Effects of repetitive SSVEPs on EEG complexity using multiscale inherent fuzzy entropy

© 2019 Elsevier B.V. Multiscale inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity, reflecting the habituation of brain systems. Entropy dynamics are generally believed to reflect the ability of the brain to adapt to a visual stimulus environment. In this study, we explored repetitive steady-state visual evoked potential (SSVEP)-based EEG complexity by assessing multiscale inherent fuzzy entropy with relative measurements. We used a wearable EEG device with Oz and Fpz electrodes to collect EEG signals from 40 participants under the following three conditions: a resting state (closed-eyes (CE) and open-eyes (OE) stimulation with five 15-Hz CE SSVEPs and stimulation with five 20-Hz OE SSVEPs. We noted monotonic enhancement of occipital EEG relative complexity with increasing stimulus times in CE and OE conditions. The occipital EEG relative complexity was significantly higher for the fifth SSVEP than for the first SSEVP (FDR-adjusted p < 0.05). Similarly, the prefrontal EEG relative complexity tended to be significantly higher in the OE condition compared to that in the CE condition (FDR-adjusted p < 0.05). The results also indicate that multiscale inherent fuzzy entropy is superior to other competing multiscale-based entropy methods. In conclusion, EEG relative complexity increases with stimulus times, a finding that reflects the strong habituation of brain systems. These results suggest that multiscale inherent fuzzy entropy is an EEG pattern with which brain complexity can be assessed using repetitive SSVEP stimuli

IMECE2009-11857 BIOMECHANICAL BASIS OF OSCILLOMETRIC BLOOD PRESSURE MEASURING TECHNIQUE

ABSTRACT Non-invasive blood pressure (BP) measurement has been used clinically for over a century to diagnose hypertension. Compared with the auscultatory technique, the oscillometric technique requires less professional training and is widely used in automatic BP measurement devices. Currently, most of these devices measure and record amplitude of cuff pressure oscillation, and then calculate diastolic and systolic pressure using characteristic ratios and designed algorithms. A finite element (FE) model is developed to study the biomechanical basis of this technique. The model identifies that errors were caused by mechanical factors of the soft tissue and the shape of the arm. By personalizing the parameters for each patient, the accuracy of the measurement will be improved for all age groups

Development of Ground Truth Data for Automatic Lumbar Spine MRI Image Segmentation

Artificial Intelligence through supervised machine learning remains an attractive and popular research area in medical image processing. The objective of such research is often tied to the development of an intelligent computer aided diagnostic system whose aim is to assist physicians in their task of diagnosing diseases. The quality of the resulting system depends largely on the availability of good data for the machine learning algorithm to train on. Training data of a supervised learning process needs to include ground truth, i.e., data that have been correctly annotated by experts. Due to the complex nature of most medical images, human error, experience, and perception play a strong role in the quality of the ground truth. In this paper, we present the results of annotating lumbar spine Magnetic Resonance Imaging images for automatic image segmentation and propose confidence and consistency metrics to measure the quality and variability of the resulting ground truth data, respectively

A comparison of characteristics of periodic surface micro/nano structures generated via single laser beam direct writing and particle lens array parallel beam processing

Abstract Changing material surface micro/nanostructures using laser beam texturing is a valuable approach in wide applications such as control of cell/bacterial adhesion and proliferation, solar cells and optical metamaterials. Here, we report a comparison of the characteristics of surface micro/nanostructures produced using single beam laser direct writing and particle lens array parallel laser beam patterning. A Nd:YVO4 nanosecond pulsed laser at the wavelength of 532 nm was used in the laser direct writing method to texture the stainless steel surface submerged in water and in air with different scanning patterns. Changes in surface morphology, wettability, surface chemistry, and optical reflectivity were analyzed. In the particle lens array method, an excimer nanosecond laser at 248 nm wavelength was adopted to produce surface patterns on GeSbTe (GST) film coated on a polycarbonate substrate by splitting and focusing a single laser beam into millions of parallel breams. Single beam laser direct writing shows that the surface of high roughness and oxygen percentage content presented high wettability and low reflectivity characteristics. However, the controllability of the type of surface micro/nanopatterns is limited. The parallel laser beam processing using particle lens array allows rapid production of user designed periodic surface patterns at nanoscale overcoming the optical diffraction limit with a high degree of controllability. Controlling the uniformity of the particle lens array is a challenge

Lumbar Spine Discs Labeling using Axial View MRI Based on the Pixels Coordinate and Gray Level Features

