Systems and methods are disclosed for determining individual-specific blood flow characteristics. One method includes acquiring, for each of a plurality of individuals, individual-specific anatomic data and blood flow characteristics of at least part of the individual's vascular system; executing a machine learning algorithm on the individual-specific anatomic data and blood flow characteristics for each of the plurality of individuals; relating, based on the executed machine learning algorithm, each individual's individual-specific anatomic data to functional estimates of blood flow characteristics; acquiring, for an individual and individual-specific anatomic data of at least part of the individual's vascular system; and for at least one point in the individual's individual-specific anatomic data, determining a blood flow characteristic of the individual, using relations from the step of relating individual-specific anatomic data to functional estimates of blood flow characteristics.

A system for monitoring hemodynamics of a subject is disclosed. The system comprises: a signal generating system configured for providing at least an output electric signal and transmitting the output signal to an organ of the subject. The system also comprises a demodulation system configured for receiving an input electrical signal sensed from the organ responsively to the output electric signal, and for modulating the input signal using the output signal to provide an in-phase component and a quadrature component of the input signal. The system also comprises a processing system configured for monitoring the hemodynamics based on the in-phase and the quadrature components.

Systems and methods are disclosed for estimating patient-specific blood flow characteristics. One method includes acquiring, for each of a plurality of individuals, a geometric model and estimated blood flow characteristics of at least part of the individual's vascular system; executing a machine learning algorithm on the geometric model and estimated blood flow characteristics for each of the plurality of individuals; identifying, using the machine learning algorithm, features predictive of blood flow characteristics corresponding to a plurality of points in the geometric models; acquiring, for a patient, a geometric model of at least part of the patient's vascular system; and using the identified features to produce estimates of the patient's blood flow characteristic for each of a plurality of points in the patient's geometric model.

Described here are meters and methods for sampling, transporting, and/or analyzing a fluid sample. The meters may include a meter housing and a cartridge. In some instances, the meter may include a tower which may engage one or more portions of a cartridge. The meter housing may include an imaging system, which may or may not be included in the tower. The cartridge may include one or more sampling arrangements, which may be configured to collect a fluid sample from a sampling site. A sampling arrangement may include a skin-penetration member, a hub, and a quantification member.

Disclosed are methods for determining biometric indicators such as plasma volume, hematocrit and glomerular filtration rate, in mammalian subjects such as humans. The methods utilize a plurality of fluorescent tags having distinct fluorescent characteristics, which may be associated with a single static molecule, or wherein the static molecule is labeled with a fluorescent tag and a dynamic molecule is labeled with another fluorescent tag. One or more measurements of the intensities of the fluorescent emissions are taken subsequent to introduction of an injectate which contains the fluorescent tags, which can be taken using a probe or via a blood or plasma sample. Compositions and apparatuses for practicing the methods are also disclosed.

Various embodiments of a monitoring system are disclosed. The monitoring system includes first and second sensors each adapted to detect a characteristic of a subject of the system and generate data representative of the characteristic of the subject, and a controller operatively connected to the first and second sensors. The controller is adapted to receive data representative of first and second characteristics of the subject from the first and second sensors, and determine statistics for first and second condition substates of the subject over a monitoring time period based upon the data received from the first and second sensors. The controller is further adapted to compare the statistics of the first and second condition substates, confirm the first condition substate if it is substantially similar to the second condition substate, and determine the statistics of an overall condition state of the subject based upon the confirmed first condition substate.

Non-invasive heartbeat sensor for determining a heart rate in a conduit of an extracorporeal blood treatment apparatus, comprising one source for directing an optical signal towards the blood flowing in the segment; one detector for receiving an optical informative signal comprising the signal emitted by said source after passing the blood, and emitting respective output signal; a controller receiving the respective output signal and retrieving a heartbeat frequency and a heart rate value, based on the output signal, wherein the informative signal is altered by flow perturbation of the blood partially generated by the flow impulses originated by the heart.

Systems and methods for a medical device are provided. The handheld medical device includes a housing including a front and rear side. A diaphragm is located in the center of the rear side of the housing. Three or less rear electrodes, configured to collect electromagnetic signals from the chest region of a patient, are located in a semi-circular in shape that encircles the diaphragm on the rear side of the housing. Two front electrodes located on the front side of the housing collect signals from the left and right index fingers of the patient. A screen is located between the two front electrodes. The device may include a transmitter, in some cases a Bluetooth module, for coupling the medical device to a user device. The user device includes an application that receives the signals and performs analysis on them. The five electrode inputs are used to calculate seven ECG channels.

A system comprises electrocardiogram sensing, glucose sensing circuitry, and processing circuitry. The sensing circuitry is configured to sense an electrocardiogram of a patient. The glucose sensing circuitry is configured to sense glucose levels of the patient. The processing circuitry configured to detect atrial fibrillation of the patient during a time unit based on the electrocardiogram of the patient, determine a first metric, wherein the first metric is associated with atrial fibrillation the patient experiences during the time unit, determine a second metric, wherein the second metric is associated with glucose levels of the patient during the time unit, and generate a health metric, wherein the health metric is determined based on the first and second metrics.

Systems and methods for determining if a wearable photoplethysmography device is correctly positioned in operating to medical signs of a user by using a classifier to determine if a signal is valid or invalid. In some embodiments, in using the classifier to determine in a signal is valid or invalid, a lean method of linear computational complexity and minimal memory complexity is provided for determining at the wearable photoplethysmography device if it is correctly positioned. In some embodiments, in using the classifier minimal computational complexity is used in determining at the wearable photoplethysmography device if it is correctly positioned.