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Browsing by Subject "sleep apnea"

Now showing 1 - 2 of 2
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    Models for Limited Labeled Time Series Data with Applications in Sleep Science
    (2023-04) Aggarwal, Karan
    Time series are encountered universally in any natural or man-made phenomenon. Time-series analysis has applications in critical domains like healthcare, meteorology, and finance. Recently, there has been a big shift in the nature of collected time-series data, with the popularity of cheaper consumer-grade sensors, e.g., smartwatches. This has provided us with a plethora of lower-quality but high-volume data. Modeling time-varying data is challenging owing to its high dimensionality and complex patterns. These challenges are compounded by issues like missing data which have detrimental effects on downstream tasks like classification. Feature engineering has been an important part of time series analysis, with the use of features like seasonality or frequency transforms. Time-series data's complexity makes feature engineering quite challenging, and hence, deep learning is quite promising. Recently, there has been a lot of work on the time-series using deep learning architectures, which requires access to labeled examples. Labeling is an expensive operation,  especially in areas requiring specialized knowledge like healthcare. In this thesis, we focus on utilizing the limited labeled data efficiently. We propose solutions that leverage: 1) unlabeled data; 2) data with missing time-series observations; and 3) effective use of scarce labels. We primarily focus on showcasing these techniques for applications in sleep science, with data from consumer-grade devices like smart watches becoming available. First, we present a method for unsupervised representation learning to create representations for human activity and sleep data. We exploit the context and content, and reduce subject-specific noise using adversarial training. These representations can be exploited to boost the performance of supervised learning models in low-labeled data settings, unlike the traditional time-series models. Empirical evaluation demonstrates that our proposed method performs better than many strong baseline methods, and adversarial learning helps improve the generalizability of our representations. Second, we use conditional random fields (CRFs) with deep neural networks to capture longer-term dependencies in the dynamics of output labels for time series segmentation tasks. This allows us to capture longer-term context while performing the segmentation labeling, allowing for more efficient usage of limited labels. Our method shows significant improvement over the baseline methods. We apply the proposed method for the detection of sleep stages from  Continuous Positive Air Pressure (CPAP) signals, an at-home therapy device for sleep apnea. Ours is the first work to detect a patient's sleep stages based on the CPAP collected data with reasonable accuracy.   Third, we present a novel semi-supervised method for time series data imputation. Observing missing data in time series is common because of issues like data drops or sensor malfunctioning. Imputation methods are used to fill in these values, with the quality of imputation having a significant impact on downstream tasks like classification. Our proposed semi-supervised approach uses unlabeled data as well as downstream task's labeled data. Our results indicate that the proposed method outperforms the existing supervised and unsupervised time series imputation methods measured on the imputation quality as well as on the downstream tasks ingesting imputed time series. Last, we adapt MixUp, a simple data augmentation technique for time series data. We show that a simple modification in the training process can improve the performance of time series classification methods. We perform data augmentation in both raw time series as well as latent space from time series classification models. The improvement in performance is observed consistently in low labeled data regimes as well as higher data regimes.
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    Predicting Therapy Adherence : A Machine Learning Approach
    (2021-12) Lima Diniz Araujo, Matheus
    Ensuring adherence to medical therapy has been an open problem in health care practices since the Hippocrates Oath (400 BC) to modern medicine. In an ideal world, people would follow their doctor's recommendations. They would stick to their diet to lose weight, take their medication on time, and use their electronic health devices as recommended by the doctors. But the planned routine is rarely followed, causing a financial burden in the order of billions of dollars for the national healthcare system and many billions of dollars worldwide. A key mechanism to revert a tendency of non-adherence is early personalized intervention, targeting the key factors of undesired behavior, but this task is not trivial. After starting their therapy, individuals have an unpredictable series of life events that may impact their willingness to keep with the necessary therapy routine. Only recently, we achieved the ability to passively collect individual-level therapy data as patients progress in their treatments using digital devices. In this thesis, we proposes various machine-learning strategies that aim to leverage the data collected at the early stages of medical therapies to predict future adherence and recommend early accurate interventions that align with each individual's desired outcomes. We narrow most of the analysis in two sleep apnea therapies, Continuous Positive Airway Pressure (CPAP) and Upper-Airway Stimulation (UAS). But to reinforce the generalization of our methods we also show how they can be applied for the growth-hormone therapy management.