126 results about "Photoplethysmogram" patented technology

In one implementation, a device detects multiple vital signs from sensors such as a digital infrared sensor, a photoplethysmogram (PPG) sensor and at least one micro dynamic light scattering (mDLS) sensor, and thereafter in some implementations the vital signs are transmitted to, and stored by, an electronic medical record system.

A photoplethysmogram (PPG) signal may be obtained from a pulse oximeter, which employs a light emitter and a light sensor to measure the perfusion of blood to the skin of a user. However, the signal may be compromised by noise due to motion artifacts. That is, movement of the body of a user may cause the skin and vasculature to expand and contract, introducing noise to the signal. To address the presence of motion artifacts, examples of the present disclosure can receive light information from two light sensors situated in a line parallel to the direction of the blood pulse wave. The light information from each sensor may include the same noise signal, and thus subtracting one from the other can result in a heart rate signal where the noise has been canceled out. In some examples, a signal from one of the light sensors may be multiplied by a scaling factor before cancellation to account for response differences in each light sensor.

A photoplethysmogram (PPG) signal may be obtained from a pulse oximeter, which employs a light emitter and a light sensor to measure the perfusion of blood to the skin of a user, and multiple wavelengths of light may be employed. For various wavelengths, relatively long wavelengths may interrogate relatively deep blood vessels in comparison to relatively short wavelengths, which may interrogate relatively shallow blood vessels. Accordingly, for co-located emitters of different wavelengths, there may be a time delay in the pulse signal measured by each wavelength. The time delay as a function of time may vary according to the constriction and dilation of the blood vessels, which itself may vary according to the respiratory rate of a user.

A system that detects heartbeats includes a sensor or a transducer and algorithms based on deep learning. The algorithms employ techniques of artificial intelligence that enable the system to extract heartbeat features under low signal-to-noise-ratio (SNR) conditions when a user is exercising. The algorithms can be applied to various technologies for heart rate monitoring such as ultrasound Doppler, photoplethysmogram (PPG), electrocardiogram (EKG), acoustic, pressure / force sensing and laser / RF Doppler, among other types of sensing methods.

A method and system for blood pressure (BP) estimation of a person is provided. The system is estimating pulse transit time (PTT) using the ECG signal and PPG signal of the person. A plurality of features are extracted from the PPG. The plurality of PPG features and the PTT are provided as inputs to an automated feature selection algorithm. This algorithm selects a set of features suitable for BP estimation. The selected features are fed to a classifier to classify the database into low / normal BP range and a high BP range. The correctly classified normal BP data are then used to create a regression model to predict BP from the selected features. The current methodology uses automated feature selection mechanism and also employs a block to reject extreme BP data. Thus the available accuracy in predicting BP is expected to be more than the existing BP estimation methods.

A system is described for providing the value of a biological characteristic. The system includes a sensor. The sensor is configured to generate a time domain photoplethysmogram (PPG) input signal. The system further includes a first computation module coupled to the sensor. The first computation module is configured to evaluate portions of the generated time domain PPG signal against predetermined quality criteria. The system further includes a second computation module coupled to the first computation module. The second computation module is configured to use portions of the generated time domain PPG signal that meet the predetermined quality criteria to generate an output value. The output value characterizes a biological characteristic at a point in time.

The invention relates to a real-time blood pressure monitoring system and method based on ballistocardiogram and photoelectrical signals. The system comprises a PC (personal computer), a bed, a photoplethysmogram acquisition device, a ballistocardiogram acquisition device and a synchronous signal acquirer; the photoplethysmogram acquisition device is a PPG (photoplethysmogram) sensor used for acquiring PPG from a testee and mounted at the testee's fingertip; the ballistocardiogram acquisition device is an acceleration sensor that is mounted on a cross beam of the bed; a signal acquiring direction of the acceleration sensor is parallel to the spine; accelerated vibration signals of a nursing bed due to ballistic shock can be acquired with no binding; both the PPG sensor and the accelerationsensor are connected to the PC through the synchronous signal acquirer; monitoring results are output from a display screen of the PC; the synchronous signal acquirer processes the two signals by means of external triggering. The system has the advantages of good timeliness, goo continuity, zero invasion and the like.

