126 results about "Photoplethysmogram" patented technology

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 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 touch-type blood pressure measurement apparatus is provided. The touch-type blood pressure measurement apparatus include: a touch sensor configured to generate a contact area signal when a finger of a user is in contact with the touch sensor; at least one photoplethysmogram (PPG) sensor configured to generate a PPG signal of the user while the finger is in contact with the touch sensor; a force sensor configured to generate a touch force signal of the finger in contact with the touch sensor; and a controller configured to obtain a blood pressure of the user based on the contact area signal, the PPG signal, and the touch force signal.

Methods and systems for detecting internal hemorrhaging in a person are provided. In an exemplary embodiment, one method includes the steps of measuring physiological conditions associated with the person and processing the measured physiological conditions using a probabilistic network to determine if the person has internal hemorrhaging. The method also includes the steps of determining the severity of any internal hemorrhaging by determining the amount of blood lost by the person and classifying this loss as non-specific, mild, moderate, and severe. The physiological measurements include an electrocardiogram, a photoplethysmogram, and oxygen saturation, respiratory, skin temperature, and blood pressure measurements. The probabilistic network included with one system determines whether there is internal hemorrhaging based on a number of factors including a physiological model, medical personnel inputs, transfer function, statistical, and spectral information, short and long term trends, and previous hemorrhage decisions.

Methods and devices for long term, cuffless, and continuous arterial blood pressure estimation using an end-to-end deep learning approach are provided. A deep learning system comprises three modules: a deep convolutional neural network (CNN) module for learning powerful features; a one- or multi-layer recurrent neural network (RNN) module for modeling the temporal dependencies in blood pressure dynamics; and a mixture density network (MDN) module for predicting final blood pressure value. This system takes raw physiological signals, such as photoplethysmogram and/or electrocardiography signals, as inputs and yields arterial blood pressure readings in real time.

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.

Systems and methods for determining a blood pressure of a subject from a photoplethysmogram are disclosed. The systems and methods include acquiring raw photoplethysmogram (PPG) data from a subject by measuring light reflected from or transmitted through a portion of the subject's body, determining a mean beat shape from the raw PPG data, and analyzing the mean beat shape to determine a blood pressure of the subject. The method can include analyzing a distinct shape of the mean beat shape to determine the blood pressure. The method can include identifying individual beats within the raw PPG data, collecting motion data and filtering the individual beats against the motion data, measuring biometric features of filtered beats, scaling individual beats in time and amplitude, and measuring additional shape features of scaled beats.

A medical system (10) and method detect arrhythmi cevents. The medical system (10)includes at least one processor (28, 34, 50) programmed to perform the method. A photoplethysmogram (PPG) signal generated using a PPG probe (12)positioned on or within a patient (14) anda pulse signal generated using an accelerometer (38)positioned on or within the patient (14) received. Features from the PPG signal are extracted to PPG feature vectors, and features are extracted from the pulse signal to pulse feature vectors. The PPG feature vectors are correlated with the pulse feature vectors, and correlated PPG feature vectors and correlated pulse feature vectors are evaluated to detect arrhythmic events.

A method, an apparatus, and a kit including the apparatus and a fluorescence agent are provided for measuring a time-varying change in an amount of blood in a tissue volume, and include exciting a fluorescence agent in the blood, acquiring a time-varying light intensity signal during a pulsatile flow of the blood through the tissue volume, the pulsatile flow having a systolic and a diastolic phase resembling a conventional photoplethysmogram, and processing the acquired signal by applying a modified Beer-Lambert law to obtain a measurement of the time-varying change in the amount of blood in the tissue volume. The instantaneous molar concentration of the fluorescence agent is determined by utilizing a concentration-mediated change in a fluorescence emission spectrum of the fluorescence agent. There is further provided a fluorescence agent for use in the method.

A pulse oximeter embedded with a novel motion and noise artifact (MNA) detection algorithm based on extraction of time-varying spectral features that are unique to the clean and corrupted components.

A device for measuring a ventricular-arterial coupling of a subject includes first and second inputs. The first input receives signals from a plurality of electrocardiogram sensors that are coupled to the subject at a plurality of first locations. The second input receives signals from a plurality of photoplethysmogram sensors that are coupled to the subject at a plurality of second locations. The second locations are selected from the group consisting of a head of the subject, an arm of the subject, and a leg of the subject. The signals received from the electrocardiogram sensors and the signals received from the photoplethysmogram sensors are received simultaneously. The device also includes a monitor configured to display the signals from the electrocardiogram sensors and the signals from the photoplethysmogram sensors.

