Sleep stage and Apnea-Hypopnea Index (AHI) are the most important metrics in the diagnosis of sleep syndrome disease. In previous studies, these two tasks are usually implemented separately, which is ...both time and resource consuming. In this work, we propose a novel single EEG based collaborative learning network (EEG-CLNet) for simultaneous sleep staging and obstructive sleep apnea (OSA) event detection through multi-task collaborative learning. The EEG-CLNet regards different tasks as a common unit to extract features from intra-groups via both local parameter sharing and cross task knowledge distillation, rather than just sharing parameters or shortening the distance between different tasks. Our approach has been validated on two datasets with the same or better performance than other methods. The experimental results show that our method achieves a performance gain of 1% - 5% compared with the baseline. Compared to previous works where two or even more models were required to perform sleep staging and OSA event detection, The EEG-CLNet could reduce the total number of model parameters and facilitate the model to mine the hidden relationships between different task semantic information. More importantly, it effectively alleviates the task bias problem in hard parameter sharing. As a consequence, this approach has notable potential to be a solution for lightweight wearable sleep monitoring system in the future.
The continuing increase in the incidence and recognition of children's sleep disorders has heightened the demand for automatic pediatric sleep staging. Supervised sleep stage recognition algorithms, ...however, are often faced with challenges such as limited availability of pediatric sleep physicians and data heterogeneity. Drawing upon two quickly advancing fields, i.e., semi-supervised learning and self-supervised contrastive learning, we propose a multi-task contrastive learning strategy for semi-supervised pediatric sleep stage recognition, abbreviated as MtCLSS. Specifically, signal-adapted transformations are applied to electroencephalogram (EEG) recordings of the full night polysomnogram, which facilitates the network to improve its representation ability through identifying the transformations. We also introduce an extension of contrastive loss function, thus adapting contrastive learning to the semi-supervised setting. In this way, the proposed framework learns not only task-specific features from a small amount of supervised data, but also extracts general features from signal transformations, improving the model robustness. MtCLSS is evaluated on a real-world pediatric sleep dataset with promising performance (0.80 accuracy, 0.78 F1-score and 0.74 kappa). We also examine its generality on a well-known public dataset. The experimental results demonstrate the effectiveness of the MtCLSS framework for EEG based automatic pediatric sleep staging in very limited labeled data scenarios.
Obstructive sleep apnea (OSA) is a common sleep disease which may cause many serious health problems, therefore timely diagnosis and treatment could bring important help for patients. The current ...research on automatic detection through bioelectric signal mainly rely on blood oxygen level (SpO2), ECG signal and airflow, with few EEG based solution proposed. More importantly, most researches on OSA detection did not realize the difference between OSA detection and the general sleep staging classification. Some of them just simply transfer sleep staging methods to OSA detection, and an EEG segment was processed as a whole, ignoring the ambiguity in feature space that some EEG frames may contain both normal fragments and OSA fragments. In this paper, we propose a framework, EEG multi-instance learning network (EEG-MIL) for automatic OSA detection based on EEG signals to alleviate this ambiguity. EEG-MIL is composed of subframe multi-resolution convolution extractor (S-MRCNN) and MIL mapping function, which could extract features from sub-frames and mine the interactive relationship between different instances (sub-frames) and bags (frame) to further distinguish OSA event fragment. Meanwhile, in order to meet the clinic needs, we define instance-level task and bag-level task in OSA events detection, and redefine the evaluation criteria to evaluate the effect of our model more comprehensively. Then we verify the performance of our framework in two public datasets and the private dataset, and provide detailed ablation experiments. We validate our method via 5-fold subject independent cross validation approach. Our model obtains 2–8.6% performance gain compared to other works and achieves the new state-of-the-arts.
A paucity of studies focused on the genetic association that tuberculosis (TB) patients with non-communicable diseases (NCDs) are more likely to be infected with Mycobacterium tuberculosis (MTB) with ...more potent virulence on anti-TB drug resistance than those without NCDs. The study aimed to document the predominant genotype, determine the association between MTB genotypes and NCD status and drug resistance.
We conducted a molecular study in 105 TB patients based on a cross-sectional study focused on the comorbid relationship between chronic conditions and TB among 1773 subjects from September 1, 2019 to August 30, 2020 in Guizhou, China. The participants were investigated through face-to-face interviews, followed by NCDs screening. The DNA of MTB isolates was extracted prior to genotyping using 24 loci MIRU-VNTR. The subsequent evaluations were performed by phylogenetic trees, combined with tests of statistical power, Chi-square or Fisher and multivariate logistic regression analysis.
The Beijing family of Lineage 2 (East Asia) was the predominant genotype accounting for 43.8% (46/105), followed by Lineage 4 (Euro-America) strains, including Uganda I (34.3%, 36/105), and the NEW-1 (9.5%, 10/105). The proportion of Beijing strain in patients with and without NCDS was 28.6% (8/28) and 49.4% (38/77), respectively, with a statistical power test value of 24.3%. No significant association was detected between MTB genotype and NCD status. A low clustering rate (2.9%) was identified, consisting of two clusters. The rates of global, mono-, poly- and multi-drug resistance were 16.2% (17/105), 14.3% (15/105), 1.0% (1/105) and 4.8% (5/105), respectively. The drug-resistant rates of rifampicin, isoniazid, and streptomycin, were 6.7% (7/105), 11.4% (12/105) and 5.7% (6/105), respectively. Isoniazid resistance was significantly associated with the Beijing genotype of Lineage 2 (19.6% versus 5.1%).
The Lineage 2 East Asia/Beijing genotype is the dominant genotype of the local MTB with endogenous infection preponderating. Not enough evidence is detected to support the association between the MTB genotype and diabetes/hypertension. Isoniazid resistance is associated with the Lineage 2 East Asia/Beijing strain.
Chinese couplets, as one of the traditional Chinese culture, is the treasure of Chinese civilization and the inheritance of Chinese history. Given a sentence (namely an antecedent clause), people ...reply with another sentence (namely a subsequent clause) equal in length. Because of the complexity of the semantic and grammatical rules of couplet, it is not easy to create a suitable couplet that meets the requirements of sentence pattern, context, and flatness. With the development of neural models and natural language processing, automatic generation of Chinese couplets has drawn significant attention due to its artistic and cultural value, most of these works mainly focus on generating couplet by given text information, while visual inspirations for couplet generation have been rarely explored. In this paper, we design a Chinese couplet generation model based on NIC (Neural Image Caption), which can compose a piece of couplet suitable to the artistic conception in an image. At first, we use the improved VGG16 model to predict the input image. The content of the image can be automatically recognized and the corresponding description are generated and translated into Chinese keywords. Then, the encoder-decoder framework is used repeatedly to process these keywords, and finally the couplet can be generated. Moreover, to satisfy special characteristics of couplets, we incorporate the attention mechanism into the encoding-decoding process, which greatly improves the accuracy of couplets generated automatically.
