• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.123-132
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• 2013

As the smartphones are equipped with various sensors such as the accelerometer, GPS, gravity sensor, gyros, ambient light sensor, proximity sensor, and so on, there have been many research works on making use of these sensors to create valuable applications. Human activity recognition is one such application that is motivated by various welfare applications such as the support for the elderly, measurement of calorie consumption, analysis of lifestyles, analysis of exercise patterns, and so on. One of the challenges faced when using the smartphone sensors for activity recognition is that the number of sensors used should be minimized to save the battery power. When the number of sensors used are restricted, it is difficult to realize a highly accurate activity recognizer or a classifier because it is hard to distinguish between subtly different activities relying on only limited information. The difficulty gets especially severe when the number of different activity classes to be distinguished is very large. In this paper, we show that a fairly accurate classifier can be built that can distinguish ten different activities by using only a single sensor data, i.e., the smartphone accelerometer data. The approach that we take to dealing with this ten-class problem is to use the ensemble of nested dichotomy (END) method that transforms a multi-class problem into multiple two-class problems. END builds a committee of binary classifiers in a nested fashion using a binary tree. At the root of the binary tree, the set of all the classes are split into two subsets of classes by using a binary classifier. At a child node of the tree, a subset of classes is again split into two smaller subsets by using another binary classifier. Continuing in this way, we can obtain a binary tree where each leaf node contains a single class. This binary tree can be viewed as a nested dichotomy that can make multi-class predictions. Depending on how a set of classes are split into two subsets at each node, the final tree that we obtain can be different. Since there can be some classes that are correlated, a particular tree may perform better than the others. However, we can hardly identify the best tree without deep domain knowledge. The END method copes with this problem by building multiple dichotomy trees randomly during learning, and then combining the predictions made by each tree during classification. The END method is generally known to perform well even when the base learner is unable to model complex decision boundaries As the base classifier at each node of the dichotomy, we have used another ensemble classifier called the random forest. A random forest is built by repeatedly generating a decision tree each time with a different random subset of features using a bootstrap sample. By combining bagging with random feature subset selection, a random forest enjoys the advantage of having more diverse ensemble members than a simple bagging. As an overall result, our ensemble of nested dichotomy can actually be seen as a committee of committees of decision trees that can deal with a multi-class problem with high accuracy. The ten classes of activities that we distinguish in this paper are 'Sitting', 'Standing', 'Walking', 'Running', 'Walking Uphill', 'Walking Downhill', 'Running Uphill', 'Running Downhill', 'Falling', and 'Hobbling'. The features used for classifying these activities include not only the magnitude of acceleration vector at each time point but also the maximum, the minimum, and the standard deviation of vector magnitude within a time window of the last 2 seconds, etc. For experiments to compare the performance of END with those of other methods, the accelerometer data has been collected at every 0.1 second for 2 minutes for each activity from 5 volunteers. Among these 5,900 ($=5{\times}(60{\times}2-2)/0.1$) data collected for each activity (the data for the first 2 seconds are trashed because they do not have time window data), 4,700 have been used for training and the rest for testing. Although 'Walking Uphill' is often confused with some other similar activities, END has been found to classify all of the ten activities with a fairly high accuracy of 98.4%. On the other hand, the accuracies achieved by a decision tree, a k-nearest neighbor, and a one-versus-rest support vector machine have been observed as 97.6%, 96.5%, and 97.6%, respectively.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.2 no.2
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• pp.139-146
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• 1999

