다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Introduction: The diagnosis of epilepsy takes a certain process, depending entirely on the attending physician. However, the human factor may cause erroneous diagnosis in the analysis of the EEG signal. In the past 2 decades, many advanced signal processing and machine learning methods have been developed for the detection of epileptic seizures. However, many of these methods require large data sets and complex operations. Methods: In this study, an end-to-end machine learning model is presented for detection of epileptic seizure using the pretrained deep two-dimensional convolutional neural network (CNN) and the concept of transfer learning. The EEG signal is converted directly into visual data with a spectrogram and used directly as input data. Results: The authors analyzed the results of the training of the proposed pretrained AlexNet CNN model. Both binary and ternary classifications were performed without any extra procedure such as feature extraction. By performing data set creation from short-term spectrogram graphic images, the authors were able to achieve 100% accuracy for binary classification for epileptic seizure detection and 100% for ternary classification. Discussion/Conclusion: The proposed automatic identification and classification model can help in the early diagnosis of epilepsy, thus providing the opportunity for effective early treatment.

Detection of Epileptic Seizure Using Pretrained Deep Convolutional Neural Network and Transfer Learning.

The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a major pandemic outbreak recently. Various diagnostic technologies have been under active development. The novel coronavirus disease (COVID-19) may induce pulmonary failures, and chest X-ray imaging becomes one of the major confirmed diagnostic technologies. The very limited number of publicly available samples has rendered the training of the deep neural networks unstable and inaccurate. This study proposed a two-step transfer learning pipeline and a deep residual network framework COVID19XrayNet for the COVID-19 detection problem based on chest X-ray images. COVID19XrayNet firstly tunes the transferred model on a large dataset of chest X-ray images, which is further tuned using a small dataset of annotated chest X-ray images. The final model achieved 0.9108 accuracy. The experimental data also suggested that the model may be improved with more training samples being released. COVID19XrayNet, a two-step transfer learning framework designed for biomedical images.

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COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images.

Objectives: The objectives of this paper are to examine the source of multifractality in uterine electromyography (EMG) signals and to study the progression of pregnancy in the term (gestation period > 37 weeks) conditions using multifractal detrending moving average (MFDMA) algorithm. Methods: The signals for the study, considered from an online database, are obtained from the surface of abdomen during the second (T1) and third trimester (T2). The existence of multifractality is tested using Hurst and scaling exponents. With the intention of identifying the origin of multifractality, the preprocessed signals are converted to shuffle and surrogate data. The original and the transformed signals are subjected to MFDMA to extract multifractal spectrum features, namely strength of multifractality, maximum, minimum, and peak singularity exponents. Results: The Hurst and scaling exponents extracted from the signals indicate that uterine EMG signals are multifractal in nature. Further analysis shows that the source of multifractality is mainly owing to the presence of long-range correlation, which is computed as 79.98% in T1 and 82.43% in T2 groups. Among the extracted features, the peak singularity exponent and strength of multifractality show statistical significance in identifying the progression of pregnancy. The corresponding coefficients of variation are found to be low, which show that these features have low intersubject variability. Conclusion: It appears that the multifractal analysis can help in investigating the progressive changes in uterine muscle contractions during pregnancy.

Multifractal Analysis of Uterine Electromyography Signals for the Assessment of Progression of Pregnancy in Term Conditions

Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest.

Evaluation of convolutional neural network for recognizing uterine contractions with electrohysterogram.

Background. Uterine contraction (UC) is the tightening and shortening of the uterine muscles which can indicate the progress of pregnancy towards delivery. Electrohysterogram (EHG), which reflects uterine electrical activities, has recently been studied for UC monitoring. In this paper, we aimed to evaluate different EHG segments for recognizing UCs using the convolutional neural network (CNN). Materials and Methods. In the open-access Icelandic 16-electrode EHG database (122 recordings from 45 pregnant women), 7136 UC and 7136 non-UC EHG segments with the duration of 60 s were manually extracted from 107 recordings of 40 pregnant women to develop a CNN model. A fivefold cross-validation was applied to evaluate the CNN based on sensitivity (SE), specificity (SP), and accuracy (ACC). Then, 1056 UC and 1056 non-UC EHG segments were extracted from the other 15 recordings of 5 pregnant women. Furthermore, the developed CNN model was applied to identify UCs using different EHG segments with the durations of 10 s, 20 s, and 30 s. Results. The CNN achieved the average SE, SP, and ACC of 0.82, 0.93, and 0.88 for a 60 s EHG segment. The EHG segments of 10 s, 20 s, and 30 s around the TOCO peak achieved higher SE and ACC than the other segments with the same duration. The values of SE from 20 s EHG segments around the TOCO peak were higher than those from 10 s to 30 s EHG segments on the same side of the TOCO peak. Conclusion. The proposed method could be used to determine the efficient EHG segments for recognizing UC with the CNN.

