Improving Sleep Quality Assessment through Clustering and Data Augmentation of Sleep Sound Events

Konsep Inti

The proposed method achieves high accuracy in classifying sleep satisfaction by clustering sleep sound events, using the cluster membership probabilities as input features, and applying data augmentation to increase the training data. The method also provides interpretable insights into important sleep sound events and individual-specific characteristics that influence sleep quality.

Abstrak

The study aims to construct a machine learning-based sleep assessment model that can provide evidence-based assessments, such as poor sleep due to frequent movement during sleep onset. The key steps of the proposed method are:

Extracting sleep sound events from audio recordings and transforming them into the frequency domain using Fast Fourier Transform (FFT).
Learning latent representations of the sleep sound events using Variational AutoEncoder (VAE) and clustering them using Gaussian Mixture Model (GMM) to obtain membership probabilities.
Performing data augmentation by sampling event sequences multiple times per night to increase the training data.
Training a Long Short-Term Memory (LSTM) model for sleep quality classification using the cluster membership probabilities as input features.
Applying TimeSHAP to the trained LSTM model to interpret the important sleep sound events and individual-specific characteristics that influence sleep quality classification.

The experimental results show that the proposed method achieves high accuracy in classifying sleep satisfaction, outperforming the conventional method that directly uses VAE latent representations as input to LSTM. The interpretation of the LSTM model using TimeSHAP reveals that the presence of body movement and breathing sounds are important for high sleep satisfaction, while noise such as deep breathing, cars, or motorcycles are important for low sleep satisfaction. The importance of sleep sound events and time periods during the night vary among individuals, demonstrating the ability of the proposed method to analyze individual sleep characteristics.

Statistik

The accuracy of the proposed method for sleep satisfaction classification ranged from 90.3% to 94.8% for the three participants.
The proposed method achieved significantly higher accuracy compared to the conventional method for two out of the three participants.
The importance of different sleep sound event clusters and time periods during the night varied among the three participants, indicating the ability to analyze individual sleep characteristics.

Kutipan

"For Subject 1, the SHAP values of Cluster 0 (large breathing sounds) are relatively large negative values in the early and late segments. However, the differences in SHAP values between clusters in the middle segment are smaller compared to the early and late segments, and their absolute values are also smaller. This indicates that the presence of large breathing sounds in the early and late segments is problematic, suggesting a possibility of sleep apnea syndrome (including pre-syndrome)."
"For Subject 2, the SHAP values for noise are significantly large negative values in all time periods, suggesting that noise is likely to disrupt sleep."
"For Subject 3, the SHAP values for noise are significantly large negative values in the early and late segments, similar to Subject 2, indicating that noise is likely to disrupt sleep."

Wawasan Utama Disaring Dari

Clustering and Data Augmentation to Improve Accuracy of Sleep Assessment and Sleep Individuality Analysis

by Shintaro Tam... pada arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10299.pdf

Pertanyaan yang Lebih Dalam

How can the proposed method be extended to provide personalized recommendations for improving sleep quality based on the identified important sleep sound events and individual characteristics?

The proposed method can be extended to provide personalized recommendations by integrating a feedback loop mechanism. Once the important sleep sound events and individual characteristics are identified through clustering and interpretation, this information can be used to create a personalized sleep profile for each individual. By continuously monitoring and analyzing the sleep patterns and sound events, the system can provide real-time feedback and suggestions for improving sleep quality.
One approach is to develop a recommendation engine that correlates specific sound events with sleep outcomes. For example, if deep breathing sounds are associated with disruptions in sleep quality for a particular individual, the system can recommend relaxation techniques or breathing exercises to alleviate this issue. Similarly, if noise disturbances are identified as a significant factor, the system can suggest using white noise machines or earplugs to create a more conducive sleep environment.
Furthermore, incorporating machine learning algorithms that adapt and learn from individual responses to interventions can enhance the personalization of recommendations over time. By continuously refining the recommendations based on feedback and outcomes, the system can tailor suggestions to address specific sleep challenges effectively.

How can the clustering and interpretation of sleep sound events be further automated to streamline the deployment of the proposed method in real-world sleep monitoring applications?

To automate the clustering and interpretation of sleep sound events for streamlined deployment in real-world sleep monitoring applications, several strategies can be implemented:

Automated Feature Extraction: Develop algorithms that automatically extract relevant features from sleep sound data without manual intervention. This can include signal processing techniques to identify key patterns and characteristics in the audio data.

Unsupervised Clustering: Implement unsupervised clustering algorithms that can automatically group similar sleep sound events together based on their features. Techniques like K-means clustering or hierarchical clustering can be utilized for this purpose.

Interpretation Models: Train machine learning models to interpret the clustered sleep sound events and identify the most significant factors influencing sleep quality. These models can learn from labeled data and provide insights into the impact of different sound events on sleep outcomes.

Real-time Monitoring: Integrate the automated clustering and interpretation algorithms into a real-time monitoring system that can process and analyze sleep sound data as it is collected. This enables immediate feedback and insights for users without delays.

By automating the clustering and interpretation processes, the proposed method can be efficiently deployed in real-world sleep monitoring applications, providing valuable insights and recommendations to users in a timely manner.

What other physiological or environmental factors, beyond sleep sounds, could be incorporated into the sleep quality assessment model to provide a more comprehensive evaluation?

In addition to sleep sounds, several other physiological and environmental factors can be incorporated into the sleep quality assessment model to offer a more comprehensive evaluation:

Heart Rate Variability (HRV): Monitoring HRV can provide insights into the autonomic nervous system's activity during sleep, reflecting stress levels and overall well-being.

Body Movement: Tracking body movements during sleep can help assess restlessness and sleep disturbances, providing information on sleep quality.

Temperature and Humidity: Environmental factors like room temperature and humidity levels can impact sleep quality. Integrating sensors to monitor these variables can offer valuable insights.

Light Exposure: Light exposure before and during sleep can influence circadian rhythms and melatonin production. Including light sensors in the assessment model can highlight the impact of light on sleep quality.

Respiratory Rate: Monitoring respiratory patterns can help detect breathing irregularities and sleep apnea, contributing to a more comprehensive evaluation of sleep health.

By incorporating these additional physiological and environmental factors into the sleep quality assessment model, a holistic view of an individual's sleep patterns and environment can be obtained, leading to more personalized and effective recommendations for improving sleep quality.