• Journal of IKEEE
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• v.25 no.1
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• pp.212-217
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• 2021

Premature contraction arrhythmia is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Most of arrhythmia clasification methods have been developed with the primary objective of the high detection performance without taking into account the computational complexity. Also, personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Therefore it is necessary to design efficient method that classifies arrhythmia by analyzing the persons's physical condition and decreases computational cost by accurately detecting minimal feature point based on only QRS features. We propose method for personalized specific classification of premature contraction arrhythmia based on QRS features in smart healthcare environments. For this purpose, we detected R wave through the preprocessing method and SOM and selected abnormal signal sets.. Also, we developed algorithm to classify premature contraction arrhythmia using QRS pattern, RR interval, threshold for amplitude of R wave. The performance of R wave detection, Premature ventricular contraction classification is evaluated by using of MIT-BIH arrhythmia database that included over 30 PVC(Premature Ventricular Contraction) and PAC(Premature Atrial Contraction). The achieved scores indicate the average of 98.24% in R wave detection and the rate of 97.31% in Premature ventricular contraction classification.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.33-40
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• 2016

Previous works for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods require accurate detection of ECG signal, higher computational cost and larger processing time. But it is difficult to analyze the ECG signal because of various noise types. Also in the healthcare system based IOT that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extrating minimal feature. In this paper, we propose R wave detection considering complexity and arrhythmia classification based on binary coding. For this purpose, we detected R wave through SOM and then RR interval from noise-free ECG signal through the preprocessing method. Also, we classified arrhythmia in realtime by converting threshold variability of feature to binary code. R wave detection and PVC, PAC, Normal classification is evaluated by using 39 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.41%, 97.18%, 94.14%, 99.83% in R wave, PVC, PAC, Normal.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.1
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• pp.7-14
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• 2021

With the increased attention about healthcare and management of heart diseases, smart healthcare services and related devices have been actively developed recently. R wave is the largest representative signal among ECG signals. R wave detection is very important because it detects QRS pattern and classifies arrhythmia. Several R wave detection algorithms have been proposed with different features, but the remaining problem is their implementation in low-cost portable platforms for real-time applications. In this paper, we propose R wave detection based on optimal threshold and arrhythmia classification through QRS pattern considering complexity in smart healthcare environments. For this purpose, we detected R wave from noise-free ECG signal through the preprocessing method. Also, we classify premature ventricular contraction arrhythmia in realtime through QRS pattern. The performance of R wave detection and premature ventricular contraction arrhythmia classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 premature ventricular contraction. The achieved scores indicate the average of 98.72% in R wave detection and the rate of 94.28% in PVC classification.

• Journal of the Korea Society of Computer and Information
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• v.18 no.8
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• pp.131-139
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• 2013

In this paper, we look into previous research in relation to each processing step for ECG diagnosis and propose detection and classification method of arrhythmia using rhythm features of ECG signal. Rhythm features for distribution of rhythm and heartbeat such as identity, regularity, etc. are extracted in feature extraction, and rhythm type is classified using rule-base constructed in advance for features of rhythm section in rhythm classification. Experimental results for all of rhythm types in the MIT-BIH arrhythmia database show detection performance of 100% for arrhythmia with only normal rhythm rule and applicability of classification for rhythm types with arrhythmia rhythm rules.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1341-1347
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• 2020

Legacy studies for classifying arrhythmia have been studied in order to improve the accuracy of classification, Neural Network, Fuzzy, Machine Learning, etc. In particular, deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose parameter extraction based on AR and arrhythmia classification through a deep learning. For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The classification rate of PVC is evaluated through MIT-BIH arrhythmia database. The achieved scores indicate arrhythmia classification rate of over 97%.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.31-40
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• 2013

In this paper, a novel method is proposed to build an ensemble of classifiers by using a feature selection schema. The feature selection schema identifies the best feature sets that affect the arrhythmia classification. Firstly, a number of feature subsets are extracted by applying the feature selection schema to the original dataset. Then classification models are built by using the each feature subset. Finally, we combine the classification models by adopting a voting approach to form a classification ensemble. The voting approach in our method involves both classification error rate and feature selection rate to calculate the score of the each classifier in the ensemble. In our method, the feature selection rate depends on the extracting order of the feature subsets. In the experiment, we applied our method to arrhythmia dataset and generated three top disjointed feature sets. We then built three classifiers based on the top-three feature subsets and formed the classifier ensemble by using the voting approach. Our method can improve the classification accuracy in high dimensional dataset. The performance of each classifier and the performance of their ensemble were higher than the performance of the classifier that was based on whole feature space of the dataset. The classification performance was improved and a more stable classification model could be constructed with the proposed approach.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.204-210
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• 2011

This paper proposes a fuzzy support vector machine ($FSVM_n$) pattern classifier to classify the arrhythmia patterns of an electrocardiograph (ECG). The $FSVM_n$ is a pattern classifier which combines n-dimensional fuzzy membership functions with a slack variable of SVM. To evaluate the performance of the proposed classifier, the MIT/BIH ECG database, which is a standard database for evaluating arrhythmia detection, was used. The pattern classification experiment showed that, when classifying ECG into four patterns - NSR, VT, VF, and NSR, VT, and VF classification rate resulted in 99.42%, 99.00%, and 99.79%, respectively. As a result, the $FSVM_n$ shows better pattern classification performance than the existing SVM and FSVM algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.117-126
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• 2019

Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.464-469
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• 2015

Long-term electrocardiogram data can be acquired by linking a Holter monitor to a mobile phone. However, most systems are designed to detect arrhythmia through heartbeat classification, and not just for supporting clinical decisions. In this paper, we propose an Abstracting algorithm, and introduce an analogous pateint search system using this algorithm. An analogous patient searcher summarizes each patient's typical pattern using the results of heartbeat, which can greatly simplify clinical activity. It helps to find patients with similar arrhythmia patterns, which can help in contributing to diagnostic clues. We have simulated these processes on data from the MIT-BIH arrhythmia database. As a result, the Abstracting algorithm provided a typical pattern to assist in reaching rapid clinical decisions for 64% of the patients. On an average, typical patterns and results generated by the abstracting algorithm summarized the results of heartbeat classification by 98.01%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1490-1499
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• 2022

Arrhythmia is a condition in which the heart has an irregular rhythm or abnormal heart rate, early diagnosis and management is very important because it can cause stroke, cardiac arrest, or even death. In this paper, we propose arrhythmia classification using hybrid combination model of CNN-BLSTM. For this purpose, the QRS features are detected from noise removed signal through pre-processing and a single bit segment was extracted. In this case, the GAN oversampling technique is applied to solve the data imbalance problem. It consisted of CNN layers to extract the patterns of the arrhythmia precisely, used them as the input of the BLSTM. The weights were learned through deep learning and the learning model was evaluated by the validation data. To evaluate the performance of the proposed method, classification accuracy, precision, recall, and F1-score were compared by using the MIT-BIH arrhythmia database. The achieved scores indicate 99.30%, 98.70%, 97.50%, 98.06% in terms of the accuracy, precision, recall, F1 score, respectively.