# US 11,298,101 B2 · Device embedded in, or attached to, a pillow configured for in-bed monitoring of respiration

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- Number: US 11,298,101 B2
- Document ID: US11298101B2
- Status: Granted
- Kind: B2
- Application number: 17/272,610
- Filed: 2019-08-29
- Published: 2021-08-12
- Granted: 2022-04-12
- Earliest claimed: 2018-08-31
- Assignees: Trustees of Dartmouth College
- Applicants: The Trustees of Dartmouth College
- Inventors: Justice Amoh; Jeffrey A. Bemowski
- Web page: https://patentgrep.com/patent/US11298101B2

## CPC

- A61B 7/003: {Detecting lung or respiration noise}
- A61B 5/0024: {for multiple sensor units attached to the patient, e.g. using a body or personal area network}
- A61B 5/0823: {Detecting or evaluating cough events}
- A61B 5/0826: {Detecting or evaluating apnoea events (sleep apnoea A61B5/4818)}
- A61B 5/4803: {Speech analysis specially adapted for diagnostic purposes}
- A61B 5/6892: {Mats}
- A61B 5/7257: {using Fourier transforms}
- A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems}
- A61B 5/0022: {Monitoring a patient using a global network, e.g. telephone networks, internet}
- A61B 5/6887: {mounted on external non-worn devices, e.g. non-medical devices}
- A61B 2560/0214: of power generation or supply
- A61B 2562/0247: Pressure sensors
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A monitoring device has microphones, an ADC; a digital radio; and a processor with firmware. The firmware includes code for digitizing audio from the microphones into time-domain audio, performing FFT to provide frequency-domain audio, running a first neural network on time domain and frequency-domain audio to extract features, executing a classifier on the features to identify candidate events, and using the digital radio to upload candidate events and features. A pressure sensor awakens the processor from a low-power state. In particular embodiments, the first neural network is an embedded Gated Recurrent Unit having weights trained to extract features of use in the classifier; and candidate events include normal inhalation and exhalation breathing sounds, crackles, wheezes, coughs, snoring, gasping, choking, and speech sounds and in some embodiments heart sounds. A method of monitoring breathing during sleep includes attaching the device to, or embedding the device within, a pillow.

## Claims

### Claim 1

A device configured for monitoring physiological sounds comprising:
at least one microphone coupled to an analog-to-digital converter (ADC);
a digital radio;
a processor configured with firmware in a memory, the firmware comprising machine readable code configured to:
use the ADC to digitize audio signals from the at least one microphone into digitized time-domain audio signals,
perform a fast Fourier transform on the digitized time-domain audio signals to provide frequency-domain audio signals,
execute a first neural network on at least the frequency-domain audio signals to extract features from the frequency-domain audio signals and at least one pressure sensor,
execute second neural network configured to classify events on the features to identify candidate events, and
use the digital radio to upload the candidate events and features;
where the at least one pressure sensor is coupled through a wake-up circuit configured to awaken the processor.

### Claim 2 (depends on 1)

The device of claim 1 wherein the first neural network is an embedded Gated Recurrent Unit (e-GRU) having weights trained to extract the features from the frequency domain audio signals for the second neural network.

### Claim 3 (depends on 2)

The device of claim 2 wherein the second neural network is a second e-GRU neural network.

### Claim 4 (depends on 1)

The device of claim 1 wherein the wake-up circuit is also coupled to the at least one microphone.

### Claim 5 (depends on 1)

The device of claim 1 wherein the at least one microphone is a plurality of microphones.

### Claim 6 (depends on 1)

The device of claim 1 wherein the candidate events comprise normal inhalation and exhalation breathing sounds, crackles, wheezes, coughs, and snoring.

### Claim 7 (depends on 6)

The device of claim 6 wherein the candidate events further comprise gasping, choking, and speech sounds.

### Claim 8 (depends on 7)

The device of claim 7 wherein the candidate events further comprise heart sounds.

### Claim 9 (depends on 8)

The device of claim 8 being embedded within, or attached to, a pillow.

### Claim 10 (depends on 1)

A system comprising:
the device configured for monitoring of physiological sounds of claim 1; and
a smart device configured to receive the uploaded candidate events and features from the digital radio, the smart device being selected from the group consisting of a body-area network (BAN) hub, a smartphone, a cellular-capable smartwatch, and a tablet computer.

### Claim 11 (depends on 10)

The system of claim 10 further comprising code configured to classify and perform statistical analysis on the candidate events and features, the candidate events being classified into events comprising normal inhalation and exhalation breathing sounds, crackles, wheezes, coughs, and snoring.

### Claim 12 (depends on 11)

The system of claim 11 wherein the first neural network is an embedded Gated Recurrent Unit (e-GRU) having weights trained to extract the features for the second neural network.

### Claim 13 (depends on 12)

The device of claim 12 wherein the second neural network is a second e-GRU neural network.

### Claim 14 (depends on 11)

The system of claim 11 wherein the smartphone is configured to relay the candidate events and features to a server, the server configured to classify and perform statistical analysis on the candidate events and features being executable on the server.

### Claim 15 (depends on 2)

The device of claim 2 wherein the candidate events comprise normal inhalation and exhalation breathing sounds, crackles, wheezes, coughs, and snoring.

### Claim 16 (depends on 3)

The device of claim 3 wherein the candidate events comprise normal inhalation and exhalation breathing sounds, crackles, wheezes, coughs, and snoring.

### Claim 17 (depends on 4)

The device of claim 4 wherein the candidate events comprise normal inhalation and exhalation breathing sounds, crackles, wheezes, coughs, and snoring.

### Claim 18 (depends on 5)

The device of claim 5 wherein the candidate events comprise normal inhalation and exhalation breathing sounds, crackles, wheezes, coughs, and snoring.