An electronic device includes housing, a user interface disposed in a first part of the housing, a photoplethysmogram (PPG) sensor disposed to be exposed through a second part of the housing, the PPG sensor configured to calculate a blood pressure value while contacting a portion of a body, at least one sensor, a wireless communication circuit disposed in the interior of the housing, a processor disposed in the interior of the housing, and operatively connected to the user interface, the PPG sensor, the at least one sensor, and the wireless communication circuit, and a memory operatively connected to the processor, wherein the memory stores instructions that, when executed by the processor, control the electronic device to: receive first data from the at least one sensor, receive second data from the PPG sensor based at least in part on the received first data, determine a pulse arrival time (PAT) value, a heart rate (HR) value, and a pulse transit time (PTT) value from the second data, calculate a first blood pressure value (BP1) and a second blood pressure value (BP2) by applying the determined values to pulse wave velocity (PWV) algorithms of Equations 1 and 2, wherein BP1≅a1PAT+b1HR+c1 . . . Equation 1, BP2≅a2 ln(PTT)+b2 . . . Equation 2 in Equations 1 and 2, a1, a2, b1, b2, and c1 are constant values for matching blood pressure values measured during calibration with blood pressure values measured by a cuff hemodynamometer, determine a calibration time point based at least in part on a difference between the first blood pressure value and the second blood pressure value, and provide guide information related to the calibration time point through the user interface based at least in part on the determination.

The invention discloses a motion noise detection method based on a photoplethysmography signal, which lays a foundation for the follow-up work of heart rate measurement. In this method, multiple photoplethysmogram signals and acceleration signals in the same time period are collected by reflective photoelectric sensors and motion sensors; the acceleration signals are processed by principal component analysis to generate reference signals related to motion noise, and combined with The least mean square adaptive filter eliminates part of the motion noise; then multiple photoplethysmography signals and acceleration signals are processed to form a spectrum matrix, and the sparse structural features of the spectrum matrix rows are extracted to construct a sparse signal reconstruction model; finally, the regularization algorithm is used to Optimize the sparse signal reconstruction model to obtain the spectral peak positions of motion noise in the spectrum of multiple photoplethysmography signals. The invention can accurately detect the motion noise in the photoplethysmography signal, and realize the high-precision measurement of the heart rate.

The present disclosure relates, in some embodiments, to devices, systems, and / or methods for collecting, processing, and / or displaying stroke volume and / or cardiac output data. For example, a device for assessing changes in cardiac output and / or stroke volume of a subject receiving airway support may comprise a processor; an airway sensor in communication with the processor, wherein the airway sensor is configured and arranged to sense pressure in the subject's airway, lungs, and / or intrapleural space over time; a blood volume sensor in communication with the processor, wherein the blood volume sensor is configured and arranged to sense pulsatile volume of blood in a tissue of the subject over time; and a display configured and arranged to display a representative of an airway pressure, a pulsatile blood volume, a photoplethysmogram, a photoplethysmogram ratio, the determined cardiac output and / or stroke volume, or combinations thereof. A method of assessing changes in cardiac output or stroke volume of a subject receiving airway support from a breathing assistance system may comprise sensing pressure in the subject's airway as a function of time, sensing pulsatile volume of blood in a tissue of the subject as a function of time, producing a photoplethysmogram from the sensed pulsatile volume, determining the ratio of the amplitude of the photoplethysmogram during inhalation to the amplitude of the photoplethysmogram during exhalation, and determining the change in cardiac output or stroke volume of the subject using the determined ratio.

In one implementation, a multi-vital-sign smartphone system detects multiple vital signs from sensors such as a digital infrared sensor, a photoplethysmogram (PPG) sensor and at least one micro dynamic light scattering (mDLS) sensor, and thereafter in some implementations the vital signs are transmitted to, and stored by, an electronic medical record system.

In one implementation, a multi-vital-sign smartphone system detects multiple vital signs from sensors such as a digital infrared sensor, a photoplethysmogram (PPG) sensor and at least one micro dynamic light scattering (mDLS) sensor, and thereafter in some implementations the vital signs are transmitted to, and stored by, an electronic medical record system.