APPARATUS AND METHOD FOR ESTIMATING BREATHING RATE USING MICROPHONE

• US 20160081651A1
• Filed: 05/04/2015
• Published: 03/24/2016
• Est. Priority Date: 09/22/2014
• Status: Abandoned Application

• Current Assignee: Soonchunyang University Industry Academy Cooperation Foundation
• Original Assignee: Soonchunyang University Industry Academy Cooperation Foundation
• Inventors: Chon, Ki H., NAM, Yunyoung, Reyes, Bersain A.
• Application Number:
  US14/702,778
• Publication Number:
  US 20160081651A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2562/0204 Acoustic sensors
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/085 Measuring impedance of respiratory organs or lung elasticity
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 7/003 Detecting lung or respiration noise

Abstract

Provided are an apparatus and method for estimating a breathing rate using a microphone, which performs band filtering and noise filtering on tracheal and nasal sounds collected from the microphone, selects a processing region of preprocessed data, calculates similarity between pieces of data using an autocorrelation function, calculates a power spectrum density, detects multiple peaks including a highest peak through the power spectrum density, analyzes the multiple peaks to determine a breathing pattern, and calculates a breathing rate in consideration of a dynamic characteristic between inhalation and exhalation, nasal congestion detection, and noise reduction. Accordingly, it is possible to accurately estimate the breathing rate because calculation is performed in consideration of a dynamic characteristic of inhalation and exhalation, nasal congestion, and white noise.

Claims

1. An apparatus for estimating a breathing rate using a microphone, the apparatus comprising:

a preprocessing unit configured to perform band filtering and noise filtering on tracheal and nasal sounds collected from the microphone;

a data selection unit configured to select a processing region of preprocessed data;

a similarity calculation unit configured to calculate similarity between pieces of data using an autocorrelation function;

a power spectrum calculation unit configured to calculate a power spectrum density;

a peak detection unit configured to detect multiple peaks including a highest peak through the power spectrum density;

a pattern determination unit configured to analyze the multiple peaks to determine a breathing pattern; and

a breathing rate calculation unit configured to calculate a breathing rate in consideration of a dynamic characteristic between inhalation and exhalation, nasal congestion detection, and noise reduction.

2. The apparatus of claim 1, wherein the breathing rate calculation unit comprises:

a nasal congestion detection unit configured to detect nasal congestion; and

a noise detection unit configured to detect background and voice noises.

3. A method of estimating a breathing rate using a microphone, the method comprising:

performing band filtering and noise filtering on tracheal and nasal sounds collected from the microphone;

selecting a processing region of preprocessed data;

calculating similarity between pieces of data using an autocorrelation function;

calculating a power spectrum density;

detecting multiple peaks including a highest peak through the power spectrum density;

analyzing the multiple peaks to determine a breathing pattern; and

calculating a breathing rate in consideration of a dynamic characteristic between inhalation and exhalation, nasal congestion detection, and noise reduction.

4. The method of claim 3, wherein the calculation of the breathing rate is performed using any one of a Welch periodogram method, an AR power spectrum (Burg algorithm), and a modified covariance method.