• Journal of Biomedical Engineering Research
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• v.43 no.6
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• pp.399-408
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• 2022

Because arrhythmia occurs irregularly, it should be examined for at least 24 hours for accurate diagnosis. For this reason, this paper developed firmware software for arrhythmia detection and prevented consumption of temporal and human resources and enabled continuous management and early diagnosis. Prior to the experiment, the interval between the R peaks of the QRS Complex was calculated using the Pan-Tompkins algorithm. The developed firmware software designed and implemented an algorithm to detect arrhythmia such as tachycardia, bradycardia, ventricular tachycardia, persistent tachycardia, and non-persistent tachycardia, and a data transmission format to monitor the collected data based on BLE. As a result of the experiment, arrhythmia was found in real time according to the change in BPM as designed in this paper. And the data quality for BLE communication was verified by comparing the sensor's serial communication value with the Android application reception value. In the future, wearable devices for real-time arrhythmia detection will be lightweight and developed firmware software will be applied.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.10
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• pp.1441-1447
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• 2014

A robust new algorithm for R wave detection named for Multiscale-based Peak Detection(MSPD) is assessed in this paper using MIT/BIH Arrhythmia Database. MSPD algorithm is based on a matrix composed of local maximum and find R peaks using result of standard deviation in the matrix. Furthermore, By reducing needless procedure of proposed algorithm, improve algorithm ability to detect R peak efficiently. And algorithm performance is assessed according to detection rates about various arrhythmia database.

• Journal of Sensor Science and Technology
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• v.21 no.1
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• pp.39-45
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• 2012

Recently the convergence of healthcare technology is used for daily life healthcare monitoring. Cardiac arrhythmia is presented by the state of the heart irregularity. Abnormal heart's electrical signal pathway or heart's tissue disorder could be the cause of cardiac arrhythmia. Fatal arrhythmia could put patient's life at risk. Therefore arrhythmia detection is very important. Previous studies on the detection of arrhythmia in various ECG analysis and classification methods had been carried out. In this paper, an ECG signal processing techniques to detect abnormal ECG based on DTW minimum accumulation distance through the template matching for normalized data and variable threshold method for ECG R-peak detection. Signal processing techniques able to determine the occurrence of normal ECG and abnormal ECG. Abnormal ECG detection algorithm using DTW minimum accumulation distance method is performed using MITBIH database for performance evaluation. Experiment result shows the average percentage accuracy of using the propose method for Rpeak detection is 99.63 % and abnormal detection is 99.60 %.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.107-116
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• 2009

This paper presents an approach to detect arrhythmia using heart rate variability and a fuzzy neural network. The proposed algorithm diagnoses arrhythmia using 32 RR-intervals that are 25 seconds on average. We extract six statistical values from the 32 RR-intervals, which are used to input data of the fuzzy neural network. This paper uses the neural network with weighted fuzzy membership functions(NEWFM) to diagnose arrhythmia. The NEWFM used in this algorithm classifies normal and arrhythmia. The performances by Tsipouras using the 48 records of the MIT-BIH arrhythmia database was below 80% of SE(sensitivity) and SP(specificity) in both. The detection algorithm of arrhythmia shows 88.75% of SE, 82.28% of SP, and 86.31% of accuracy.

• Journal of Satellite, Information and Communications
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• v.9 no.1
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• pp.85-89
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• 2014

Electrocardiogram (ECG) is a diagnostic test which records the electrical activity of the heart, shows abnormal rhythms and detects heart muscle damages. With this ECG signal, medical centers diagnose patients' heart disease symptoms. A normal resting heart rate for adults rages from 60 to 100 beats a minute. An irregular heartbeat is called "arrhythmia", and arrhythmia is also called "cardiac dysrhythmia". In an arrhythmia, the heartbeat maybe too slow(slower than 60beats), too rapid(faster than 100beats), too irregular, etc. Among these symptoms of arrhythmia, if the heart beat is slower than the normal range, the symptom is called "bradycardia", and if it is faster than the range, it is called "tachycardia" In this letters, we proposed the detection algorithm of cardiac arrhythmia in ECG signal using R-R interval through the detection of R-peak.

• Journal of Sensor Science and Technology
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• v.22 no.5
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• pp.338-345
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• 2013

Premature ventricular contractions are the most common of all arrhythmias and may cause more serious situation like ventricular fibrillation and ventricular tachycardia in some patients. Therefore, the detection of this arrhythmia becomes crucial in the early diagnosis and the prevention of possible life threatening cardiac diseases. Most methods for detecting arrhythmia require pp interval, or the diversity of P wave morphology, but they are difficult to detect the p wave signal because of various noise types. Thus, it is necessary to use noise-free R wave. So, the new approach for the detection of PVC is presented based on the rhythm analysis and the beat matching in this paper. For this purpose, we removed baseline wandering of low frequency band and made summed signals that are composed of two high frequency bands including the frequency component of QRS complex using the wavelet filter. And then we designed R wave detection algorithm using the adaptive threshold and window through RR interval. Also, we developed algorithm to classify PVC using RR interval. The performance of R wave and PVC detection is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate average detection rate of 99.76%, sensitivity of 99.30% and specificity of 98.66%; accuracy respectively for R wave and PVC detection.

• 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 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.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.29-31
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• 2005

Detection methods for atrial tachycardia and fibrillation on the time axis have the advantages of light operational load and are easy to apply to various applications. Despite these advantages, arrhythmia detection algorithm on the time axis cannot stand much noise such as motion artifacts, moreover the peak detection algorithm has high complexity. In this paper, we use a spectrum analysis method for the detection of atrial tachycardia and fibrillation. By applying spectrum analysis and digital filtering on obtained electrogram signals, we can diagnose heart arrhythmia without using peak detection algorithm.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.3
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• pp.142-145
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• 2006

Detection methods for atrial tachycardia and atrial fibrillation on the time axis have the advantages of light operational load and are easy to apply to various applications. Despite these advantages, arrhythmia detection algorithm on the time axis cannot stand much noise such as motion artifacts, moreover the peak detection algorithm has high complexity. In this paper, we use a spectrum analysis method for the detection of atrial tachycardia and atrial fibrillation. By applying spectrum analysis and digital filtering on obtained electrogram signals, we can diagnose heart arrhythmia without using peak detection algorithm.