The present disclosure relates to a device, method and system for calculating, estimating, or monitoring the blood pressure of a subject based on physiological features and personalized models. At least one processor, when executing instructions, may perform one or more of the following operations. A first signal representing a pulse wave relating to heart activity of a subject may be received. A plurality of second signals representing time-varying information on a pulse wave of the subject may be received. A personalized model for the subject may be designated. Effective physiological features of the subject based on the plurality of second signals may be determined. A blood pressure of the subject based on the effective physiological features and the designated model for the subject may be calculated.

An implantable monitoring device includes first sensors to measure state information of one or both of a posture and an activity of a user and second sensors to measure bioinformation of two or more of an electrocardiogram (ECG) of a heart of the user, a pulmonary impedance of a lung of the user, a movement of the heart, a movement of a thorax including the lung, and a respiratory quotient (RQ) of the lung, two electrodes to detect bioinformation to measure one or both of the ECG and the pulmonary impedance, an analog circuit to process the detected bioinformation to measure the one or both of the ECG and the pulmonary impedance, and a processor to monitor an abnormal state of the heart and the lung of the user based on the state information and the bioinformation.

Methods of measuring biometric characteristics using a sensor positioned in an ear canal of a user are provided. The sensor is positioned on or connected to an ear tip, a contact hearing device, or one or more components thereof. One or more biometric signals may be sensed from the sensor. The biometric characteristic of the user is measured or derived from these sensed signals, and include but are not limited to the temperature of the user, acoustic signal(s) from the user, movement(s) of the user, a ballistocardiogram, an electrocardiogram, oxygen saturation, and blood pressure.

An exemplary embodiment of the present disclosure provides systems and methods for non-invasively measuring blood pressure, the system and methods comprise a wearable device having a first surface, a first sensor positioned on the first surface of the wearable device, the first sensor configured to receive a first signal, wherein the first signal is indicative of a first blood-volume change in a first vessel of a subject, a second sensor positioned within the wearable device, the second sensor configured to receive a second signal, wherein the second signal is indicative of a cardiac mechanical motion of the subject, and a processor positioned within the wearable device, the processor configured to generate an output based at least on the first signal and the second signal, the output representing a blood pressure measurement of the subject.

Certain aspects of the disclosure are directed to an apparatus including a scale and external circuitry. The scale includes a platform, and data-procurement circuitry for collecting signals indicative of the user's identity and cardio-physiological measurements. The scale includes processing circuitry to process data obtained by the data-procurement circuitry, therefrom generate cardio-related physiologic data, and to send user data to the external circuitry. The external circuitry identifies a risk that the user has a condition based on the reference information and the user data provided by the scale and outputs generic information correlating to the condition to the scale that is tailored based on the identified risk.

An approach for determining an estimated pressure curve for the ventricle of the heart, the method comprising: using data from a motion sensor that has been implanted at the heart to determine the timing of heart cycle events; scaling a reference pressure-time curve including timing of reference heart cycle events in order to fit the reference pressure-time curve to the motion sensor data, the scaling comprising scaling the reference curve along the time axis to fit it to the measured timing of the heart cycle events; and thereby obtaining an estimated pressure-time curve in the form of the scaled reference pressure-time curve.

An ambulatory medical device including a plurality of sensing electrodes and one or more processors operably coupled to the plurality of sensing electrodes is provided. Each sensing electrodes is configured to be coupled eternally to a patient and to detect one or more ECG signals. The one or more processors are configured to receive at least one electrode-specific digital signal for each of the plurality of sensing electrodes, determine a noise component for each of the electrode-specific digital signals, analyze each of the noise components for each of the plurality of sensing electrodes, generate electrode matching information for each sensing electrode of the plurality of sensing electrodes based upon analysis of each of the noise components, determine one or more sensing electrode pairs based upon the electrode matching information, and monitor each of the one or more sensing electrode pairs for ECG activity of the patient.

A computer implemented method for determining heart arrhythmias based on cardiac activity that includes under control of one or more processors of an implantable medical device (IMD) configured with specific executable instructions to obtain far field cardiac activity (CA) signals at electrodes located remote from the heart, and obtain acceleration signatures, at an accelerometer of the IMD, indicative of heart sounds generated during the cardiac beats. The IMD is also configured with specific executable instructions to declare a candidate arrhythmia based on a characteristic of at least one R-R interval from the cardiac beats, and evaluate the acceleration signatures for ventricular events (VEs) to re-assess a presence or absence of at least one R-wave from the cardiac beats and based thereon confirming or denying the candidate arrhythmia.

Devices and methods for generating synthetic cardio-respiratory signals from one or more ballistocardiogram (BCG) sensors. A method for determining item specific parameters includes obtaining ballistocardiogram (BCG) data from one or more sensors, where the one or more sensors capture BCG data for one or more subjects in relation to a substrate. For each subject, the captured BCG data is pre-processed to obtain cardio-respiratory BCG data. The cardio-respiratory BCG data is sub-sampled to generate the cardio-respiratory BCG data at a cardio-respiratory sampling rate conducive to cardio-respiratory signal generation. The sub-sampled cardio-respiratory BCG data is cardio-respiratory processed to generate a cardio-respiratory parameter set. A synthetic cardio-respiratory signal is generated from at least the cardio-respiratory parameter set and a cardio-respiratory event morphology template. A condition of the subject is determined based on the synthetic cardio-respiratory signal.

Various arrangements for performing ballistocardiography using a mobile device are presented. A radar integrated circuit of a mobile device may emit frequency-modulated continuous-wave (FMCW) radar. Reflected radio waves based on the FMCW radar being reflected off objects may be received and used to create a raw radar waterfall. The raw radar waterfall may be analyzed to create a ballistocardiography waveform. Data based on the ballistocardiography waveform may be output, such as to a machine-learning application installed on the mobile device.