A Hidden Markov Model for Seismocardiography

This is the author accepted manuscript. The final version is available from Institute of Electrical and Electronics Engineers (IEEE) via the DOI in this record.We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate, heart rate variability measures, and cardiac time intervals (the latter requiring a small number of manual annotations). As is shown in the conducted experimental study, the presented algorithm outperforms the state-of-the-art in seismocardiogram-based heart rate and heart rate variability estimation. Moreover, the isovolumic contraction time and the left ventricular ejection time are estimated with mean absolute errors of about 5 [ms] and 9 [ms], respectively. The proposed algorithm can be applied to any set of inertial sensors; does not require access to any additional sensor modalities; does not make any assumptions on the seismocardiogram morphology; and explicitly models sensor noise and beat-to-beat variations (both in amplitude and temporal scaling) in the seismocardiogram morphology. As such, it is well suited for low-cost implementations using off-the-shelf inertial sensors and targeting, e.g., at-home medical services

Cardio-postural Interactions and Muscle-pump Baroreflex Are Severely Impacted by 60-day Bedrest Immobilization

To understand fundamental mechanisms associated with post-flight orthostatic intolerance we investigated the interaction between the cardiovascular and postural functions before and after 60 days of head down bedrest (HDBR). Twenty healthy young males (35.0 ± 1.7 years) were subjected to 60-day HDBR at 6˚ to simulate spaceflight-induced fluid shifts. A supine-to-stand (STS) test was conducted to evaluate cardio-postural control before and after (R) HDBR while an assessment of cardiovascular function was performed during HDBR. Beat-to-beat heart period, systolic blood pressure, and electromyography impulses were derived for wavelet transform coherence and causality analyses of the cardio-postural control and used to assess changes in the muscle-pump baroreflex. During quiet stand of the STS test, compared to baseline, heart rate was 50% higher on the day of exit from bedrest (R0) and 20% higher eight days later (R8). There was a 50% increase in deoxygenated hemoglobin on R0 and R8. Leg muscle activity reduced, and postural sway increased after HDBR. Causality of the muscle-pump baroreflex was reduced on R0 (0.73 ± 0.2) compared to baseline (0.87 ± 0.2) with complete recovery by R8. The muscle-pump baroreflex also had decreased gain and fraction time active following HDBR. Overall, our data show a significantly impaired muscle-pump baroreflex following bedrest

Role of deep levels and interface states in the capacitance characteristics of all‐sputtered CuInSe2/CdS solar cell heterojunctions

All‐sputtered CuInSe2/CdS solar cellheterojunctions have been analyzed by means of capacitance‐frequency (C‐F) and capacitance‐bias voltage (C‐V) measurements. Depending on the CuInSe2 layer composition, two kinds of heterojunctions were analyzed: type 1 heterojunctions (based on stoichiometric or slightly In‐rich CuInSe2 layers) and type 2 heterojunctions (based on Cu‐rich CuInSe2 layers). In type 1 heterojunctions, a 80‐meV donor level has been found. Densities of interface states in the range 101 0–101 1 cm2 eV− 1 (type 1) and in the range 101 2–101 3 cm− 2 eV− 1 (type 2) have been deduced. On the other hand, doping concentrations of 1.6×101 6 cm− 3 for stoichiometric CuInSe2 (type 1 heterojunction) and 8×101 7 cm− 3 for the CdS (type 2 heterojunction) have been deduced from C‐Vmeasurements

Warum die Deutsche Gesellschaft für Neurologie einen industrieunabhängigen Kongress braucht [Why German neurology needs an annual meeting without industry sponsorship]

Scientific conferences need to be independent from commercial interests. In this contribution to an ongoing debate the German initiative NeurologyFirst argues for professional autonomy and reduction of industry influence on the annual neurology meeting. Our key concern is the abolition of industry symposia which pursue mainly commercial interests and are designed to shed a favourable light on the sponsor's product. Industry symposia are usually based on industry-sponsored studies which are often burdened with methodological shortcomings including selection of positive data. The industry exhibition appears similarly problematic as it pursues a commercial agenda in scientific clothing. Giving up industry support will help us to appraise pharmacological treatments from the perspective of evidence-based medicine and thus serve our patients. Several national and international examples demonstrate that large conferences can be organized without industry at moderate prices

