# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
## 1 Introduction  
Sleep is an essential physiological process for brain and body restoration. During sleep, significant changes occur in brain, muscle, ocular, cardiac, and respiratory activity [1]. Heart rate and respiration decrease, muscles enter a state of paralysis, and the brain processes daily experiences, storing them in memory [2]. A healthy sleep cycle, characterized by an adequate duration, quality, consistency, and regularity, improves cognitive development, consolidates learning, and supports physical and emotional well-being [3]. It alsostrengthens the immune system and regulates functions such as metabolism, insulin sensitivity, and glucose tolerance, helping to prevent metabolic diseases such as diabetes and obesity [4]. In contrast, sleep deprivation has widespread negative effects. It can lead to a decrease in body temperature, disrupted growth hormone release, diminished immune function, drowsiness, a lack of concentration, and memory loss [4]. It is also associated with increased appetite, mood disturbances, slower reaction times, cognitive impairment, and the development of neurodegenerative diseases such as Parkinson's and Alzheimer's [5]. Beyond these vital functions, sleep disorders have a huge impact on quality of life. According to the American Academy of Sleep Medicine (AASM), sleep disorders affect a third of the global population [3; 6]. The most common include insomnia, hypersonnia, parasomnia, sleep-related breathing disorders, narcolepsy, circadian rhythm disorders, and abnormal movements during sleep [6; 7]. An example is obstructive sleep apnea (OSA), which disrupts the oxygen supply of the body and, if left untreated, can lead to hypertension, cardiovascular diseases, and other serious conditions [8]. These disorders not only affect individual health, but also have societal consequences. They are related to increased medical and psychiatric morbidity, traffic accidents, reduced work productivity, lower quality of life, and even higher mortality rates [9]. Given the importance of sleep for human health, it is fundamental to understand its behavior and define patterns or stages that facilitate the early diagnosis of related disorders. The accurate classification of these stages can improve medical interventions. In fact, this task is typically performed manually by experts, but it is time-consuming and labor-intensive, requiring detailed visual analysis. In addition, measurements are often invasive, performed in clinical settings or at the patient's home under expert supervision. Recent technological developments aim to reduce invasiveness through non-contact or wearable alternatives. For example, optical fiber-based systems embedded in mattresses have been investigated to monitor heart rate and respiration with promising precision [10], and similar efforts include textile-integrated sensors in garments designed for overnight monitoring [11]. These approaches offer greater comfort and practicality for home environments and also facilitate the acquisition of biomedical signals during sleep, making them highly useful for classification-related applications. In relation to sleep stage classification, various mathematical models have been explored to automate the identification of sleep stages. Advances in deep learning have shown promising results, achieving high precision in multiclass classification tasks [12]. Beyond neurological disorders, these methods have also been applied in medical engineering, such as in the evaluation of muscle strength using wearable sensors with temporal convolutional and transformer-based models [13], and in breast cancer imaging [14], supporting more accurate screening and diagnosis. These developments illustrate the growing impact of deep learning across clinical domains and its potential to improve diagnostic precision and therapeutic decision-making. To assess sleep quality, neurophysiologists collect various physiological data from patients during sleep, including respiration, heart rate, body temperature, and movements [2].