PPG-Based Sleep Staging Using SleepPPGNet: Extension to Wearables, Improvements, Limitations.

The diagnosis of sleep disorders is still often based on polysomnography, an in-lab exam allowing experts to perform accurate sleep staging, although this is labor-intensive, expensive, and exposing patients to unusual sleep conditions. A state-of-the-art deep learning model - called SleepPPGNet - was recently proposed. It achieves an accuracy of 82% and a Cohen's kappa of 0.

Changes in sensor recorded activity patterns and neuropsychiatric symptoms after deep brain stimulation for Parkinson's disease: 5 case reports.

Background: Effects of subthalamic nucleus deep brain stimulation (STN-DBS) on neuropsychiatric symptoms of Parkinson's disease (PD) remain debated. Sensor technology might help to objectively assess behavioural changes after STN-DBS.

Case Presentation: 5 PD patients were assessed 1 before and 5 months after STN-DBS with the Movement Disorders Society Unified Parkinson's Disease Rating Scale part III in the medication ON (plus postoperatively stimulation ON) condition, the Montreal Cognitive Assessment, the Questionnaire for Impulsive-Compulsive Behaviors in Parkinson's Disease Rating Scale present version, the Hospital Anxiety and Depression Scale and the Starkstein Apathy Scale.

Smartphone based blood pressure measurement: accuracy of the OptiBP mobile application according to the AAMI/ESH/ISO universal validation protocol.

Objective: The aim of this study was to assess the accuracy of the OptiBP mobile application based on an optical signal recorded by placing the patient's fingertip on a smartphone's camera to estimate blood pressure (BP). Measurements were carried out in a general population according to existing standards of the Association for the Advancement of Medical Instrumentation (AAMI), the European Society of Hypertension (ESH) and the International Organization for Standardization (ISO).

Methods: Participants were recruited during a scheduled appointment at the hypertension clinic of Lausanne University Hospital in Switzerland.

Blood pressure measurements with the OptiBP smartphone app validated against reference auscultatory measurements.

Mobile health diagnostics have been shown to be effective and scalable for chronic disease detection and management. By maximizing the smartphones' optics and computational power, they could allow assessment of physiological information from the morphology of pulse waves and thus estimate cuffless blood pressure (BP). We trained the parameters of an existing pulse wave analysis algorithm (oBPM), previously validated in anaesthesia on pulse oximeter signals, by collecting optical signals from 51 patients fingertips via a smartphone while simultaneously acquiring BP measurements through an arterial catheter.

Pulse Oximetry at the Wrist During Sleep: Performance, Challenges and Perspectives.

The state-of-the-art non-invasive measurement of peripheral oxygen saturation (SpO2) during sleep is mainly based on pulse oximetry at the fingertip. Although this approach is noninvasive, it can still be obtrusive and cumbersome to apply, in particular for ambulatory monitoring over several nights.We developed a wrist-worn reflectance pulse oximetry device which can be embedded in a watch, making it less obtrusive and easy to apply.

Machine Learning Approaches For Improved Continuous, Non-occlusive Arterial Pressure Monitoring Using Photoplethysmography.

Arterial pressure (AP) is a crucial biomarker for cardiovascular disease prevention and management. Photoplethysmography (PPG) could provide a novel, paradigm-shifting approach for continuous, non-obtrusive AP monitoring, comfortably integrated in wearable and mobile devices; yet, it still faces challenges in accuracy and robustness. In this work, we sought to integrate machine learning (ML) techniques into a previously established, clinically-validated classical approach (oBPM) to develop new accurate AP estimation tools based on PPG, and at the same time improve our understanding of the underlying physiological parameters.

Author Correction: The challenge of mapping the human connectome based on diffusion tractography.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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.

Performance Assessment of a Dedicated Reflectance Pulse Oximeter in a Neonatal Intensive Care Unit.

The measurement of peripheral oxygen saturation (SpO2) in neonatal intensive care units (NICUs) poses a significant challenge. Motion artifacts due to the patient's limb motion induce many false alarms, which in turn cause an additional workload for the medical staff and anxiety for the parents. We developed a reflectance pulse oximeter dedicated to be placed at the patient's forehead, which is less prone to such artifacts.

The challenge of mapping the human connectome based on diffusion tractography.

Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups.

Global tractography with embedded anatomical priors for quantitative connectivity analysis.

Tractography algorithms provide us with the ability to non-invasively reconstruct fiber pathways in the white matter (WM) by exploiting the directional information described with diffusion magnetic resonance. These methods could be divided into two major classes, local and global. Local methods reconstruct each fiber tract iteratively by considering only directional information at the voxel level and its neighborhood.

COMMIT: Convex optimization modeling for microstructure informed tractography.

Tractography is a class of algorithms aiming at in vivo mapping the major neuronal pathways in the white matter from diffusion magnetic resonance imaging (MRI) data. These techniques offer a powerful tool to noninvasively investigate at the macroscopic scale the architecture of the neuronal connections of the brain. However, unfortunately, the reconstructions recovered with existing tractography algorithms are not really quantitative even though diffusion MRI is a quantitative modality by nature.

Connectivity and tissue microstructural alterations in right and left temporal lobe epilepsy revealed by diffusion spectrum imaging.

Focal epilepsy is increasingly recognized as the result of an altered brain network, both on the structural and functional levels and the characterization of these widespread brain alterations is crucial for our understanding of the clinical manifestation of seizure and cognitive deficits as well as for the management of candidates to epilepsy surgery. Tractography based on Diffusion Tensor Imaging allows non-invasive mapping of white matter tracts in vivo. Recently, diffusion spectrum imaging (DSI), based on an increased number of diffusion directions and intensities, has improved the sensitivity of tractography, notably with respect to the problem of fiber crossing and recent developments allow acquisition times compatible with clinical application.

Tracking the source of cerebellar epilepsy: hemifacial seizures associated with cerebellar cortical dysplasia.

Traditionally, subcortical structures such as the cerebellum are supposed to exert a modulatory effect on epileptic seizures, rather than being the primary seizure generator. We report a 14-month old girl presenting, since birth, with seizures symptomatic of a right cerebellar dysplasia, manifested as paroxystic contralateral hemifacial spasm and ipsilateral facial weakness. Multimodal imaging was used to investigate both anatomical landmarks related to the cerebellar lesion and mechanisms underlying seizure generation.

The connectome mapper: an open-source processing pipeline to map connectomes with MRI.

Researchers working in the field of global connectivity analysis using diffusion magnetic resonance imaging (MRI) can count on a wide selection of software packages for processing their data, with methods ranging from the reconstruction of the local intra-voxel axonal structure to the estimation of the trajectories of the underlying fibre tracts. However, each package is generally task-specific and uses its own conventions and file formats. In this article we present the Connectome Mapper, a software pipeline aimed at helping researchers through the tedious process of organising, processing and analysing diffusion MRI data to perform global brain connectivity analyses.

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.