Abstract: A method of transforming Monte Carlo (MC) simulations for diffuse reflectance spectroscopy (DRS) may include obtaining, by a DRS device, MC simulated DRS measurements using a pre-defined number of photons; pre-processing, by the DRS device, the MC simulated DRS measurements to obtain normalized DRS measurements; correcting, by the DRS device, non-monotonicity of the normalized DRS measurements to obtain monotonic DRS measurements; converting, by the DRS device, the monotonic DRS measurements to a logarithmic domain to obtain logarithmic DRS measurements; performing, by the DRS device, curve fitting on the logarithmic DRS measurements in the logarithmic domain to obtain curve-fitted logarithmic DRS measurements; and transforming, by the DRS device, the curve-fitted logarithmic DRS measurements to a non-logarithmic domain to obtain transformed MC simulated DRS measurements.

Abstract: A method of predicting a blood compound concentration of a target may include receiving, by a system, spectral data associated with a region of the target, using near-infrared (NIR) spectroscopy. The method may include classifying, by the system, each of the plurality of data instances of the spectral data to one of a plurality of labelled classes. The method may include obtaining, by the system, one or more best fit models from a plurality of prediction models based on the classification. The method may include determining, by the system, blood compound concentration values corresponding to each of the one or more best fit models. The method may include predicting, by the system, the blood compound concentration of the target using the blood compound concentration values predicted using the best fit models.

Abstract: A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform functions comprising: receiving a drug profile for a drug identified as part of a high-risk look-alike-sound-alike (LASA) drug pair prior to filling a prescription for the drug; generating an anomaly score based on direction components of the drug profile; and transmitting an alert to a fulfilment screen when the anomaly score exceeds a predetermined threshold. Other embodiments are disclosed.

Abstract: A method and apparatus for pre-processing a photoplethysmogram (PPG) signal include receiving, by an apparatus, the PPG signal including a plurality of pulses, obtaining, by the apparatus, a smoothed PPG signal based on smoothing the PPG signal using a moving average filter, obtaining, by the apparatus, a drift removed PPG signal based on removing drift from the smoothed PPG signal using a fitted polynomial function, and obtaining, by the apparatus, a rectified PPG signal based on correcting a motion artifact of the drift removed PPG signal by replacing each of the plurality of pulses using a combination of a global signal pulse and a corresponding local signal pulse.

Abstract: The present disclosure relates to a method and system for processing a photoplethysmogram (PPG) signal to improve accuracy of measurement of physiological parameters of a subject. The method may include removing a baseline drift from the PPG signal; obtaining a drift removed signal; filtering the drift removed signal; obtaining a filtered signal; performing motion artifact correction on the filtered signal; and obtaining a corrected signal.

Abstract: A method of estimating concentration of a blood compound may include: removing a baseline drift from Near-Infrared (NIR) spectroscopy data to obtain drift-free spectral features; obtaining a set of global features based on the drift-free spectral features; and estimating the concentration of the blood compound by regression using the set of global features.

Abstract: Provided is a method for predicting optical properties of a sample, the method including obtaining, by a device, a plurality of diffuse reflectance values based on optical energy diffusely reflected from the sample, generating, by a multi-layered Deep Fully Connected Neural Network (DFCNN) in the device, a first set of intermediate values by non-linearly mapping the plurality of diffuse reflectance values to the first set of intermediate values, generating, by a One-Dimensional-Convolutional Neural Network (1D-CNN) in the device, a second set of intermediate values by non-linearly mapping the plurality of diffuse reflectance values to the second set of intermediate values, and predicting, by the device, values of the optical properties of the sample based on the first set of intermediate values and the second set of intermediate values.

Abstract: A method for obtaining blood glucose concentration using near infrared spectroscopy (NIR) data is provided. The method includes obtaining, by an independent component analysis (ICA) temporal module, orthogonal pure spectra from human NIR spectra; performing, by a processing module, one or more preprocessings and drift removal on the human NIR spectra and the orthogonal pure spectra to obtain preprocessed spectra; and obtaining, by a regression block, the blood glucose concentration from the preprocessed spectra.

Abstract: A method for obtaining blood glucose concentration using near infrared spectroscopy (NIR) data is provided. The method includes obtaining, by an independent component analysis (ICA) temporal module, orthogonal pure spectra from human NIR spectra; performing, by a processing module, one or more preprocessings and drift removal on the human NIR spectra and the orthogonal pure spectra to obtain preprocessed spectra; and obtaining, by a regression block, the blood glucose concentration from the preprocessed spectra.