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Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks.

Uterine contraction is one of the most important indication in the labor progression. Electrohysterogram (EHG) is a promising method for monitoring uterine contraction and discriminating efficient and inefficient contractions. This study aims to analyze the difference of EHG signals between two groups. EHG signals are recorded with abdominal electrodes from 20 pregnant women, including 10 in term labor group and 10 in non-labor group. Typical linear and nonlinear characteristics of EHG signals, including root mean square (RMS), peak frequency (PF), median frequency (MDF), mean frequency (MNF), parameters from wavelet decomposition (W4, W5) and time reversibility (Tr) are extracted. These characteristics are compared between contraction and non-contraction in term labor group and non-labor group. The result shows that RMS, W4 and W5 of contraction are significantly larger than non-contraction both within term labor group and between two groups (all p<0.001). However, MDF and MNF are significantly smaller (all p<0.05). Furthermore, all characteristics of non-contraction show no significant difference between two groups, except MNF. The variability of RMS, W4, W5 and Tr of contraction are significantly larger than non-contraction both within term labor group and between two groups (all p<0.05, with p<0.001 for W5 and Tr). However, the variability of MDF, PF and MNF are significantly smaller (all p<0.05). Moreover, the variability of all characteristics of non-contraction shows no significant difference between two groups, except MNF. We have shown that characteristics of EHG signals and their variability during contraction are quite different from non-contraction. Therefore, it is feasible to separate uterine contractions and monitor uterine activity with EHG signals.

Comparison of electrohysterogram characteristics during uterine contraction and non-contraction during labor

Aim Gitelman syndrome (GS) is a rare inherited salt-losing renal tubulopathy. Data on clinical features and the pregnancy outcome for female GS patients in a large cohort are lacking. The study was aimed to explore the phenotype and pregnant issue for female GS patients. Methods GS cases from the National Rare Diseases Registry System of China (NRSC) were collected, and detailed clinical, laboratory and genetic data were analysed. Articles on pregnancy in GS were also systemically reviewed. Results A total of 101 GS patients were included; among them, 42.6% were female and 79.2% showed hypomagnesaemia. A lower proportion of female patients presented before 18 years of age, with less frequently reported polyuria, higher serum potassium and less urine sodium and chloride excretions. There was no gender difference in the sodium-chloride cotransporter (NCC) dysfunction evaluated by hydrochlorothiazide test. Twelve of the 43 female GS patients delivered after disease symptom onset, and their pregnancies were generally uneventful. As a group, pregnant GS patients had lower potassium levels in the first-trimester (P = .002) requiring higher potassium supplementation. After delivery, serum potassium (P = .02) and magnesium (P = .03) increased significantly. Both caesarean section and vaginal delivery were safe. Conclusion Female GS patients may have a less severe phenotype with generally favourable outcomes of pregnancy. Intensive monitoring and increased potassium supplementation are necessary during pregnancy, especially in the first-trimester.

Clinical and laboratory features of female Gitelman syndrome and the pregnancy outcomes in a Chinese cohort.

The invention discloses a method and device for identifying uterine contraction based on uterine eletrohystrogram signals, and belongs to the technical field of delivery monitoring. The method comprises the steps of collecting the uterine eletrohystrogram signals within a certain duration; preprocessing the uterine eletrohystrogram signals to remove interference; setting the length of windows as L, and extracting features of the uterine eletrohystrogram signals in the windows; adopting a fuzzy clustering algorithm to cluster all features of the windows into two types, wherein if the center distance of the two types of features is smaller than or equal to a set threshold value D, it is regarded that no uterine contraction signal exists, and if the center distance of the two types of features is larger than the set threshold value, it is regarded that the type with the largest type center value is the type corresponding to the uterine contraction; calculating the time interval of the centers of every two windows in the uterine contraction types; in all the newly generated windows, searching for the starting point, the end point and the peak point of the uterine contraction; calculating uterine contraction parameters, wherein the uterine contraction parameters comprise the uterine contraction frequency, the average lasting time, the average intensity and the average interval. By means of the method and device for identifying the uterine contraction based on the uterine eletrohystrogram signals, the uterine contraction signals are automatically recognized, the uterine contraction feature parameters are calculated, and a new means is provided for monitoring the uterine activities.

Juntao Liu

Papers

Evaluation of convolutional neural network for recognizing uterine contractions with electrohysterogram.

Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks.

Comparison of electrohysterogram characteristics during uterine contraction and non-contraction during labor

Clinical and laboratory features of female Gitelman syndrome and the pregnancy outcomes in a Chinese cohort.

Method and device for identifying uterine contraction based on uterine eletrohystrogram signals