• The Journal of the Society of Korean Medicine Diagnostics
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• v.9 no.2
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• pp.123-144
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• 2005

Objectives: Pulse-Respiration Ratio has been used for estimating subject's Han-Yeol [寒熱] status since it mentioned in suwen [素問]. In practicing Pulse-Respiration Ratio over 5 means the status of Yeol [熱], Pulse-Respiration Ratio below 3 means the status of Han [寒]. We performed this study to examine the Optimum Standard for Measuring Pulse-Respiration Ratio on the Basis of Repeatability and Reproducibility. Methods: After subject's 5 minutes rest we measured subject's ECG, respiration pattern, EEG, EMG simultaneously. In this research examiner's number is two, subject's number is four, and the number of repeat is two. We calculated Pulse-Respiration Ratio through dividing Respiration cycle average by Pulse cycle average according to each standard including time section, $EEG(relative-{\alpha}$ density, $relative-{\beta}$ density, ${\alpha}/{\beta}$ and EMG. We analyzed these data through Gage R&R study using MINITAB 13.20 program and considered the results of below 30 %R&R and over 4 Number of Distinct Categories to have a significance. Results: 1. In the applying of time standard, Pulse-Respiration Ratio from section 3, 4, 6, 8 had a significant meaning in the aspect of Repeatability and Reproducibility. 2. In the applying of $EEG({\alpha}$ I , ${\beta}$ I , ${\alpha}/{\beta})$, EMG(E I) standard, there was no significant results. 3. In the applying of time standard(section 5, 6, 7), $EEG({\alpha}$ I , ${\beta}$ I , ${\alpha}/{\beta})$ and EMG(E I) standard simultaneously, Pulse-Respiration Ratio from ${\alpha}/{\beta}$ in section 6, ${\beta}$ I in section 8 had a significant meaning in the aspect of Repeatability and Reproducibility. Conclusions: We can suggest the Optimum Standard for Measuring Pulse-Respiration Ratio on the basis of Repeatability and Reproducibility as followings; 1. Pulse-Respiration Ratio Measuring time should be at least 15 minutes. 2. Applying of time(section 6, 8) and $EEG({\beta}$ I, ${\alpha}/{\beta})$ standard simultaneously is recommended considering reliability and validity but more study is needed. 3. EMG(E I) may be helpful to detect the segment of physical rest and exclude artifacts but more study is needed.

• The Korean Journal of Emergency Medical Services
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• v.24 no.3
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• pp.141-145
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• 2020

The most common symptom of aortic dissection is chest pain, which is similar to acute coronary artery syndrome, making it difficult to diagnose with clinical pattern, requiring various diagnostic methods. About 10-15% of the aortic dissection patients are accompanied by changes in the ST segment by the dissecting flap of the coronary opening, which can lead to delayed diagnosis of aortic dissection, or can adversely affect the patient by administration to unnecessary drugs such as nitroglycerin, thrombolytic agent, and anticoagulants. It is difficult to distinguish aortic dissection from an acute myocardial infarction only through a 12-Lead electrocardiogram at the pre-hospital. The application of cardiac ultrasonography through medical direction to chest pain patients who show ST segmental changes in pre-hospital phase will contribute to the diagnosis of aortic dissection and the improvement of survival rate, such as anticoagulant administration, to patients with acute myocardial infarction.

• 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.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.10 no.2
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• pp.104-120
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• 2006

Background : Pulse-respiration ratio has been used for estimating subject's status in oriental medicine. Pulse and respiration is strongly associated with autonomic nerve system. But there is no research about correlation between pulse-respiration ratio and autonomic nerve system. Objectives : We performed this study to know correlation between pulse-respiration ratio and HRV(heart rate variability) that shows autonomic nerve system status well and to clarify clinical meaning of pulse-respiration ratio. Methods : After subject's 10 minutes rest, we measured subject's ECG, respiration pattern and HRV. In this research, subject's number is 95(Male 50/Female 45). We calculated pulse-respiration ratio from ECG and respiration pattern. Then, we analyzed correlation between pulse-respiration ratio and HRV parameters in all subjects, 2 group divided by Wan-Maek(P-R ratio 4.28). We tried to compare HRV parameters among Wan-Maek, Sak-Maek and Ji-Maek group. Correlation analysis between pulse-respiration Ratio and Pulse rate, respiration rate is performed. Finally correlation analysis between Respiration and HRV parameters in all subjects, 2 group divided by Wan-Maek(4.28) is studied. Results : 1. Mean pulse-respiration is 4.10${\pm}$0.67, Mean pulse rate is 68.06${\pm}$7.82bpm, Mean respiration rate is 16.81${\pm}$2.72 times per minute in all subjects. 2. Correlation analysis between pulse-respiration ratio and HRV parameters of high pulse-respiration ratio group is not significant. But, in low pulse-respiration ratio group, HFnorm(correlation coefficient 0.306, p= 0.018), lnHF (0.308, p=0.002) is significantly correlated with pulse-respiration ratio. 3. Comparison of HRV parameters among Wan-Maek, Sak-Maek and Ji-Maek Group is not significant. 4. Pulse-respiration ratio is more affected by respiration rate(correlation coefficient-0.17, p=0.000) than pulse rate (correlation coefficient 0.396, p=0.000). 5. Correlation analysis between respiration rate and HRV parameters of high pulse-respiration ratio group is not significant. But, in low pulse-respiration ratio group, HFnorm (correlation coefficient -0.327, p=0.011), LF/HF(0.346, p=0.007), lnHF (-0.355, p=0.006) are significantly correlated with respiration rate. Conclusion : Pulse-respiration ratio and parasympathetic index has positive correlation. The closer Wan-Maek, The higher parasympathetic index in low pulse-respiration ratio group. Respiration rate is more related with pulse-respiration ratio than pulse rate. Respiration is negatively correlated with autonomic parameters. And the slower respiration, the higher parasympathetic index in low pulse-respiration ratio group.

• Journal of Biomedical Engineering Research
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• v.41 no.5
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• pp.185-194
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• 2020

Arrhythmia is basically diagnosed with the electrocardiogram (ECG) signal, however, ECG is difficult to measure and it requires expert help in analyzing the signal. On the other hand, the radial pulse can be measured with easy and uncomplicated way in daily life, and could be suitable bio-signal for the recent untact paradigm and extensible signal for diagnosis of Korean medicine based on pulse pattern. In this study, we developed an irregular radial pulse detection algorithm based on a learning model and considered its applicability as arrhythmia screening. A total of 1432 pulse waves including irregular pulse data were used in the experiment. Three data sets were prepared with minimal preprocessing to avoid the heuristic feature extraction. As classification algorithms, elastic net logistic regression, random forest, and extreme gradient boosting were applied to each data set and the irregular pulse detection performances were estimated using area under the receiver operating characteristic curve based on a 10-fold cross-validation. The extreme gradient boosting method showed the superior performance than others and found that the classification accuracy reached 99.7%. The results confirmed that the proposed algorithm could be used for arrhythmia screening. To make a fusion technology integrating western and Korean medicine, arrhythmia subtype classification from the perspective of Korean medicine will be needed for future research.