Classification of health deterioration by geometric invariants.

BACKGROUND AND OBJECTIVES: Prediction of patient deterioration is essential in medical care, and its automation may reduce the risk of patient death. The precise monitoring of a patient's medical state requires devices placed on the body, which may cause discomfort. Our approach is based on the processing of long-term ballistocardiography data, which were measured using a sensory pad placed under the patient's mattress. METHODS: The investigated dataset was obtained via long-term measurements in retirement homes and intensive care units (ICU). Data were measured unobtrusively using a measuring pad equipped with piezoceramic sensors. The proposed approach focused on the processing methods of the measured ballistocardiographic signals, Cartan curvature (CC), and Euclidean arc length (EAL). RESULTS: For analysis, 218,979 normal and 216,259 aberrant 2-second samples were collected and classified using a convolutional neural network. Experiments using cross-validation with expert threshold and data length revealed the accuracy, sensitivity, and specificity of the proposed method to be 86.51 CONCLUSIONS: The proposed method provides a unique approach for an early detection of health concerns in an unobtrusive manner. In addition, the suitability of EAL over the CC was determined.

An extensive individual particle analysis of solid airborne particles collected in a moderately urbanized area.

Detailed individual particle characterization of PM10, in terms of particle size, morphology, and elemental composition, was done using scanning electron microscopy combined with energy-dispersive X-ray spectroscopy. The samples were collected in four localities in the Czech Republic (Central Europe), three of which are medium-sized cities, and one is a natural locality in the mountains. More than 1600 particles obtained from each locality were evaluated. During the sampling period (1.9.-8.9.2019), the atmospheric conditions were similar in the localities, which enabled the identification of PM10 characteristics common to all the sampling sites. Some differences in the particles' morphology and composition, arising from site-specific conditions, were observed too. The most abundant elements in the PM10 were C, O, Si, Fe, Al, Ca, Na, K, Mg, and S, but some toxic elements (Cr, Cu, and Ni) were also detected. The main component of the PM10 is carbon, whose multimodal distribution indicates that the particles contain different carbonaceous chemical compounds. The distribution of carbon in the natural locality was different compared to the other sites, suggesting a specific character of the sources of carbonaceous compounds in this region. Last but not least, a relationship between Al, Si, and O concentrations was found, which implies the presence of aluminosilicates and silicon dioxide (possibly sand) of crustal origin in the particles.

Possibilities and limits of using gyroscopic sensors in the diagnosis of progression of osteoarthritis and femoroacetabular impingement syndrome.

Osteoarthritis is the most common type of degenerative joint disease and affects millions of people. In this paper, we propose a non-obtrusive and straightforward method to assess the progression of osteoarthritis. In standard medicine praxis, osteoarthritis is observed with X-rays. In this study, we use widely available wearable sensors with gyroscopes to make the observation. Two novel methods are proposed for gyroscope data processing. A small-scale study has shown that these methods can be used to monitor osteoarthritis's progression, and to differentiate between healthy subjects and subjects with femoroacetabular impingement syndrome.

The Changes of Cholesterol Profile at the Different Insulin Resistance Range in the Czech Republic.

Background and Objectives: The mechanism of the relationship between glycemia and lipid metabolism has not been completely clarified, and slight differences may be found between authors and the kinds of evaluated parameters. Therefore, this study focused on possible changes of lipoprotein profile with regards to HOMA IR (Homeostatic Model Assessment for Insulin Resistance) cut-off 3.63, considered a signal of glucose metabolism alterations. Materials and Methods: The metabolic profiles of 3051 individuals were divided by HOMA IR values into two groups below cut-off 3.63, including (n = 2627) and above cut-off (n = 424). Patients taking medication or supplements to affect lipid, insulin, or glucose metabolism were excluded. Fasting glucose levels, insulin, and lipoproteins (total, HDL-high density and LDL-low density lipoprotein cholesterol) were compared between the groups with different HOMA IR. After analysis of data distribution, F-test and t-test were provided to compare variances and mean values. Results: The evaluation shows that the kind of cholesterol is crucial for a possible relationship with glucose metabolism and consequently confirms the changes of lipoproteins (HDL and LDL) by HOMA IR cut-off 3.63. Conclusions: The results of patients divided by HOMA IR cut-off 3.63 also suggest possible changes in the regulation of glucose metabolism and lipoprotein concentrations (HDL and LDL).