Soo Beom Choi

Heart Rate Detection During Sleep Using a Flexible RF Resonator and Injection-Locked PLL Sensor

Novel nonintrusive technologies for wrist pulse detection have been developed and proposed as systems for sleep monitoring using three types of radio frequency (RF) sensors. The three types of RF sensors for heart rate measurement on wrist are a flexible RF single resonator, array resonators, and an injection-locked PLL resonator sensor. To verify the performance of the new RF systems, we compared heart rates between presleep time and postsleep onset time. Heart rates of ten subjects were measured using the RF systems during sleep. All three RF devices detected heart rates at 0.2 to 1 mm distance from the skin of the wrist over clothes made of cotton fabric. The wrist pulse signals of a flexible RF single resonator were consistent with the signals obtained by a portable piezoelectric transducer as a reference. Then, we confirmed that the heart rate after sleep onset time significantly decreased compared to before sleep. In conclusion, the RF system can be utilized as a noncontact nonintrusive method for measuring heart rates during sleep.

Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study

Background Knee osteoarthritis (OA) is the most common joint disease of adults worldwide. Since the treatments for advanced radiographic knee OA are limited, clinicians face a significant challenge of identifying patients who are at high risk of OA in a timely and appropriate way. Therefore, we developed a simple self-assessment scoring system and an improved artificial neural network (ANN) model for knee OA. Methods The Fifth Korea National Health and Nutrition Examination Surveys (KNHANES V-1) data were used to develop a scoring system and ANN for radiographic knee OA. A logistic regression analysis was used to determine the predictors of the scoring system. The ANN was constructed using 1777 participants and validated internally on 888 participants in the KNHANES V-1. The predictors of the scoring system were selected as the inputs of the ANN. External validation was performed using 4731 participants in the Osteoarthritis Initiative (OAI). Area under the curve (AUC) of the receiver operating characteristic was calculated to compare the prediction models. Results The scoring system and ANN were built using the independent predictors including sex, age, body mass index, educational status, hypertension, moderate physical activity, and knee pain. In the internal validation, both scoring system and ANN predicted radiographic knee OA (AUC 0.73 versus 0.81, p<0.001) and symptomatic knee OA (AUC 0.88 versus 0.94, p<0.001) with good discriminative ability. In the external validation, both scoring system and ANN showed lower discriminative ability in predicting radiographic knee OA (AUC 0.62 versus 0.67, p<0.001) and symptomatic knee OA (AUC 0.70 versus 0.76, p<0.001). Conclusions The self-assessment scoring system may be useful for identifying the adults at high risk for knee OA. The performance of the scoring system is improved significantly by the ANN. We provided an ANN calculator to simply predict the knee OA risk.

Prediction of ATLS hypovolemic shock class in rats using the perfusion index and lactate concentration.

ABSTRACT It is necessary to quickly and accurately determine Advanced Trauma Life Support (ATLS) hemorrhagic shock class for triage in cases of acute hemorrhage caused by trauma. However, the ATLS classification has limitations, namely, with regard to primary vital signs. This study identified the optimal variables for appropriate triage of hemorrhage severity, including the peripheral perfusion index and serum lactate concentration in addition to the conventional primary vital signs. To predict the four ATLS classes, three popular machine learning algorithms with four feature selection methods for multicategory classification were applied to a rat model of acute hemorrhage. A total of 78 anesthetized rats were divided into four groups for ATLS classification based on blood loss (in percent). The support vector machine one-versus-one model with the Kruskal-Wallis feature selection method performed best, with 80.8% accuracy, relative classifier information of 0.629, and a kappa index of 0.732. The new hemorrhage-induced severity index (lactate concentration/perfusion index), diastolic blood pressure, mean arterial pressure, and the perfusion index were selected as the optimal variables for predicting the four ATLS classes by support vector machine one-versus-one with the Kruskal-Wallis method. These four variables were also selected for binary classification to predict ATLS classes I and II versus III and IV for blood transfusion requirement. The suggested ATLS classification system would be helpful to first responders by indicating the severity of patients, allowing physicians to prepare suitable resuscitation before hospital arrival, which could hasten treatment initiation.

