The present disclosure relates to a coil assembly of an MRI device. The MRI device may be configured to perform an MR scan on a subject. The coil assembly may include one or more coil units, a substrate, and a sensor mounted within or on the substrate. The one or more coil units may be configured to receive an MR signal from the subject during the MR scan. The substrate may be configured to position the one or more coil units during the MR scan. The one or more coil units may be mounted within or on the substrate. The sensor may be configured to detect a motion signal relating to a physiological motion of the subject before or during the MR scan.

Disclosed herein is a system and method directed to detecting placement of a medical device within a patient body, where the system includes a medical device including an optical fiber having core fibers, each of the one or more core fibers including a plurality of sensors each configured to (i) reflect a light signal having an altered characteristic due to strain experienced by the optical fiber. The system further includes logic configured to cause operations of providing an incident light signal to the optical fiber, receiving reflected light signals of different spectral widths of the incident light from the sensors, processing the reflected light signals to detect fluctuations of a portion of the optical fiber, and determining a location of the portion of the optical fiber based on the detected fluctuations. In some instances, the detected fluctuations are caused by anatomical movement of the patient body.

Sensor apparatuses, methods of operating the sensor apparatuses, and systems including the sensor apparatuses are disclosed. Methods of analyzing electromechanical characteristics of a heart are also disclosed.

Apparatus and method are provided for synchronized multiple vital health measurements. In one novel aspect, an integrated wearable device with multiple sensors that can collect multiple vital health signals, digitize them, send them through wireless network to a receiver. In one embodiment, the wearable device has a plurality of different types of sensors including at least one or more acoustic-to-electric sensors collecting phonocardiogram (PCG) electrical signal and one or more electrocardiogram (ECG) sensors, a control module includes a synchronization circuitry that synchronizes measurements of the plurality of different types of sensors. In another novel aspect, a system performs a synchronized measurement using a plurality type of health-monitoring sensors, performs a correlation analysis of the plurality of measurement results using selected one or more analytical rules, and obtains a set of parameters with recognized medical values and generating one or more medical health records based on the correlation analysis.

A smartphone plugin determines the heart rate and the breathing rate of a user, who is either holding the smartphone in his/her hand or who has the smartphone resting on his/her chest when lying in a supine position, using only smartphone accelerometer output data and no external sensors. The smartphone is preloaded with spectral entropy to weight mapping information for each of a plurality of use cases. The plugin performs frequency domain processing on accelerometer output data to determine an estimated heart rate EHR and an estimated breathing rate EBR. The spectral entropy of accelerometer output data is determined, and is used along with an appropriate spectral entropy to weight mapping, to determine an EHR weight for each EHR value and an EBR weight for each EBR value. The weights are used to adjust the EHR and EBR values to generate more accurate heart rate and breathing rate values.

A bed comprises substrate support members, each including a load bearing and a base configured to provide contact with a floor. The load bearing member is configured to move vertically relative to the base, while the base and the load bearing member are configured to fit together to maintain lateral alignment of the base and the load bearing member. A load sensor is positioned between the base and the load bearing member, the load bearing member configured to transmit a load from the substrate to the load sensor. A printed circuit board is in communication with the load sensor. A controller is in communication with the printed circuit board of each substrate support member and is configured to receive and process data output by the printed circuit boards.

A leadless implantable medical device (IMD) and method of using same are provided. The IMD comprises: a housing, a fixation element, electrodes configured to sense electrical cardiac activity (CA) signals over a period of time, an HS sensor configured to sense HS signals over the period of time, memory to store specific executable instructions, and one or more processors. The one or more processors and method: identify a characteristic of interest (COI) of a heartbeat from the CA signals, calculate a center of mass (COM) for at least one HS based on the HS signals to obtain a corresponding at least one HS COM, and calculate at least one of a therapy-related (TR) delay or a sensing-related (SR) blanking interval (BI) based on the at least one HS COM.

A patient-worn arrhythmia monitoring and treatment device includes a pair of therapy electrodes and at least one pair of sensing electrodes disposed proximate to the skin and configured to continually sense at least one ECG signal of the patient over an extended period of time. The device includes a therapy delivery circuit coupled to the pair of therapy electrodes and configured to deliver one or more therapeutic pulses. A controller coupled to therapy delivery circuit is configured to analyze the at least one ECG signal and detect one or more treatable arrhythmias and cause the therapy delivery circuit to deliver the one or more therapeutic pulses to the patient. At least one of the one or more therapeutic pulses is formed as a biphasic waveform delivering within 15 percent of 360 J of energy to a patient body having a transthoracic impedance from about 20 to about 200 ohms.

A cardiac health assessment system includes a memory, a circuit board, and a touchscreen controller integrated into a handheld electronic device (HED). The memory stores a classification model, a regression model, and instructions about a cardiac monitoring application. The circuit board includes a microphonic sensor, an Inertial Measurement Unit (IMU) sensor, a camera sensor, and a processor. The microphonic sensor captures cardiac sound wave signals indicative of the cardiac health of a user. The IMU sensor captures seismic signals indicative of the cardiac health of the user. The camera sensor enables visual data collection of tissue and photoplethysmography. The processor is configured to: execute the instructions, display commands to position the HED against the chest of the user, detect abnormal heart activity by deploying the classification model, and estimate intracardiac pressure by deploying the regression model. The touchscreen controller displays cardiac diagnostic information.

A system for marking cardiac time intervals from heart valve signals includes a non-invasive sensor unit for capturing electrical signals and composite vibration objects, a memory containing computer instructions, and one or more processors coupled to the memory. The one or more processors causes the one or more processors to perform operations including separating a plurality of individual heart vibration events into heart valve signals from the composite vibration objects, and marking cardiac time interval from the heart valve signals by detecting individual heartbeats using at least one or more of a PCA algorithm or deep learning.