Systems and methods for detecting cardiac arrhythmia are discussed. An exemplary medical-device system includes an arrhythmia detector circuit that receives physiologic information, including respiration and heart beat information a patient, and determines whether a respiratory sinus arrhythmia (RSA) is present or absent using the respiration and the heart beat information. An indication of the presence or absence of RSA may be stored in a memory. The arrhythmia detector circuit can detect an AT episode using the indication of RSA.

In one embodiment, a medical data processing apparatus includes processing circuitry. The processing circuitry obtains medical data relating to a subject, and outputs medical diagnostic image data obtained by performing predetermined processing on the medical data, along with standardized medical image data based on the medical data, the standardized medical image data being standardized for machine learning without performing part or all of the predetermined processing.

An electromyography (EMG) device according to an aspect of the present disclosure includes a main circuit board having opposing first and second faces. A plurality of first connectors of a first type are provided on the first face, and a plurality of input contacts are provided on the second face. An EMG circuit is provided on the main circuit board. The EMG circuit is configured to utilize the input contacts as inputs to obtain an EMG input signal, and process the EMG input signal to provide an EMG output signal that is based on, but different from, the EMG input signal. For each of the input contacts, there is no conductive path directly between the input contact and any of the first connectors.

Systems, methods, apparatuses, and computer program products for contact-free heart rate monitoring and/or measurement are provided. One method may include receiving video(s) that include visual frame(s) of individual s) performing exercises, detecting some exposed skin from the video(s), and performing motion compensation to generate color signals for the exposed skin to precisely align frames of the exposed skin. The method may also include generating the color signals by estimating a skin color for each frame by taking a spatial average over pixels of a cheek of the face(s) for R, G, and B channels, respectively, applying an operation to remove remaining motion traces from the frames such that the heart rate traces dominate, and extracting and/or outputting the heart rate of the individuals using a frequency estimator of the skin color signals.

Systems, devices, and methods are disclosed for characterizing joint attention. A method includes dynamically obtaining video streams of participants; dynamically obtaining gaze streams; dynamically providing a cue to the participants to view the object; dynamically detecting a joint gaze based on the gaze streams focusing on the object over a time interval; and dynamically providing feedback based on detecting the joint gaze.

Disclosed is a natural movement electroencephalogram (EEG) recognition method based on source localization and a brain network, which includes the following steps: (1) performing multi-channel EEG measurement for natural movements; (2) preprocessing acquired EEG signals, and extracting the movement-related cortical potential (MRCP), and θ, α, β, and γ rhythms; (3) determining a lead field matrix of the signals, calculating initial solutions of sources by means of L1 regularization constraint, and then performing iteration by means of successive over-relaxation to obtain a source localization result; (4) by using the sources as nodes, calculating PLV between each pair of sources at each time point by means of short-time sliding window, and establishing brain networks; and (5) calculating a network adjacency matrix at each time point and five brain network indicators, introducing these features into a classifier for training and testing, and conducting a statistical test for the brain network indicators. The present disclosure makes improvements to the conventional source localization method by using the T-wMNE algorithm in combination with successive over-relaxation, and establishes brain networks by using the sources as nodes, thus improving the EEG decoding accuracy for natural movements and revealing the neural mechanism of the human body.

This disclosure relates generally to patient invariant model for freezing of gait detection based on empirical wavelet decomposition. The method receives a motion data from an accelerometer sensor coupled to an ankle of a subject. The motion data is further processed to denoise a plurality of data windows using a peak detection technique to classify into a real motion data window or a noisy data window. Further, a plurality of denoised data windows are generated by processing spectrums associated with each real motion data window and a plurality of empirical modes using an empirical wavelet decomposition technique (EWT). Then, a resultant acceleration is computed, and a plurality of features are extracted from the denoised data window which enables detection of freezing of gait based on a pretrained classifier model into a (i) a positive class, or (ii) a negative class.

An inertial sensor based surgical navigation system for knee replacement surgery is disclosed. Inertial sensors composed of six-degree-of-freedom inertial chips, whose measurements are processed through a series of integration, quaternion, and kalman filter algorithms, are used to track the position and orientation of bones and surgical instruments. The system registers anatomically significant geometry, calculates joint centers and the mechanical axis of the knee, develops a visualization of the lower extremity that moves in real time, assists in the intra-operative planning of surgical cuts, determines the optimal cutting planes for cut guides and the optimal prosthesis position and orientation, and finally navigates the cut guides and the prosthesis to their optimal positions and orientations using a graphical user interface.

The present disclosure provides a method for determining preference implemented by a processor, comprising: providing image content to a user; receiving electroencephalogram (EEG) data and gaze data including a series of gaze position data or gaze speed data which is measured while the content is provided; determining the user's region of interest with respect to the content based on the gaze data; determining a saccade onset time based on the gaze data; extracting EEG data during a time period including the saccade onset time, based on the EEG data; and determining whether the user prefers the region of interest based on the EEG data during the time period, and a device for determining preference using the same.

An apparatus for estimating bio-information includes a pulse wave sensor configured to measure a pulse wave signal from an object, for a predetermined period of time, a processor configured to extract DC components of the pulse wave signal measured for the predetermined period of time, normalize the extracted DC components, based on at least one of the extracted DC components of the pulse wave signal measured at a time when a reference force is applied by the object to the pulse wave sensor, and estimate the bio-information, based on the normalized DC components.