Jong-Uk Park

Orbital rendezvous using two-step sliding mode control

Abstract The problem of spacecraft rendezvous is studied, using sliding mode control in the presence of the earths gravitational perturbation. The impulsive solution of Lamberts problem is obtained using the combined equations method to minimize total ΔV via an iterative method, and it is used as the desired trajectory for the rendezvous. In this paper, a two-step sliding mode control method is introduced for solving the rendezvous problem with finite-thrust including unmodeled dynamics. The thrust-coast-thrust type control laws for the system to follow the desired trajectories are represented and resultant trajectories are close enough to the Lamberts orbit with comparable amount of ΔV to the Lamberts impulsive solution. All state variables matched the final boundary conditions reasonably well at the end of maneuver.

Automatic classification of apnea/hypopnea events through sleep/wake states and severity of SDB from a pulse oximeter.

This study proposes a method of automatically classifying sleep apnea/hypopnea events based on sleep states and the severity of sleep-disordered breathing (SDB) using photoplethysmogram (PPG) and oxygen saturation (SpO2) signals acquired from a pulse oximeter. The PPG was used to classify sleep state, while the severity of SDB was estimated by detecting events of SpO2 oxygen desaturation. Furthermore, we classified sleep apnea/hypopnea events by applying different categorisations according to the severity of SDB based on a support vector machine. The classification results showed sensitivity performances and positivity predictive values of 74.2% and 87.5% for apnea, 87.5% and 63.4% for hypopnea, and 92.4% and 92.8% for apnea + hypopnea, respectively. These results represent better or comparable outcomes compared to those of previous studies. In addition, our classification method reliably detected sleep apnea/hypopnea events in all patient groups without bias in particular patient groups when our algorithm was applied to a variety of patient groups. Therefore, this method has the potential to diagnose SDB more reliably and conveniently using a pulse oximeter.

Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network

In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from a single-lead electrocardiogram (ECG) using a convolutional neural network (CNN). A CNN model was designed with six optimized convolution layers including activation, pooling, and dropout layers. One-dimensional (1D) convolution, rectified linear units (ReLU), and max pooling were applied to the convolution, activation, and pooling layers, respectively. For training and evaluation of the CNN model, a single-lead ECG dataset was collected from 82 subjects with OSA and was divided into training (including data from 63 patients with 34,281 events) and testing (including data from 19 patients with 8571 events) datasets. Using this CNN model, a precision of 0.99%, a recall of 0.99%, and an F1-score of 0.99% were attained with the training dataset; these values were all 0.96% when the CNN was applied to the testing dataset. These results show that the proposed CNN model can be used to detect OSA accurately on the basis of a single-lead ECG. Ultimately, this CNN model may be used as a screening tool for those suspected to suffer from OSA.

Obstructive Sleep Apnea Screening Using a Piezo-Electric Sensor

In this study, we propose a novel method for obstructive sleep apnea (OSA) detection using a piezo-electric sensor. OSA is a relatively common sleep disorder. However, more than 80% of OSA patients remain undiagnosed. We investigated the feasibility of OSA assessment using a single-channel physiological signal to simplify the OSA screening. We detected both snoring and heartbeat information by using a piezo-electric sensor, and snoring index (SI) and features based on pulse rate variability (PRV) analysis were extracted from the filtered piezo-electric sensor signal. A support vector machine (SVM) was used as a classifier to detect OSA events. The performance of the proposed method was evaluated on 45 patients from mild, moderate, and severe OSA groups. The method achieved a mean sensitivity, specificity, and accuracy of 72.5%, 74.2%, and 71.5%; 85.8%, 80.5%, and 80.0%; and 70.3%, 77.1%, and 71.9% for the mild, moderate, and severe groups, respectively. Finally, these results not only show the feasibility of OSA detection using a piezo-electric sensor, but also illustrate its usefulness for monitoring sleep and diagnosing OSA.

Estimation of Respiration Using Photoplethysmograph During Sleep

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-fre- quency 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 ± 37.69 mHz, 10s: 36.53 ± 38.16 mHz, 60s: 30.35 ± 38.72 mHz), in PRV(1s: 1.45 ± 1.38 mHz, 10s: 1.44 ± 1.37 mHz, 60s: 0.45 ± 0.56 mHz), and in PAV(1s: 1.05 ± 0.81 mHz, 10s: 1.05 ± 0.79 mHz, 60s: 0.56 ± 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.

Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram

OBJECTIVE In this paper, we propose a convolutional neural network (CNN)-based deep learning architecture for multiclass classification of obstructive sleep apnea and hypopnea (OSAH) using single-lead electrocardiogram (ECG) recordings. OSAH is the most common sleep-related breathing disorder. Many subjects who suffer from OSAH remain undiagnosed; thus, early detection of OSAH is important. APPROACH In this study, automatic classification of three classes-normal, hypopnea, and apnea-based on a CNN is performed. An optimal six-layer CNN model is trained on a training dataset (45 096 events) and evaluated on a test dataset (11 274 events). The training set (69 subjects) and test set (17 subjects) were collected from 86 subjects with length of approximately 6 h and segmented into 10 s durations. MAIN RESULTS The proposed CNN model reaches a mean [Formula: see text]-score of 93.0 for the training dataset and 87.0 for the test dataset. SIGNIFICANCE Thus, proposed deep learning architecture achieved a high performance for multiclass classification of OSAH using single-lead ECG recordings. The proposed method can be employed in screening of patients suspected of having OSAH.

Automatic detection of sleep-disordered breathing events using recurrent neural networks from an electrocardiogram signal

In this study, we propose a novel method for automatically detecting sleep-disordered breathing (SDB) events using a recurrent neural network (RNN) to analyze nocturnal electrocardiogram (ECG) recordings. We design a deep RNN model comprising six stacked recurrent layers for the automatic detection of SDB events. The proposed deep RNN model utilizes long short-term memory (LSTM) and a gated-recurrent unit (GRU). To evaluate the performance of the proposed RNN method, 92 SDB patients were enrolled. Single-lead ECG recordings were measured for an average 7.2-h duration and segmented into 10-s events. The dataset comprised a training dataset (68,545 events) from 74 patients and test dataset (17,157 events) from 18 patients. The proposed method achieved high performance with an F1-score of 98.0% for LSTM and 99.0% for GRU. The results demonstrate superior performance over conventional methods. The proposed method can be used as a precise screening and diagnosing tool for patients with SDB disorders.

Estimating sleep parameters using nasal pressure signals applicable to continuous positive airway pressure devices

OBJECTIVE This paper proposes a method for classifying sleep-wakefulness and estimating sleep parameters using nasal pressure signals applicable to a continuous positive airway pressure (CPAP) device. APPROACH In order to classify the sleep-wakefulness states of patients with sleep-disordered breathing (SDB), apnea-hypopnea and snoring events are first detected. Epochs detected as SDB are classified as sleep, and time-domain- and frequency-domain-based features are extracted from the epochs that are detected as normal breathing. Subsequently, sleep-wakefulness is classified using a support vector machine (SVM) classifier in the normal breathing epoch. Finally, four sleep parameters-sleep onset, wake after sleep onset, total sleep time and sleep efficiency-are estimated based on the classified sleep-wakefulness. In order to develop and test the algorithm, 110 patients diagnosed with SDB participated in this study. Ninety of the subjects underwent full-night polysomnography (PSG) and twenty underwent split-night PSG. The subjects were divided into 50 patients of a training set (full/split: 42/8), 30 of a validation set (full/split: 24/6) and 30 of a test set (full/split: 24/6). MAIN RESULTS In the experiments conducted, sleep-wakefulness classification accuracy was found to be 83.2% in the test set, compared with the PSG scoring results of clinical experts. Furthermore, all four sleep parameters showed higher correlations than the results obtained via PSG (r  ⩾  0.84, p  <  0.05). In order to determine whether the proposed method is applicable to CPAP, sleep-wakefulness classification performances were evaluated for each CPAP in the split-night PSG data. The results indicate that the accuracy and sensitivity of sleep-wakefulness classification by CPAP variation shows no statistically significant difference (p  <  0.05). SIGNIFICANCE The contributions made in this study are applicable to the automatic classification of sleep-wakefulness states in CPAP devices and evaluation of the quality of sleep.

Classification of Sleep/Wakefulness using Nasal Pressure for Patients with Sleep-disordered Breathing

The present invention relates to a device and method for classifying the sleep/wakefulness of a patient with sleep-disordered breathing using a nasal pressure signal obtainable from continuous positive airway pressure (CPAP). The method for classifying the sleep/wakefulness of a patient with sleep-disordered breathing using a nasal pressure signal according to the present invention includes a step of detecting a sleep-disordered breathing event; a step of identifying a sleep-disordered breathing interval; a step of extracting the feature vector of a normal breathing interval; and a step of classifying the sleep and wakefulness of a support vector machine classifier.