Multicategory classification of 11 neuromuscular diseases based on microarray data using support vector machine

We applied multicategory machine learning methods to classify 11 neuromuscular disease groups and one control group based on microarray data. To develop multicategory classification models with optimal parameters and features, we performed a systematic evaluation of three machine learning algorithms and four feature selection methods using three-fold cross validation and a grid search. This study included 114 subjects of 11 neuromuscular diseases and 31 subjects of a control group using microarray data with 22,283 probe sets from the National Center for Biotechnology Information (NCBI). We obtained an accuracy of 100%, relative classifier information (RCI) of 1.0, and a kappa index of 1.0 by applying the models of support vector machines one-versus-one (SVM-OVO), SVM one-versus-rest (OVR), and directed acyclic graph SVM (DAGSVM), using the ratio of genes between categories to within-category sums of squares (BW) feature selection method. Each of these three models selected only four features to categorize the 12 groups, resulting in a time-saving and cost-effective strategy for diagnosing neuromuscular diseases. In addition, a gene symbol, SPP1 was selected as the top-ranked gene by the BW method. We confirmed relationships between the gene (SPP1) and Duchenne muscular dystrophy (DMD) from a previous study. With our models as clinically helpful tools, neuromuscular diseases could be classified quickly using a computer, thereby giving a time-saving, cost-effective, and accurate diagnosis.

ATLS Hypovolemic Shock Classification by Prediction of Blood Loss in Rats Using Regression Models.

ABSTRACT In our previous study, our input data set consisted of 78 rats, the blood loss in percent as a dependent variable, and 11 independent variables (heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, respiration rate, temperature, perfusion index, lactate concentration, shock index, and new index (lactate concentration/perfusion)). The machine learning methods for multicategory classification were applied to a rat model in acute hemorrhage to predict the four Advanced Trauma Life Support (ATLS) hypovolemic shock classes for triage in our previous study. However, multicategory classification is much more difficult and complicated than binary classification. We introduce a simple approach for classifying ATLS hypovolaemic shock class by predicting blood loss in percent using support vector regression and multivariate linear regression (MLR). We also compared the performance of the classification models using absolute and relative vital signs. The accuracies of support vector regression and MLR models with relative values by predicting blood loss in percent were 88.5% and 84.6%, respectively. These were better than the best accuracy of 80.8% of the direct multicategory classification using the support vector machine one-versus-one model in our previous study for the same validation data set. Moreover, the simple MLR models with both absolute and relative values could provide possibility of the future clinical decision support system for ATLS classification. The perfusion index and new index were more appropriate with relative changes than absolute values.

Exposure of surgeons to extremely low-frequency magnetic fields during laparoscopic and robotic surgeries.

AbstractThe development of new medical electronic devices and equipment has increased the use of electrical apparatuses in surgery. Many studies have reported the association of long-term exposure to extremely low-frequency magnetic fields (ELF-MFs) with diseases or cancer. Robotic surgery has emerged as an alternative tool to overcome the disadvantages of conventional laparoscopic surgery. However, there has been no report regarding how much ELF-MF surgeons are exposed to during laparoscopic and robotic surgeries. In this observational study, we aimed to measure and compare the ELF-MFs that surgeons are exposed to during laparoscopic and robotic surgery.The intensities of the ELF-MFs surgeons are exposed to were measured every 4 seconds for 20 cases of laparoscopic surgery and 20 cases of robotic surgery using portable ELF-MF measuring devices with logging capability.The mean ELF-MF exposures were 0.6 ± 0.1 mG for laparoscopic surgeries and 0.3 ± 0.0 mG for robotic surgeries (significantly lower with P < 0.001 by Mann–Whitney U test).Our results show that the ELF-MF exposure levels of surgeons in both robotic and conventional laparoscopic surgery were lower than 2 mG, which is the most stringent level considered safe in many studies. However, we should not overlook the effects of long-term ELF-MF exposure during many surgeries in the course of a surgeons career.

Classification of serous ovarian tumors based on microarray data using multicategory support vector machines

Ovarian cancer, the most fatal of reproductive cancers, is the fifth leading cause of death in women in the United States. Serous borderline ovarian tumors (SBOTs) are considered to be earlier or less malignant forms of serous ovarian carcinomas (SOCs). SBOTs are asymptomatic and progression to advanced stages is common. Using DNA microarray technology, we designed multicategory classification models to discriminate ovarian cancer subclasses. To develop multicategory classification models with optimal parameters and features, we systematically evaluated three machine learning algorithms and three feature selection methods using five-fold cross validation and a grid search. The study included 22 subjects with normal ovarian surface epithelial cells, 12 with SBOTs, and 79 with SOCs according to microarray data with 54,675 probe sets obtained from the National Center for Biotechnology Information gene expression omnibus repository. Application of the optimal model of support vector machines one-versus-rest with signal-to-noise as a feature selection method gave an accuracy of 97.3%, relative classifier information of 0.916, and a kappa index of 0.941. In addition, 5 features, including the expression of putative biomarkers SNTN and AOX1, were selected to differentiate between normal, SBOT, and SOC groups. An accurate diagnosis of ovarian tumor subclasses by application of multicategory machine learning would be cost-effective and simple to perform, and would ensure more effective subclass-targeted therapy.

