudur surname: Rahman fullname: Rahman, Md. Masudur email: masudur_2006@yahoo.com organization: Graduate School of Engineering, Kanagawa University – sequence: 2 givenname: Sergio surname: Albeverio fullname: Albeverio, Sergio organization: Inst. Angewandte Mathematik, and HCM, University of Bonn – sequence: 3 givenname: Toshinao surname: Kagawa fullname: Kagawa, Toshinao organization: School of General Education and Management Studies, Suwa University of Science – sequence: 4 givenname: Shuji surname: Kawasaki fullname: Kawasaki, Shuji organization: Faculty of Science and Engineering, Iwate University – sequence: 5 givenname: Takayuki surname: Okai fullname: Okai, Takayuki organization: Faculty of Information Engineering, Tokyo City University – sequence: 6 givenname: Hidetoshi surname: Oya fullname: Oya, Hidetoshi organization: Faculty of Information Engineering, Tokyo City University – sequence: 7 givenname: Yumi surname: Yahagi fullname: Yahagi, Yumi organization: Department of Information Systems, Tokyo City University – sequence: 8 givenname: Minoru W. surname: Yoshida fullname: Yoshida, Minoru W. organization: Graduate School of Engineering, Kanagawa University |
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| Cites_doi | 10.1109/78.640725 10.1016/j.ins.2021.05.035 10.1007/s00521-019-04061-8 10.1016/j.resuscitation.2010.08.008 10.1109/TBME.2010.2048568 10.1016/j.measurement.2017.04.041 10.1109/TSP.2008.924856 10.1007/s11004-017-9691-0 10.1016/j.aci.2018.08.003 10.1109/TNNLS.2020.3008938 10.1088/1538-3873/abcc4e 10.1161/CIRCULATIONAHA.110.970889 10.1038/s41598-018-33424-9 10.3390/s21248210 10.1590/S0001-37652010000200019 10.1159/000354195 10.17226/21723 10.1007/s00220-021-04186-9 10.3389/fphys.2018.00722 10.1016/B978-1-59749-272-0.X0001-2 10.1016/j.bspc.2020.102031 10.1109/ACCESS.2017.2723258 10.1007/s10916-016-0441-5 10.1109/ICACEA.2015.7164783 10.1109/ITAIC.2019.8785851 10.1109/IEMBS.2001.1019019 10.15748/jasse.9.96 10.1109/EMBC.2019.8856504 10.1109/ICSPCS47537.2019.9008726 10.1109/SPMB.2017.8257022 10.2316/P.2017.848-042 10.1016/j.future.2017.08.039 10.1109/ICoICT.2018.8528737 10.23919/ChiCC.2019.8866462 10.1145/3454127.3457628 10.1109/BIBM.2013.6732594 |
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| Snippet | 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... 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... |
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| SubjectTerms | 639/166/985 639/705 Accuracy Algorithms Arrhythmia Arrhythmias, Cardiac Automation Cardiac arrhythmia Death, Sudden Diagnosis Heart Humanities and Social Sciences Humans Medicine multidisciplinary Q R Science Science (multidisciplinary) Survival Topology Wavelet Analysis Wavelet transforms |
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| Title | High accuracy distinction of shockable and non-shockable arrhythmias in abnormal classes through wavelet transform with pseudo differential like operators |
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