Abstract:
A method includes receiving a time series of slice images of medical imaging. The images have a region of interest located at a lung lesion. The method also includes tracking over at least one subset of slice images in a time series of slice images variations over time of at least one image parameter at the set of points in the region of interest. Classifier processing is applied to set of signals indicative of tracked time variations of the at least one image parameter at respective points in the set of points. A classification signal is indicative of the tracked time variations of the at least one image parameter reaching or failing to reach at least one classification threshold.
Abstract:
In an embodiment, a method of processing an electrophysiological signal includes collecting the electrophysiological signal that is indicative of a level of attention of a human; filtering the electrophysiological signal via joint low-pass and high-pass filtering using a set of filtering parameters including low-pass filters parameters and high-pass filters parameters having a set of low-pass cut-off frequencies and a set of high-pass cut-off frequencies respectively. The method further includes applying artificial neural network processing to the filtered electrophysiological signal to extract therefrom a set of features of the electrophysiological signal. The method further includes applying classifier processing to the set of features extracted from the filtered electrophysiological signal and producing a classification signal indicative of the level of attention of the human. The method further includes generating a trigger signal to trigger a user circuit based on the classification signal.
Abstract:
A microfluidic device, comprising: a semiconductor body, having a first side and a second side, opposite to one another in a first direction; and at least one well, configured for containing a fluid, extending in the semiconductor body starting from the first side and being delimited at the bottom by a bottom surface. The microfluidic device further comprises a stirring structure integrated in the well at the bottom surface, the stirring structure including a first stirring portion coupled to the semiconductor body and provided with at least one first suspended beam configured for being moved in a second direction so as to generate, in use, agitation of the fluid present in said well.
Abstract:
A microfluidic device, comprising: a semiconductor body, having a first side and a second side, opposite to one another in a first direction; and at least one well, configured for containing a fluid, extending in the semiconductor body starting from the first side and being delimited at the bottom by a bottom surface. The microfluidic device further comprises a stirring structure integrated in the well at the bottom surface, the stirring structure including a first stirring portion coupled to the semiconductor body and provided with at least one first suspended beam configured for being moved in a second direction so as to generate, in use, agitation of the fluid present in said well.
Abstract:
The present invention relates to the use of a nitroaniline derivative of Formula I for the production of nitric oxide and for the preparation of a medicament for the treatment of a disease wherein the administration of nitric oxide is beneficial. The present invention furthermore relates to a method for the production of NO irradiating a nitroaniline derivative of Formula I, a kit comprising a nitroaniline derivative of Formula I and a carrier and to a system comprising a source of radiations and a container associated to a nitroaniline derivative of Formula I. In Formula I, R and RI are each independently hydrogen or a C1-C3 alkyl group; RII is hydrogen or an alkyl group.
Abstract:
A method includes receiving a video signal that comprises a time series of images of a face of a human, wherein the images in the time series of images comprise a set of landmark points in the face, applying tracking processing to the video signal to reveal variations over time of at least one image parameter at the set of landmark points in the human face, generating a set of variation signals indicative of variations revealed at respective landmark points in the set of landmark points, applying processing to the set of variation signals, the processing comprising artificial neural network processing to produce a reconstructed PhotoPletysmoGraphy (PPG) signal, and estimating a heart rate variability of a variable heart rate of the human as a function of the reconstructed PPG signal.
Abstract:
A method includes receiving a time series of slice images of medical imaging. The images have a region of interest located at a lung lesion. The method also includes tracking over at least one subset of slice images in a time series of slice images variations over time of at least one image parameter at the set of points in the region of interest. Classifier processing is applied to set of signals indicative of tracked time variations of the at least one image parameter at respective points in the set of points. A classification signal is indicative of the tracked time variations of the at least one image parameter reaching or failing to reach at least one classification threshold.
Abstract:
A microreactor includes: a substrate (2; 102; 202) made of semiconductor material; a plurality of wells (5; 105; 205) separated by walls (6; 106; 206) in the substrate (2; 102; 202); a dielectric structure (7; 107; 207a, 207b) coating at least the top of the walls (6; 106; 206); a cap (3; 103; 203), bonded to the substrate (2; 102; 202) and defining a chamber (10; 110; 210) above the wells (5; 105; 205); and a biasing structure (2, 8, 13; 102, 108, 113; 202, 208a, 208b, 213), configured for setting up a voltage (VB) between the substrate (2; 102; 202) and the chamber (10; 110; 210).
Abstract:
Blood pressure signals are reconstructed from PhotoPlethysmoGraphy (PPG) signals by: receiving PPG signals including systolic, diastolic and dicrotic phases; and determining first and second derivatives of the PPG signals and: a first set of values indicative of lengths of the signal paths of the PPG signal, the first derivative and the second derivative thereof in the systolic, diastolic and dicrotic phases; a second set of values indicative of relative durations of the PPG signal and the first and second derivatives thereof in the systolic, diastolic and dicrotic phases; and a third set of values indicative of the time separation of peaks and/or valleys in subsequent waveforms of the PPG signal. Reconstruction also includes applying artificial neural network processing to the first, second and third set of values. The artificial neural network processing includes artificial neural network training as a function of blood pressure signals to produce reconstructed blood pressure signals.
Abstract:
An embodiment method includes segmenting at least one electrophysiological signal and producing a set of sampled waveforms, applying artificial neural network processing to the set of sampled waveforms and a set of randomly generated noise samples and producing at least one altered data pattern, the altered data pattern comprising the set of filtered waveforms altered as a function of the randomly generated noise samples, providing calibration data comprising expected waveforms for filtered waveforms in the set of filtered waveforms, applying classifier processing to the produced at least one altered data pattern to detect a degree of resemblance between the produced at least one altered data pattern and the calibration data patterns, the classifier processing producing classification signals having values above or below at least one threshold value as a function of the detected degree of resemblance, and triggering a user circuit as a function of the classification signal.