A method and system for detecting and removing EEG artifacts is disclosed herein. Each source of a plurality of sources for an EEG signal is separated for a selected artifact type. Each source of the plurality of sources is reconstituted into a recorded montage and an optimal reference montage for recognizing the selected artifact type of each source. The sources with artifacts are removed and the remaining sources are reconstituted into a filtered montage for the EEG signal.

A method is provided. The method is implemented by a device orientation engine being executed by one or more processors. The method includes determining a movement between each electrode group of a catheter to provide movements and determining a total movement of electrodes of the catheter. The method also includes removing a standard component from the movements and the total movement and outputting a movement indication for the catheter based on the movements and the total movement with the standard component.

There is provided a heart rate detection device including a sensing unit for sensing emergent light from subcutaneous tissues illuminated by a single light source of multiple light colors to output multiple light detection signals associated with multiple wavelengths. The heart rate detection device further includes a processor uses the multiple light detection signals associated with the multiple wavelengths to cancel motion artifact to obtain a clean heart rate signal.

A physiological activity detection system comprises a signal acquisition module configured for non-invasively acquiring a signal from an anatomical structure of a user, the acquired signal having a physiological-encoded component and a periodic artifact component that dominates the physiological-encoded component. The physiological activity detection system further comprises a phase-locked loop (PLL) component configured for estimating a phase of the periodic artifact component of the acquired signal, and generating a periodic reference signal having a phase representative of the estimated phase of the periodic artifact component of the acquired signal.

A biological information processing apparatus according to an embodiment of the present technology is provided with a sphygmographic sensor unit, a plurality of calculation units, and an output unit. The sphygmographic sensor unit outputs a pulse wave signal. The plurality of calculation units respectively calculate heart rate candidate information with a reliability on a basis of the output pulse wave signal. The output unit outputs heart rate information on a basis of the heart rate candidate information and the reliability thereof calculated by each of the plurality of calculation units.

This document discusses, among other things, systems and methods to produce a composite heart sound signal of a patient using a first signal including heart sound information over a first physiologic interval and a second signal including heart sound information over the first physiologic interval.

An Electrocardiography (ECG) noise-filtering device is provided in the invention. The ECG device includes a filter and a calculation circuit. The filter receives a first ECG signal and performs a Savitzky-Golay algorithm to generate a second ECG signal. The calculation circuit is coupled to the filter to receive the second ECG signal and processes the second ECG signal according to a Stationary Wavelet Transform (SWT) algorithm to generate a noise signal, and subtracts the noise signal from the second ECG signal to filter the noise signal in the first ECG signal.

A computer method for managing, processing and handling sensor signals from an in-ear monitor for immersive reality applications is provided. The method includes receiving, from a first electrode, a first electronic signal from a skin in a first ear canal of a user of an in-ear device, receiving, from a first microphone, a first acoustic signal from the first ear canal of the user of the in-ear monitor, and a second acoustic signal from a second ear canal of the user (binaural data capture), forming an acoustic waveform with the first acoustic signal, forming an electronic waveform with the first electronic signal, and identifying a health condition of the user based on the acoustic waveform and the electronic waveform. A device including a memory storing instructions and processors to execute the instructions to perform the above method are also provided.

A system is provided that includes one or more electrodes configured to be implanted proximate to a sensing site, and a memory configured to store first and second sets of filter parameters that define first and second noise stop bands. The system also includes an implantable medical device (IMD) that has inputs configured to receive sensed signals, the sensed signals include frequency components associated with physiology activity and frequency components associated with noise. The IMD also includes a band-stop filter communicating with the sensing channel inputs. When executing program instructions, a processor switches the band-stop filter from the first set of filter parameters to the second set of filter parameters to shift from the first noise stop band to the second noise stop band based on the noise in the environment of the patient.

A physiological signal feature extraction method includes receiving a physiological signal, determining in a situation that a second pulse in a plurality of pulses of the physiological signal being correlated to a first pulse in the plurality of pulses, selecting the second pulse as one of a plurality of reference pulses in a reference waveform sequence after the second pulse is determined as correlated to the first pulse, averaging the plurality of reference pulses into a template pulse, performing match comparison on the plurality of pulses by the template pulse one by one, and extracting a plurality of features from each of the plurality of pulses that matches the template pulse.