Improving Sleep Disorder Diagnosis with Technology

Polysomnography (PSG) is a test that records various body functions during sleep. It helps doctors diagnose sleep disorders like sleep apnea, insomnia, and restless leg syndrome. PSG can track brain activity, eye movements, heart rate, and breathing. However, analyzing this data can be tough and time-consuming. Traditionally, sleep technologists, who are trained professionals, have to score the recordings manually based on guidelines. This process can take a long time and can lead to differences of opinion among technologists.

With advances in technology, particularly in machine learning, there is potential to automate some parts of the Scoring process. Machine learning uses algorithms to learn from data and make predictions or decisions without being explicitly programmed. Different algorithms have been developed to score sleep recordings automatically, but not much focus has been placed on how these tools fit into the daily work of sleep technologists.

This article discusses a new digital platform developed to improve the scoring of PSG data by combining automatic scoring with human input. The goal is to make the process faster, more accurate, and easier for sleep technologists.

#The Current State of Polysomnography

Polysomnography involves recording and analyzing multiple signals from individuals during sleep. This includes electrical signals from the brain (EEG), muscle activity (EMG), and eye movements (EOG), as well as breathing patterns and oxygen levels. After the data is collected overnight, sleep technologists manually review the recordings and label various sleep stages and events, a process known as scoring.

Scoring takes time, and technologists follow guidelines from the American Academy of Sleep Medicine (AASM) to categorize each 30-second snippet of the recording into different sleep stages. This categorization results in a visual tool called a hypnogram, which shows how a person's sleep progresses throughout the night. By understanding the patterns in the hypnogram, healthcare professionals can diagnose sleep disorders.

However, manual scoring has its drawbacks. It can take several hours to analyze just one night of sleep data, delaying reports to healthcare providers and possibly affecting patient care. Furthermore, there can be disagreements between different sleep technologists when scoring the same data. These differences can reach levels of over 19% for sleep stages and 11% for respiratory events.

#The Approach to Improve Scoring Efficiency

With the aim of improving this scoring process, a new platform was designed. This platform comprises three main components:

1. Web Platform: A user-friendly interface where sleep technologists can upload recordings from multiple nights of PSG data.
2. Splitter: A tool that breaks down three-night recordings into individual one-night segments for easier analysis.
3. Processor: A system that applies automatic scoring algorithms to enhance the analysis of the sleep recordings.

The platform aims to reduce the time taken to score PSG data by allowing technologists to focus more on areas that the algorithms find challenging. By highlighting these challenging areas-referred to as "gray areas"-the technologists can direct their efforts where they are most needed, rather than reviewing the entire recording.

#Understanding Gray Areas in Scoring

In this context, gray areas refer to segments of the sleep data where the automatic scoring algorithms are less confident about their predictions. By signaling these segments to the technologists, the platform helps them to prioritize their manual review on parts of the data that require closer examination. This focus on gray areas not only enhances the accuracy of the scoring process but also optimizes the technologist's time.

Using this approach, the platform allows for more efficient use of the technologists' expertise. Instead of having to review every single part of the data, they can concentrate on the areas that could benefit from their insight, thus speeding up the overall scoring process.

#The Role of Automation

Automation plays a crucial role in this platform. Machine learning algorithms can analyze sleep data far faster than a human can. With this capability, parts of the scoring process can be done quickly and efficiently, which would normally take hours. This is especially beneficial in busy clinical settings where many sleep studies need to be processed in a short amount of time.

When the platform was tested, it showed significant improvements in scoring times. For instance, some technologists reported reductions in their scoring times by as much as 65 minutes when using the platform compared to traditional methods. This improvement allows healthcare providers to receive necessary reports quicker, enabling more timely diagnoses and treatment plans.

#Assessing the Effectiveness of the Platform

The effectiveness of the platform was evaluated through a study involving three sleep technologists, each with varying levels of experience. They were asked to score a selection of sleep recordings using two methods: with the automatic scoring algorithms and gray area highlighting, and without them. By comparing their scoring times and the agreement with a standardized scoring benchmark, researchers assessed how well the platform improved workflow.

The results showed a marked increase in efficiency. The technologists using the platform were able to complete their scoring tasks faster without compromising the quality of their work, as evidenced by the levels of agreement with the benchmark scoring.

#The Importance of Trust and Understanding

Despite the progress made with the platform, trust remains a critical factor in its adoption. Interviews with the technologists revealed that while they found the system useful, they also expressed concerns about the accuracy of the automatic scoring. Trust in the technology can be shaped by the transparency of the algorithms used and their ability to provide reliable outputs.

For the system to be fully embraced, the technologists emphasized the need for enhanced accuracy in the scoring algorithms. They wanted to know more about how the system arrives at its decisions, particularly in gray areas. This feedback highlights the need for ongoing development of the algorithms to ensure they not only produce accurate results but also are comprehensible to the users.

#Future Directions

Looking ahead, the development of this platform opens up new possibilities for research and clinical applications. There are plans to include additional scoring algorithms and enhancements to the existing framework. This flexibility allows for the incorporation of new technologies and methods as they become available.

Moreover, the potential for further studies is significant. Researchers aim to expand the dataset to include more sleep recordings, which will allow for more robust analyses and validations of the platform's effectiveness. Through continuous improvement and adaptation, the platform could help bridge the gap between more experienced and less experienced sleep technologists, facilitating better training outcomes.

#Conclusion

The integration of automatic scoring algorithms into the workflow of sleep technologists offers a promising avenue for improving the efficiency and accuracy of sleep disorder diagnoses. By addressing the challenges of traditional scoring methods and enhancing user trust in the technology, the platform not only streamlines the scoring process but also maintains the essential human oversight needed for quality patient care. As research progresses, the continual evolution of this platform will likely have a meaningful impact on the field of sleep medicine, leading to better patient outcomes and a more efficient healthcare system.