Hello everyone, my name is Xianda Chen.
I'm a PhD student from the Pennsylvania State University.
The paper I will present today is ApneaDetector: detecting sleep apnea with smartwatches.
Sleep apnea is a serious sleep disorder, in which breathing is briefly and repeatedly interrupted.
The PSG test is the standard clinical test to diagnose sleep apnea.
However, it is very inconvenient since it requires the subjects to wear many wire attachments.
Also the PSG test is very expensive and time-consuming
since it involves many hospital visits.
In this paper, we propose to use the built-in sensors in smartwatches to detect sleep apnea.
There are three kinds of sleep apnea events:
central sleep apnea, obstructive sleep apnea and hypopnea.
This figure shows the airflow for a central sleep event.
Since the subject holds the breathing for more than 10 seconds,
the airflow during this period remains flat, and since the airway remains open,
the subject can return back to normal breathing after the event.
This shows for an obstructive sleep apnea.
Since the airflow is blocked then the airflow is flat, and because of this airflow
blockage, the subject is more likely to make one or more intensive breaths after the event.
Similar situation happens for hypopnea events.
Using accelerometer sensor in smartwatch, we can detect sleep apneas.
This is because
respiration leads to the periodical movement of the arms and wrists during breathing.
This figure shows the accelerometer data for this central sleep apnea event.
We can see here during the central sleep apnea event, the accelerometer data remains flat.
This shows the same pattern as the pattern shown in the airflow.
For obstructive sleep apnea events, since the subjects make intensive breaths,
these intensive breaths will cause a signal spike in the accelerometer data.
Similar for hypopnea events.
Detecting these signal spikes can detect most of obstructive sleep apnea and hypopnea events.
In this paper, we propose a smartwatch-based system
called ApneaDetector to detect sleep apnea events.
Since the data is noisy, there are several challenges to be addressed.
First, how to process the noisy data, and how to detect the signal spikes,
and then how to identify features for apnea detection
To process the noisy data, we propose techniques for signal denoising and data calibration.
In signal denoising, the signal noise is smoothed
away from the raw data while preserving the spikes, which reflect the possible apnea events.
In data calibration,
trend effects are removed from the accelerometer data to preserve the apnea event patterns.
After data preprocessing, we define a metric called maximum standardized residual
to identify the signal spike, which represents the existence of an OSA or hypopnea event.
As can be seen in this figure, the signal spike usually lags behind intensive breath,
so we can see there's a lagging time here.
We consider the lagging time of this spike occurrence and
the sub-window size of the apnea event for calculating maximum standardized residual.
Besides the spike feature, we also extract features related to signal peaks, such as
the distance between two consecutive peaks, the number of peaks, and the amplitude of peaks.
We also consider multi-axis correlation features, we extract the feature, the normalized covariance
between the accelerometer data along every two different axes for apnea detection.
To evaluate the performance of ApneaDetector,
we conducted a clinical study with 20 subjects at Penn State medical center.
The subjects were prescribed to do the regular PSG test.
During the PSG test, each subject also won a smartwatch running our
application to collect the accelerometer data of the wrist movement during sleep.
The collected data covers a diversity of different sleep apnea events.
We use the PSG reports as the ground truth to evaluate the performance of ApneaDetector.
The ApneaDetector can achieve much high performance,
above 96% for precision, recall and f1-score.
It also performs much better than the amplitude-based method.
More results can be found in the paper.
In summary, in this paper we designed and evaluated
a smartwatch-based system called ApneaDetector for apnea detection.
We identified the characteristics of sleep apnea,
which can be captured by smartwatch through a clinical study.
We proposed techniques to process the noisy data
and extract features to train classifiers for apnea detection.
Using our collected data, we demonstrated that
ApneaDetector can detect sleep apnea events with high precision.
Thank you!