A Two Stage Approach for the Automatic Detection of Insomnia

Advanced sleep disorder detection using multi-layered ensemble learning and advanced data balancing techniques

Sleep disorder detection has greatly improved with the integration of machine learning, offering enhanced accuracy and effectiveness. However, the labor-intensive nature of diagnosis still presents challenges. To address these, we propose a novel coordination model aimed at improving detection accuracy and reliability through a multi-model ensemble approach. The proposed method employs a multi-layered ensemble model, starting with the careful selection of N models to capture essential features. Techniques such as thresholding, predictive scoring, and the conversion of Softmax labels into multidimensional feature vectors improve interpretability. Ensemble methods like voting and stacking are used to ensure collaborative decision-making across models. Both the original dataset and one modified using the Synthetic Minority Oversampling Technique (SMOTE) were evaluated to address data imbalance issues. The ensemble model demonstrated superior performance, achieving 96.88% accuracy on the SMOTE-implemented dataset and 95.75% accuracy on the original dataset. Moreover, an eight-fold cross-validation yielded an impressive 99.5% accuracy, indicating the reliability of the model in handling unbalanced data and ensuring precise detection of sleep disorders. Compared to individual models, the proposed ensemble method significantly outperformed traditional models. The combination of models not only enhanced accuracy but also improved the system's ability to handle unbalanced data, a common limitation in traditional methods. This study marks a significant advancement in sleep disorder detection through the integration of innovative ensemble techniques. The proposed approach, combining multiple models and advanced interpretability methods, promises improved patient outcomes and greater diagnostic accuracy, paving the way for future applications in medical diagnostics.

Effective diagnosis of sleep disorders using EEG and EOG signals

This work focuses on the diagnosis of various sleep disorders such as insomnia, narcolepsy, periodic leg movement, nocturnal frontal lobe epilepsy, bruxism, REM behavior disorder, and sleep-disordered breathing. We utilize SVM for classifying each of the sleep disorders from healthy controls. The proposed approach is evaluated on the publicly available CAP dataset comprising 108 overnight recordings from healthy controls and patients with sleep disorders. A single feature called gridded distribution entropy derived from Poincaré plots of EEG signal provides 100% accuracy in distinguishing healthy controls from each pathology, except insomnia and PLM. With the EOG channel, we are able to classify these two groups as well with 100% accuracy, demonstrating the effectiveness of EOG in disambiguating insomnia and PLM from controls.Clinical relevance- Diagnosis of sleep disorders is important to facilitate appropriate treatment. It is challenging due to the diverse nature and inter-subject variation of the physiological symptoms. Automated sleep disorder detection can improve cost efficiency and reduce variability.

A Review on Automated Sleep Study

In recent years, research on automated sleep analysis has witnessed significant growth, reflecting advancements in understanding sleep patterns and their impact on overall health. This review synthesizes findings from an exhaustive analysis of 87 papers, systematically retrieved from prominent databases such as Google Scholar, PubMed, IEEE Xplore, and ScienceDirect. The selection criteria prioritized studies focusing on methods employed, signal modalities utilized, and machine learning algorithms applied in automated sleep analysis. The overarching goal was to critically evaluate the strengths and weaknesses of the proposed methods, shedding light on the current landscape and future directions in sleep research. An in-depth exploration of the reviewed literature revealed a diverse range of methodologies and machine learning approaches employed in automated sleep studies. Notably, K-Nearest Neighbors (KNN), Ensemble Learning Methods, and Support Vector Machine (SVM) emerged as versatile and potent classifiers, exhibiting high accuracies in various applications. However, challenges such as performance variability and computational demands were observed, necessitating judicious classifier selection based on dataset intricacies. In addition, the integration of traditional feature extraction methods with deep structures and the combination of different deep neural networks were identified as promising strategies to enhance diagnostic accuracy in sleep-related studies. The reviewed literature emphasized the need for adaptive classifiers, cross-modality integration, and collaborative efforts to drive the field toward more accurate, robust, and accessible sleep-related diagnostic solutions. This comprehensive review serves as a solid foundation for researchers and practitioners, providing an organized synthesis of the current state of knowledge in automated sleep analysis. By highlighting the strengths and challenges of various methodologies, this review aims to guide future research toward more effective and nuanced approaches to sleep diagnostics.

