How Big Data Is Helping Fight Sleep Disorders
Sleep disorders are taking over the world as a silent pandemic. Youths and elders worldwide are suffering from sleeping problems. Some have insomnia, while others have hypersomnia. <!--more--> Healthcare research has focused on finding the best way to deal with this issue. Despite positive advances, the health outcomes for the patients have not been satisfactory.
Now, big data is posing a potential solution to sleep disorders. Many healthcare institutions have become interested in its applications.
Big data offers the chance to dive deeper into the functioning of a human being. It treats a person as a primary data source that offers insight into the causes of the noted sleep disorders. This technology could change current treatment approaches towards sleep disorders.
Why big data?
Using big data in assessing and treating sleep disorders has been a significant leap in sleep science.
Until recently, the primary way of getting clinically relevant data was through polysomnography. However, this method is expensive and requires complex skills to operate. Even so, its invasiveness makes it a less desirable test. Using the approach can make people uncomfortable, and some patients may refuse the test.
Big data comes in as a cost-effective, simple, and non-invasive method of collecting patient data regarding their sleeping patterns. Besides, big data collects data on a large scale, making it more appropriate than polysomnography.
The bulky nature of big data enhances data analytics and arrival at an accurate diagnosis of a sleep disorder.
Sleep and digital and computational big data
Digital and computation big data is crucial when monitoring, diagnosing and treating sleep disorders. Sleep science relies on algorithms that create automated, robust, and proactive data-guided approaches. The application of deep learning models is now driving the collection and analysis of digital and computation big data related to sleep.
The deep learning approach enhances access to sleep diagnostics. Researchers have developed a robust deep learning algorithm to evaluate sleep quality as effectively as a sleep specialist. Currently, certified sleep technologists can take up to two hours to assess and obtain polysomnography scores.
These are results obtained following just one night of sleep. This method has proven inefficient because it takes several tests over a long period to diagnose a sleep disorder accurately.
The new deep learning-based approach combines deep recurrent and convolutional neural networks to give accurate results in a fraction of time. The system relies on designed algorithms that assess a large pool of data. They then filter the data and pick the most relevant features to evaluate an individual's sleeping patterns.
The new approach is flexible and utilizes artificial intelligence to understand people better. Since the system can quickly process large amounts of data, big data becomes valid and provides more reliable patterns. Deep learning can identify essential sleep features and use them to arrive at final decisions.
Through digital and computational big data, new deep learning algorithms can help healthcare institutions improve their level of care for patients with sleep disorders.
Sleep and imaging-based big data
Imaging-based big data is a crucial aspect of sleep science. Sleep studies have ventured into neuro-imaging using connectomics. Connectomics refers to a branch of neuroscience that focuses on mapping and analyzing data on neural connections.
A combination of diffusion tensor imaging and resting-state functional magnetic resonance imaging (fMRI) is necessary to form a connectome that captures brain function during sleep. The imaging produced through connectomics helps explain the physiology of sleep and the neurophysiology of sleep disorders.
The fMRI-based connectomics helps monitor sleep patterns by automatically staging sleep. Functional MRI can combine binary support vector machine classifiers to discriminate between all pairs of sleep stages. The approach facilitates proper modeling of vigilance states.
Researchers use connectomics to obtain imaging-based big data, which is vital in treating sleep disorders. In a study, connectomics noted that individuals with primary insomnia have small-world architecture. The patients had specific anomalies, as noted through a circuit linking the cortex and basal ganglia of the right limbic system. In another study, connectomics displayed the mechanisms underlying an inability to sleep.
Imaging-based big data is thus critical in understanding sleeping disorders. Healthcare centers are adopting more and more imaging-based big data-related technologies. These systems give a clear pictorial impression of brain activity to determine the sleep disorder in a specific individual.
Sleep and portable devices
The concept of sleep tracking has become more mainstream. Today, people monitor their sleep patterns to maximize health benefits. But this is more important in hospital settings where patients present with sleep disorders.
One of the biggest game-changers in the healthcare industry has been the introduction of wearable sensors to monitor people's sleeping patterns. Wearables are now leading in tracking consumer sleep patterns.
Some of the renowned portable devices with these capabilities include Apple Watch, Amazon Halo, and Fitbit. Further advances have seen the incorporation of sleep tracking technology into jewelry, brain-tracing caps, and textiles.
These wearables include sensors that collect data waveforms like body movement, electrocardiograms, and pulse oximetry.
These waveforms measure the body's electrical activities at rates of up to 500 times per second. The sleep trackers also contain accelerometers that measure the quantity of an individual's sleep.
Motion detectors collect data on the number of movements an individual makes while asleep. These trackers also monitor data for vital signs, like breathing and heart rates in different stages of sleep. Based on a large amount of collected data, the algorithms estimate the amount and quality of sleep an individual experiences.
But what exactly do sleep trackers monitor?
Sleep trackers measure several data types. Below is a breakdown of some of the most important when assessing sleep disorders:
- Sleep quality tracking: The trackers detect any form of interruption during sleep. It records data that shows if the individuals had poor quality sleep due to disturbances like constant turning and tossing in bed.
- Sleep cycle tracking: Sleep tracker assesses an individual through different sleep phases.
- Sleep duration tracking: The trackers record an individual's inactive time. If the inactive time noted is lower or higher than expected, the data could help pinpoint a sleeping disorder.
- Lifestyle influences: A tracker may also assess one's dietary intake. Data from diet plans can be crucial in making clinical decisions regarding the etiology of one's sleeping disorder. For instance, high caffeine intake can be a significant factor when evaluating an insomniac.
- Environmental influences: Some sleep trackers monitor the surroundings to record factors influencing one's sleep quality. They may record the light intensity and temperature in an area, among other factors. Such influences help determine why someone is having sleeping issues.
Conclusion
Big data plays a critical role in making people's lives more bearable. Its role in fighting sleep disorders has a significant impact on the healthcare industry.
An individual's quantity and quality of sleep are crucial factors to lead a healthy life. They influence a person's stress levels, mood, and ability to perform in social and workplace settings.
Big data's intervention to solve sleep disorders has come when many people need it. Wearable sensors fitted on portable devices now play a vital role in tracking sleep. They monitor people's movements, sleeping phases, and vital signs. They collect large amounts of data that healthcare centers analyze to determine a patient's sleeping disorder.
Healthcare centers must take big data seriously as they tackle the challenge of sleep disorders.
Further reading
- Introduction to Neural Networks
- Basics of Convolution Neural Networks
- Introduction to Deep Learning
- Fundamentals of Big Data Supply Chain
Happy learning!
Peer Review Contributions by: Briana Nzivu