Disc herniation is a major reason for lower back pain (LBP), it cost the United Kingdom (UK) government over £1.3 million per day. In fact a very high proportion of the UK population will complain from their back pain. Fur-thermore, Magnetic Resonance Imaging (MRI) is one of the main diagnosing procedure for LBP. Automatic disc labeling in the MRI to detect the herniation area will reduce the required time to issue the report from the radiologist. We present a method for automatic labeling for the lumbar spine disc area using the axial view MRI based on the pixels coordinate and gray level features. We use a clinical MRI for the training and testing. Moreover, the accuracy and the recon-structed images was the main indicator for our result. The highest achieved ac-curacy was 98.9 and 91.1 for Weighted KNN and Fine Gaussian SVM respec-tively

Lumbar spine discs labeling using axial view MRI based on the pixels coordinate and gray level features

© Springer International Publishing AG 2017. Disc herniation is a major reason for lower back pain (LBP), a health issue that affects a very high proportion of the UK population and is costing the UK government over £1.3 million per day in health care cost. Currently, the process to diagnose the cause of LBP involves examining a large number of Magnetic Resonance Images (MRI) but this process is both expensive in terms time and effort. Automatic labeling of lumbar disc pixels in the MRI to detect the herniation area will reduce the time to diagnose and detect the cause of LBP by the physicians. In this paper, we present a method for automatic labeling of the lumbar spine disc pixels in axial view MRI using pixels locations and gray level as features. Clinical MRIs are used for the training and testing of the method. The pixel classification accuracy and the quality of the reconstructed disc images are used as the main performance indicators for our method. Our experiments show that high level of classification accuracy of 91.1% and 98.9% can be achieved using Weighted KNN and Fine Gaussian SVM classifiers respectively

The influence of picosecond laser generated periodic structures on bacterial behaviour

The formation of a biofilm is preceded by bacterial retention and proliferation on a surface. Biofilm development on surfaces can cause numerous issues in terms of fouling and bacterial transmission and contamination. The design and fabrication of surfaces that prevent bacterial retention and biofilm formation may provide a potential solution to reduce bacterial fouling of surfaces. An EdgeWave, Nd:YVO4 picosecond laser was used to generate two periodic surface topographies on 316L stainless steel surfaces with and without fluoroalkylsilane (FAS) treatment. These were characterised using Optical Laser Microscopy (OLM), Scanning Electron Microscopy (SEM), contact angle measurements, and Energy Dispersive X-ray Spectroscopy (EDX). The surface wettability and retention of Escherichia coli bacteria on the laser generated surfaces were analysed over one month. Without chemical treatment, and with increasing the time to one month, the results showed that the wettability of laser treated surfaces was decreased as was subsequent bacterial retention. However, the control surface recorded the lowest number of adhered bacteria. After reducing the surface tension, the number of bacteria retention was decreased on all surfaces and one of laser generated surfaces which presented higher contact angle and lower surface tension components (CA = 132°, ΔGiwi = −85.26, γs = 13.81, γsLW = 13.37, and γs− = 0.13) recorded the minimal number of bacteria retention. The results showed that reducing the surface tension played an important role which reduced bacterial fouling

Segmentation of Lumbar Spine MRI Images for Stenosis Detection using Patch-based Pixel Classification Neural Network

This paper addresses the central problem of automatic segmentation of lumbar spine Magnetic Resonance Imaging (MRI) images to delineate boundaries between the anterior arch and posterior arch of the lumbar spine. This is necessary to efficiently detect the occurrence of lumbar spinal stenosis as a leading cause of Chronic Lower Back Pain. A patch-based classification neural network consisting of convolutional and fully connected layers is used to classify and label pixels in MRI images. The classifier is trained using overlapping patches of size 25x25 pixels taken from a set of cropped axial-view T2-weighted MRI images of the bottom three intervertebral discs. A set of experiment is conducted to measure the performance of the classification network in segmenting the images when either all or each of the discs separately is used. Using pixel accuracy, mean accuracy, mean Intersection over Union (IoU), and frequency weighted IoU as the performance metrics we have shown that our approach produces better segmentation results than eleven other pixel classifiers. Furthermore, our experiment result also indicates that our approach produces more accurate delineation of all important boundaries and making it best suited for the subsequent stage of lumbar spinal stenosis detection

Global Retinoblastoma Presentation and Analysis by National Income Level.

