• Korean Journal of Psychosomatic Medicine
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• v.5 no.2
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• pp.214-217
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• 1997

Electroconvulsive therapy(ECT) is one of the most effective treatment modalities for the treatment of depression, mania, schizophrenia, or other neuropsychiatric disorders. But, reportedly ECT also can produce various forms of cardiac arrhythmia. We experienced a case of ECT-induced arrhythmia(PVC) accompanied with chest pain in a schizophrenic patient during the course of plain ECT. We conclude that there is a possible causal relationship between ECT and cardiac arrhythmia(PVC). The mechanisms of cardiac arrhythmia(PVC) due to ECT may be explained by the effects of ECT to vagal and sympathetic nervous systems. from this case report, We suggest that careful cardiac monitoring before, during, and after ECT with appropriate anesthetic preparation to a patient may enable to minimize the cardiovascular side effects of ECT in the patients with neuropsychiatric disorders.

• The Korean Nurse
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• v.33 no.4
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• pp.62-85
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• 1994

The data were analyzed by using an S. P. S. S. computerized program for mean, standard deviation, percentage and paired t-test. The results of this study were as follows: 1. The increase in $PaO_2$ after hyperoxygenation and hyperinflation was highly statistically significant(p=0.041), and the increase in $PaO_2$ immediately after suctioning was not significant (p=0.752). The time of lowest $PaO_2$ was 30 seconds after the endotracheal suction. 2. The occurrance of cardiac arrhythmia after the endotracheal suction included sinus tachycardia, sinus arrhythmia, sinus bradycardia, premature atrial contraction (PAC), and premature ventricular contraction (PVC). The most frequent cardiac arrhythmia was sinus tachycardia (a subjects). Sinus arrhythmia was observed in 5 subjects and continued till 10 minutes after suctioning in two of these. Sinus bradycardia occurred in only 3 subjects and among them, 1 subjects shows sinus arrythmia till 10 minutes after suctioning along. PAC was observed in only one subject and continued till five minutes after suctining along with sinus arrhythmia. PVC was observed in three subjects: it lasted for only 30 seconds after suctioning in two subjects. but continued for 10 minutes after suctioning in the third. 6 subjects manifested two kinds of Cardiac arrhythmia Three of them showed sinus tachycardia with PVC, another 2 showed sinus bradycardia with sinus arrhythmia, and the other subject showed sinus arrhythmia with PAC. 3. The increases in heart rate during the endotracheal suction immediately after and at 30 seconds after suctioning were statistically significant (p=0.005). The increase in heart rate at one minute after suctioning was also significant (p=0.023). The increase in heart rate continued until 10 minutes after the endotracheal suction, but was not statistically significant In this study, endotracheal suctioning with hyperoxygenation and hyperinflation was effective in preventing a decrease in $PaO_2$ after suctioning, but not in preventing cardiac arrhythmias. Nurses should be aware of the complications of endotracheal suctioning and do effective hyperoxygenation and hyperinflation before and after suctioning. Further research is needed to develop a efficient endotracheal suction method which will minimize complications. This study needs to be replicated with different population of patients intubatted or having a tracheostomy, specifically, patients who cardiac or pulmonary desease. The data were analyzed by using an S. P. S. S. computerized program for mean, standard deviation, percentage and paired t-test. The results of this study were as follows: 1. The increase in $PaO_2$ after hyperoxygenation and hyperinflation was highly statistically significant(p=0.041), and the increase in $PaO_2$ immediately after suctioning was not significant (p=0.752). The time of lowest $PaO_2$ was 30 seconds after the endotracheal suction. 2. The occurrance of cardiac arrhythmia after the endotracheal suction included sinus tachycardia, sinus arrhythmia, sinus bradycardia, premature atrial contraction (PAC), and premature ventricular contraction (PVC). The most frequent cardiac arrhythmia was sinus tachycardia (a subjects). Sinus arrhythmia was observed in 5 subjects and continued till 10 minutes after suctioning in two of these. Sinus bradycardia occurred in only 3 subjects and among them, 1 subjects shows sinus arrythmia till 10 minutes after suctioning along. PAC was observed in only one subject and continued till five minutes after suctining along with sinus arrhythmia. PVC was observed in three subjects: it lasted for only 30 seconds after suctioning in two subjects. but continued for 10 minutes after suctioning in the third. 6 subjects manifested two kinds of Cardiac arrhythmia Three of them showed sinus tachycardia with PVC, another 2 showed sinus bradycardia with sinus arrhythmia, and the other subject showed sinus arrhythmia with PAC. 3. The increases in heart rate during the endotracheal suction immediately after and at 30 seconds after suctioning were statistically significant (p=0.005). The increase in heart rate at one minute after suctioning was also significant (p=0.023). The increase in heart rate continued until 10 minutes after the endotracheal suction, but was not statistically significant In this study, endotracheal suctioning with hyperoxygenation and hyperinflation was effective in preventing a decrease in $PaO_2$ after suctioning, but not in preventing cardiac arrhythmias. Nurses should be aware of the complications of endotracheal suctioning and do effective hyperoxygenation and hyperinflation before and after suctioning. Further research is needed to develop a efficient endotracheal suction method which will minimize complications. This study needs to be replicated with different population of patients intubatted or having a tracheostomy, specifically, patients who cardiac or pulmonary desease.

