Effect of potassium present in stratum corneum during non-invasive measurement of potassium in human subjects using reverse iontophoresis

Purpose: Reverse iontophoresis (RI) is one of the potential techniques used to monitor the concentration of various analytes in body fluids non -invasively. Transdermal extraction of potassium is investigated using RI. In the present work, the effect of potassium on stratum corneum (SC) during RI, feasibility of RI for continuous monitoring of potassium, and use of potassium as internal standard in RI, are investigated. Methods: Tape stripping experiment is carried out to find potassium concentration in SC. RI is carried out continuously for 180 min without passive diffusion and after passive diffusion for 60 min. Skin impedance measurements are done at 20 Hz and 20 kHz. Results: Potassium is found to be in the range 300-650 nmol/cm(2) on SC by tape stripping experiment. Correlation coefficient between blood potassium and extracted potassium through RI after passive diffusion (R-2 = 0.5870) is more than without passive diffusion (R-2 = 0.5117). The skin impedance measurement shows that RI has more effect on SC than superficial layer of SC during RI. Conclusion: The present investigations conclude that it is possible to monitor potassium continuously through RI and using potassium as internal standard in RI.

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

A novel non-intrusive sampling technique for CO2 laser on-line beam monitoring utilising a silicon mirror

The measurement of high speed laser beam parameters during processing is a topic that has seen growing attention over the last few years as quality assurance places greater demand on the monitoring of the manufacturing process. The targets for any monitoring system is to be non-intrusive, low cost, simple to operate, high speed and capable of operation in process. A new ISO compliant system is presented based on the integration of an imaging plate and camera located behind a proprietary mirror sampling device. The general layout of the device is presented along with the thermal and optical performance of the sampling optic. Diagnostic performance of the system is compared with industry standard devices, demonstrating the high quality high speed data which has been generated using this system.

Non-invasive methods for identifying oocysts of Sarcocystis spp. from definitive hosts

Because the excreted sporocysts and/or oocysts of various species of Sarcocystis may not be discriminated morphologically, we sought to validate a diagnostic technique based on variation in the 18S rDNA sequence. Oocysts and/or sporocysts; from three taxa

SLP: A zero-contact non-invasive method for pulmonary function testing

Structured Light Plethysmography (SLP) is a novel non-invasive method that uses structured light to perform pulmonary function testing that does not require physical contact with a patient. The technique produces an estimate of chest wall volume changes over time. A patient is observed continuously by two cameras and a known pattern of light (i.e. structured light) is projected onto the chest using an off-the-shelf projector. Corner features from the projected light pattern are extracted, tracked and brought into correspondence for both camera views over successive frames. A novel self calibration algorithm recovers the intrinsic and extrinsic camera parameters from these point correspondences. This information is used to reconstruct a surface approximation of the chest wall and several novel ideas for 'cleaning up' the reconstruction are used. The resulting volume and derived statistics (e.g. FVC, FEV) agree very well with data taken with a spirometer. © 2010. The copyright of this document resides with its authors.

Computer vision tools for the non-invasive assessment of autism-related behavioral markers

The early detection of developmental disorders is key to child outcome, allowing interventions to be initiated that promote development and improve prognosis. Research on autism spectrum disorder (ASD) suggests behavioral markers can be observed late in the first year of life. Many of these studies involved extensive frame-by-frame video observation and analysis of a child's natural behavior. Although non-intrusive, these methods are extremely time-intensive and require a high level of observer training; thus, they are impractical for clinical and large population research purposes. Diagnostic measures for ASD are available for infants but are only accurate when used by specialists experienced in early diagnosis. This work is a first milestone in a long-term multidisciplinary project that aims at helping clinicians and general practitioners accomplish this early detection/measurement task automatically. We focus on providing computer vision tools to measure and identify ASD behavioral markers based on components of the Autism Observation Scale for Infants (AOSI). In particular, we develop algorithms to measure three critical AOSI activities that assess visual attention. We augment these AOSI activities with an additional test that analyzes asymmetrical patterns in unsupported gait. The first set of algorithms involves assessing head motion by tracking facial features, while the gait analysis relies on joint foreground segmentation and 2D body pose estimation in video. We show results that provide insightful knowledge to augment the clinician's behavioral observations obtained from real in-clinic assessments.