Sleep Apnea Detection Using Multi-Error-Reduction Classification System with Multiple Bio-Signals.

INTRODUCTION: Obstructive sleep apnea (OSA) can cause serious health problems such as hypertension or cardiovascular disease. The manual detection of apnea is a time-consuming task, and automatic diagnosis is much more desirable. The contribution of this work is to detect OSA using a multi-error-reduction (MER) classification system with multi-domain features from bio-signals. METHODS: Time-domain, frequency-domain, and non-linear analysis features are extracted from oxygen saturation (SaO2), ECG, airflow, thoracic, and abdominal signals. To analyse the significance of each feature, we design a two-stage feature selection. Stage 1 is the statistical analysis stage, and Stage 2 is the final feature subset selection stage using machine learning methods. In Stage 1, two statistical analyses (the one-way analysis of variance (ANOVA) and the rank-sum test) provide a list of the significance level of each kind of feature. Then, in Stage 2, the support vector machine (SVM) algorithm is used to select a final feature subset based on the significance list. Next, an MER classification system is constructed, which applies a stacking with a structure that consists of base learners and an artificial neural network (ANN) meta-learner. RESULTS: The Sleep Heart Health Study (SHHS) database is used to provide bio-signals. A total of 66 features are extracted. In the experiment that involves a duration parameter, 19 features are selected as the final feature subset because they provide a better and more stable performance. The SVM model shows good performance (accuracy = 81.68%, sensitivity = 97.05%, and specificity = 66.54%). It is also found that classifiers have poor performance when they predict normal events in less than 60 s. In the next experiment stage, the time-window segmentation method with a length of 60s is used. After the above two-stage feature selection procedure, 48 features are selected as the final feature subset that give good performance (accuracy = 90.80%, sensitivity = 93.95%, and specificity = 83.82%). To conduct the classification, Gradient Boosting, CatBoost, Light GBM, and XGBoost are used as base learners, and the ANN is used as the meta-learner. The performance of this MER classification system has the accuracy of 94.66%, the sensitivity of 96.37%, and the specificity of 90.83%

Hallucinating Agnostic Images to Generalize Across Domains

The ability to generalize across visual domains is crucial for the robustness of artificial recognition systems. Although many training sources may be available in real contexts, the access to even unlabeled target samples cannot be taken for granted, which makes standard unsupervised domain adaptation methods inapplicable in the wild. In this work we investigate how to exploit multiple sources by hallucinating a deep visual domain composed of images, possibly unrealistic, able to maintain categorical knowledge while discarding specific source styles. The produced agnostic images are the result of a deep architecture that applies pixel adaptation on the original source data guided by two adversarial domain classifier branches at image and feature level. Our approach is conceived to learn only from source data, but it seamlessly extends to the use of unlabeled target samples. Remarkable results for both multi-source domain adaptation and domain generalization support the power of hallucinating agnostic images in this framework

Urban tree classification using discrete-return LiDAR and an object-level local binary pattern algorithm

© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Urban trees have the potential to mitigate some of the harm brought about by rapid urbanization and population growth, as well as serious environmental degradation (e.g. soil erosion, carbon pollution and species extirpation), in cities. This paper presents a novel urban tree extraction modelling approach that uses discrete laser scanning point clouds and object-based textural analysis to (1) develop a model characterised by four sub-models, including (a) height-based split segmentation, (b) feature extraction, (c) texture analysis and (d) classification, and (2) apply this model to classify urban trees. The canopy height model is integrated with the object-level local binary pattern algorithm (LBP) to achieve high classification accuracy. The results of each sub-model reveal that the classification of urban trees based on the height at 47.14 (high) and 2.12 m (low), respectively, while based on crown widths were highest and lowest at 22.5 and 2.55 m, respectively. Results also indicate that the proposed algorithm of urban tree modelling is effective for practical use

Surface dissolution UV imaging for characterization of superdisintegrants and their impact on drug dissolution

