• Science of Emotion and Sensibility
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• v.14 no.1
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• pp.147-156
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• 2011

Basic emotions such as happiness, sadness, anger, fear, and disgust have been widely used to investigate emotion-specific autonomic nervous system activity in many studies. On the contrary, surprise emotion, Suggested also as one of the basic emotions suggested by Ekman et al. (1983), has been least investigated. The purpose of this study was to provide a description of cardiovascular responses on surprise stimulus using electrocardiograph (ECG) and photoplethysmograph (PPG). ECG and PPG were recorded from 76 undergraduate students, as they were exposed to a visuo-acoustic surprise stimulus. Heart rate (HR), standard deviation of R-R interval (SD-RR), root mean square of successive R-R interval difference (RMSSD-RR), respiratory sinus arrhythmia (RSA), finger blood volume pulse amplitude (FBVPA), and finger pulse transit time (FPTT) were calculated before and after the stimulus presentation. Results show significant increase in HR, SD-RR, and RMSSD-RR, decreased FBVPA, and shortened FPTT. Evidence suggests that surprise emotion can be characterized by vasoconstriction and accelerated heart rate, sympathetic activation, and increased heart rate variability, parasympathetic activation. These results can be useful in developing an emotion theory, or profiling surprise-specific physiological responses, as well as establishing the basis for emotion recognition system in human-computer interaction.

• Journal of the Korean Society of Safety
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• v.35 no.1
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• pp.62-69
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• 2020

Most people suffer from stress during day life because modernized society is very complex and changes fast. Because stress can affect to many kind of physiological phenomena it is even considered as a disease. Therefore, it should be detected earlier, then must be released. When a person is being stressed several bio-signals such as heart rate, etc. are changed. So, those can be detected using medical electronics techniques. In this paper, stress assessment system is studied using random forest algorithm based on heart rate, RR interval and Galvanic skin response. The random forest model was trained and tested using the data set obtained from the bio-signals. It is found that the stress assessment procedure developed in this paper is very useful.

• Journal of Biomedical Engineering Research
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• v.12 no.4
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• pp.277-284
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• 1991

This study describes the ambulatory ECG monitoring system for the remote autom atic diagnosis. System: tlardware is based on one chip microcomputer(80c31) and its peripherals which consists of A/D, EPROM, RAM, LCD display and two preamplifiers, Power circuits, control logic circuits. A/D converted data were differentiated and low pass filtered. The detection of QRS complex and R point were accomplished by software algorithm based on adaptive threshold computed on low pass fi:leered signal. Rhythm analysis is performed by RR interval and average RR interval. The performance of QRS detection algorithm is evaluated by using MIT/BIH data base. Using this system, the trends of the arrythmia during the long term could be saved and displayed.

• YAKHAK HOEJI
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• v.55 no.1
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• pp.22-31
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• 2011

This systematic review was conducted to assess the clinical effect of ocular fluoroquinolones used for the treatment of bacterial conjunctivitis. A literature search for randomized controlled clinical trials registered up to January 2010 based on PubMed database, using the following search terms: conjunctivitis and fluoroquinolones (besifloxacin, moxifloxacin, gatifloxacin, levofloxacin, lomefloxacin, ciprofloxacin and ofloxacin) were performed. Pooled data on the clinical resolution and bacterial eradication rates derived from selected 16 studies were reported as the relative risk (RR) and 95% confidence interval (95% CI) compared with placebo. Early clinical resolution and microbiological eradication rates in placebo were 28% and 62% respectively. Fluoroquinolones were significantly effective comparing to placebo: early RR 1.94 (95% CI 1.60~2.34) and late RR 1.30 (1.19~1.43) in clinical resolution rates, and early RR 1.75 (1.58~1.94) and late RR 1.28 (1.18~1.39) in microbiological eradication rates. Besifloxacin, ciprofloaxain and moxifloxacin in clinical resolution, and besifloxacin and levofloxacin in microbiological eradication showed higher RRs than pooled overall fluoroquinolones' RRs. New quinolones had higher antibacterial potencies for all pathogens isolated from bacterial conjunctivitis and resistant isolates than old generation quinolones. In conclusion, ocular 7 fluoroquinolones were all effective than placebo for bacterial conjunctivitis and there were differences between quinolones in early and late clinical resolutions and microbiological eradications, and no differences in safety comparing to placebo.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.346-355
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• 2017

