• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.988-1001
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• 2018

Arrhythmia has recently emerged as one of the major causes of death in Koreans. Premature Ventricular Contraction (PVC) is the most common arrhythmia that can be found in clinical practice, and it may be a precursor to dangerous arrhythmias, such as paroxysmal insomnia, ventricular fibrillation, and coronary artery disease. Therefore, we need for a method that can detect an abnormal heart beat and diagnose arrhythmia early. We extracted the features corresponding to the QRS pattern from the subject's ECG signal and classify the premature ventricular contraction waveform using the features. We modified the weighting and bias values based on the error back-propagation algorithm through learning data. We classify the normal signal and the premature ventricular contraction signal through the modified weights and deflection values. MIT-BIH arrhythmia data sets were used for performance tests. We used RR interval, QS interval, QR amplitude and RS amplitude features. And the hidden layer with two nodes is composed of two layers to form a total three layers (input layer 0, output layer 3).

• Archives of Plastic Surgery
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• v.39 no.5
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• pp.463-468
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• 2012

Background In the extremities of premature infants, the skin and subcutaneous tissue are very pliable due to immaturity and have a greater degree of skin laxity and mobility. Thus, we can expect wounds to heal rapidly by wound contraction. This study investigates wound healing of full-thickness defects in premature infant extremities. Methods The study consisted of 13 premature infants who had a total of 14 cases of full-thickness skin defects of the extremities due to extravasation after total parenteral nutrition. The wound was managed with intensive moist dressings with antibiotic and anti-inflammatory agents. After wound closure, moisturization and mild compression were performed. Results Most of the full-thickness defects in the premature infants were closed by wound contraction without granulation tissue formation on the wound bed. The defects resulted in 3 pinpoint scars, 9 linear scars, and 2 round hypertrophic scars. The wounds with less granulation tissue were healed by contraction and resulted in linear scars parallel to the relaxed skin tension line. The wounds with more granulation tissue resulted in round scars. There was mild contracture without functional abnormality in 3 cases with a defect over two thirds of the longitudinal length of the dorsum of the hand or foot. The patients' parents were satisfied with the outcomes in 12 of 14 cases. Conclusions Full-thickness skin defects in premature infants typically heal by wound contraction with minimal granulation tissue and scar formation probably due to excellent skin mobility.

• 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 Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.437-444
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• 2016

Most methods for detecting arrhythmia require pp interval, diversity of P wave morphology, but it is difficult to detect the p wave signal because of various noise types. Therefore it is necessary to use noise-free R wave. In this paper, we propose algorithm for premature contraction arrhythmia classification through ECG pattern analysis and template threshold. For this purpose, we detected R wave through the preprocessing method using morphological filter, subtractive operation method. Also, we developed algorithm to classify premature contraction wave pattern using weighted average, premature ventricular contraction(PVC) and atrial premature contraction(APC) through template threshold for R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 6 record of MIT-BIH arrhythmia database that included over 30 PVC and APC. The achieved scores indicate the average of 99.77% in R wave detection and the rate of 94.91%, 95.76% in PVC and APC classification.

• Journal of the Korea Society of Computer and Information
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• v.22 no.5
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• pp.65-72
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• 2017

Premature Ventricular Contraction(PVC) arrhythmia is most common abnormal-heart rhythm that may increase mortal risk of a cardiac patient. Thus, it is very important issue to identify the specular portraits of PVC pattern especially from the patient. In this paper, we propose a new method to extract the characteristics of PVC pattern by applying K-means machine learning algorithm on Heart Rate Variability depicted in Poinecare plot. For the quantitative analysis to distinguish the trend of cluster patterns between normal sinus rhythm and PVC beat, the Euclidean distance measure was sought between the clusters. Experimental simulations on MIT-BIH arrhythmia database draw the fact that the distance measure on the cluster is valid for differentiating the pattern-traits of PVC beats. Therefore, we proposed a method that can offer the simple remedy to identify the attributes of PVC beats in terms of K-means clusters especially in the long-period Electrocardiogram(ECG).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.233-242
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• 2018

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting feature point based on only R peak through optimal R wave. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 94.85% in PVC classification.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.2
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• pp.15-21
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• 2009

This paper proposes a novel algorithm to assess the abnormal heart beats such as PVC (Premature Ventricular Contraction) and its subsequent RUNs. Our Arrhythmic detection scheme is based on only the R-R Interval features extracted from ECG waveforms and MIT-BIH arrhythmia database is evaluated to validate the efficiency of our algorithm in terms of sensitivity, specificity, FPR(%) and FNR(%).

• Biomolecules & Therapeutics
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• v.9 no.1
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• pp.51-54
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• 2001

Bufonis Venenum is a toad venom and its main components are bufadienolides, namely resibufogenin, bufalin and cinobufagin. The desensitizing effect of Bufonis Venenum is useful for the treatment of the premature ejaculation in Chinese medicine. But, minor components of Bufonis Venenum cause problems such as topical burring, pain, and erectile dysfunction. To clarify and eliminate the components responsible for these side effects, we prepared two extracts of Bufonis Venenum with either 70% ethanol or ethylacetate and tested their pharmacological effects. The extract of Bufonis Venenum with 70% ethanol produced pain response in rat hind paw, and exhibited contraction of rabbit corpus cavernosal muscle in vitro. On the other hand, the ethylacetate extract did not cause pain and smooth muscle contraction. The desensitizing effect of the ethylacetate extract was similar to that of the 70% ethanol extract. In conclusion, these results show that the extract of Bufonis Venenum with ethylacetate does not have the components causing side effects and deserve further study for therapeutic potential in premature ejaculation in men.

• The Journal of Internal Korean Medicine
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• v.37 no.5
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• pp.796-805
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• 2016

Objective: To examine the effects of Gagam-Samryoungbeakchul-san (加減 蔘苓白朮散) on a premature ventricular contraction patient with dizziness and chest discomfort. Methods: A patient diagnosed with premature ventricular contraction was treated with herbal medicine and acupuncture. The period of admission was 15 days, and we measured the electrocardiogram before and after treatment. We evaluated the improvement in symptoms by Global Assessment (G/A), and checked the pulse rate by oximetry three times a day. We estimated the efficacy of treatment by analyzing the relationship between the average pulse rate and symptoms. Results: After Gagam-Samryoungbeakchul-san treatment and acupuncture therapy, the average pulse rate increased from 36.5 to 58. This increase in average pulse rate was accompanied by a reduction in dizziness of 40%, chest discomfort of 30%, and frequency of bigeminy in the electrocardiogram. Conclusions: This case report confirmed the effectiveness of Gagam-Samryoungbeakchul-san on premature ventricular contraction, but further study is warranted.