• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.200-207
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• 2014

Arrhythmia electrocardiogram signal contains a specific unusual heartbeat with abnormal morphology. Because unusual heartbeat is useful for diagnosis and classification of various diseases, such as arrhythmia, detection of unusual heartbeat from the arrhythmic ECG signal is very important. Amplitude and kurtosis at R-peak point and RR interval are characteristics of ECG signal on R-wave. In this paper, we provide a method for detecting unusual heartbeat based on these. Through the value of the attribute deviates more from the average value if unusual heartbeat is more certainly, the proposed method detects unusual heartbeat in order using the mean and standard deviation. From 15 ECG signals of MIT-BIH arrhythmia database which has R-wave distortion, we compare the result of conventional method which uses the fixed threshold value and the result of proposed method. Throughout the experiment, the sensitivity is significantly increased to 97% from 50% using the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.4
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• pp.731-739
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• 2015

In arrhythmia ECG signal, abnormal beat that has various abnormal shape depending on the generation site and conduction disorders is included and it is very important to diagnose heart disease such as arrhythmia. In this paper, we propose a PVC abnormal beat detection algorithm associated with ventricular disease. The PVC abnormal beat is characterized by distortion of the QRS complex occurs among the components of the ECG signal. Therefore it is possible to detect PVC abnormal beat according to the degree of distortion of the QRS complex. First, quantify the distortion of the QRS complex by using the potential of the R-peak, kurtosis and period. By using the mean and standard deviation, PVC abnormal beat is detected depending on the degree of distortion from the normal beat. The proposed algorithm can detect the average over 98% of the AAMI-V class type abnormal beat associated with ventricular disease in MIT-BIH arrhythmia database.

• 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 Biomedical Engineering Research
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• v.29 no.6
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• pp.466-476
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• 2008

The more people use ambulatory electrocardiogram(ECG) for arrhythmia detection, the more researchers report the automatic classification algorithms. Most of the previous studies don't consider the un-balanced data distribution. Even in patients, there are much more normal beats than abnormal beats among the data from 24 hours. To solve this problem, the hierarchical classification using 21 features was adopted for arrhythmia abnormal beat detection. The features include R-R intervals and data to describe the morphology of the wave. To validate the algorithm, 44 non-pacemaker recordings from physionet were used. The hierarchical classification model with 2 stages on domain knowledge was constructed. Using our suggested method, we could improve the performance in abnormal beat classification from the conventional multi-class classification method. In conclusion, the domain knowledge based hierarchical classification is useful to the ECG beat classification with unbalanced data distribution.

• Journal of Embryo Transfer
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• v.23 no.1
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• pp.5-11
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• 2008

Serial ultrasonographic examinations were daily performed from 15 days after ovulation until parturition to determine the time of first detection and ultrasonographic appearance of the fetal and extra-fetal structures in pregnant 10 Maltese, 10 Yorkshire Terrier, 15 Shih-tzu, and 10 Miniature Schnauzer bitches, respectively. Gestational age was timed from the day of ovulation (day 0), which was estimated to occur when plasma progesterone concentration was first increased above 4.0ng/ml. The gestational length was $63.4{\sim}63.6$ (range: $61{\sim}65$) days and the geatational length was no statistically significant difference among bitches (p>0.05). The initial detection of the extra-fetal structures were; gestational sac at days $18.9{\sim}19.5\;(17{\sim}22)$, zonary placenta at days $24.6{\sim}25.5\;(23{\sim}28)$, yolk sac membrane at days $24.6{\sim}25.5\;(23{\sim}27)$, yolk sac tubular shape at days $26.1{\sim}26.3\;(24{\sim}28)$, and amniotic membrane at days $26.1{\sim}28.2\;(24{\sim}31)$, respectively. The time of the first detection of the extra-fetal structures were no statistically significant difference among bitches (p>0.05). The initial detection of the fetal structures were; embryo initial detection at days $22.5{\sim}22.9\;(21{\sim}24)$, heartbeat at days $23.2{\sim}23.8\;(21{\sim}25)$, embryo bipolar shape $27.6{\sim}28.9\;(26{\sim}30)$, fetal movement at days $31.9{\sim}32.8\;(27{\sim}34)$, limb buds at days $29.1{\sim}30.7\;(27{\sim}33)$, stomach at days $31.1{\sim}33.1\;(29{\sim}34)$, urinary bladder at days $32.4{\sim}33.2\;(29{\sim}35)$, skeleton at days $34.7{\sim}35.9\;(34{\sim}39)$, and kidney at days $42.1{\sim}44.7\;(41{\sim}48)$, respectively. The the time of the first detection of the fetal structures were no statistically significant difference among bitches (p>0.05). These results indicate the evaluation of the time of first detection and ultrasonographic characteristics of the gestational structures might be useful for pregnancy diagnosis, estimating fetal age, embryonic resorption, fetal monster, abnormal fetal growth and fetal viability, respectively.