Purpose: The aim of this study was to evaluate the clinical significance of 24 hour pH monitoring in the pediatric patients with recurrent vomiting or regurgitation. Methods: We performed 24 hour pH monitoring on 87 pediatric patients with recurrent vomiting or regurgitation using GastrograpH with glass electrode. The pathologic GER was determined by the reflux index (RI). RIs>10% were considered positive in patients <1 year of age, whereas RIs of >5% were positive in other age groups. We evaluated the mean and standard deviation of the reflux parameters between physiologic and pathologic GER groups, and also compared the reflux indices of each group with respect to time zones of the day. Results: Pathologic GER was found in 32 of 87 patients (36.8%), and the age incidence included 32.5% in infants <6 months old, 13.3% in infants aged 6 months-1 year old, 61.5% in children aged 1~2 years old, 14.3% in children aged 2~3 years old and 66.7% in children >3 years old. In physiologic GER patients, the RI was $3.7{\pm}2.9%$ for the patients <1 year old (group A), and $1.8{\pm}1.5%$ for those ${\geq}1$ year old (group B) which was statistically significant between the 2 age groups (p=0.02). The number of long refluxes more than 5 minutes was significantly increased (p=0.03) in group A ($1.7{\pm}1.9$) than in group B ($0.8{\pm}1.0$). The duration of the longest reflux was significantly longer (p=0.007) in group A ($604{\pm}551$ sec) than in group B ($275{\pm}296$ sec). In pathologic GER patients, the RI was $17.7{\pm}11.6%$ for the patients <1 year old and $7.8{\pm}2.9$ for those ${\geq}1$ year old. The number of long refluxes of more than 5 minutes were $8.9{\pm}4.6$ and $3.2{\pm}1.8$, and the duration of the longest reflux were $1955{\pm}2190$ sec and $1093{\pm}706$ sec for each age group. In both physiologic and pathologic GER patients, there was no significant difference of RI among the time zones of the day. Conclusion: Pathologic GER was found in 36.8% of patients. There was significant difference of RI between those <1 year old and those ${\geq}1$ year old in physiologic GER patients. There was no significant difference of RI among the time zones of the day in both pathologic and physiologic groups. In our study, the frequency of pathplogic GER was too much higher in age group of 1~2 years old (61.5%) than in group of 6 months-1 year old (13.3%), which means that further study is needed to determine the pathologic criteria of RI (Vandenplas criteria is >5%) in the age group of 1~2 years old.

• Journal of Food Hygiene and Safety
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• v.34 no.1
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• pp.22-29
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• 2019

Validamycin A is an aminoglycoside fungicide produced by Streptomyces hygroscopicus that inhibits trehalase. The purpose of this study was to develop a method for detecting validamycin A in agricultural samples to establish MRL values for use in Korea. The validamycin A residues in samples were extracted using methanol/water (50/50, v/v) and purified with a hydrophilic-lipophilic balance (HLB) cartridges. The analyte was quantified and confirmed by liquid chromatograph-tandem mass spectrometer (LC-MS/MS) in positive ion mode using multiple reaction monitoring (MRM). Matrix-matched calibration curves were linear over the calibration ranges (0.005~0.5 ng) into a blank extract with $R^2$ > 0.99. The limits of detection and quantification were 0.005 and 0.01 mg/kg, respectively. For validation validamycin A, recovery studies were carried out three different concentration levels (LOQ, $LOQ{\times}10$, $LOQ{\times}50$, n = 5) with five replicates at each level. The average recovery range was from 72.5~118.3%, with relative standard deviation (RSD) less than 10.3%. All values were consistent with the criteria ranges requested in the Codex guidelines (CAC/GL 40-1993, 2003) and the NIFDS (National Institute of Food and Drug Safety) guideline (2016). Therefore, the proposed analytical method is accurate, effective and sensitive for validamycin A determination in agricultural commodities.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Food Hygiene and Safety
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• v.34 no.2
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• pp.124-134
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• 2019

An analytical method was developed for the determination of broflanilide and its metabolites in agricultural products. Sample preparation was conducted using the QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) method and LC-MS/MS (liquid chromatograph-tandem mass spectrometer). The analytes were extracted with acetonitrile and cleaned up using d-SPE (dispersive solid phase extraction) sorbents such as anhydrous magnesium sulfate, primary secondary amine (PSA) and octadecyl ($C_{18}$). The limit of detection (LOD) and quantification (LOQ) were 0.004 and 0.01 mg/kg, respectively. The recovery results for broflanilide, DM-8007 and S(PFP-OH)-8007 ranged between 90.7 to 113.7%, 88.2 to 109.7% and 79.8 to 97.8% at different concentration levels (LOQ, 10LOQ, 50LOQ) with relative standard deviation (RSD) less than 8.8%. The inter-laboratory study recovery results for broflanilide and DM-8007 and S (PFP-OH)-8007 ranged between 86.3 to 109.1%, 87.8 to 109.7% and 78.8 to 102.1%, and RSD values were also below 21%. All values were consistent with the criteria ranges requested in the Codex guidelines (CAC/GL 40-1993, 2003) and the Food and Drug Safety Evaluation guidelines (2016). Therefore, the proposed analytical method was accurate, effective and sensitive for broflanilide determination in agricultural commodities.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.3-8
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• 2010

