Complex sleep disorder

Wulterkens livens up. Whereas Huijben spent a lot of time behind her computer for data analysis, she could often be found at the patient’s bedside. “There’s a group of people who are diagnosed with sleep apnea but for whom the treatment doesn’t work. In many cases, they suffer from a combination of insomnia and obstructive sleep apnea: COMISA. When a patient with suspected sleep apnea is referred to the hospital for a sleep study via their general practitioner, COMISA can easily be missed and is often only diagnosed at a later stage. And that’s worrisome, because patients with COMISA experience more symptoms and limitations than those with only one of the two conditions. In the sleep studies, we examined whether COMISA has certain characteristics that might allow for an earlier diagnosis.”

Initially, Wulterkens used data from Kempenhaeghe, which is where patients are referred to when they cannot be treated at their local hospital. These people often have a more complex sleep disorder which Kempenhaeghe is better equipped to deal with, Wulterkens emphasizes. That helped her in the search for COMISA characteristics. “Whereas sleep apnea involves many brief wakeful moments throughout the night, lasting a few tens of seconds each, we observed that in COMISA patients, these periods lasted much longer, around five minutes for example.” But when she switched to hospital data – Wulterkens also collaborated with the Amphia Hospital in Breda – she ran into practical issues. “Based on standard sleep measurements, it’s difficult to diagnose COMISA, as not every COMISA patient suffers from insomnia every night. Ideally, you’d want to monitor a patient over several nights, at home without all the wires.”

Although many advancements have been made in wireless sleep monitoring and many consumers are already tracking their sleep using wearables, Wulterkens could not use them. “Consumer devices are tested on young people with healthy sleep patterns and are not able to detect sleep disorders. Moreover, we as researchers don’t have access to the raw data, and a manufacturer can change their algorithms at any time. Not ideal for a scientific study.” Using wearables from industrial partner Philips, Wulterkens collected two weeks of (raw) sleep measurement data from people who wore this wristband at night. Then, she used a machine learning algorithm to score the sleep of patients with sleep disorders. The first clinical studies are promising, Wulterkens says enthusiastically. “With an improved way of measuring, we can better distinguish between wake and sleep. Current wearables often can’t differentiate between a patient who is asleep and a patient who is awake in bed without moving. And this is essential for a correct diagnosis.”

Sitting upright in bed

Huijben is also eager to make the switch to wearables. “We can now perform state-of-the-art machine learning with real clinical data. Models are usually fed with synthetic data sets, for example simple pictures of dogs and cats that must then be classified. With the professional data from Kempenhaeghe, we’ve managed to bring machine learning closer to the clinic.” That offers new opportunities to look at sleep patterns from a different angle, Huijben explains. “We can now represent sleep as a continuous process, just as it really is. Changes within and between sleep stages are gradual; the transitions aren’t abrupt. We can also use the developed machine learning models to examine sleep measured at home in a new way, in collaboration with industrial partner Onera Health. They are developing wireless polysomnography sensors for home measurements.”

In addition to analyzing regular sleep patterns, Huijben also used her ML model to examine data from sleepwalkers. “We still don’t understand much about why and when such sleepwalking behavior occurs; it’s a unique state of the body. This doesn’t necessarily involve physical walking, by the way. It also includes talking and suddenly sitting upright in bed. Sleepwalkers are also very difficult to wake up, and if they do wake up, they are often aggressive. With the standard way of visualizing sleep cycles, it is very hard to distinguish periods of sleepwalking in the brain measurements. Our ML models show that the longer a stretch of deep sleep, the more likely it is that sleepwalking behaviors will occur. We understand relatively little about what exactly happens during such a period. We hope this method will provide us with more insight, also through the use of Onera Health’s sensors to monitor sleepwalkers at home over several days.”

Listening to your body

Despite not having worked together directly, Huijben and Wulterkens were able to learn a lot from each other. Huijben: “Our group includes the entire sleep pipeline. I didn’t work with patients directly, but through Bernice I heard stories about the enormous impact of sleep disorders. And at the drinks parties we organized together, centered on sleep, there was room for sharing new ideas in addition to collegial conviviality.” And that also led to a completely different perspective – literally, Huijben laughs. Because both she and Wulterkens got to join their PhD supervisor Sebastiaan Overeem, who is not only a TU/e professor and somnologist but also an amateur pilot, on a flight around Eindhoven and see their work from above.

They have certainly become very aware of what bad sleep does to you, Wulterkens concludes. “What many people don’t realize is that there’s a lot they can do to improve their sleep habits. “No coffee at night, a consistent sleep rhythm, no screens. Especially the latter is a serious problem, particularly among young people who can’t put their phones down. And in today’s data-centric culture, more and more people monitor their sleep on a standard basis, but that, in turn, can be a source of sleep issues. Measure in moderation, and don’t forget to simply listen to your body.”

Bernice Wulterkens defended her dissertation “Shining light on a Dark Duo: Advancing the analysis of sleep in the assessment of COMISA” on Monday, October 14, 2024. Iris Huijben will defend her dissertation “Uncovering sleep structure through discrete representation learning” on Thursday, October 17.