• 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.14 no.1
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• pp.16-22
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• 2010

Electrocardiogram(ECG) analysis and arrhythmia recognition are critical for diagnosis and treatment of ill patients. Cardiac arrhythmia is a condition in which heart beat may be irregular and presents a serious threat to the patient recovering from ventricular tachycardia (VT) and ventricular fibrillation (VF). Other arrhythmias like atrial premature contraction (APC), Premature ventricular contraction (PVC) and superventricular tachycardia (SVT) are important in diagnosing the heart diseases. This paper presented new method to classify various arrhythmias contrary to other techniques which are limited to only two or three arrhythmias. ECG is decomposed into Intrinsic Mode Functions (IMFs) by Empirical Mode Decomposition (EMD). Burg algorithm was performed on IMFs to obtain AR coefficients which can reduce the dimension of feature vector and utilized as Multi-class SVM inputs which is basically extended from binary SVM. We chose optimal parameters for SVM classifier, applied to arrhythmias classification and achieved the accuracies of detecting NSR, APC, PVC, SVT, VT and VP were 96.8% to 99.5%. The results showed that EMD was useful for the preprocessing and feature extraction and multi-class SVM for classification of cardiac arrhythmias, with high usefulness.

• 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 the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2391-2398
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• 2009

Premature ventricular contractions are the most common of all arrhythmias and may cause more serious situation in some patients. Therefore, the detection of this arrhythmia becomes crucial in the early diagnosis and prevention of possible life threatening cardiac diseases. Most of the algorithms detecting PVC reported in literature is not always feasible due to the presence of noise and P wave making the detection difficult, and the process being time consuming and ineffective for real time analysis. To solve this problem, a new approach for the detection of PVC is presented based rhythm analysis and beat matching in this paper. For this purpose, the ECG signals are first processed by the usual preprocessing method and R wave was detected. The algorithm that decides beat type using the rhythm analysis of RR interval and beat matching of QRS width is developed. The performance of R wave and PVC detection is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate sensitivity of 99.74%, positive predictivity of 99.81% and sensitivity of 93.91%, positive predictivity of 96.48% accuracy respectively for R wave and PVC detection.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11B
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• pp.1289-1295
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• 2009

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 prevention of possible life threatening cardiac diseases. Particularly, in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, design of algorithm that exactly detects R wave using minimal computation and classifies PVC is needed. So, R wave detection algorithm based adaptive threshold and window for the classification of PVC is presented in this paper. For this purpose, ECG signals are first processed by the usual preprocessing method and R wave was detected and adaptive window through R-R interval is used for efficiency of the detection. The performance of R wave detection and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate 99.33%, 88.86% accuracy respectively for R wave detection and PVC classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.2021-2030
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• 2012

Premature ventricular contraction(PVC) is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Particularly, in the healthcare system that must continuously monitor patient's situation, it is necessary to process ECG (Electrocardiography) signal in realtime. In other words, the design of algorithm that exactly detects R wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, the patient adaptive pattern matching algorithm for the classification of PVC is presented in this paper. For this purpose, we detected R wave through the preprocessing method, adaptive threshold and window. Also, we applied pattern matching method to classify each patient's normal cardiac behavior through the Hash function. The performance of R wave detection and abnormal beat classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.33% in R wave detection and the rate of 0.32% in abnormal beat classification error.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.705-711
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• 2013

QRS detection of ECG is the most popular and easy way to detect cardiac-disease. But it is difficult to analyze the ECG signal because of various noise types. Also in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, the design of algorithm that exactly detects QRS wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, efficient QRS detection and PVC classification based on profiling method is presented in this paper. For this purpose, we detected QRS through the preprocessing method using morphological filter, adaptive threshold, and window. Also, we applied profiling method to classify each patient's normal cardiac behavior through hash function. The performance of R wave detection, normal beat and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.77% in R wave detection and the rate of 0.65% in normal beat classification error and 93.29% in PVC classification.

• The Journal of Internal Korean Medicine
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• v.27 no.4
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• pp.798-810
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• 2006

Objectives : This study was designed to investigate heart rate variability(HRV) in patients with abnormality on EKG by power spectrum analysis of HRV. Methods : The patient group consisted of 147 patients diagnosed as abnormal on EKG at the Oriental Medical Hospital of Dong-eui University from November 2003 to September 2005. We divided the patient group into 9 subgroups (bradycardia, arrhythmia, PVC, AF, AV block, RBBB, LVH, cardiac ischemia, LAD ). The control group consisted of 117 patients who were diagnosed as normal on EKG at the same hospital during the same period. We checked HRV of the two groups over 5 minutes and compared the HRV index between groups. Results and Conclusions : In the time domain analysis, SDNN was significantly higher in the PVC and AF groups than control group and RMSSD was significantly higher in the all patient group and the bradycardia, PVC and AF groups than in the control group. In the frequency domain analysis, Ln(LF) was significantly higher in the all patient group and the PVC and AF groups than the control group but lower in the LAD group. Ln(HF) was significantly higher in The all patient group and bradycardia, PVE and AF groups than control group. LF/HF ratio was significantly lower in the all patient group and bradycardia, arrhythmia, AF, AV block and LAD groups than control group. The autonomic nerve system and parasympathetic nerve system were higher in the patient group with abnormal EKG compared with the control group.