Superdisintegrants are a key excipient used in immediate release formulations to promote fast tablet disintegration, therefore understanding the impact of superdisintegrant variability on product performance is important. The current study examined the impact of superdisintegrant critical material attributes (viscosity for sodium starch glycolate (SSG), particle size distribution (PSD) for croscarmellose sodium (CCS)) on their performance (swelling) and on drug dissolution using surface dissolution UV imaging. Acidic and basic pharmacopoeia (compendial) media were used to assess the role of varying pH on superdisintegrant performance and its effect on drug dissolution. A highly soluble (paracetamol) and a poorly soluble (carbamazepine) drug were used as model compounds and drug compacts and drug-excipient compacts were prepared for the dissolution experiments. The presence of a swelled SSG or CCS layer on the compact surface, due to the fast excipient hydration capacity, upon contact with dissolution medium was visualized. The swelling behaviour of superdisintegrants depended on excipient critical material attributes and the pH of the medium. Drug dissolution was faster in presence compared to superdisintegrant absence due to improved compact wetting or compact disintegration. The improvement in drug dissolution was less pronounced with increasing SSG viscosity or CCS particle size. Drug dissolution was slightly more complete in basic compared to acidic conditions in presence of the studied superdisintegrants for the highly soluble drug attributed to the increased excipient hydration capacity and the fast drug release through the swelled excipient structure. The opposite was observed for the poorly soluble drug as potentially the improvement in drug dissolution was compromised by drug release from the highly swelled structure. The use of multivariate data analysis revealed the influential role of excipient and drug properties on the impact of excipient variability on drug dissolution.</p

D4AM: A General Denoising Framework for Downstream Acoustic Models

The performance of acoustic models degrades notably in noisy environments. Speech enhancement (SE) can be used as a front-end strategy to aid automatic speech recognition (ASR) systems. However, existing training objectives of SE methods are not fully effective at integrating speech-text and noisy-clean paired data for training toward unseen ASR systems. In this study, we propose a general denoising framework, D4AM, for various downstream acoustic models. Our framework fine-tunes the SE model with the backward gradient according to a specific acoustic model and the corresponding classification objective. In addition, our method aims to consider the regression objective as an auxiliary loss to make the SE model generalize to other unseen acoustic models. To jointly train an SE unit with regression and classification objectives, D4AM uses an adjustment scheme to directly estimate suitable weighting coefficients rather than undergoing a grid search process with additional training costs. The adjustment scheme consists of two parts: gradient calibration and regression objective weighting. The experimental results show that D4AM can consistently and effectively provide improvements to various unseen acoustic models and outperforms other combination setups. Specifically, when evaluated on the Google ASR API with real noisy data completely unseen during SE training, D4AM achieves a relative WER reduction of 24.65% compared with the direct feeding of noisy input. To our knowledge, this is the first work that deploys an effective combination scheme of regression (denoising) and classification (ASR) objectives to derive a general pre-processor applicable to various unseen ASR systems. Our code is available at https://github.com/ChangLee0903/D4AM

RURAL SUICIDE: A THREE MANUSCRIPT DISSERTATION UTILIZING THE NATIONAL VIOLENT DEATH REPORTING SYSTEM

Purpose: Rural residents and veterans are at a greater risk of death by suicide but there is little research to compare rural versus urban suicide decedents. There is also a lack of research specific to rural veteran suicide. This three-manuscript dissertation study explores 1. epidemiology of suicide specific to rural areas comparing rural veterans to rural non-veterans 2. veteran suicide decedents that lived in rural areas compared to veterans that live in urban areas and 3. How the continuum of rurality is related to demographic and circumstantial variables associated with suicide Methods: Data was obtained from the Centers for Disease Control Restricted Access Database. The data included suicide decedents from 40 states from 2003-2017 n=199,730. Within this sample, the rural population was n=36,032 and the veteran population was n=7,421. Findings: Rural decedents had a mean age (M=61.16 SD=18.08 when compared to urban decedents (M=45.14 SD=16.45). Rural decedents died using firearm (77.9%) compared to urban residents (58.6%). Rural veterans had a reported issue with on-going physical health problems 35.7% compared to rural non-veterans 17.2%. When controlling for age the suicide decedents in the sample were 11.70 times likely to be male veterans. When looking at only the veteran population within the sample rural veterans were 1.43 times more likely to die using firearm compared to urban veterans. When looking at suicide across the rurality gradient death by firearms increased as the gradient moves from urban to rural. Conclusions: Rurality influences the reported characteristics of suicide decedents. Rural residents are less likely to have reported mental health treatment, report of alcohol problems, report of substance abuse problems, are more likely to die by suicide using a firearm, and there is increased use of long guns as rurality increases. Rural veterans were 1.43 times more likely to die using firearm compared to rural non-veterans. Firearms are more accessible in rural areas, rural residents are more familiar with firearms, and there is greater variety of firearms, namely long guns, in rural areas