Purpose: $^{11}C$-Acetate PET/CT is useful in detecting lesions that are related to livers in the human body and leads to a sensitivity of 87.3%. On the other hand, $^{18}F$-FDG PET/CT has a sensitivity of 47.3% and it has been reported that if both $^{18}F$-FDG and $^{11}C$-Acetate PET/CT are carried out together, their cumulative sensitivity is around 100%. However, the normal intake of the pancreas and the spleen in $^{11}C$-Acetate PET/CT can influence the $^{18}F$-FDG PET/CT leading to an inaccurate diagnosis. This research was aimed at the verification of the usefulness of how much influence these two radioactive medical supplies can cause on the medical images through comparative analysis between the one-day and two-day protocol. Materials and Methods: This research was carried out based on 46 patients who were diagnosed with liver cancer and have gone through the PET/CT (35 male, 11 female participants, average age: $54{\pm}10.6$ years, age range: 29-69 years). The equipment used for this test was the Biograph TruePoint40 PET/CT (Siemens Medical Systems, USA) and 21 participants who went through the one-day protocol test were first given the $^{11}C$-Acetate PET/CT and the $^{18}F$-FDG PET/CT, the latter exactly after one hour. The other 25 participants who went through the two-day protocol test were given the $^{11}C$-Acetate PET/CT on the first day and the $^{18}F$-FDG PET/CT on the next day. These two groups were then graded comparatively by assigning identical areas of interest of the pancreas and the spleen in the $^{18}F$-FDG images and by measuring the Standard Uptake Value (SUV). SPSS Ver.17 (SPSS Inc., USA) was used for statistical analysis, where statistical significance was found through the unpaired t-test. Results: After analyzing the participants' medical images from each of the two different protocol types, the average${\pm}$standard deviation of the SUV of the pancreas carried out under the two-day protocol were as follows: head $1.62{\pm}0.32$ g/mL, body $1.57{\pm}0.37$ g/mL, tail $1.49{\pm}0.33$ g/mL and the spleen $1.53{\pm}0.28$ g/mL. Whereas, the results for participants carried out under the one-day protocol were as follows: head $1.65{\pm}0.35$ g/mL, body $1.58{\pm}0.27$ g/mL, tail $1.49{\pm}0.28$ g/mL and the spleen $1.66{\pm}0.29$ g/mL. Conclusion: It was found that no statistical significant difference existed between the one-day and two-day protocol SUV in the pancreas and the spleen (p<0.05), and nothing which could be misconceived as false positive were found from the PET/CT medical image analysis. From this research, it was also found that no overestimation of the SUV occurred from the influence of $^{11}C$-Acetate on the $^{18}F$-FDG medical images where those two tests were carried out for one day. This result was supported by the statistical significance of the SUV of measurement. If $^{11}C$-Acetate becomes commercialized in the future, the diagnostic ability of liver diseases can be improved by $^{18}F$-FDG and one-day protocol. It is from this result where tests can be accomplished in one day without the interference phenomenon of the two radioactive medical supplies and furthermore, could reduce the waiting time improving customer satisfaction.

• The Korean Journal of Nuclear Medicine Technology
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• v.20 no.2
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• pp.62-68
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• 2016

