# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
## 1 Introduction
These parameters provide insights into the sleep duration, onset latency, interruptions, efficiency, and other key aspects. The clinical standard for recording biological signals during sleep is polysomnography (PSG). PSG recordings typically include electroencephalography (EEG) to measure cortical electrical activity through scalp electrodes, electrooculography (EOG) used to track eye movements, electromyography (EMG) to measure muscle activity, and electrocardiography (ECG) to monitor cardiovascular activity. Additional PSG recordings may include body temperature, respiratory effort, motion annotations, and other physiological parameters. By integrating these signals, PSG enables an extensive assess ment of neural and physical patterns during sleep, resulting in multidimensional datasets. Figure 1 illustrates the first 100 s of several signals from a PSG recording in the publicly available Sleep EDF Expanded database, utilized in this research. This database includes various signals, such as event markers, temperature, EMG, respiration, EOG, and two EEG channels. The latter were recorded at different cortical locations after the international 10-20 electrode placement standard for EEG (Figure 2). The 10-20 placement protocol divides the cortex into areas based on the main brain lobes, assigning each a combination of letters and numbers. Fp, F, C, P, T, O, and A correspond to the frontal polar, frontal central, parietal, temporal, occipital, and auricular regions, respectively [15]. Even and odd numbers refer to the right and left brain hemispheres, respectively, while the letter z designates the central region of each lobe. Information for the analysis of brain activity during sleep is obtained through EEG recordings. These signals are characterized by their non-linear, nonstationary, and often random nature, with voltage amplitudes ranging from 5 \(\upmu\)V to 200 \(\upmu\)V [16]. EEG signals are classified into five main wave types, varying in frequency from 0.5 to 128 Hz, and are identified by Greek letters: gamma (30-128 Hz), beta (13-30 Hz), alpha (8-13 Hz), theta (4-8 Hz), and delta (0.5-4 Hz) [12]. Figure 3 presents a temporal representation of brain waves over a 5 s interval. These waves were extracted from a signal within the Sleep EDF Expanded database. In this case, gamma wave activity above 50 Hz was discarded due to the 100 Hz sampling frequency, in accordance with the Nyquist criterion. Electrical patterns captured by EEG, such as variations in amplitude, frequency, and waveform morphology, are key indicators in identifying and characterizing different stages of sleep. Figure 1: Physiological signals recorded in a PSG session. Figure 2: International 10–20 electrode placement standard. The first set of rules for categorizing sleep stages was introduced by [PERSON] and [PERSON] in 1968, known as the R&K rules [17]. These rules define a sleep cycle with a duration between 90 and 110 min, comprising wakefulness (W), rapid eye movement (REM, hereafter referred to as R) sleep, and non-REM (NREM) sleep stages. The NREM phase consists of four distinct stages: S1, S2, S3, and S4. Sleep stages are typically assessed in 30 s intervals. In Table 1, each sleep stage with its physiological characteristics is presented [17]. In 2007, the AASM revised the classification system to improve its clinical applicability. The update merged states S3 and S4 into a unified stage, designated N3, while stages S1