Techniques and devices for implementing the techniques for adjusting atrial arrhythmia detection based on analysis of one or more P-wave sensing windows associated with one or more R-waves. An implantable medical device may determine signal characteristics of the cardiac signal within the P-wave sensing window, determine whether the cardiac signal within the sensing window corresponds to a P-wave based on the determined signal characteristics, determine a signal to noise ratio of the cardiac signal within the sensing window, update the arrhythmia score when the P-wave is identified in the sensing window and the determined signal to noise ratio satisfies a signal to noise threshold.

Techniques and devices for implementing the techniques for adjusting atrial arrhythmia detection based on analysis of one or more P-wave sensing windows associated with one or more R-waves. An implantable medical device may determine signal characteristics of the cardiac signal within the P-wave sensing window, determine whether the cardiac signal within the sensing window corresponds to a P-wave based on the determined signal characteristics, determine a signal to noise ratio of the cardiac signal within the sensing window, update the arrhythmia score when the P-wave is identified in the sensing window and the determined signal to noise ratio satisfies a signal to noise threshold.

Apparatus and methods for monitoring electrical activity within the brain of a person (“brainwaves”) employing electrodes or other sensors placed proximate to portions of the body below the head to develop raw signals without physically touching the body and penetrating hair and clothing. Additionally, apparatus and methods for monitoring electrical activity within the brain of a person (“brainwaves”) employing non-contacting sensors placed proximate to portions of the head to develop raw signals. The raw signals are filtered to produce analysis signals including frequency components relevant to brain electrical activity while attenuating unrelated frequency components. The apparatus and methods can be used for biofeedback-based attention training, human performance training, gaming, biometrics, cognitive state detection, and relaxation training. Either wired or wireless signal connections are made to electronic circuitry, typically including a digital computer, for performing signal processing and analysis functions.

Apparatus and methods for monitoring electrical activity within the brain of a person (“brainwaves”) employing electrodes or other sensors placed proximate to portions of the body below the head to develop raw signals without physically touching the body and penetrating hair and clothing. Additionally, apparatus and methods for monitoring electrical activity within the brain of a person (“brainwaves”) employing non-contacting sensors placed proximate to portions of the head to develop raw signals. The raw signals are filtered to produce analysis signals including frequency components relevant to brain electrical activity while attenuating unrelated frequency components. The apparatus and methods can be used for biofeedback-based attention training, human performance training, gaming, biometrics, cognitive state detection, and relaxation training. Either wired or wireless signal connections are made to electronic circuitry, typically including a digital computer, for performing signal processing and analysis functions.

The present specification describes systems and methods that enable the automatic detection, analysis and calculation of various processes and parameters associated with electromyography. The methods of the present specification include the automated modulation of analytical or recording states based on the nature of the signal, optimal reference fiber selection, modulating a trigger level, and determining firing parameters.

The present specification describes systems and methods that enable the automatic detection, analysis and calculation of various processes and parameters associated with electromyography. The methods of the present specification include the automated modulation of analytical or recording states based on the nature of the signal, optimal reference fiber selection, modulating a trigger level, and determining firing parameters.

A method of processing of electrocardiogram includes the steps of providing an electrocardiogram comprising signals in at least two channels; selecting at least two frequency ranges of the signal in each of the said at least two channels; calculating an envelope for the signal in each frequency range in each channel; dividing the calculated envelope of the signal in each frequency range in each channel into QRS complex segment envelopes; and computing an average or median envelope as an average or mean of QRS complex segment envelopes for each frequency range in each channel.

A method of medical imaging including receiving k-space data that is divided into multiple k-space data groups, selecting one of the multiple k-space data groups as a reference k-space data group, and calculating spatial transform data for each of the multiple k-space data groups by inputting the multiple k-space data groups and the reference k-space data group into a transformation estimation module. The spatial transformation estimation module is configured for outputting spatial transform data descriptive of a spatial transform between a reference k-space data group and multiple k-space data groups in response to receiving the reference k-space data group and the multiple k-space data groups as input. The method further comprises reconstructing a corrected magnetic resonance image according to the magnetic resonance imaging protocol using the multiple k-space data groups and the spatial transform data for each of the multiple k-space data groups.

A computer implemented method and system for detecting premature ventricular contractions (PVCs) are provided. The method is under control of one or more processors configured with specific executable instructions. The method obtains a cycle length (CL) distribution metric that plots a series of cardiac beats into one of a set of transition types based on R-R interval (RRI) difference pairs associated with the cardiac beats. The CL distribution metric plots the cardiac beats based on a comparison between combinations of the RRI difference pairs for corresponding combinations of the cardiac beats. The method calculates a distribution characteristic for the cardiac beats, from the series of cardiac beats that exhibit a first transition type from the set of transition types and calculates a discrimination score based on the distribution characteristic of the cardiac beats across the CL distribution metric. The method designates the CA signals to include a predetermined level of PVC burden based on the discrimination score.

A system (1) for estimating the brain blood volume and/or brain blood flow and/or depth of anesthesia of a patient, comprises at least one excitation electrode (110E) to be placed on the head (20) of a patient (2) for applying an excitation signal, at least one sensing electrode (110S) to be placed on the head (20) of the patient (2) for sensing a measurement signal caused by the excitation signal, and a processor device (12) for processing said measurement signal (VC) sensed by the at least one sensing electrode (110S) for determining an output indicative of the brain blood volume and/or the brain blood flow. Herein, the processor device (12) is constituted to reduce noise in the measurement signal (VC) by applying a non-linear noise-reduction algorithm. In this way a system for estimating the brain blood volume and/or the brain blood flow of a patient is provided which may lead to an increased accuracy and hence more exact estimates.