In this work we show preliminary advance in the development of a low-cost wrist wearable that measures levels of oxygen saturation levels in blood (SpO2) and physical activity simultaneously. For the monitoring of SpO2, we use pulse oximetry (a non invasive technique) by backscattering detection at the wrist. The device consists in an optoelectronics layer based on two light-emitting diodes (650nm and 960 nm) and a photodiode, a 3-axis gyroscope, a 3-axis accelerometer and a microcontroller capable of data processing and data transmission via bluetooth. In addition to, our sensor allows to save data for later analysis.
In this work we present a method to perform pituitary cell segmentation in image stacks acquired by fluorescence microscopy from pituitary slice preparations. Although there exist many procedures developed to achieve cell segmentation tasks, they are generally based on the edge detection and require high resolution images. However in the biological preparations that we worked on, the cells are not well defined as experts identify their intracellular calcium activity due to fluorescence intensity changes in different regions over time. This intensity changes were associated with time series over regions, and because they present a particular behavior they were used into a classification procedure in order to perform cell segmentation.
Two logistic regression classifiers were implemented for the time series classification task using as features the area under the curve and skewness in the first classifier and skewness and kurtosis in the second classifier. Once we have found both decision boundaries in two different feature spaces by training using 120 time series, the decision boundaries were tested over 12 image stacks through a python graphical user interface (GUI), generating binary images where white pixels correspond to cells and the black ones to background. Results show that area-skewness classifier reduces the time an expert dedicates in locating cells by up to 75% in some stacks versus a 92% for the kurtosis-skewness classifier, this evaluated on the number of regions the method found. Due to the promising results, we expect that this method will be improved adding more relevant features to the classifier.
This work presents the development of a low cost pulse oximeter prototype consisting of pulsed red and infrared commercial LEDs and a broad spectral photodetector used to register time series of heart rate and oxygen saturation of blood. This platform, besides providing these values, like any other pulse oximeter, processes the signals to compute a power spectrum analysis of the patient heart rate variability in real time and, additionally, the device allows access to all raw and analyzed data if databases construction is required or another kind of further analysis is desired. Since the prototype is capable of acquiring data for long periods of time, it is suitable for collecting data in real life activities, enabling the development of future wearable applications.