Deep Learning versus Professional Healthcare Equipment: A Fine-Grained Breathing Rate Monitoring Model

Bang Liu,Xiaomin Wang,Ming Liu,Haigang Gong,Xili Dai,Nianbo Liu,Zihao Guo

doi:10.1155/2018/5214067

Abstract

In mHealth field, accurate breathing rate monitoring technique has benefited a broad array of healthcare-related applications. Many approaches try to use smartphone or wearable device with fine-grained monitoring algorithm to accomplish the task, which can only be done by professional medical equipment before. However, such schemes usually result in bad performance in comparison to professional medical equipment. In this paper, we propose DeepFilter, a deep learning-based fine-grained breathing rate monitoring algorithm that works on smartphone and achieves professional-level accuracy. DeepFilter is a bidirectional recurrent neural network (RNN) stacked with convolutional layers and speeded up by batch normalization. Moreover, we collect 16.17 GB breathing sound recording data of 248 hours from 109 and another 10 volunteers to train and test our model, respectively. The results show a reasonably good accuracy of breathing rate monitoring.

Highlights

Introduction e emergence ofmHealth draws much attention both in industry and academy [1]
We propose a deep learning model such as DeepFilter, which can filter the breathing from low signal-to-noise ratio (SNR) data
Our model is a recurrent neural network (RNN) that begins with several convolutional input layers, followed by a fully connected layer and multiple recurrent layers, and ends with an output layer. e network is trained end to end and is added batch normalization with cross entropy loss function

Summary

Related Work

Since our scheme involves accurate sleep monitoring and deep learning, we mainly discuss the previous work on the two aspects. Polysomnography [12] is used in medical facilities to perform accurate sleep monitoring by attaching multiple sensors on patients, which requires professional installation and maintenance. It can measure many body functions during sleep, including breathing functions, eye movements, heart rhythm, and muscle activity. DoppleSleep [13] is a contactless sleep sensing system that continuously and unobtrusively tracks sleep quality, by using commercial off-the-shelf radar modules Some smartphone apps, such as Sleep as Android, Sleep Cycle Alarm Clock, and iSleep [14], can perform low-cost sleep monitoring by using the smartphone built-in microphone and motion sensors. We are inspired by the good performance of the previous work on speech recognition and introduce deep learning algorithm into the problem of fine-grained breathing rate monitoring [21]

DeepFilter

Fine-Grained Breathing Rate Monitoring

Experiment

Findings

Conclusion