Sensors, Vol. 20, Pages 6481: Classification and Detection of Breathing Patterns with Wearable Sensors and Deep Learning
Sensors, Vol. 20, Pages 6481: Classification and Detection of Breathing Patterns with Wearable Sensors and Deep Learning
Sensors doi: 10.3390/s20226481
Authors:
Kristin McClure
Brett Erdreich
Jason H. T. Bates
Ryan S. McGinnis
Axel Masquelin
Safwan Wshah
Rapid assessment of breathing patterns is important for several emergency medical situations. In this research, we developed a non-invasive breathing analysis system that automatically detects different types of breathing patterns of clinical significance. Accelerometer and gyroscopic data were collected from light-weight wireless sensors placed on the chest and abdomen of 100 normal volunteers who simulated various breathing events (central sleep apnea, coughing, obstructive sleep apnea, sighing, and yawning). We then constructed synthetic datasets by injecting annotated examples of the various patterns into segments of normal breathing. A one-dimensional convolutional neural network was implemented to detect the location of each event in each synthetic dataset and to classify it as belonging to one of the above event types. We achieved a mean F1 score of 92% for normal breathing, 87% for central sleep apnea, 72% for coughing, 51% for obstructive sleep apnea, 57% for sighing, and 63% for yawning. These results demonstrate that using deep learning to analyze chest and abdomen movement data from wearable sensors provides an unobtrusive means of monitoring the breathing pattern. This could have application in ...
Source: Sensors - Category: Biotechnology Authors: Kristin McClure Brett Erdreich Jason H. T. Bates Ryan S. McGinnis Axel Masquelin Safwan Wshah Tags: Article Source Type: research