Detailed Performance Analysis of Recognition Algorithm Based on Spectrum Feature Parameters for Electrocardiogram

Okai, Takayuki; Hirata, Shogo; Oya, Hidetoshi; Hoshi, Yoshikatsu; Nakano, Kazushi; Yamaguchi, Yoshihiro; Igarashi, Takashi; Miyauchi, Hiroshi

doi:10.1109/icspcs47537.2019.9008726

Cited by 4 publications

(22 citation statements)

References 5 publications

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“…As shown in the our previous results [11,12], one can see that there are the following 5 classes for ECGs;…”

Section: Shockable and Non-shockable Ecgssupporting

confidence: 63%

“…[3]- [7]). Furthermore, one can see that either systems proposed in our results [10,11,12] can detect "Sinus Rhythm (SR)" perfectly. However, there are still an important problem which should be solved as soon as possible.…”

Section: Introductionmentioning

confidence: 60%

“…In our existing result [10], firstly some spectrum feature parameters based on are extracted, and next detection systems based on such spectrum feature parameters based on gabor wavelet transform (GWT) are presented. In addition, the detailed analysis result of our recognition system has also been presented [11,12]. In these results [11,12], the detection performance has been evaluated by using the average value for AUC (Area Under the Curve), and our results achieve more higher recognition performance comparing with the other systems (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the detailed analysis result of our recognition system has also been presented [11,12]. In these results [11,12], the detection performance has been evaluated by using the average value for AUC (Area Under the Curve), and our results achieve more higher recognition performance comparing with the other systems (e.g. [3]- [7]).…”

Section: Introductionmentioning

confidence: 99%

“…In other words, the detection performance for all combinations for spectrum feature parameters has not verified. Additionally, recognition systems in our results [10,11,12] consist of three classifiers (SR, Schockable (VF/VT) and Non-Shockable (PEA)), and those have same input parameters, and thus it may be able to reduce computational amounts in our results.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A New Recongnition System Based on Gabor Wavelet Transform for Shockable Electrocardiograms

Okai

Akimoto

Oya

et al. 2021

JALSI

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This paper presents a new recognition system for shockable arrhythmias for patients suffering from sudden cardiac arrest. In order to develop the recognition system, lots of electrocardiogram (ECGs) have been analyzed by using gabor wavelet transform (GWT). Although, there is a huge number of spectrum feature parameters, recognition performance for all combinations for spectrum feature parameters are evaluated, and on the basis of the evaluation results, useful and effective spectrum features for ECGs are extracted. As a result, the proposed recognition system based on the selected effective spectrum feature parameters can achieved good performance comparing with the existing results.

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“…As shown in the our previous results [11,12], one can see that there are the following 5 classes for ECGs;…”

Section: Shockable and Non-shockable Ecgssupporting

confidence: 63%

Section: Introductionmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A New Recongnition System Based on Gabor Wavelet Transform for Shockable Electrocardiograms

Okai

Akimoto

Oya

et al. 2021

JALSI

Self Cite

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Prediction of the effect of electrical defibrillation by using spectral feature parameters

Yoshikawa,

Ogino,

Okai

et al. 2024

Computers in Biology and Medicine

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

Rahman

Albeverio

Kagawa

et al. 2023

Sci Rep

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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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Detailed Performance Analysis of Recognition Algorithm Based on Spectrum Feature Parameters for Electrocardiogram

Cited by 4 publications

References 5 publications

A New Recongnition System Based on Gabor Wavelet Transform for Shockable Electrocardiograms

A New Recongnition System Based on Gabor Wavelet Transform for Shockable Electrocardiograms

Prediction of the effect of electrical defibrillation by using spectral feature parameters

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

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