Search results for "medicine.diagnostic_test"

Impact loading history modulates hip fracture load and location : A finite element simulation study of the proximal femur in female athletes

2018

Sideways falls impose high stress on the thin superolateral cortical bone of the femoral neck, the region regarded as a fracture-prone region of the hip. Exercise training is a natural mode of mechanical loading to make bone more robust. Exercise-induced adaptation of cortical bone along the femoral neck has been previously demonstrated. However, it is unknown whether this adaption modulates hip fracture behavior. The purpose of this study was to investigate the influence of specific exercise loading history on fall-induced hip fracture behavior by estimating fracture load and location with proximal femur finite element (FE) models created from magnetic resonance images (MRI) of 111 women w…

Hybrid Deep Shallow Network for Assessment of Depression Using Electroencephalogram Signals

2020

Depression is a mental health disorder characterised by persistently depressed mood or loss of interest in activities resulting impairment in daily life significantly. Electroencephalography (EEG) can assist with the accurate diagnosis of depression. In this paper, we present two different hybrid deep learning models for classification and assessment of patient suffering with depression. We have combined convolutional neural network with Gated recurrent units (RGUs), thus the proposed network is shallow and much smaller in size in comparison to its counter LSTM network. In addition to this, proposed approach is less sensitive to parameter settings. Extensive experiments on EEG dataset shows…

Effect of high hydrostatic pressure on extraction of B-phycoerythrin from Porphyridium cruentum: Use of confocal microscopy and image processing

2019

International audience; The aim of the study was to extract B-phycoerythrin from Porphyridium cruentum while preserving its structure. The high hydrostatic pressure treatments were chosen as extraction technology. Different methods have been used to observe the effects of the treatment: spectrophotometry and confocal laser scanning microscopy followed by image processing analysis. Image processing led to the generation of masks used for the identification of three clusters: intra, extra and intercellular. All methods showed that high hydrostatic pressure treatments between 50 and 500 MPa failed to extract B-phycoerythrin from Porphyridium cruentum cells. The fluorescence emission was negati…

On the Influence of Affect in EEG-Based Subject Identification

2021

Biometric signals have been extensively used for user identification and authentication due to their inherent characteristics that are unique to each person. The variation exhibited between the brain signals (EEG) of different people makes such signals especially suitable for biometric user identification. However, the characteristics of these signals are also influenced by the user’s current condition, including his/her affective state. In this paper, we analyze the significance of the affect-related component of brain signals within the subject identification context. Consistent results are obtained across three different public datasets, suggesting that the dominant component of the sign…

2020

Limited data are available regarding strength and endurance training adaptations to occupational physical performance during deployment. This study assessed acute training-induced changes in neuromuscular (electromyography; EMG) and metabolic (blood lactate, BLa) responses during a high-intensity military simulation test (MST), performed in the beginning (PRE) and at the end (POST) of a six-month crisis-management operation. MST time shortened (145 ± 21 vs. 129 ± 16 s, −10 ± 7%, p < 0.001) during the operation. Normalized muscle activity increased from PRE to POST in the hamstring muscles by 87 ± 146% (116 ± 52 vs. 195 ± 139%EMGMVC, p < 0.001) and in the quadriceps by 54 ± 81% (26 ± 8…

Image-Evoked Affect and its Impact on Eeg-Based Biometrics

2019

Electroencephalography (EEG) signals provide a representation of the brain’s activity patterns and have been recently exploited for user identification and authentication due to their uniqueness and their robustness to interception and artificial replication. Nevertheless, such signals are commonly affected by the individual’s emotional state. In this work, we examine the use of images as stimulus for acquiring EEG signals and study whether the use of images that evoke similar emotional responses leads to higher identification accuracy compared to images that evoke different emotional responses. Results show that identification accuracy increases when the system is trained with EEG recordin…

ES1D: A Deep Network for EEG-Based Subject Identification

2017

Security systems are starting to meet new technologies and new machine learning techniques, and a variety of methods to identify individuals from physiological signals have been developed. In this paper, we present ESID, a deep learning approach to identify subjects from electroencephalogram (EEG) signals captured by using a low cost device. The system consists of a Convolutional Neural Network (CNN), which is fed with the power spectral density of different EEG recordings belonging to different individuals. The network is trained for a period of one million iterations, in order to learn features related to local patterns in the spectral domain of the original signal. The performance of the…

2020

Atopic dermatitis (AD) is characterized by chronic, relapsing, pruritic skin inflammation and does not have a well-understood pathogenesis. In this study, we addressed the contribution of adipokines to AD eczema based on the assessment of blood levels of adiponectin, resistin, leptin, lipocalin-2, and vaspin in adult non-obese patients suffering from chronic extrinsic childhood-onset AD. We investigated 49 AD patients with a median age of 37 years. The control group consisted of 30 age-matched healthy subjects. Adipokines were assessed in the serum by ELISA assays and the severity of AD with the SCORing Atopic Dermatitis (SCORAD) index. We found that adiponectin and resistin decreased and l…

Group analysis of ongoing EEG data based on fast double-coupled nonnegative tensor decomposition

2019

Abstract Background Ongoing EEG data are recorded as mixtures of stimulus-elicited EEG, spontaneous EEG and noises, which require advanced signal processing techniques for separation and analysis. Existing methods cannot simultaneously consider common and individual characteristics among/within subjects when extracting stimulus-elicited brain activities from ongoing EEG elicited by 512-s long modern tango music. New method Aiming to discover the commonly music-elicited brain activities among subjects, we provide a comprehensive framework based on fast double-coupled nonnegative tensor decomposition (FDC-NTD) algorithm. The proposed algorithm with a generalized model is capable of simultaneo…

Detecting differences with magnetoencephalography of somatosensory processing after tactile and electrical stimuli.

2018

Abstract Background Deviant stimuli within a standard, frequent stimulus train induce a cortical somatosensory mismatch response (SMMR). The SMMR reflects the brain’s automatic mechanism for the detection of change in a somatosensory domain. It is usually elicited by electrical stimulation, which activates nerve fibers and receptors in superficial and deep skin layers, whereas tactile stimulation is closer to natural stimulation and activates uniform fiber types. We recorded SMMRs after electrical and tactile stimuli. Method 306-channel magnetoencephalography recordings were made with 16 healthy adults under two conditions: electrical (eSMMR) and tactile (tSMMR) stimulations. The SMMR proto…