Purpose In a cyclosporine experiment using a robotic liquid handing system has found a deviation of its standard curve and low reproducibility of patients's results. The difference of the test is that methanol is mixed with samples and the extractions are used for the test. Therefore, we assumed that the abnormal test results came from using methanol and conducted this test. In a manual of a robotic liquid handling system mentions that we can choose several setting parameters depending on the viscosity of the liquids being used, the size of the sampling tips and the motor speeds that you elect to use but there's no exact order. This study was undertaken to confirm pipetting ability depending on types of liquids and investigate proper setting parameters for the optimum dispensing ability. Materials and Methods 4types of liquids(water, serum, methanol, PEG 6000(25%)) and $TSH^{125}I$ tracer(515 kBq) are used to confirm pipetting ability. 29 specimens for Cyclosporine test are used to compare results. Prepare 8 plastic tubes for each of the liquids and with multi pipette $400{\mu}l$ of each liquid is dispensed to 8 tubes and $100{\mu}l$ of $TSH^{125}I$ tracer are dispensed to all of the tubes. From the prepared samples, $100{\mu}l$ of liquids are dispensed using a robotic liquid handing system, counted and calculated its CV(%) depending on types of liquids. And then by adjusting several setting parameters(air gap, dispense time, delay time) the change of the CV(%)are calcutated and finds optimum setting parameters. 29 specimens are tested with 3 methods. The first(A) is manual method and the second(B) is used robotic liquid handling system with existing parameters. The third(C) is used robotic liquid handling system with adjusted parameters. Pipetting ability depending on types of liquids is assessed with CV(%). On the basis of (A), patients's test results are compared (A)and(B), (A)and(C) and they are assessed with %RE(%Relative error) and %Diff(%Difference). Results The CV(%) of the CPM depending on liquid types were water 0.88, serum 0.95, methanol 10.22 and PEG 0.68. As expected dispensing of methanol using a liquid handling system was the problem and others were good. The methanol's dispensing were conducted by adjusting several setting parameters. When transport air gap 0 was adjusted to 2 and 5, CV(%) were 20.16, 12.54 and when system air gap 0 was adjusted to 2 and 5, CV(%) were 8.94, 1.36. When adjusted to system air gap 2, transport air gap 2 was 12.96 and adjusted to system air gap 5, Transport air gap 5 was 1.33. When dispense speed was adjusted 300 to 100, CV(%) was 13.32 and when dispense delay was adjusted 200 to 100 was 13.55. When compared (B) to (A), the result increased 99.44% and %RE was 93.59%. When compared (C-system air gap was adjusted 0 to 5) to (A), the result increased 6.75% and %RE was 5.10%. Conclusion Adjusting speed and delay time of aspiration and dispense was meaningless but changing system air gap was effective. By adjusting several parameters proper value was found and it affected the practical result of the experiment. To optimize the system active efforts are needed through the test and in case of dispensing new types of liquids proper test is required to check the liquid is suitable for using the equipment.

• Korean Journal of Soil Science and Fertilizer
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• v.21 no.4
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• pp.386-408
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• 1988

A study was conducted to develop a model for estimating evapotranspiration and yield of Chinese cabbages from meteorological factors from 1981 to 1986 in Suweon, Korea. Lysimeters with water table maintained at 50cm depth were used to measure the potential evapotranspiration and the maximum evapotranspiration in situ. The actual evapotranspiration and the yield were measured in the field plots irrigated with different soil moisture regimes of -0.2, -0.5, and -1.0 bars, respectively. The soil water content throughout the profile was monitored by a neutron moisture depth gauge and the soil water potentials were measured using gypsum block and tensiometer. The fresh weight of Chinese cabbages at harvest was measured as yield. The data collected in situ were analyzed to obtain parameters related to modeling. The results were summarized as followings: 1. The 5-year mean of potential evapotranspiration (PET) gradually increased from 2.38 mm/day in early April to 3.98 mm/day in mid-June, and thereafter, decreased to 1.06 mm/day in mid-November. The estimated PET by Penman, Radiation or Blanney-Criddle methods were overestimated in comparison with the measured PET, while those by Pan-evaporation method were underestimated. The correlation between the estimated and the measured PET, however, showed high significance except for July and August by Blanney-Criddle method, which implied that the coefficients should be adjusted to the Korean conditions. 2. The meteorological factors which showed hgih correlation with the measured PET were temperature, vapour pressure deficit, sunshine hours, solar radiation and pan-evaporation. Several multiple regression equations using meteorological factors were formulated to estimate PET. The equation with pan-evaporation (Eo) was the simplest but highly accurate. PET = 0.712 + 0.705Eo 3. The crop coefficient of Chinese cabbages (Kc), the ratio of the maximum evapotranspiration (ETm) to PET, ranged from 0.5 to 0.7 at early growth stage and from 0.9 to 1.2 at mid and late growth stages. The regression equation with respect to the growth progress degree (G), ranging from 0.0 at transplanting day to 1.0 at the harvesting day, were: $$Kc=0.598+0.959G-0.501G^2$$ for spring cabbages $$Kc=0.402+1.887G-1.432G^2$$ for autumn cabbages 4. The soil factor (Kf), the ratio of the actual evapotranspiration to the maximum evapotranspiration, showed 1.0 when the available soil water fraction (f) was higher than a threshold value (fp) and decreased linearly with decreasing f below fp. The relationships were: Kf=1.0 for $$f{\geq}fp$$ Kf=a+bf for f$$I{\leq}Esm$$ Es = Esm for I > Esm 6. The model for estimating actual evapotranspiration (ETa) was based on the water balance neglecting capillary rise as: ETa=PET. Kc. Kf+Es 7. The model for estimating relative yield (Y/Ym) was selected among the regression equations with the measured ETa as: Y/Ym=a+bln(ETa) The coefficients and b were 0.07 and 0.73 for spring Chinese cabbages and 0.37 and 0.66 for autumn Chinese cabbages, respectively. 8. The estimated ETa and Y/Ym were compared with the measured values to verify the model established above. The estimated ETa showed disparities within 0.29mm/day for spring Chinese cabbages and 0.19mm/day for autumn Chinese cabbages. The average deviation of the estimated relative yield were 0.14 and 0.09, respectively. 9. The deviations between the estimated values by the model and the actual values obtained from three cropping field experiments after the completion of the model calibration were within reasonable confidence range. Therefore, this model was validated to be used in practical purpose.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.4 no.1
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• pp.31-71
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• 1968