Importance: Early diagnosis of retinoblastoma, the most common intraocular cancer, can save both a child's life and vision. However, anecdotal evidence suggests that many children across the world are diagnosed late. To our knowledge, the clinical presentation of retinoblastoma has never been assessed on a global scale. Objectives: To report the retinoblastoma stage at diagnosis in patients across the world during a single year, to investigate associations between clinical variables and national income level, and to investigate risk factors for advanced disease at diagnosis. Design, Setting, and Participants: A total of 278 retinoblastoma treatment centers were recruited from June 2017 through December 2018 to participate in a cross-sectional analysis of treatment-naive patients with retinoblastoma who were diagnosed in 2017. Main Outcomes and Measures: Age at presentation, proportion of familial history of retinoblastoma, and tumor stage and metastasis. Results: The cohort included 4351 new patients from 153 countries; the median age at diagnosis was 30.5 (interquartile range, 18.3-45.9) months, and 1976 patients (45.4%) were female. Most patients (n = 3685 [84.7%]) were from low- and middle-income countries (LMICs). Globally, the most common indication for referral was leukocoria (n = 2638 [62.8%]), followed by strabismus (n = 429 [10.2%]) and proptosis (n = 309 [7.4%]). Patients from high-income countries (HICs) were diagnosed at a median age of 14.1 months, with 656 of 666 (98.5%) patients having intraocular retinoblastoma and 2 (0.3%) having metastasis. Patients from low-income countries were diagnosed at a median age of 30.5 months, with 256 of 521 (49.1%) having extraocular retinoblastoma and 94 of 498 (18.9%) having metastasis. Lower national income level was associated with older presentation age, higher proportion of locally advanced disease and distant metastasis, and smaller proportion of familial history of retinoblastoma. Advanced disease at diagnosis was more common in LMICs even after adjusting for age (odds ratio for low-income countries vs upper-middle-income countries and HICs, 17.92 [95% CI, 12.94-24.80], and for lower-middle-income countries vs upper-middle-income countries and HICs, 5.74 [95% CI, 4.30-7.68]). Conclusions and Relevance: This study is estimated to have included more than half of all new retinoblastoma cases worldwide in 2017. Children from LMICs, where the main global retinoblastoma burden lies, presented at an older age with more advanced disease and demonstrated a smaller proportion of familial history of retinoblastoma, likely because many do not reach a childbearing age. Given that retinoblastoma is curable, these data are concerning and mandate intervention at national and international levels. Further studies are needed to investigate factors, other than age at presentation, that may be associated with advanced disease in LMICs

Burden of non-communicable diseases among adolescents aged 10–24 years in the EU, 1990–2019: a systematic analysis of the Global Burden of Diseases Study 2019

Background: Disability and mortality burden of non-communicable diseases (NCDs) have risen worldwide; however, the NCD burden among adolescents remains poorly described in the EU. Methods: Estimates were retrieved from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019. Causes of NCDs were analysed at three different levels of the GBD 2019 hierarchy, for which mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) were extracted. Estimates, with the 95% uncertainty intervals (UI), were retrieved for EU Member States from 1990 to 2019, three age subgroups (10–14 years, 15–19 years, and 20–24 years), and by sex. Spearman's correlation was conducted between DALY rates for NCDs and the Socio-demographic Index (SDI) of each EU Member State. Findings: In 2019, NCDs accounted for 86·4% (95% uncertainty interval 83·5–88·8) of all YLDs and 38·8% (37·4–39·8) of total deaths in adolescents aged 10–24 years. For NCDs in this age group, neoplasms were the leading causes of both mortality (4·01 [95% uncertainty interval 3·62–4·25] per 100 000 population) and YLLs (281·78 [254·25–298·92] per 100 000 population), whereas mental disorders were the leading cause for YLDs (2039·36 [1432·56–2773·47] per 100 000 population) and DALYs (2040·59 [1433·96–2774·62] per 100 000 population) in all EU Member States, and in all studied age groups. In 2019, among adolescents aged 10–24 years, males had a higher mortality rate per 100 000 population due to NCDs than females (11·66 [11·04–12·28] vs 7·89 [7·53–8·23]), whereas females presented a higher DALY rate per 100 000 population due to NCDs (8003·25 [5812·78–10 701·59] vs 6083·91 [4576·63–7857·92]). From 1990 to 2019, mortality rate due to NCDs in adolescents aged 10–24 years substantially decreased (–40·41% [–43·00 to –37·61), and also the YLL rate considerably decreased (–40·56% [–43·16 to –37·74]), except for mental disorders (which increased by 32·18% [1·67 to 66·49]), whereas the YLD rate increased slightly (1·44% [0·09 to 2·79]). Positive correlations were observed between DALY rates and SDIs for substance use disorders (rs=0·58, p=0·0012) and skin and subcutaneous diseases (rs=0·45, p=0·017), whereas negative correlations were found between DALY rates and SDIs for cardiovascular diseases (rs=–0·46, p=0·015), neoplasms (rs=–0·57, p=0·0015), and sense organ diseases (rs=–0·61, p=0·0005). Interpretation: NCD-related mortality has substantially declined among adolescents in the EU between 1990 and 2019, but the rising trend of YLL attributed to mental disorders and their YLD burden are concerning. Differences by sex, age group, and across EU Member States highlight the importance of preventive interventions and scaling up adolescent-responsive health-care systems, which should prioritise specific needs by sex, age, and location. Funding: Bill &amp; Melinda Gates Foundation