80 results about "Biological signal processing" patented technology

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.

A wearable action-assist device for assisting the wearer to act or doing an action for the wearer (1) comprises an action-assist wearable tool (2) having an actuator (201) to give the wearer a mechanical force, a biosignal sensor (221) for detecting a biosignal of the wearer (1), a biosignal processing means (3) for obtaining, from the biosignal (a) detected by the biosignal sensor, a neurotransmitting signal (b) to move the musculoskeletal system of the wearer (1) and a muscle potential signal (c) generated when a muscle activity occurs , a voluntary control means (4) for generating, using the obtained neurotransmitting signal (b) and muscle potential signal (c), a command signal (d) to generate a mechanical force reflecting the intention of the wearer (1) in the actuator (201), and a drive current generating means (5) for producing, according to the command signal (d), currents corresponding to the neurotransmission signal (b) and the muscle potential signal (c) respectively and supplying them to the actuator (201).

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.

According to an embodiment of the present disclosure, there is a wearable electronic device, comprising: a first sensor configured to sense a movement of the electronic device; a second sensor configured to sense a biological signal for a user wearing the electronic device; and a processor configured to compute a movement value of the electronic device using the first sensor, to detect a resting state when the movement value lasts within a predetermined first threshold range during a first time period, and to configure biological information of the user based on a biological signal measured after detection of the resting state.

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.

A biological activity detection apparatus includes: a drive unit that actively or passively drives in conjunction with a limb motion of a subject; a periarticular detection unit that detects a periarticular physical quantity around a joint in association with the limb motion of the subject based on an output signal from the drive unit; a biological signal detection unit located at a body surface site of the subject with reference to the joint and includes an electrode group for detecting a biological signal of the subject, wherein an activity status of a musculoskeletal system and / or a transmission status of a peripheral nervous system around the joint are converted into quantitative musculoskeletal system data and / or nervous system data on the basis of the physical quantity acquired from the periarticular detection unit and a myogenic potential signal and a neural transmission signal which are acquired from the biological signal processing unit.

Biosignal processing method and apparatus are provided. The biosignal processing method includes: detecting a biosignal, which is generated by a movement of a heart existing in a second area of a subject, from a first area of the subject; generating of a biosignal waveform from the biosignal; determining a relative position of the first area with respect to the second area based on at least one of the biosignal waveform and a direction of the first area; and converting the biosignal waveform to a reference biosignal waveform based on the relative position.

An exemplary electronic device is in a housing to be gripped by both hands, and measures the potentials of first and second fingers of respectively different hands. The device includes: a first electrode group provided in a position to come in contact with the first finger, including a first main electrode and at least one first auxiliary electrode provided in a position away from the first main electrode; a second electrode group provided in a position to come in contact with the second finger, including a second main electrode and at least one second auxiliary electrode provided in a position away from the second main electrode; a biological signal processor for, from potential values measured at the first and second electrode groups, determining respective contact states concerning the first and second fingers; and a transmission circuit for presenting information concerning a finger contact state based on a result of determination.

The invention discloses an R wave detection algorithm based on extremum field mean mode decomposition and improved Hilbert enveloping and belongs to the technical field of weak biological signal processing. An electrocardio signal pre-processing algorithm based on the extremum field mean mode decomposition and the improved Hilbert enveloping and an R wave detection algorithm based on slope threshold are provided. Detection criteria are set according to wave form characteristics and time domain distribution characters of electrocardio signals, and positions of R waves with most obvious characters and highest information amount in the electrocardio signals are detected. An extremum field mean mode decomposition algorithm improves empirical mode decomposition speed and can effectively restrain mode superimposition and boundary effect. The improved Hilbert enveloping can effectively restrain interference of noise and other characteristic waves an can also enhance energy of the R waves. The R wave detection algorithm based on the extremum field mean mode decomposition and the improved Hilbert enveloping can also detect positions of R points accurately even if interference of strong noise and large P / T waves exists. A Massachusetts institute of technology-Beth Israel hospital (MIT-BIH) data base is used for detecting the R wave detection algorithm. Sensitivity of the R wave detection algorithm is 99.94%, and positive predictive rate is 99.87%.

