A device and method are provided for real-time classification of covert speech. The device comprises a plurality of sensors for capturing real-time bio-signal data for brain monitoring in response to mental tasks delivered to a user, and a brain computer interface with memory storing instructions to configure a processor to perform a method of real-time classification of covert speech. The method comprises capturing real-time bio-signal data for brain monitoring in response to mental tasks delivered to a user, pre-processing the raw bio-signal data, extracting a vector of features from the raw bio-signal data, selecting features from the vector of features, building classification model to generate classified covert speech data using the selected features, and controlling a display device with visual elements based on the classified covert speech data.

A personal protection device includes one or two elongate members for engaging into the nostrils of the nose of a user. The elongate members each includes gridded ports for allowing air to pass through the elongate member when breathing in and for allowing air to exit via the elongate member when breathing out. Sensors are placed in the air flow pathway to detect pathogen presence, and the primary air filter is positioned in the elongate member. In yet other embodiments, a disposable version is disclosed. The eyewear can also be used as part of an extended reality system, and when game playing, particularized scents can be rendered to the nose to enhance the gaming experience. In another aspect, systems and methods protect against pathogen by sampling an environment of a travel path with a plurality of pathogen detectors along the travel path to detect a presence of one or more pathogens, wherein at least one detector includes a nano-sensor with receptacles to bind to the pathogens and wherein the nano-sensor changes resistivity, inductance or capacitance upon pathogen binding; directing air towards said pathogen detectors; contact tracing a user mobile device having a mobile identification (ID) carried by each user, wherein the mobile device comprises a memory storing mobile IDs of all devices within a predetermined radius of the user mobile device; and performing deep learning with a neural network receiving data from the pathogen detectors and to the user mobile device to detect a presence of one or more pathogens.

Devices and methods to mitigate the erroneous signal imparted by physical and/or chemical process incident upon analyte-selective electrochemical sensors that are non-analyte-related in origin are disclosed herein. A sensing system featuring at least one of an analyte-selective sensor and at least one of an analyte-invariant sensor.

The present disclosure facilitates the evaluation of wide-band phase gradient information of the heart tissue to assess, e.g., the presence of heart ischemic heart disease. Notably, the present disclosure provides an improved and efficient method to identify and risk stratify coronary stenosis of the heart using a high resolution and wide-band cardiac gradient obtained from the patient. The patient data are derived from the cardiac gradient waveforms across one or more leads, in some embodiments, resulting in high-dimensional data and long cardiac gradient records that exhibit complex nonlinear variability. Space-time analysis, via numeric wavelet operators, is used to study the morphology of the cardiac gradient data as a phase space dataset by extracting dynamical and geometrical properties from the phase space dataset.

One or more embodiments relate to a bio-signal measurement apparatus and a bio-signal measurement method for detecting peaks and a computer program for executing the method. Sensed ECG signal is wavelet transformed into one or more levels, and an effective peak of a bio-signal is detected using a complexity value of a signal surrounding the peak in the converted signals.

Pulmonary vein isolation has become a first-line treatment for symptomatic drug refractory atrial fibrillation (AF). In the context of PVI procedures, linear ablation lesions are delivered in order to achieve PV isolation. Electrophysiological maps from data collected by high density (HD) grid catheters can be used to identify conduction gaps associated within circumferential pulmonary vein isolation lesions.

An electronic device and a method for predicting a blockage of a coronary artery are provided. The method includes: obtaining multiple pieces of electrocardiogram (ECG) data respectively corresponding to a coronary artery set; generating multiple first probabilities corresponding to the multiple pieces of electrocardiogram data respectively according to the multiple pieces of electrocardiogram data and a first phase model, generating a first determined result according to the multiple first probabilities, and selecting a first data subset corresponding to a first probability subset from the multiple pieces of electrocardiogram data in response to each one in the first data subset of the multiple first probabilities being greater than a first threshold; generating multiple second probabilities corresponding to the first data subset according to the first data subset and a second phase model, and generating a second determined result according to the multiple second probabilities.

A subject information acquisition apparatus, comprises: a signal generation unit configured to generate a high-frequency signal corresponding to each of the frequencies; an acquisition unit configured to acquire a plurality of detection signals based on at least one of a reflection signal and a transmission signal; a signal selection unit configured to select at least one detection signal from the plurality of detection signals based on an index value of the plurality of detection signals; a coupling amount detecting unit configured to detect a coupling amount of near-field coupling due to an electric field between the antenna and the subject based on a detection signal; and a displacement detecting unit configured to generate a displacement signal indicating a displacement of the subject based on the coupling amount.

In one embodiment, a system for identifying a biomedical condition of a subject includes apparatus for collecting blood flow sounds from the subject and a computing device that stores computer-executable instructions that are configured to: receive the collected blood flow sounds, extract acoustic heart pulses from the collected blood flow sounds, segment the acoustic heart pulses to obtain acoustic heart pulse segments, compute a continuous time wavelet transform-based feature for each acoustic heart pulse segment, and perform clustering on the computed continuous time wavelet transform-based features to determine whether or not the subject is experiencing the biomedical condition.

A lesion characterization process is disclosed. According to one aspect, a method includes obtaining measurements of at least one of an impedance magnitude, impedance phase, a temperature, and electrical properties of tissue of the lesion. The method further includes determining at least one lesion property including at least one of a depth of the lesion, percent transmurality of the lesion, lesion surface area and lesion volume based on at least one of the obtained measurements.