Depression, age, and weight were three factors that the artificial intelligence model identified as predictive of an insomnia diagnosis

A machine learning model can effectively predict a patient’s risk for a sleep disorder using demographic and lifestyle data, physical exam results, and laboratory values, according to a new study published this week in the open-access journal PLOS ONE by Samuel Y. Huang of Virginia Commonwealth University School of Medicine, and Alexander A. Huang of Northwestern Feinberg University School of Medicine, US.

The prevalence of diagnosed sleep disorders among American patients has significantly increased over the past decade. This trend is important to better understand and reverse since sleep disorders are a significant risk factor for diabetes, heart disease, obesity, and depression.

In the new work, the researchers used the machine learning model XGBoost to analyze publicly available data on 7,929 patients in the US who completed the National Health and Nutrition Examination Survey. The data contained 684 variables for each patient, including demographic, dietary, exercise, and mental health questionnaire responses, as well as laboratory and physical exam information.

Overall, 2,302 patients in the study had a physician diagnosis of a sleep disorder. XGBoost could predict the risk of sleep disorder diagnosis with a strong accuracy (AUROC=0.87, sensitivity=0.74, specificity=0.77), using 64 of the total variables included in the full dataset. The greatest predictors for a sleep disorder, based on the machine learning model, were depression, weight, age, and waist circumference.

The authors conclude that machine learning methods may be effective first steps in screening patients for sleep disorder risk without relying on physician judgment or bias. 

Samuel Y. Huang adds: “What sets this study on the risk factors for insomnia apart from others is seeing not only that depressive symptoms, age, caffeine use, history of congestive heart failure, chest pain, coronary artery disease, liver disease, and 57 other variables are associated with insomnia, but also visualizing the contribution of each in a very predictive model.”

The multi-functional filter could help advance autonomous driving and the Internet of Things

Researchers have developed a new chip-sized microwave photonic filter to separate communication signals from noise and suppress unwanted interference across the full radio frequency spectrum. The device is expected to help next-generation wireless communication technologies efficiently convey data in an environment that is becoming crowded with signals from devices such as cell phones, self-driving vehicles, internet-connected appliances, and smart city infrastructure. 

“This new microwave filter chip has the potential to improve wireless communication, such as 6G, leading to faster internet connections, better overall communication experiences, and lower costs and energy consumption for wireless communication systems,” said researcher Xingjun Wang from Peking University. “These advancements would, directly and indirectly, affect daily life, improving the overall quality of life and enabling new experiences in various domains, such as mobility, smart homes, and public spaces.”

In the Photonics Research journal co-published by Chinese Laser Press and Optica Publishing Group, the researchers describe how their new photonic filter overcomes the limitations of traditional electronic devices to achieve multiple functionalities on a chip-sized device with low power consumption. They also demonstrate the filter’s ability to operate across a broad radio frequency spectrum extending to over 30 GHz, showing its suitability for envisioned 6G technology.

“As the electro-optic bandwidth of optoelectronic devices continues to increase unstoppably, we believe that the integrated microwave photonics filter will certainly be one of the important solutions for future 6G wireless communications,” said Wang. “Only a well-designed integrated microwave photonics link can achieve low cost, low power consumption, and superior filtering performance.”

Stopping interference

6G technology is being developed to improve upon currently-deployed 5G communications networks. To convey more data faster, 6G networks are expected to use millimeter wave and even terahertz frequency bands. As this will distribute signals over an extremely wide frequency spectrum with an increased data rate, there is a high likelihood of interference between different communication channels.

To solve this problem, researchers have sought to develop a filter to protect signal receivers from various types of interference across the full radio frequency spectrum. To be cost-effective and practical for widespread deployment, this filter needs to be small, consume little power, achieve multiple filtering functions, and be integrated into a chip. However, previous demonstrations have been limited by their few functions, large size, limited bandwidth, or requirements associated with electrical components.

For the new filter, researchers created a simplified photonic architecture with four main parts. First, a phase modulator serves as the input of the radio frequency signal, which modulates the electrical signal onto the optical domain. Next, the double-ring acts as a switch to shape the modulation format. An adjustable microring is the core unit for processing the signal. Finally, a photodetector serves as the output of the radio frequency signal and recovers the radio frequency signal from the optical signal.

“The greatest innovation here is breaking the barriers between devices and achieving mutual collaboration between them,” said Wang. “The collaborative operation of the double-ring and microring enables the realization of the intensity-consistent single-stage-adjustable cascaded-microring (ICSSA-CM) architecture. Owing to the high reconfigurability of the proposed ICSSA-CM, no extra radio frequency device is needed for the construction of various filtering functions, which simplifies the whole system composition.”

Demonstrating performance

To test the device, researchers used high-frequency probes to load a radio frequency signal into the chip and collected the recovered signal with a high-speed photodetector. They used an arbitrary waveform generator and directional antennas to simulate the generation of 2Gb/s high-speed wireless transmission signals and a high-speed oscilloscope to receive the processed signal. By comparing the results with and without using the filter, the researchers demonstrated the filter’s performance.

Overall, the findings show that the simplified photonic architecture achieves comparable performance with lower loss and system complexity compared with previous programmable integrated microwave photonic filters composed of hundreds of repeating units. This makes it more robust, energy-efficient, and easier to manufacture than previous devices.

The researchers plan to further optimize the modulator and improve the overall filter architecture to achieve a high dynamic range and low noise while ensuring high integration at both the device and system levels.