The invention relates to a method and system for obtaining a refined biosignal, typically an electrocardiogram (ECG), of a living being located in a turbulent electromagnetic environment. Reference data indicative of statistical properties of biosignal artifacts generated by the turbulent electromagnetic environment is collected and multiple channels of the biosignal of the patient are measured in the turbulent electromagnetic environment. Artifacts in one or more of the multiple channels are detected using the reference data and parameters are derived for a linear combination of the multiple channels, which is less prone to the artifacts than the measured multiple channels of the biosignal. The refined biosignal is obtained by applying a linear combination to desired signal samples of the multiple channels, the linear combination being defined by the parameters derived.

The present invention discloses an ultrasonic diagnosis and treatment method and system. The system comprises a plurality of biological signal processing devices, an ultrasonic wave transmitting device and a central processing device. The biological signal processing devices calculate the difference between the biological signals of a positioning target before and after the excitation of difference frequency ultrasound, generates a first index change amount, and calculate the difference between the biological signals of a positioning target before the excitation of the difference frequency ultrasound and after ablation of differential frequency ultrasound stimulation, and generates a second index change amount. The central processing apparatus transmits corresponding instructions to the ultrasonic wave transmitting device based on a comparison result of the first index change amount and a first preset index change amount as well as a comparison result of the second index change amount and a second preset index change amount, wherein the instructions include a target positioning instruction and a difference frequency ultrasound stimulation instruction, the same frequency ultrasound ablation instruction or a difference frequency ultrasound ablation instruction to achieve the diagnosis of a positioning target and the evaluation of ablation effect for the integration and automation of diagnosis and treatment.

An apparatus for use in connection with biosignal measurement from the skin of a living object, comprising means for applying a test input impedance to the biosignal measurement, means for determining a response of a biosignal characteristics to the test input impedance, and means for setting a usage input impedance on the basis of the response.

The invention is applicable to the field of biological signal processing, and provides an electrocardiographic scatter point display method and device. The electrocardiographic scatter point display method comprises: respectively acquiring data characteristics and a heartbeat type of each QRS wave group in an electrocardiographic signal; determining a layer corresponding to the heartbeat type in an electrocardiographic scatter point diagram; calculating a position point of the QRS group in the layer according to the data characteristics, and generating an electrocardiographic scatter point corresponding to the QRS group at the position point; acquiring a heart rate ratio of the position point, and calculating the color brightness based on the heart rate ratio; and rendering a color to theelectrocardiographic scatter point of the position point according to the color brightness. The invention reflects the overall signs of different heartbeat type electrocardiographic scatter points onthe basis of the degree of scatter point colors and different layers, can avoid the problem that the electrocardiographic scatter point display mode is too single and the interaction performance is poor, can ensure that in the case of a large number of electrocardiographic scatter points, a user does not appear visually confusing, can enable the user to quickly identify the QRS wave group of theabnormal rhythm from the electrocardiographic scatter point diagram, thereby improving the diagnostic efficiency.

A portable biological signal measurement / transmission system includes: a body unit; and at least one biological signal processing unit detachably connected to the body unit, and including a signal processor which processes a biological signal, the biological signal processing unit including a first transmitter which transmits the biological signal to the body unit when the biological signal processing unit is connected to the body unit.

According to an embodiment of the present disclosure, there is a wearable electronic device, comprising: a first sensor configured to sense a movement of the electronic device; a second sensor configured to sense a biological signal for a user wearing the electronic device; and a processor configured to compute a movement value of the electronic device using the first sensor, to detect a resting state when the movement value lasts within a predetermined first threshold range during a first time period, and to configure biological information of the user based on a biological signal measured after detection of the resting state.

A method processes a signal by storing a template sequence composed of a number N of consecutive digital samples of the signal; calculating a sequence of extended sums that includes, for each digital sample, an extended sum of absolute values of differences between N most recent digital samples and the samples of the template sequence; detecting minima of the sequence of extended sums; and estimating a period of the signal as a time interval between two consecutive minima of the sequence of extended sums. The method also estimates the noise level of the signal as a ratio between most recently-detected minimum and maximum of said sequence of extended sums. The method also generates a reduced-noise replica of the signal as a weighted average of the template sequence and of the current samples of the signal processed between two consecutive minima of said extended sum, the weighted average being calculated using weights based on the estimated noise level.

