• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.76-84
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• 2022

Arrhythmia is a condition in which the heart beats abnormally or irregularly, early detection is very important because it can cause dangerous situations such as fainting or sudden cardiac death. However, performance degradation occurs due to personalized differences in ECG signals. In this paper, we propose arrhythmia classification using hybrid combination model of CNN-LSTM. For this purpose, the R wave is detected from noise removed signal and a single bit segment was extracted. It consisted of eight convolutional layers to extract the features of the arrhythmia in detail, used them as the input of the LSTM. The weights were learned through deep learning and the model was evaluated by the verification data. The performance was compared in terms of the accuracy, precision, recall, F1 score through MIT-BIH arrhythmia database. The achieved scores indicate 92.3%, 90.98%, 92.20%, 90.72% in terms of the accuracy, precision, recall, F1 score, respectively.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.521-526
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• 2015

Research on how to use the biological characteristics of human to confirm the identity of the individual is being actively conducted. Electrocardiogram(: ECG) based biometric system is difficult to counterfeit and does not cause skin irritation on the subject. It can be easily combined with conventional biometrics such as fingerprint and face recognition to give multimodal biometric systems. In this thesis, biometric identification method analysing ECG waveform characteristics from Discrete Wavelet Transform(DWT) coefficients is suggested. Feature selection is performed on the 9 coefficients of DWT using the correlation analysis. The verification is achieved by using the error back propagation neural networks. Using the proposed approach on 24 subjects of MIT-BIH QT Database, 98.88% verification rate has been obtained.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.4
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• pp.727-732
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• 2012

This paper presents an approach to classify normal and arrhythmia from the MIT-BIH Arrhythmia Database using Discrete Cosine Transform(DCT), Discrete Wavelet Transform(DWT) and neural network. In the first step, Discrete Cosine Transform is used to obtain the representative 15 coefficients for input features of neural network. In the second step, Discrete Wavelet Transform are used to extract maximum value, minimum value, mean value, variance, and standard deviation of detail coefficients. Neural network classifies normal and arrhythmia beats using 55 numbers of input features, and then the accuracy rate is 98.8%.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.1
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• pp.17-22
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• 2011

In this study, a P wave emphasizing and detection algorithm from ECG signal was proposed to read arrhythmia. The algorithm uses two slope tracing waveform, the descending slope tracing wave and the ascending slope tracing wave, developed for efficient determination of slope inverting points and sudden slope changing points. The algorithm generates the slope tracing waveform which trace the original ECG wave, and subtracts one tracing wave from the other to detect P and T waves. The algorithm has been applied to MIT/BIH database in order to verify its efficacy and validity in practical applications.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.1
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• pp.7-12
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• 2008

This paper studies a method to minimize the baseline wandering that make hard to extract R-wave in ECG. This method uses a difference signal between ECG and ascending slope tracing waves to minimize the baseline wandering. When the slope of ECG signal maintains the value or falls, the ascending slope tracing wave follows ECG signal directly, and this wave holds that value of ECG signal when the slope begins to rises in a certain time(=hold time). After this hold time, this wave traces ECG signal again. This method has been applied to MIT/BIH database to verify its efficacy and validity in practical applications.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.378-382
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• 2006

This paper presents an approach to detect premature ventricular contractions(PVC) using the neural network with weighted fuzzy membership functions(NEWFM). NEWFM classifies normal and PVC beats by the trained weighted fuzzy membership functions using wavelet transformed coefficients extracted from the MIT-BIH PVC database. The two most important coefficients are selected by the non-overlap area distribution measurement method to minimize the classification rules that show PVC classification rate of 99.90%. By Presenting locations of the extracted two coefficients based on the R wave location, it is shown that PVC can be detected using only information of the two portions.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.5
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• pp.205-214
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• 2020

With the recent development of hardware and software technology, interest in the development of wearable devices is increasing. In particular, wearable devices require algorithms suitable for low-power and low-capacity embedded devices. Among them, there is an increasing demand for a signal compression algorithm that reduces communication overhead, in order to increase the efficiency of storage and transmission of electrocardiogram (ECG) signals requiring long-time measurement. Because normal beats occupy most of the signal with similar shapes, a high rate of signal compression is possible if normal beats are represented by a template. In this paper, we propose an algorithm for determining the normal beat template using the template cluster and Pearson similarity. Also, the template is expressed effectively as a few vertices through linear approximation algorithm. In experiment of Datum 234 of MIT-BIH arrhythmia database (MIT-BIH ADB) provided by Physionet, a compression ratio was 33.44:1, and an average distribution of root mean square error (RMSE) was 1.55%.

• Journal of the Korea Society of Computer and Information
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• v.19 no.8
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• pp.107-117
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• 2014

This paper proposed the method of removing baseline drift by eliminating local maxima such as P, R, T-wave signal region and local minima Q, S-wave signal region. We applied morphology-pair operations improved from morphology operation to the ECG signal. To eliminate overshoot in the result of morphology-pair operation, we apply median value operation to the result of morphology-pair operation. We use MIT/BIH database to estimate the proposed algorithm. Experiment result show that proposed algorithm removing baseline drift effectively without orignal ECG signal distortion.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.3
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• pp.467-472
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• 2005

ECG signals are recorded for diagnostic purposes in many clinical situations. Also, In order to permit good clinical interpretation, data is needed at high resolutions and sampling rates. Therefore In this paper, we designed to compression structure using multiple wavelet basis function(SWBF) and compared to single wavelet basis function(SWBF) and discrete cosine transform(DCT). For experience objectivity, Simulation was performed using the arrhythmia data with sampling frequency 360Hz, resolution lIbit at MIT-BIH database. An estimate of performance estimate evaluate the reconstruction error. Consequently compression structure using MWBF has high performance result.