To clarify the breeding behavior of the hybrids between tropical and temperate area rice varieties, investigations were made on heading days and grain sterility. In this study, crosses were made in half way diallel involving 7 varieties: 2 photoperied sensitive Indicas, 2 less sensitive intermediate Indicas, 1 Ponlai Japonica and 2 high temperature sensitive Japonicas. The parents and $F_1$s were grown under 10 hours and 14 hours daylength controlled conditions at both IRRI(International Rice Research Institute, N$14^{\circ}$17') and Suwon(N$37^{\circ}$16'). F2s with their parents were grown at IRRI in the short day season, and at Suwon under natural conditions. Fa lines with their parents were grown at Suwon under natural conditions. Observations were made for heading days and sterility. The results are summarized as follow; 1. Heading days : 1. For the $F_1$s, earliness showed dominance or overdominance to lateness under the 10 hours condition, and dominance or partial dominance under the 14 hours conditions, at both IRRI and Suwon. 2. For the $F_2$s grown at IRRI during the shortday season earliness appeared to be dominant over lateness and segregation was not distinct and continuous. In the early season culture of $F_2$s at Suwon earliness showed partial dominance or was intermediate. In the proper season culture of $F_2$s lateness showed partial dominance or was intermediate. 3. In the combinations between late parental varieties which do not head at Suwon, transgressive segregants bearing effective panicles were obtained. 4. The crosses of parental varieties having long basic vegetative growth duration showed bigger variance in heading days, and significant correlation was found between of parental varieties and the mean coefficient of variance for parental arrays. 5. The means of heading days of F2 populations were significantly correlated with those of $F_1$ or mid-parents. The means of F 8 lines were also highly correlated with the means of $F_2$s, but, the means of $F_3$ lines grown at Suwon and of their parental $F_2$ individual, grown at IRRI were not correlated. 6. A faint heritability was calculated from the regression of $F_3$ lines grown at Suwon on the $F_2$ individuals grown at IRRI for most combinations, especially in the combinations involving shortday sensitive varieties. This implies low efficiency for the selection of heading days of $F_2$ individuals at IRRI to be grown in lines at Suwon. 7. No significant reciprocal effects were measured for $F_1$ and $F_2$ mean heading days. 8. Partitioning the observed photoperiod sensitivity. into two components, parental array mean md the deviation from this array mean, the parental photoperiod sensitivity contributing to the hybrids was measured in terms of general and specific combining ability for photoperiod sensitivity. 9. The photoperiod sensitivity of $F_1$s was higher than that of the parents, and it decreased as the generation progressed in most combinations of tested varieties. 10. The response of heading days to difference of temperature was weaker for $F_1$ hybrids than for the parents. The differences of temperature responses between the longday and shortday treatments were specific for the variety. 2. Sterility : 1. The $F_1$ sterility was specific for the combinations and not correlated to the parental sterility. The sterility of $F_1$s grown under the 10 hours condition was higher than of those grown under 14 hours. These results were the same at both locations, IRRI and Suwon. 2. The high sterile combinations in $F_1$ showed high sterility in $F_2$. The combinations between a high photoperiod sensitive variety and a high temperature sensitive variety showed high sterility and wider variance. 3. The mean sterility of $F_2$s was lower than of $F_1$s and the mean of $F_3$ lines was lower than of $F_2$s. Sterility decreased as the generation progressed, and the differences of $F_3$ sterility of different combinations were not significant. 4. A faint correlation between grain sterility and pollen sterility was observed in $F_2$ populations. 5. No significant reciprocal effects were measured in $F_1$ and $F_2$ sterility. 6. Following Griffing's method, specific combining ability effects were higher than general combining ability effects, especially in the combinations between highly photoperiod sensitive varieties and highly temperature sensitive varieties. 7. No distinct correlations were found between $F_2$ individual sterility grown at IRRI and $F_3$ line sterility grown at Suwon. 8. No distinct correlations were observed between heading days and sterility of $F_2$ individuals.