Earphones comprise a first earphone for collecting a first biosignal and a second earphone for collecting a second biosignal. The second earphone is wirelessly connected to the first earphone and configured to quantize the second biosignal into a second digital signal and transmit the second digital signal to the first earphone together with a set of marker bits representing a time at which the second biosignal is collected. The first earphone includes a biosignal processor configured to quantize the first biosignal into a first digital signal and synchronize the first and second digital signals with reference to the set of marker bits to generate an electrocardiogram (ECG) signal; and a transmitter configured to transmit the ECG signal to a digital device wirelessly connected to the first earphone.

Provided are a biosignal processing apparatus and a biosignal processing method. The biosignal processing apparatus includes: a first measurement module configured to detect a biosignal of a subject in a non-invasive manner in response to the biosignal processing apparatus being in contact with the subject; a second measurement module configured to measure a contact state with respect to the subject; and a controller configured to determine whether the contact state is equal to or greater than a reference value.

A secondary battery built in a bio-signal measuring instrument (a wearable biosensor) is charged without using a dedicated charging terminal. A bio-signal measuring device includes: a battery as an internal power source and a charging circuit for the battery; electrodes which are brought into contact with the skin surface of the human body at the time of bio-signal measurement and are connected to a predetermined power feeder at the time of charging of the battery; a bio-signal processing circuit which processes bio-signals detected at the electrodes in a predetermined manner; and a bio-signal and feed power distribution device, in which the bio-signal processing circuit and the charging circuit are switchably connected to the electrodes through the bio-signal and feed power distribution device, and at the time of the charging of the battery, the electrodes are used as charging terminals.

The invention provides a bio-signal quality assessment apparatus and a bio-signal quality assessment method. The bio-signal quality assessment apparatus may include: a bio-signal obtainer configured to obtain a bio-signal; and a processor configured to extract periodic signals from the obtained bio-signal, and determine a signal quality index based on at least one of similarity between the extracted periodic signals and signal variability of the obtained bio-signal.

A biosignal processing apparatus includes a communication interface configured to receive a biosignal, and a processor configured to set a target interval of the biosignal, calculate a quality metric corresponding to the target interval based on a target component that is a frequency component of the target interval corresponding to a set value and a non-target component that is a frequency component of the target interval not corresponding to the set value, and estimate a quality of the biosignal based on the quality metric.

A computer-implemented method for biological signal recording, including modulating a sampled evoked biological signal with a carrier sequence code resulting in a modulated evoked biological signal. The carrier sequence code has an autocorrelation function. The method includes demodulating the modulated evoked biological signal by calculating a convolution of the modulated evoked biological signal with the carrier sequence code resulting in an evoked biological signal spectrum. The evoked biological signal spectrum has a peak to sideband ratio as a function of the carrier sequence code. The method includes calculating deviations between each element of the sampled evoked biological signal and the peak to sideband ratio and filtering noise artifacts from the sampled evoked biological signal based on the deviations. Peak to sideband ratios may also be optimized by varying the sampling rate.

The invention discloses an electroencephalogram signal analysis method based on a complex network and an application, and particularly relates to the technical field of biological signal processing. The method specifically comprises the following steps of 1, collecting signals; 2, constructing an electroencephalogram signal network; 3, performing quantitative analysis; 4, performing matching and tracking; 5, carrying out wavelet transformation; and step 6, carrying out data analysis. According to the method, resolution analysis is carried out on signal data of which local features are obviousbut relatively difficult to process in a time domain and a frequency domain, a rapid instantaneous phenomenon displayed by the data can be identified more accurately and effectively, the original complex electroencephalogram network can be extracted rapidly and effectively by using multi-scale analysis, and the electroencephalogram signals needed by researchers are extracted to cope with the phenomenon that the stimulation occurrence time cannot be known accurately but the real-time electroencephalogram signals need to be captured.

An apparatus and method for detecting a bio-signal feature are provided. The apparatus according to one aspect may include: a bio-signal acquirer configured to acquire a bio-signal; and a processor configured to generate an envelope signal of the bio-signal, and detect at least one feature of the bio-signal based on a difference between the envelope signal and the bio-signal.