Respiratory and cardiac monitoring at night using a wrist wearable optical system

Sleep monitoring provides valuable insights into the general health of an individual and helps in the diagnostic of sleep-derived illnesses. Polysomnography, is considered the gold standard for such task. However, it is very unwieldy and therefore not suitable for long-term analysis. Here, we present a non-intrusive wearable system that, by using photoplethysmography, it can estimate beat-to-beat intervals, pulse rate, and breathing rate reliably during the night. The performance of the proposed approach was evaluated empirically in the Department of Psychology at the University of Fribourg. Each participant was wearing two smart-bracelets from Ava as well as a complete polysomnographic setup as reference. The resulting mean absolute errors are 17.4 ms (MAPE 1.8%) for the beat-to-beat intervals, 0.13 beats-per-minute (MAPE 0.20%) for the pulse rate, and 0.9 breaths-per-minute (MAPE 6.7%) for the breath rate.Comment: Submitted to the 40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC

The Connectome Viewer Toolkit: An Open Source Framework to Manage, Analyze, and Visualize Connectomes

Advanced neuroinformatics tools are required for methods of connectome mapping, analysis, and visualization. The inherent multi-modality of connectome datasets poses new challenges for data organization, integration, and sharing. We have designed and implemented the Connectome Viewer Toolkit – a set of free and extensible open source neuroimaging tools written in Python. The key components of the toolkit are as follows: (1) The Connectome File Format is an XML-based container format to standardize multi-modal data integration and structured metadata annotation. (2) The Connectome File Format Library enables management and sharing of connectome files. (3) The Connectome Viewer is an integrated research and development environment for visualization and analysis of multi-modal connectome data. The Connectome Viewer's plugin architecture supports extensions with network analysis packages and an interactive scripting shell, to enable easy development and community contributions. Integration with tools from the scientific Python community allows the leveraging of numerous existing libraries for powerful connectome data mining, exploration, and comparison. We demonstrate the applicability of the Connectome Viewer Toolkit using Diffusion MRI datasets processed by the Connectome Mapper. The Connectome Viewer Toolkit is available from http://www.cmtk.org

Global tractography with embedded anatomical priors and microstructure information for quantitative connectomics

Over the last decades new technologies have shed the light on an improved understanding of brain structure and function. Recently, an imaging modality referred to as diffusion Magnetic Resonance Imaging (dMRI) has emerged. By exploiting the natural motion of water molecules undergoing a thermal agitation, i.e. Brownian motion, dMRI allows to estimate the biological tissue structure. The promising potentials of dMRI relies on the fact that it is the only imaging modality that maps the architecture of the cerebral white matter in-vivo and non-invasively. In the brain, experimental evidences suggest that the tissue component mainly responsible for diffusion anisotropy in white matter is the cell membrane. Consequently, the anisotropy of molecular diffusion in the white matter can be exploited to map the structural neuronal connectivity. The study of connectivity would allow to develop a clinical comprehension of the brain function. The precise mapping of the connectivity from dMRI involves an image processing step referred to as tractography. These algorithms produce trajectories capturing coherent orientations of maximal diffusion that are likely to represent real axonal fiber bundles. Tractography will play a major role in this thesis and we will focus on two points that needs to be taken into consideration when conducting whole brain connectivity analysis and comparing controls vs patients. First, the reproducibility of the measures describing the mutual connections between a pair of brain regions across healthy subjects and scans. Second, the accuracy and reliability of these measures. A multi-center study using healthy subjects and phantoms was performed to test whether dMRI data are reproducible across different MRI scanners and subjects. Measures describing the anisotropy were investigated both using region- and tract-based approaches. The main outcomes supports the feasibility of pooling dMRI data due to a reasonably low variability of these measures. This grants us now the possibility to study pathological cases using these measures and scanners. In this framework, we conducted a whole brain connectivity analysis comparing a group of healthy subjects vs a group of patients with epilepsy. In addition, more intrinsic micro-structural features were derived to further describe the connectivity and to better understand the changes in diffusion anisotropy. We observed that global connectivity, hub architecture and regional connectivity patterns were altered in epilepsy patients. Finally, to increase the sensitivity of dMRI study for other brain disorders, we developed a global tractography algorithm that reconstructs simultaneously the fibers of the entire brain by solving an energy minimization problem. Our approach was specifically designed for connectivity analysis applications, with the following main contributions: (i) explicitly enforces anatomical priors of the tracts in the optimization, (ii) considers the effective contribution of each of them to the acquired dMRI image. This algorithm was first tested on a realistic phantom and further applied to in-vivo human brain data. The new connectivity measures were in agreement with the ground truth. These results were improved compared to the current state-of-the-art tractography methods. We believe that the findings in this thesis will be of big value for the community performing connectivity analysis on the human brain data

CONDOMINIUM DENGAN PENERAPAN \'SKYRISE GREENERY\' TERKAIT PENDEKATAN ARSITEKTUR HIJAU DI JAKARTA TIMUR

CONDOMINIUM DENGAN PENERAPAN \'SKYRISE GREENERY\' TERKAIT PENDEKATAN ARSITEKTUR HIJAU DI JAKARTA TIMUR

Structural Graph Analysis of Left and Right Temporal Lobe Epilepsy using Diffusion Spectrum Imaging

Patients with Temporal Lobe Epilepsy (TLE) suffer from widespread subtle white matter abnormalities and abnormal functional connectivity extending beyond the affected lobe, as revealed by volumetric and functional MRI studies. Diffusion Magnetic Resonance Imaging and fiber-tracking offer a noninvasive technique for mapping human brain connectivity and have been increasingly used to study patients with epilepsy. In this study we investigated the effects of two types of TLE ( right-sided and left-sided ) on the global characteristics of brain connectivity estimated by topological measures to reduce the complexity of its interpretation. We used Diffusion Spectrum Imaging (DSI), a high angular resolution diffusion technique, to address the difficulty of Diffusion Tensor Imaging (DTI) to disentangle multiple fiber orientations in a single voxel. Further, a global tractography method was utilized to reconstruction the non-dominate pathways

Anatomical Priors to improve Global Tractography

The main assumption of fiber-tracking algorithms is that fiber trajectories are represented by paths of highest diffusion, which is usually accomplished by following the principal diffusion directions in every voxel. The state-of-the-art approaches known as “global tractography” reconstruct all the fiber tracts of the whole brain simultaneously by solving global energy minimization inverse problems. In this work we have reformulated global tractography to explicitly enforce anatomical priors in the optimization with the aim of (i) improving the quality of reconstructions and (ii) reducing the computation time