Analog Gated Recurrent Unit Neural Network for Detecting Chewing Events.

We present a novel gated recurrent neural network to detect when a person is chewing on food. We implemented the neural network as a custom analog integrated circuit in a 0.18 μm CMOS technology.

Inferring COPD Severity from Tidal Breathing.

Objective: To develop an algorithm that can infer the severity level of a COPD patient's airflow limitation from tidal breathing data that is collected by a wearable device.

Methods: Data was collected from 25 single visit adult volunteers with a confirmed or suspected diagnosis of chronic obstructive pulmonary disease (COPD). The ground truth airflow limitation severity of each subject was determined by applying the Global Initiative for Chronic Obstructive Lung Disease (GOLD) staging criteria to the subject's spirometry results.

Cardiac eigen imaging: a novel method to isolate cardiac activity in thoracic electrical impedance tomography.

Objective: As the global burden of cardiovascular disease increases, proactive cardiovascular healthcare by means of accurate, precise, continuous, and non-invasive monitoring is becoming crucial. However, no current device is able to provide cardiac hemodynamic monitoring with the aforementioned criterion. Electrical impedance tomography (EIT) is an inexpensive, non-invasive imaging modality that can provide real-time images of internal conductivity distributions that describe physiological activity.

A 1 MHz Miniaturized Electrical Impedance Tomography System for Prostate Imaging.

An ASIC for a high frequency electrical impedance tomography (EIT) imaging system for prostate cancer screening is presented. The ASIC enables a small form-factor architecture, which ensures high signal-to-noise ratio (SNR) at MHz frequencies. The 4-channel ASIC was designed and fabricated in a standard CMOS 0.

A Power Adaptive, 1.22-pW/Hz, 10-MHz Read-Out Front-End for Bio-Impedance Measurement.

This paper presents a read-out front-end application-specific integrated circuit (ASIC) for the measurement of tissue impedances. The 10 mm 2-channel front-end ASIC is fabricated in a 0.18 μm CMOS technology.

Noise-robust bioimpedance approach for cardiac output measurement.

Objective: Congestive heart failure is a problem affecting millions of Americans. A continuous, non-invasive, telemonitoring device that can accurately monitor cardiac metrics could greatly help this population, reducing unnecessary hospitalizations and cost.

Approach: Machine learning (ML) algorithms trained on electrical-impedance tomography (EIT) data are presented for portable cardiac monitoring.

Structured Design Methodology to Achieve a High SNR Electrical Impedance Tomography.

In this paper, we present a methodology for designing the main circuit building blocks of an electrical impedance tomography (EIT) system. In particular, we derive equations that map system-level EIT specifications to the performance requirements of each circuit block. We also review the circuit architectures that are best suited for meeting a given set of performance requirements.

An Analog Front End ASIC for Cardiac Electrical Impedance Tomography.

In this paper, an end-to-end CMOS application specific integrated circuit (ASIC) for readout channel in a cardiac electrical impedance tomography system is presented. The ASIC consists of an integrated current driver for current injection, an instrumentation amplifier, variable gain amplifier at the analog front end for voltage readout from electrodes, and an on-chip 10-bit successive approximation register analog to digital converter with serial peripheral interface. The ASIC is fabricated in the CMOS 0.

A 10 MHz Read-Out Chain for Electrical Impedance Tomography.

This paper presents the design and implementation of a read-out chain for electrical impedance tomography (EIT) imaging. The EIT imaging approach can be incorporated to take spectral images of the tissue under study, offering an affordable, portable device for home health monitoring. A fast read-out channel covering a wide range of frequencies is a must for such applications.

Signal-to-Noise Ratio Analysis of a Phase-Sensitive Voltmeter for Electrical Impedance Tomography.

In this paper, thorough analysis along with mathematical derivations of the matched filter for a voltmeter used in electrical impedance tomography systems are presented. The effect of the random noise in the system prior to the matched filter, generated by other components, are considered. Employing the presented equations allow system/circuit designers to find the maximum tolerable noise prior to the matched filter that leads to the target signal-to-noise ratio (SNR) of the voltmeter, without having to over-design internal components.

Deep Neural Networks for Identifying Cough Sounds.

In this paper, we consider two different approaches of using deep neural networks for cough detection. The cough detection task is cast as a visual recognition problem and as a sequence-to-sequence labeling problem. A convolutional neural network and a recurrent neural network are implemented to address these problems, respectively.

Real-time embedded implementation of the binary mask algorithm for hearing prosthetics.

In this paper, we present a real-time embedded implementation of the binary masking algorithm, which has been shown to significantly improve speech-in-noise intelligibility. Our real-time implementation relies on a balance of parallel processing and hardware pipelining. We have tested and evaluated our implementation on a Spartan 3A FPGA.

Real-time source separation on a field programmable gate array platform.

In this paper, we present a real-time implementation of the ideal binary-mask algorithm, which is a promising approach for enhancing speech intelligibility. Our implementation is hardware efficient, making it suitable for embedded biomedical devices such as hearing aids and cochlear implants. We tested our algorithm implementation on an FPGA platform, and produced results that verify that it effectively performs source separation with 25 µs latency.

Technologies for developing ambulatory cough monitoring devices.

Cough is a prevailing symptom in most lung diseases. While cough sounds themselves can be very instrumental in the diagnosis of certain diseases, their intensity and frequency also infers the intensity of the particular illness. There is an imperative need for a robust system for identifying and analyzing cough sounds.