Interpretation of movement during stair ascent for predicting severity and prognosis of knee osteoarthritis in elderly women using support vector machine

Several studies have demonstrated that pathologic movement changes in knee osteoarthritis (OA) may contribute to disease progression. The aim of this study was to investigate the association between movement changes during stair ascent and pain, radiographic severity, and prognosis of knee OA in the elderly women using machine learning (ML) over a seven-year follow-up period. Eighteen elderly female patients with knee OA and 20 healthy controls were enrolled. Kinematic data for stair ascent were obtained using a 3D-motion analysis system at baseline. Kinematic factors were analyzed based on one of the popular ML methods, support vector machines (SVM). SVM was used to search kinematic predictors associated with pain, radiographic severity of knee OA, and unfavorable outcomes, which were defined as persistent knee pain as reported at the seven-year follow-up or as having undergone total knee replacement during the follow-up period. Six patients (46.2%) had unfavorable outcomes at the seven-year follow-up. SVM showed accuracy of detection of knee OA (97.4%), prediction of pain (83.3%), radiographic severity (83.3%), and unfavorable outcomes (69.2%). The predictors with SVM included the time of stair ascent, maximal anterior pelvis tilting, knee flexion at initial foot contact, and ankle dorsiflexion at initial foot contact. The interpretation of movement during stair ascent using ML may be helpful for physicians not only in detecting knee OA, but also in evaluating pain and radiographic severity.

Intraoperative Diagnosis Support Tool for Serous Ovarian Tumors Based on Microarray Data Using Multicategory Machine Learning.

Objectives Serous borderline ovarian tumors (SBOTs) are a subtype of serous ovarian carcinoma with atypical proliferation. Frozen-section diagnosis has been used as an intraoperative diagnosis tool in supporting the fertility-sparing surgery by diagnosing SBOTs with accuracy of 48% to 79%. Using DNA microarray technology, we designed multicategory classification models to support frozen-section diagnosis within 30 minutes. Materials and Methods We systematically evaluated 6 machine learning algorithms and 3 feature selection methods using 5-fold cross-validation and a grid search on microarray data obtained from the National Center for Biotechnology Information. To validate the models and selected biomarkers, expression profiles were analyzed in tissue samples obtained from the Yonsei University College of Medicine. Results The best accuracy of the optimal machine learning model was 97.3%. In addition, 5 features, including the expression of the putative biomarkers SNTN and AOX1, were selected to differentiate between normal, SBOT, and serous ovarian carcinoma groups. Different expression levels of SNTN and AOX1 were validated by real-time quantitative reverse-transcription polymerase chain reaction, Western blotting, and immunohistochemistry. A multinomial logistic regression model using SNTN and AOX1 alone was used to construct a simple-to-use equation that gave a diagnostic test accuracy of 91.9%. Conclusions We identified 2 biomarkers, SNTN and AOX1, that are likely involved in the pathogenesis and progression of ovarian tumors. An accurate diagnosis of ovarian tumor subclasses by application of the equation in conjunction with expression analysis of SNTN and AOX1 would offer a new accurate diagnosis tool in conjunction with frozen-section diagnosis within 30 minutes.

Screening for pre-diabetes using support vector machine model.

The global prevalence of diabetes is rapidly increasing. Studies support screening and interventions for pre-diabetes, which results in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for pre-diabetes that could assist with decreasing the prevalence of diabetes through early identification and subsequent interventions. Data from the Korean National Health and Nutrition Examination Survey (KNHANES) were used, excluding subjects with diabetes. The KNHANES 2010 data (n = 4,685) were used for training and internal validation, while data from KNHANES 2011 (n = 4,566) were used for external validation. We developed a model to screen for pre-diabetes using support vector machine (SVM), and performed a systematic evaluation of the SVM model using internal and external validation. We compared the performance of the SVM model with that of a screening score model based on logistic regression analysis for pre-diabetes that had been developed previously. Backward elimination logistic regression resulted in associations between pre-diabetes and age, sex, waist circumference, body mass index, alcohol intake, family history of diabetes, and hypertension. The areas under the curves (AUCs) for the SVM model in the internal and external datasets were 0.761 and 0.731, respectively, while the AUCs for the screening score model were 0.734 and 0.712, respectively. The SVM model developed in this study performed better than the screening score model that had been developed previously and may be more effective for pre-diabetes screening.