Abstract: A method of transforming Monte Carlo (MC) simulations for diffuse reflectance spectroscopy (DRS) may include obtaining, by a DRS device, MC simulated DRS measurements using a pre-defined number of photons; pre-processing, by the DRS device, the MC simulated DRS measurements to obtain normalized DRS measurements; correcting, by the DRS device, non-monotonicity of the normalized DRS measurements to obtain monotonic DRS measurements; converting, by the DRS device, the monotonic DRS measurements to a logarithmic domain to obtain logarithmic DRS measurements; performing, by the DRS device, curve fitting on the logarithmic DRS measurements in the logarithmic domain to obtain curve-fitted logarithmic DRS measurements; and transforming, by the DRS device, the curve-fitted logarithmic DRS measurements to a non-logarithmic domain to obtain transformed MC simulated DRS measurements.

Abstract: A method includes processing near infrared spectroscopy (NIR) spectra and pure spectra, to obtain a preprocessed NIR spectra, wherein the preprocessed NIR spectra includes any one or any combination of a training data of a plurality of subjects, a calibration data of a test subject and a validation data of the test subject, extracting a dominant feature set from the preprocessed NIR spectra, wherein the dominant feature set includes at least one preprocessed NIR spectrum corresponding to each of the training data, the calibration data and the validation data, and determining a blood glucose concentration of the test subject, using the training data of the plurality of subjects and based on the extracted dominant feature set.

Abstract: A method of predicting a blood compound concentration of a target may include receiving, by a system, spectral data associated with a region of the target, using near-infrared (NIR) spectroscopy. The method may include classifying, by the system, each of the plurality of data instances of the spectral data to one of a plurality of labelled classes. The method may include obtaining, by the system, one or more best fit models from a plurality of prediction models based on the classification. The method may include determining, by the system, blood compound concentration values corresponding to each of the one or more best fit models. The method may include predicting, by the system, the blood compound concentration of the target using the blood compound concentration values predicted using the best fit models.

Abstract: A method of extracting glucose feature, a method for monitoring glucose using near-infrared (NIR) spectroscopy and a glucose monitoring device are provided. The method comprising: removing noise from a near-infrared (NIR) data; extracting a glucose signal from the NIR data; and removing temporal drift components from the extracted glucose signal.

Abstract: A method for predicting a blood glucose level using a near-Infrared (NIR) spectrometer is provided. The method may include obtaining a feature set from an NIR glucose spectra; and predicting glucose values from the feature set based on a binary classification of the NIR glucose spectra and an in-class prediction of glucose using Machine Learning Regression.

Abstract: A method to detect a packet includes: receiving an input sequence including preambles; detecting a transition from a noise period to a signal period in the input sequence; dynamically adjusting a gain of the input sequence in response to the signal period being initiated; and distinguishing an intended packet from other packets, among packets received in the preambles.

Abstract: The present disclosure relates to a method and system for preprocessing near infrared (NIR) spectroscopy data for non-invasive monitoring of blood glucose. In accordance with an embodiment, the method receiving the NIR spectroscopy data from a subject; performing a scatter correction on the NIR spectroscopy data to obtain scatter corrected NIR spectra; removing interference from the scatter corrected NIR spectra to obtain glucose spectra; removing noise from the glucose spectra to obtain noise removed glucose spectra; obtaining noise removed NIR glucose data as a set of noise removed glucose spectra corresponding to a plurality of reference glucose values; removing drift from the noise removed NIR glucose data to obtain preprocessed NIR glucose data; and obtaining a set of global features from the preprocessed NIR glucose data for non-invasive monitoring of blood glucose of the subject.

Abstract: The present disclosure relates to a method and system for processing a photoplethysmogram (PPG) signal to improve accuracy of measurement of physiological parameters of a subject. The method may include removing a baseline drift from the PPG signal; obtaining a drift removed signal; filtering the drift removed signal; obtaining a filtered signal; performing motion artifact correction on the filtered signal; and obtaining a corrected signal.

Abstract: A method and a transmitter for transmitting a pay load sequence are provided. The transmitter includes a ternary sequence mapper configured to map a binary data sequence to a ternary sequence stored in the transmitter, and a pulse shaping filter configured to generate a first signal based on the mapped ternary sequence. The ternary sequence includes elements of ?1, 0, and 1.

Abstract: A method and apparatus for pre-processing a photoplethysmogram (PPG) signal include receiving, by an apparatus, the PPG signal including a plurality of pulses, obtaining, by the apparatus, a smoothed PPG signal based on smoothing the PPG signal using a moving average filter, obtaining, by the apparatus, a drift removed PPG signal based on removing drift from the smoothed PPG signal using a fitted polynomial function, and obtaining, by the apparatus, a rectified PPG signal based on correcting a motion artifact of the drift removed PPG signal by replacing each of the plurality of pulses using a combination of a global signal pulse and a corresponding local signal pulse.

Abstract: A method to detect a packet includes: receiving an input sequence including preambles; detecting a transition from a noise period to a signal period in the input sequence; dynamically adjusting a gain of the input sequence in response to the signal period being initiated; and distinguishing an intended packet from other packets, among packets received in the preambles.