A thermosensitive electromechanical model for detecting biological particles

Miniature electromechanical systems form a class of bioMEMS that can provide appropriate sensitivity. In this research, a thermo-electro-mechanical model is presented to detect biological particles in the microscale. Identification in the model is based on analyzing pull-in instability parameters and frequency shifts. Here, governing equations are derived via the extended Hamilton’s principle. The coupled effects of system parameters such as surface layer energy, electric field correction, and material properties are incorporated in this thermosensitive model. Afterward, the accuracy of the present model and obtained results are validated with experimental, analytical, and numerical data for several cases. Performing a parametric study reveals that mechanical properties of biosensors can significantly affect the detection sensitivity of actuated ultra-small detectors and should be taken into account. Furthermore, it is shown that the number or dimension of deposited particles on the sensing zone can be estimated by investigating the changes in the threshold voltage, electrode deflection, and frequency shifts. The present analysis is likely to provide pertinent guidelines to design thermal switches and miniature detectors with the desired performance. The developed biosensor is more appropriate to detect and characterize viruses in samples with different temperatures

Video classification using deep autoencoder network

Abstract We present a deep learning framework for video classification applicable to face recognition and dynamic texture recognition. A Deep Autoencoder Network Template (DANT) is designed whose weights are initialized by conducting unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines. In order to obtain a class specific network and fine tune the weights for each class, the pre-initialized DANT is trained for each class of video sequences, separately. A majority voting technique based on the reconstruction error is employed for the classification task. The extensive evaluation and comparisons with state-of-the-art approaches on Honda/UCSD, DynTex, and YUPPEN databases demonstrate that the proposed method significantly improves the performance of dynamic texture classification

The role of celecoxib in glioblastoma treatment: a review of literature

Objective: Glioblastoma (GB) is the most aggressive and lethal type of brain tumor. Despite the standard treatments and improvements, the overall survival (OS) and progression free survival (PFS) are not optimal. Celecoxib (CEL) has been considered as one of the adjuvant agents in patients with GB due to its different mechanisms in recent years. Materials and Methods: A systematic search was performed in EMBASE, MEDLINE, ClinicalTrials.gov, Web of Science, Google Scholar and Cochrane Central Register of the Controlled Trials databases to get access to the trials that investigated the potential benefits of CEL in the treatment regimen of patients with GB. Results: From 77 studies, twelve clinical trials with 690 patients from 2004 to 2015 were included. The trials were often in phase II and temozolamide was the main agent of the treatment regimen. CEL was administered mostly at high dose of 400 mg twice daily and it was well tolerated. CEL has shown some promising effects but only in studies which patients were not eligible for standard treatment due to their age or clinical conditions. Conclusions: CEL administration in tested doses is safe and practical for GBM patients. It could be considered as one of the choices in the therapeutic protocol of GB along with the main drugs commonly used in chemotherapy regimen especially in the elderly patients who are not eligible for standard treatment

Dynamic Texture Comparison Using Derivative Sparse Representation: Application to Video-Based Face Recognition

© 2013 IEEE. Video-based face, expression, and scene recognition are fundamental problems in human-machine interaction, especially when there is a short-length video. In this paper, we present a new derivative sparse representation approach for face and texture recognition using short-length videos. First, it builds local linear subspaces of dynamic texture segments by computing spatiotemporal directional derivatives in a cylinder neighborhood within dynamic textures. Unlike traditional methods, a nonbinary texture coding technique is proposed to extract high-order derivatives using continuous circular and cylinder regions to avoid aliasing effects. Then, these local linear subspaces of texture segments are mapped onto a Grassmann manifold via sparse representation. A new joint sparse representation algorithm is developed to establish the correspondences of subspace points on the manifold for measuring the similarity between two dynamic textures. Extensive experiments on the Honda/UCSD, the CMU motion of body, the YouTube, and the DynTex datasets show that the proposed method consistently outperforms the state-of-the-art methods in dynamic texture recognition, and achieved the encouraging highest accuracy reported to date on the challenging YouTube face dataset. The encouraging experimental results show the effectiveness of the proposed method in video-based face recognition in human-machine system applications