Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG

Background@#This paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal. @*Methods@#A convolutional neural network (CNN) was used as an identification model and implemented using a one-dimensional convolutional, pooling, and fully connected layer.An optimal architecture is incorporated into the CNN model for the precise identification of SA severity. A total of 144 subjects were studied. The nocturnal single-lead ECG signal was collected, and the short-term normal ECG was extracted from them. The short-term normal ECG was segmented for a duration of 30 seconds and divided into two datasets for training and evaluation. The training set consists of 82,952 segments (66,360 training set, 16,592 validation set) from 117 subjects, while the test set has 20,738 segments from 27 subjects. @*Results@#F1-score of 98.0% was obtained from the test set. Mild and moderate SA can be identified with an accuracy of 99.0%. @*Conclusion@#The results showed the possibility of automatically identifying SA severity based on a short-term normal ECG signal.

Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG

Background@#This paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal. @*Methods@#A convolutional neural network (CNN) was used as an identification model and implemented using a one-dimensional convolutional, pooling, and fully connected layer.An optimal architecture is incorporated into the CNN model for the precise identification of SA severity. A total of 144 subjects were studied. The nocturnal single-lead ECG signal was collected, and the short-term normal ECG was extracted from them. The short-term normal ECG was segmented for a duration of 30 seconds and divided into two datasets for training and evaluation. The training set consists of 82,952 segments (66,360 training set, 16,592 validation set) from 117 subjects, while the test set has 20,738 segments from 27 subjects. @*Results@#F1-score of 98.0% was obtained from the test set. Mild and moderate SA can be identified with an accuracy of 99.0%. @*Conclusion@#The results showed the possibility of automatically identifying SA severity based on a short-term normal ECG signal.

Automatic Prediction of Atrial Fibrillation Based on Convolutional Neural Network Using a Short-term Normal Electrocardiogram Signal

BACKGROUND: In this study, we propose a method for automatically predicting atrial fibrillation (AF) based on convolutional neural network (CNN) using a short-term normal electrocardiogram (ECG) signal. METHODS: We designed a CNN model and optimized it by dropout and normalization. One-dimensional convolution, max-pooling, and fully-connected multiple perceptron were used to analyze the short-term normal ECG. The ECG signal was preprocessed and segmented to train and evaluate the proposed CNN model. The training and test sets consisted of the two AF and one normal dataset from the MIT-BIH database. RESULTS: The proposed CNN model for the automatic prediction of AF achieved a high performance with a sensitivity of 98.6%, a specificity of 98.7%, and an accuracy of 98.7%. CONCLUSION: The results show the possibility of automatically predicting AF based on the CNN model using a short-term normal ECG signal. The proposed CNN model for the automatic prediction of AF can be a helpful tool for the early diagnosis of AF in healthcare fields.

Design of a stimulation protocol to predict temperature distribution in subcutaneous tissue using the finite element model

Moxibustion is a traditional Oriental medicine therapy that treats the symptoms of a disease with thermal stimulation. However, it is difficult to control the strength of the thermal or chemical stimulus generated by the various types and amounts of moxa and to prevent energy loss through the skin. To overcome these problems, we previously developed a method to efficiently provide RF thermal stimulation to subcutaneous tissue. In this paper, we propose a finite element model (FEM) to predict temperature distributions in subcutaneous tissue after radio-frequency thermal stimulation. To evaluate the performance of the developed FEM, temperature distributions were obtained from the FEM, and in vivo experiments were conducted using the RF stimulation system at subcutaneous tissue depths of 5 and 10 mm in the femoral region of a rabbit model. High correlation coefficients between simulated and actual temperature distributions—0.98 at 5 mm and 0.99 at 10 mm—were obtained, despite some slight errors in the temperature distribution at each depth. These results demonstrate that the FEM described here can be used to determine thermal stimulation profiles produced by RF stimulation of subcutaneous tissue.

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.

The Usefulness of Trunk Accelerometry in Evaluating Walking Ability of Hemiparetic Patients

OBJECTIVE: Accelerometer is a convenient device that can easily measure human movement. The purpose of this study was to evaluate its usefulness in the assessment of hemiparetic gait after stroke. METHOD: Twenty hemiparetic stroke patients were enrolled in the study. A portable accelerometer was attached between L3,4 intervertebral area. Vertical and medio-lateral acceleration was measured while walking 10 m. Walking ability of each subject was classified by Functional Walking Category (FWC). RESULTS: Accelerometric parameters, such as walking speed, a mean amount of peak vertical acceleration in one gait cycle, mean peak value of unaffected side, peak vertical acceleration ratio, step time ratio were significantly higher in groups of FWC 4, 5, 6 than in groups of FWC 2, 3. In subjects using cane there was an extra peak other than peaks observed in non-users. Mean peak value, step length of unaffected side and cadence were significantly higher in non-users than in users. CONCLUSION: Trunk accelerometer can be used as an objective method to evaluate walking ability in hemiparetic patients after stroke.