Detection of Abnormal Respiration from Multiple-Input Respiratory Signals.

Ju O Kim,Deokwoo Lee

doi:10.3390/s20102977

Abstract

In this paper, we propose a novel approach for the detection of abnormal signals from multiple respiration signals. An ultrawide-band (UWB) radar was used to acquire respiration signals that represent a distance from the chest to the radar sensor, i.e., shape variation of the chest due to breathing (inhaling or exhaling) activity provides quantitative information (distance values) about respiratory status. Distribution, shape, and variation of values across time provide information to determine respiratory status, one of the most important indicators of human health. In this paper, respiratory status was categorized into two classes, normal and abnormal. Abnormal respiration (apnea in this paper) was emulated by interrupting breathing activity because it is difficult to acquire real apnea from patients in hospital wards. This paper considered two cases, single and multiple respiration. In the first case, a single normal- or abnormal-respiration signal was used as input, and output was the classified status of respiration. In the second case, multiple respiration signals were simultaneously used as inputs, and we focused on determining the existence of abnormal signals in multiple respiration signals. In the case of multiple inputs, filters with varying cut-off frequency were applied to input signals followed by the analysis of output signals in response to the filters. To substantiate the proposed method, experiment results are provided. In this paper, classification results showed of the successful rate in the case of multiple inputs, and results are promising for applications to monitoring systems of human respiration.

Highlights

Rates or states of respiration are considered one of the most important indicators to evaluate the physical, physiological, and psychological state of human health because breathing activity is the most important and crucial task for humans
We focus on the detection and classification of respiratory status that is categorized into two classes, normal and abnormal respiration
Simulations were conducted using a UWB radar that was connected to a PC, monitoring and measuring the shape or positional variation of the chest surface owing to respiration

Summary

Introduction

Rates or states of respiration are considered one of the most important indicators to evaluate the physical, physiological, and psychological state of human health because breathing activity is the most important and crucial task for humans. In contact- or noncontact-based methods, numerous algorithms have been proposed to accurately estimate the respiratory rate from an electrocardiogram (ECG) on the basis of information about chest movements (this is related to heart rate and lung volume) indicating human respiratory status. Noncontact-based methods are considered an alternative to contact-based ones, practically providing convenience and simplicity to monitor respiratory status because sensing devices do not have to be attached to the human body. Thermal image (or infrared thermography)-based methods monitor respiration profiles by measuring thermal variations and hyperventilation, detecting or tracking target areas related to breathing activity (e.g., nose, throat, or chest) to detect unusual respiratory patterns [24].

Acquisition of Respiration Signals

Detection of Existence of Abnormal Status

Case 1

Case 2

Simulation Results

Conclusions