University of Michigan

Sardar Ansari

Kevin R Ward

Kayvan Najarian

Kenn Oldham

Ashwin Belle

Lu Wang

Christopher Elliot Gillies

Rodney C Daniels

Parag G Patil

Mohamad Hakam Tiba

Hamid Ghanbari

Daniel Slavin

A method includes receiving raw input signals, analyzing the raw signals using a trained coding architecture including an encoding layer; and displaying an output. A computing system includes a processor and a memory storing instructions that when executed by the processor, cause the computing system to receive raw input signals, analyzing the raw signals using a trained coding architecture and display an output. A non-transitory computer readable medium includes program instructions that when executed cause a computer to receive raw input signals, analyze the raw signals using a trained coding architecture and display an output.

Coding architectures for automatic analysis of waveforms

Devices and techniques for continuously monitoring pressure and simultaneously monitoring changes in blood flow, vascular resistance, and/or vascular behavior are provided. The techniques are employed in measuring intracranial pressure (ICP), while simultaneously measuring cerebral blood flow and/or cerebrovascular resistance or behavior. A sensor device includes an optical and piezoelectric sensing assembly integrated into a deployable ICP monitoring device.

Multi-sensor intracranial pressure monitor for cerebral hemodynamic monitoring

Methods and systems monitor and assess brain bioimpedance through the use of an ocular window that assesses dynamic changes in cerebral blood volume (CBV). That ocular window is implemented through an ocular bioimpedance device that, in a non-invasive manner, measures numerous different brain health indicators using the bioimpedance measurements collected through the regions around the eyes. The ocular bioimpedance device may be goggles with localized measurement electrodes that include cathodes and anodes.

Ocular impedance-based system for brain health monitoring

A wearable assembly has a pulse plethysmography (PPG) sensor and a piezoelectric pressure sensor and is attachable to a patient's finger or other area corresponding to a peripheral vascular region, and further includes a signal processor configured to monitor blood flow dependent measurements and pressure measurements over time, comparing these measurements to determine properties of the vascular region, such as vascular resistance of a blood vessel, vascular radius of the blood vessel, vascular stiffness of the vascular region, blood pressure, and/or cardiac vascular power. The signal processor may apply a hysteresis comparison of the sensor outputs, e.g., using an elliptical model, and in some examples may apply an extended Kalman filter for optimizing output of the vascular region properties.

Estimation of peripheral vascular resistance using a miniature piezoelectric sensor

The invention is a passive, wearable sensor that uses a thin piezoelectric material to produce a time history of blood pressure of the patient, with signal processing algorithms to extract physiological information. The sensor consists of a piezoelectric transducer set in a polymer laminate that can be applied to the finger or wrist of the patient. During use, a combination of compressive and bending deformation in the piezoelectric layer in response to blood pressure in the finger or wrist as a voltage output. Using signal processing techniques, the raw signal is filtered and decomposed to obtain a information to form derivative signals such as blood pressure, pulse pressure, pulse pressure variability, heart rate, heart rate variability, and respiratory rate which can be very important pre-cursors in the monitoring of the patient's physiological conditions.

Miniature piezoelectric cardiovascular monitoring system

The present application describes techniques to filter signals contaminated with blunt noise. Calculated filter coefficients may be applied to signals to generate filtered output signals without the blunt noise. Sets of filter coefficients may be calculated utilizing an &#x3b5;-tube filter process in conjunction with an autoregressive exogenous (ARX) model. Sets of filter coefficients may be calculated in accordance with a constrained optimization algorithm using data indicative of a source of the blunt noise. When the blunt noise is modeled in accordance with the ARX model, filtered output signals are generated having amplitudes constrained to a selected Epsilon value, which may be the amplitude of a primary component of the unfiltered signal. A set of filter coefficients may be calculated by determining, from the set of filter coefficients that satisfy the constrained optimization algorithm, a solution that produces a filtered output signal having the most time-invariant frequency composition.

Epsilon-tube filter for blunt noise removal

Techniques for motion artifact (MA) reduction in impedance plethysmography (IP) and other physiological signals are provided. The techniques limit the amplitude of MA filtered signals by imposing an '&#x3b5;-tube.' The techniques may include the introduction of a regularization term to ensure that the pattern of a filtered signal is similar to the pattern of the primary component of the original, unfiltered signal by maximizing the regularity of the filtered signal. The techniques may be integrated into a portable monitoring device, such as an armband, to remove MA from various diagnostic signals and to extract primary signal components for producing enhanced device performance.

Growing community of inventors

Richmond, VA, United States of America

Sardar Ansari