The human body constantly generates a variety of signals that can be measured from outside the body with wearable devices. These bio-signals – ranging from heart rate to sleep state and blood oxygen levels – can indicate whether someone is having mood swings or can be used to diagnose a variety of body or brain disorders.
It can be relatively cheap to gather a lot of bio-signal data. Researchers can organize a study and ask participants to use a wearable device akin to a smartwatch for a few days. However, to teach a machine learning algorithm to find a relationship between a specific bio-signal and a health disorder, you first need to teach the algorithm to recognize that disorder. That’s where computer engineers like myself come in.
Many commercial smartwatches, such as ones by Apple, AliveCor, Google and Samsung, currently support atrial fibrillation detection. Atrial fibrillation is a common type of irregular heart rhythm, and leaving it untreated can lead to a stroke. One way to automatically detect atrial fibrillation is to train a machine learning algorithm to recognize what atrial fibrillation looks like in the data.
This machine learning approach requires large bio-signal datasets in which instances of atrial fibrillation are labeled. The algorithm can use the labeled instances to learn to recognize a relationship between the bio-signal and atrial fibrillation.
The labeling process can be quite expensive because it requires experts, such as cardiologists, to go through millions of data points and label each instance of atrial fibrillation. The same problem extends to many other bio-signals and disorders.
To resolve this issue, researchers have been developing new ways to train machine learning algorithms with fewer labels. By first training a machine learning model to fill in the blanks of large-scale unlabeled bio-signal data, the machine learning model is primed to learn the relationship between a bio-signal and a disorder with fewer labels. This is called pretraining. Pretraining even helps a machine learning model learn a relationship between a bio-signal and a disorder when it is pretrained on a completely unrelated bio-signal.
Bio-signals are found all over the body and provide information about different bodily functions. Each of these is a bio-signal that measures a specific physiological signal in a noninvasive way.
Eloy Geenjaar
Challenges of working with bio-signals
Finding relationships between bio-signals and disorders can be difficult because of noise, or irrelevant data, differences between people’s bio-signals, and because the relationship between a bio-signal and disorder may not be clear.
First, bio-signals contain a lot of noise. For example, when you’re wearing a smartwatch while running, the watch will move around. This causes the sensor for the bio-signal to record at different locations during the run. Since the locations vary across the run,…