Dual-Teacher Feature Distillation: A Transfer Learning Method for Insomniac PSG Staging

Insomnia is the most common sleep disorder linked with adverse long-term medical and psychiatric outcomes. Automatic sleep staging plays a crucial role in aiding doctors to diagnose insomnia disorder. Only a few studies have been conducted to develop automatic sleep staging methods for insomniacs, and most of them have utilized transfer learning methods, which involve pre-training models on healthy individuals and then fine-tuning them on insomniacs. Unfortunately, significant differences in feature distribution between the two subject groups impede the transfer performance, highlighting the need to effectively integrate the features of healthy subjects and insomniacs. In this paper, we propose a dual-teacher cross-domain knowledge transfer method based on the feature-based knowledge distillation to improve the performance of sleep staging for insomniacs. Specifically, the insomnia teacher directly learns from insomniacs and feeds the corresponding domain-specific features into the student network, while the health domain teacher guide the student network to learn domain-generic features. During the training process, we adopt the OFD (Overhaul of Feature Distillation) method to build the health domain teacher. We conducted the experiments to validate the proposed method, using the Sleep-EDF database as the source domain and the CAP-Database as the target domain. The results demonstrate that our method surpasses advanced techniques, achieving an average sleep staging accuracy of 80.56% on the CAP-Database. Furthermore, our method exhibits promising performance on the private dataset.

Fine tuned personalized machine learning models to detect insomnia risk based on data from a smart bed platform

Introduction

Insomnia causes serious adverse health effects and is estimated to affect 10-30% of the worldwide population. This study leverages personalized fine-tuned machine learning algorithms to detect insomnia risk based on questionnaire and longitudinal objective sleep data collected by a smart bed platform.

Methods

Users of the Sleep Number smart bed were invited to participate in an IRB approved study which required them to respond to four questionnaires (which included the Insomnia Severity Index; ISI) administered 6 weeks apart from each other in the period from November 2021 to March 2022. For 1,489 participants who completed at least 3 questionnaires, objective data (which includes sleep/wake and cardio-respiratory metrics) collected by the platform were queried for analysis. An incremental, passive-aggressive machine learning model was used to detect insomnia risk which was defined by the ISI exceeding a given threshold. Three ISI thresholds (8, 10, and 15) were considered. The incremental model is advantageous because it allows personalized fine-tuning by adding individual training data to a generic model.

Results

The generic model, without personalizing, resulted in an area under the receiving-operating curve (AUC) of about 0.5 for each ISI threshold. The personalized fine-tuning with the data of just five sleep sessions from the individual for whom the model is being personalized resulted in AUCs exceeding 0.8 for all ISI thresholds. Interestingly, no further AUC enhancements resulted by adding personalized data exceeding ten sessions.

Discussion

These are encouraging results motivating further investigation into the application of personalized fine tuning machine learning to detect insomnia risk based on longitudinal sleep data and the extension of this paradigm to sleep medicine.

Verification methodology for Smart Awakening Systems

A mental and physical recovery after an awakening moment depends not only on the overall sleep duration and quality but mostly on the sleep stage in the waking moment. The most comfortable awakening moment is during the Light or Wake sleep stages. But the fix-time alarm clock doesn't take into account the sleep stage in the awakening moment, which often results in awakening during the Deep or Rapid Eyes Movement stages. To reduce the negative recovery effect, big companies and research groups develop various awakening systems. Such systems recognize sleep stages based on wearable sensors' data (mostly from accelerometer sensors) and thus can find the easiest awakening moment time with minimal recovery effects.However, it is quite hard to measure and verify the efficiency of such systems without using polysomnography (which can be performed only in clinical conditions by medical experts). To solve this problem we developed a methodology based on questionnaires and psychological tests. Such an approach has big scalability, does not require special medical equipment, and can be evaluated in a home environment with minimal support effort. The proposed verification approach has been tested on smartwatches with the sleep stages forecast model. The proposed model accuracy was 78%. Results of our experiment show that the majority of users demonstrated a correlation between awakening quality and the verification tests performance.

Progress in Detection of Insomnia Sleep Disorder: A Comprehensive Review

Lack of adequate sleep is a major source of many harmful diseases related to heart, brain, psychological changes, high blood pressure, diabetes, weight gain, etc. 40 to 50% of the world's population is suffering from poor or inadequate sleep. Insomnia is a sleep disorder in which an individual complaint of difficulties in starting/continuing sleep at least four weeks regularly. It is estimated that 70% of heart diseases are generated during insomnia sleep disorder. The main objective of this study is to determine all work conducted on insomnia detection and to make a database. We used two procedures including network visualization techniques on two databases including PubMed and Web of Science to complete this study. We found 169 and 36 previous publications of insomnia detection in the PubMed and the Web of Science databases, respectively. We analyzed 10 datasets, 2 databases, 21 genes, and 23 publications with 30105 subjects of insomnia detection. This work has revealed the future way and gap so far directed on insomnia detection and has also tried to provide objectives for the future work to be proficient in a scientific and significant manner.