The invention provides a system for characterizing a patient undergoing hemodialysis, featuring: 1) a body-worn biometric sensor, worn on a single location of the patient, and featuring: i) sensing elements for measuring electrocardiogram (ECG), thoracic bio-impedance (TBI), photoplethysmogram (PPG), and phonocardiogram (PCG) waveforms; ii) a processor for collectively analyzing the ECG, TBI, PPG, and PCG waveforms to determine a set of physiological parameters; and iii) a first wireless transceiver configured to transmit the set of physiological parameters; 2) a gateway system comprising a second wireless transceiver configured to receive the set of physiological parameters; and 3) a data-analytics system configured to analyze the set of physiological parameters to determine the patient's status.

A biological state estimating apparatus includes a pair of electrocardiographic electrodes, a photoplethysmographic sensor, and a controller that includes a peak detecting section, a pulse transmission time measuring section, a correlation information storing section, and a biological state estimating section. The electrocardiographic electrodes detect an electrocardiogram signal and the photoplethysmographic sensor, which has light-emitting and light-receiving elements, detects a photoplethysmogram signal. The controller detects peaks of the electrocardiogram and photoplethysmogram signals and determines a pulse transmission time from the time difference between the respective peaks of the photoplethysmogram and the electrocardiogram signal. Memory stores information determined in advance based on the relationship between pulse transmission time and biological state. The controller further estimates the biological state of the user on the basis of the pulse transmission time, and the correlation information stored in the correlation information storing section.

When evaluating the quality of photoplethysmography (PPG) signal (52) measured from a patient monitor (e.g., a finger sensor or the like), multiple features of the PPG signal are extracted and analyzed to facilitate assigning a score to the PPG signal or portions (e.g., heartbeats) thereof. Heartbeats in the PPG signal are segmented out using concurrently captured electrocardiograph (ECG) signal (50), and for each heartbeat, a plurality of extracted features are analyzed. If all extracted features satisfy one or more predetermined criteria for each feature, then the heartbeat waveform is compared to a predefined heartbeat template. If the waveform matches the template (e.g., within a predetermined match percentage or the like), then the heartbeat is classified as 'clean'. If the heartbeatdoes not patch the template, or if one or more of the extracted features fails to satisfy its one or more predetermined criteria, the heartbeat is classified as 'noisy'.

Described herein is a device and system and method for the detection of naturally occurring gas bubbles in the bloodstream; in one embodiment, a wireless optical device may be placed on various parts of the body including a finger or earlobe or is coupled with the SCUBA mouthpiece and detects certain vital signs such as heart rate, blood oxygen saturation (SpO2) and respiration rate by analyzing the photoplethysmogram (PPG) from a miniature sensor. There is also a method and apparatus for real-time non-invasive optical monitoring of actual state of decompression sickness are presented. The method is based on simultaneous measurements of PPG signals in different locations of human body and analysis of such a factor as phase shifts between various locations, change the shape of PPG signal between different locations, existence and modification of slow trends and low frequency modulations and angular distributions of the out-coming light.

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.

According to an aspect, there is provided an apparatus for measuring the blood pressure, BP, of a user, the apparatus comprising a volume-clamp BP monitoring device that comprises a first pressure device for applying pressure to a first part of the body of the user, a first photoplethysmogram, PPG, sensor for obtaining a first PPG signal from the first part of the body of the user, and a control unit that is configured to analyse the first PPG signal and to control the pressure of the first pressure device; wherein the control unit is configured to adjust the pressure of the first pressure device to maintain the first PPG signal at a constant level and to determine the BP of the user from the pressure of the first pressure device; and a second sensor, separate from the first PPG sensor, for measuring a physiological characteristic of the user in a second part of the body of the user, wherein the second part of the body is separate from the first part of the body; wherein the apparatus is configured to analyse the measured physiological characteristic to determine a measure of the blood perfusion in the second part of the body of the user, and to determine whether to perform a recalibration of the volume-clamp BP monitoring device on the basis of changes in the blood perfusion.

A physiological monitoring system features a Floormat and Handheld Sensor connected by a cable. A user stands on the Floormat and grips the Handheld Sensor. These components measure time-dependent physiological waveforms from a user over a conduction pathway extending from the user's hand or wrist to their feet. The Handheld Sensor and Floormat use a combination of electrodes that inject current into the user's body and collect bioelectric signals that, with processing, yield ECG, impedance, and bioreactance waveforms. Simultaneously, the Handheld Sensor measures photoplethysmogram waveforms with red and infrared radiation and pressure waveforms from the user's fingers and wrist, while the Floormat measures signals from load cells to determine ‘force’ waveforms to determine the user's weight, and ballistocardiogram waveforms to determine parameters related to cardiac contractility. Processing these waveforms with algorithms running on a microprocessor yield the vital sign, hemodynamic, and biometric parameters.