Atrial fibrillation (AF) and Congestive heart failure (CHF) are increasingly widespread, costly, deadly diseases and are associated with significant morbidity and mortality. In this study, we analyzed three statistical methods for automatic detection of AF and CHF based on the randomness, variability and complexity of the heart beat interval, which is RRI time series. Specifically, we used short RRI time series with 16 beats and employed the normalized root mean square of successive RR differences (RMSSD), the sample entropy and the Shannon entropy. The detection performance was analyzed using four large well documented databases, namely the MIT-BIH Atrial fibrillation (n=23), the MIT-BIH Normal Sinus Rhythm (n=18), the BIDMC Congestive Heart Failure (n=13) and the Congestive Heart Failure RRI databases (n=25). Using thresholds by Receiver Operating Characteristic (ROC) curves, we found that the normalized RMSSD provided the highest accuracy. The overall sensitivity, specificity and accuracy for AF and CHF were 0.8649, 0.9331 and 0.9104, respectively. Regarding CHF detection, the detection rate of CHF (NYHA III-IV) was 0.9113 while CHF (NYHA I-II) was 0.7312, which shows that the detection rate of CHF with higher severity is higher than that of CHF with lower severity. For the clinical 24 hour data (n=42), the overall sensitivity, specificity and accuracy for AF and CHF were 0.8809, 0.9406 and 0.9108, respectively, using normalized RMSSD.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.117-126
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• 2019

Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1542-1550
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• 2019

Legacy studies for classifying arrhythmia have been studied to improve the accuracy of classification, Neural Network, Fuzzy, etc. Deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose optimal parameter extraction method based on a deep learning. For this purpose, R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 97.84% in PVC classification.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.96-101
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• 2011

In recent days, the demand for the remote ECG monitoring system has been increasing and the automation of the monitoring system is becoming quite of a concern. Automatic detection of the abnormal ECG beats must be a necessity for the successful commercialization of these real time remote ECG monitoring system. From these viewpoints, in this paper, we proposed an automatic detection algorithm for the abnormal ECG beats using QRS width and RR interval patterns. In the previous research, many efforts have been done to classify the ECG beats into detailed categories. But, these approaches have disadvantages such that they produce lots of misclassification errors and variabilities in the classification performance. Also, they require large amount of training data for the accurate classification and heavy computation during the classification process. But, we think that the detection of abnormality from the ECG beats is more important that the detailed classification for the automatic ECG monitoring system. In this paper, we tried to detect the VEB which is most frequently occurring among the abnormal ECG beats and we could achieve satisfactory detection performance when applied the proposed algorithm to the MIT/BIH database.

• Clinics in Shoulder and Elbow
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• v.25 no.1
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• pp.36-41
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• 2022

Background: Distal biceps rupture is a relatively uncommon injury that can significantly affect quality of life. Early complications following biceps tendon repair are not well described in the literature. This study utilizes a national surgical database to determine the incidence of and predictors for short-term complications following distal biceps tendon repair. Methods: The American College of Surgeons' National Surgical Quality Improvement Program database was used to identify patients undergoing distal biceps repair between January 1, 2011, and December 31, 2017. Patient demographic variables of sex, age, body mass index, American Society of Anesthesiologists class, functional status, and several comorbidities were collected for each patient, along with 30-day postoperative complications. Binary logistic regression was used to calculate risk ratios for these complications using patient predictor variables. Results: Early postoperative surgical complications (0.5%)-which were mostly infections (0.4%)-and medical complications (0.3%) were rare. A readmission risk factor was diabetes (risk ratio [RR], 4.238; 95% confidence interval [CI], 1.180-15.218). Non-home discharge risk factors were smoking (RR, 3.006; 95% CI, 1.123-8.044) and ≥60 years of age (RR, 4.150; 95% CI, 1.611-10.686). Maleness was protective for medical complications (RR, 0.024; 95% CI, 0.005-0.126). Surgical complication risk factors were obese class II (RR, 4.120; 95% CI, 1.123-15.120), chronic obstructive pulmonary disease (COPD; RR, 21.981; 95% CI, 3.719-129.924), and inpatient surgery (RR, 8.606; 95% CI, 2.266-32.689). Conclusions: Complication rates after distal biceps repair are low. Various patient demographics, medical comorbidities, and surgical factors were all predictive of short-term complications.