• Tuberculosis and Respiratory Diseases
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• v.41 no.4
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• pp.379-388
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• 1994

Background : In sleep apnea syndrome, arterial oxygen saturation($SaO_2$) decreases at a variable rate and to a variable degree for a given apneic period from patient to patient, and various kinds of cardiac arrythmia are known to occur. Factors supposed to affect arterial oxygen desaturation during apnea are duration of apnea, lung voulume at which apnea occurs, and oxygen consumption rate of the subject. The lung serves as preferential oxygen source during apnea, and there have been many reports related with the influence of lung volume on $SaO_2$ during apnea, but there are few, if any, studies about the influence of oxygen consumption rate of an individual on $SaO_2$ during breath holding or about the profile of arterial oxygen resaturation after breathing resumed. Methods : To investigate the changes of $SaO_2$ and heart rate(HR) during breath holding(BH) and rebreathing(RB) and to evaluate the physiologic factors responsible for the changes, lung volume measurements, and arterial blood gas analyses were performed in 17 healthy subjects. Nasal airflow by thermistor, $SaO_2$ by pulse oxymeter and ECG tracing were recorded on Polygraph(TA 4000, Gould, U.S.A.) during voluntary BH & RB at total lung capacity(TLC), at functional residual capacity(FRC) and at residual volume(RV), respectively, for the study subjects. Each subject's basal metabolic rate(BMR) was assumed on Harris-Benedict equation. Results: The time needed for $SaO_2$ to drop 2% from the basal level during breath holding(T2%) were $70.1{\pm}14.2$ sec(mean${\pm}$standard deviation) at TLC, $44.0{\pm}11.6$ sec at FRC, and $33.2{\pm}11.1$ sec at RV(TLC vs. FRC, p<0.05; FRC vs. RV, p<0.05). On rebreathing after $SaO_2$ decreased 2%, further decrement in $SaO_2$ was observed and it was significantly greater at RV($4.3{\pm}2.1%$) than at TLC($1.4{\pm}1.0%$)(p<0.05) or at FRC($1.9{\pm}1.4%$)(p<0.05). The time required for $SaO_2$ to return to the basal level after RB(Tr) at TLC was not significantly different from those at FRC or at RV. T2% had no significant correlation either with lung volumes or with BMR respectively. On the other hand, T2% had significant correlation with TLC/BMR(r=0.693, p<0.01) and FRC/BMR (r=0.615, p<0.025) but not with RV/BMR(r=0.227, p>0.05). The differences between maximal and minimal HR(${\Delta}HR$) during the BH-RB manuever were $27.5{\pm}9.2/min$ at TLC, $26.4{\pm}14.0/min$ at RV, and $19.1{\pm}6.0/min$ at FRC which was significantly smaller than those at TLC(p<0.05) or at RV(p<0.05). The mean difference of 5 p-p intervals before and after RB were $0.8{\pm}0.10$ sec and $0.72{\pm}0.09$ sec at TLC(p<0.001), $0.82{\pm}0.11$ sec and $0.73{\pm}0.09$ sec at FRC(p<0.025), and $0.77{\pm}0.09$ sec and $0.72{\pm}0.09$ sec at RV(p<0.05). Conclusion Healthy subjects showed arterial desaturation of various rates and extent during breath holding at different lung volumes. When breath held at lung volume greater than FRC, the rate of arterial desaturation significantly correlated with lung volume/basal metabolic rate, but when breath held at RV, the rate of arterial desaturation did not correlate linearly with RV/BMR. Sinus arrythmias occurred during breath holding and rebreathing manuever irrespective of the size of the lung volume at which breath holding started, and the amount of change was smallest when breath held at FRC and the change in vagal tone induced by alteration in respiratory movement might be the major responsible factor for the sinus arrythmia.