• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2304-2320
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• 2021

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.96-101
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• 2011

In recent days, the demand for the remote ECG monitoring system has been increasing and the automation of the monitoring system is becoming quite of a concern. Automatic detection of the abnormal ECG beats must be a necessity for the successful commercialization of these real time remote ECG monitoring system. From these viewpoints, in this paper, we proposed an automatic detection algorithm for the abnormal ECG beats using QRS width and RR interval patterns. In the previous research, many efforts have been done to classify the ECG beats into detailed categories. But, these approaches have disadvantages such that they produce lots of misclassification errors and variabilities in the classification performance. Also, they require large amount of training data for the accurate classification and heavy computation during the classification process. But, we think that the detection of abnormality from the ECG beats is more important that the detailed classification for the automatic ECG monitoring system. In this paper, we tried to detect the VEB which is most frequently occurring among the abnormal ECG beats and we could achieve satisfactory detection performance when applied the proposed algorithm to the MIT/BIH database.

• Journal of Chest Surgery
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• v.50 no.1
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• pp.41-43
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• 2017

A 38-year-old female patient with a history of tetralogy of Fallot repair at 10 years of age underwent pulmonary valve replacement with a mechanical prosthesis, tricuspid annuloplasty, and right ventricular outflow tract cryoablation due to pulmonary regurgitation, tricuspid regurgitation, and multiple premature ventricular contractions with sustained ventricular tachycardia. After surgery, she had an uneventful postoperative course with arrhythmia monitoring. She was discharged without incident, and a follow-up Holter examination showed a decrease in the number of ventricular ectopic beats from 702 to 41.

• Journal of Yeungnam Medical Science
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• v.4 no.2
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• pp.23-27
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• 1987

Mass electrocardiographic (ECG) examination was performed on 13,801 children (male 7,526 and female 6,275) of elementary and middle school in Taegu from May 1. 1986. to April 30. 1987. We read their ECG according to the "Pediatric Electrocardiography." The results were as following; The Incidence of ECG abnormality was 1.05%(male 1.3% and female 0.75%). Fifty eight children (0.42%) had atrial and ventricular hypertrophy; two right atrial hypertrophy, five left atrial hypertrophy, thirty five fight ventricular hypertrophy and sixteen left ventricular hypertrophy respectively. Ectopic beats occurred in 25 children (0.18%) ; They were atrial in 12 children, ventricular in 8 children and junctional in 5 children. There were 62 children (0.45%) of conduction disturbance ; They were first degree atrioventricular (A-V) block in 21 children, type I second degree A-V block in 1 child, A-V dissociation in 1 child, right bundle branch block in 36 children, left bundle branch block in 1 child and WPW syndrome in 2 children. Nonspecific ST, T changes and sinus tachycardia were found in 3 and one children respectively.