Towards Sleep Apnea Screening with an Under-the-Mattress IR-UWB Radar Using Machine Learning

Ghaffar

et al. 2020

Non-invasive remote health monitoring plays a vital role in epidemiological situations such as SARS outbreak (2003), MERS (2015) and the recently ongoing outbreak of COVID-19 because it is extremely risky to get close to the patient due to the spread of contagious infections. Non-invasive monitoring is also extremely necessary in situations where it is difficult to use complicated wired connections, such as ECG monitoring for infants, burn victims or during rescue missions when people are buried during building collapses/earthquakes. Due to the unique characteristics such as higher penetration capabilities, extremely precise ranging, low power requirement, low cost, simple hardware and robustness to multipath interferences, Impulse Radio Ultra Wideband (IR-UWB) technology is appropriate for non-invasive medical applications. IR-UWB sensors detect the macro as well as micro movement inside the human body due to its fine range resolution. The two vital signs, i.e., respiration rate and heart rate, can be measured by IR-UWB radar by measuring the change in the magnitude of signal due to displacement caused by human lungs, heart during respiration and heart beating. This paper reviews recent advances in IR-UWB radar sensor design for healthcare, such as vital signs measurements of a stationary human, vitals of a non-stationary human, vital signs of people in a vehicle, through the wall vitals measurement, neonate's health monitoring, fall detection, sleep monitoring and medical imaging. Although we have covered many topics related to health monitoring using IR-UWB, this paper is mainly focused on signal processing techniques for measurement of vital signs, i.e., respiration and heart rate monitoring.

Section: Sleep Monitoringmentioning

confidence: 99%

An Overview of Signal Processing Techniques for Remote Health Monitoring Using Impulse Radio UWB Transceiver

Ghaffar

et al. 2020

“…Previously, the pattern recognition method for UWB sensor signals was researched primarily on pulse or apnea pattern detection based on machine learning [ 6 , 7 , 8 , 9 , 10 ]. Since then, as ANN(Artificial Neural Network)-based AI(Artificial Intelligence) technology has emerged, methods using this technique have been investigated; however, in such methods, preprocessing steps are required to construct learning data of a specific pattern.…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter Optimization Method Based on Harmony Search Algorithm to Improve Performance of 1D CNN Human Respiration Pattern Recognition System

Kim

Geem

Han

2020

In this study, we propose a method to find an optimal combination of hyperparameters to improve the accuracy of respiration pattern recognition in a 1D (Dimensional) convolutional neural network (CNN). The proposed method is designed to integrate with a 1D CNN using the harmony search algorithm. In an experiment, we used the depth of the convolutional layer of the 1D CNN, the number and size of kernels in each layer, and the number of neurons in the dense layer as hyperparameters for optimization. The experimental results demonstrate that the proposed method provided a recognition rate for five respiration patterns of approximately 96.7% on average, which is an approximately 2.8% improvement over an existing method. In addition, the number of iterations required to derive the optimal combination of hyperparameters was 2,000,000 in the previous study. In contrast, the proposed method required only 3652 iterations.

“…Especially, it is known that UWB Radar can detect respiratory and pulse signals due to its signal characteristics. UWB radar has been used to detect apnea and respiratory rate [15,16,17,18,19]. However, in order to be eventually used for polysomnography or healthcare, it is necessary to recognize different breathing patterns as well as to measure the number of breaths per minute or detect the apnea.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, recognition rate enhancement methods using artificial neural network technology are being studied in signal pattern recognition [20,21,22,23,24]. The existing UWB radar-based methods for recognizing apnea patterns are based on classical machine learning algorithms or on breathing frequency detection [15,16,17,18,19].…”

Section: Introductionmentioning

confidence: 99%

A Novel Human Respiration Pattern Recognition Using Signals of Ultra-Wideband Radar Sensor

Kim

Geem

Han

2019

Recently, various studies have been conducted on the quality of sleep in medical and health care fields. Sleep analysis in these areas is typically performed through polysomnography. However, since polysomnography involves attaching sensor devices to the body, accurate sleep measurements may be difficult due to the inconvenience and sensitivity of physical contact. In recent years, research has been focused on using sensors such as Ultra-wideband Radar, which can acquire bio-signals even in a non-contact environment, to solve these problems. In this paper, we have acquired respiratory signal data using Ultra-wideband Radar and proposed 1D CNN (1-Dimension Convolutional Neural Network) model that can classify and recognize five respiration patterns (Eupnea, Bradypnea, Tachypnea, Apnea, and Motion) from the signal data. Also, in the proposed model, we find the optimum parameter range through the recognition rate experiment on the combination of parameters (layer depth, size of kernel, and number of kernels). The average recognition rate of five breathing patterns experimented by applying the proposed method was 93.9%, which is about 3%~13% higher than that of conventional methods (LDA, SVM, and MLP).