http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
박종욱,이전,이효기,김호중,이경중,Park, Jong-Uk,Lee, Jeon,Lee, Hyo-Ki,Kim, Hojoong,Lee, Kyoung-Joung 대한의용생체공학회 2013 의공학회지 Vol.34 No.3
Respiratory signal is one of the important physiological information indicating the status and function of the body. Recent studies have provided the possibility of being able to estimate the respiratory signal by using a change of PWV(pulse width variability), PRV(pulse rate variability) and PAV(pulse amplitude variability) in the PPG (photoplethysmography) signal during daily life. But, it is not clear whether the respiratory monitoring is possible even during sleep. Therefore, in this paper, we estimated the respiration from PWV, PRV and PAV of PPG signals during sleep. In addition, respiration rates of the estimated respiration signal were calculated through a time-frequency analysis, and errors between respiration rates calculated from each parameter and from reference signal were evaluated in terms of 1 sec, 10 sec and 1 min. As a result, it showed the errors in PWV(1s: $36.38{\pm}37.69$ mHz, 10s: $36.53{\pm}38.16$ mHz, 60s: $30.35{\pm}38.72$ mHz), in PRV(1s: $1.45{\pm}1.38$ mHz, 10s: $1.44{\pm}1.37$ mHz, 60s: $0.45{\pm}0.56$ mHz), and in PAV(1s: $1.05{\pm}0.81$ mHz, 10s: $1.05{\pm}0.79$ mHz, 60s: $0.56{\pm}0.93$ mHz). The errors in PRV and PAV are lower than that of PWV. Finally, we concluded that PRV and PAV are more effective than PWV in monitoring the respiration in daily life as well as during sleep.
비강압력신호를 이용한 수면호흡장애 환자의 수면/각성 분류
박종욱,정필수,강규민,이경중,Park, Jong-Uk,Jeoung, Pil-Soo,Kang, Kyu-Min,Lee, Kyoung-Joung 대한의용생체공학회 2016 의공학회지 Vol.37 No.4
This study proposes the feasibility for automatic classification of sleep/wakefulness using nasal pressure in patients with sleep-disordered breathing (SDB). First, SDB events were detected using the methods developed in our previous studies. In epochs for normal breathing, we extracted the features for classifying sleep/wakefulness based on time-domain, frequency-domain and non-linear analysis. And then, we conducted the independent two-sample t-test and calculated Mahalanobis distance (MD) between the two categories. As a results, $SD_{LEN}$ (MD = 0.84, p < 0.01), $P_{HF}$ (MD = 0.81, p < 0.01), $SD_{AMP}$ (MD = 0.76, p = 0.031) and $MEAN_{AMP}$ (MD = 0.75, p = 0.027) were selected as optimal feature. We classified sleep/wakefulness based on support vector machine (SVM). The classification results showed mean of sensitivity (Sen.), specificity (Spc.) and accuracy (Acc.) of 60.5%, 89.0% and 84.8% respectively. This method showed the possibilities to automatically classify sleep/wakefulness only using nasal pressure.
수면호흡장애 환자의 인공지능 기반 심혈관질환 예측 모델
박종욱(Jong-Uk Park),류지승(Ji-Seung Rye),강승영(Seung-Young Kang),김윤지(Yun-Ji Kim),김이웅(Yeewoong Kim),이경중(Kyoung-Joung Lee) 대한전자공학회 2020 대한전자공학회 학술대회 Vol.2020 No.8
This study proposes a method of prediction of cardiovascular disease (CVD) that can develop within ten years in patients with sleep-disordered breathing (SDB). From the data during a baseline period when patients did not have any CVD, we extracted 18 features from electrography (ECG) based on signal processing methods, 30 ECG features based on artificial intelligence (AI), ten clinical risk factors for CVD. We trained the model and evaluated it by using CVD outcomes result, monitored in follow-ups. The optimal feature vectors were selected through statistical analysis and support vector machine recursive feature elimination (SVM-RFE) of the extracted feature vectors. Features based on AI, a novel proposal from this study, showed excellent performance out of all selected feature vectors. Also, new parameters based on AI were possibly meaningful predictors for CVD, when used in addition to the predictors for CVD that are already known. The selected features were used as inputs to the prediction model based on SVM. As a result, the respective recall and precision values were 82.9% and 87.5% for CVD-free. The F1-score between CVD and CVD-free was 76.5. In conclusion, our results confirm the excellence of the prediction model for CVD in patients with SDB and verify the possibility of prediction within ten years of the CVD that may occur in patients with SDB.
에어 매트리스와 산소 포화도 측정기를 이용한 수면호흡장애 자동 검출 시스템 개발
정필수,박종욱,주은연,이경중,Jeong, Pil-Soo,Park, Jong-Uk,Joo, Eun-Youn,Lee, Kyoung-Joung 대한의용생체공학회 2017 의공학회지 Vol.38 No.4
The present study proposes a system that can detect sleep-disordered breathing automatically using an air mattress and oxygen saturation. A thin air mattress was fabricated to reduce discomfort during sleep, and respiration signals were acquired. The system was configured to be synchronized with a polysomnography to receive signals simultaneously with other bio-signals. The present study has been conducted with nine adult male and female patients with sleep-disordered breathing, and sleep-disordered breathing events have been detected by applying the signals acquired from the subjects to the rule-based detection algorithm. The sensitivity and positive predictive values were found to evaluate the performance of the system, which are 91.4% and 89.7% for all events, respectively. The comparison of apnea hypopnea index(AHI) between the polysomnography and the proposed method showed squared R-value of 0.9. This study presents the possibility of detecting sleep-disordered breathing at hospitals or homes using the proposed system.
압전센서를 이용한 코골이와 심박 검출을 위한 자동 알고리즘
에르덴바야르,박종욱,정필수,이경중,Urtnasan, Erdenebayar,Park, Jong-Uk,Jeong, Pil-Soo,Lee, Kyoung-Joung 대한의용생체공학회 2015 의공학회지 Vol.36 No.5
In this paper, we proposed a novel method for automatic detection for snoring and heart beat using a single piezoelectric sensor. For this study multi-rate signal processing technique was applied to detect snoring and heart beat from the single source signal. The sound event duration and intensity features were used to snore detection and heart beat was found by autocorrelation. The performance of the proposed method was evaluated on clinical database, which is the nocturnal piezoelectric snoring data of 30 patients that suffered obstructive sleep apnea. The method achieved sensitivity of 88.6%, specificity of 96.1% with accuracy of 95.6% for snoring and sensitivity of 94.1% and positive predictive value of 87.6% for heart beat, respectively. These results suggest that the proposed method can be a useful tool in sleep monitoring and sleep disordered breathing diagnosis.