A MATLAB-Based EEG Sleep Classification System from Brain Waves to Sleep Stages

Author : Waqas Javaid

Abstract

This study presents a comprehensive MATLAB-based framework for the automated classification of sleep stages from multi-channel electroencephalogram (EEG) data. The system implements a complete processing pipeline, beginning with simulated EEG, electrooculogram (EOG), and electromyogram (EMG) signals that model the five primary sleep stages: Wake, N1, N2, N3, and REM [1]. Following bandpass and notch filtering for artifact removal, a suite of twelve temporal and spectral features is extracted from 30-second epochs. A transparent, rule-based classifier, grounded in standard polysomnography scoring criteria, is applied to distinguish sleep stages based on physiological thresholds for power bands and muscle tone [2]. The framework is evaluated through six integrated visualizations, including hypnograms, spectrograms, and feature distributions, providing intuitive interpretation of both signals and classification results. Achieving accurate sleep architecture quantification, the system demonstrates the effective fusion of digital signal processing and sleep medicine principles [3]. This work offers a modular, educational foundation for developing automated sleep scoring tools with applications in clinical diagnostics and sleep research.

1. Introduction

Sleep is a complex, dynamic physiological process essential for human health, traditionally characterized by distinct stages Wake, N1, N2, N3, and REM that cycle throughout the night. Accurate classification of these stages, known as sleep scoring, is a cornerstone of sleep medicine, critical for diagnosing disorders like insomnia, sleep apnea, and narcolepsy.

Conventionally, this task is performed manually by trained technicians analyzing overnight polysomnography (PSG) recordings, a method that is labor-intensive, time-consuming, and prone to inter-rater subjectivity. The advent of digital signal processing and computational analytics offers a transformative solution through automated sleep staging, promising enhanced consistency, scalability, and objective analysis [4]. This article details the development of a comprehensive, MATLAB-based framework designed to replicate and automate this intricate process. The system processes multi-channel biosignals including electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) data through a structured pipeline encompassing simulation, preprocessing, feature extraction, and classification. By implementing physiologically grounded rules and generating multifaceted visualizations, the model not only classifies sleep stages but also provides insightful analytics on sleep architecture [5]. This work bridges the gap between clinical sleep science and engineering methodologies, serving as an educational platform and a foundational tool for advancing automated diagnostic technologies in sleep research and clinical practice [6].

1.1 The Clinical Foundation of Sleep Analysis

Sleep is not a uniform state but a cyclical journey through distinct neurophysiological stages, each characterized by unique brain wave patterns, eye movements, and muscle activity. These stages Wake, N1 (light sleep), N2, N3 (deep or slow-wave sleep), and REM (rapid eye movement) sleep form the architecture of sleep, which is critically assessed in sleep medicine. The gold standard for evaluation is overnight polysomnography (PSG), a multi-parameter test that records biophysical changes during sleep [7]. A core component of PSG is the electroencephalogram (EEG), which measures electrical activity from the scalp and provides the primary data for visual sleep stage scoring. This manual scoring, following standardized rules like those from the American Academy of Sleep Medicine (AASM), is the definitive diagnostic method for sleep disorders [8]. However, this process requires expert technicians to examine 30-second epochs of data across an entire night, making it profoundly tedious and subjective. Variability between scorers can affect diagnosis and treatment plans, highlighting a significant need for standardization. Thus, the quest for reliable, automated sleep scoring systems is a major focus in biomedical engineering, aiming to augment clinical practice with objective, efficient tools.

1.2 The Engineering Challenge and Computational Approach

Translating the nuanced visual patterns recognized by a sleep technician into a robust computational algorithm presents a formidable engineering challenge. The solution lies in the interdisciplinary field of biomedical signal processing, which applies mathematical techniques to extract meaningful information from physiological data [9]. An automated system must reliably mimic the expert’s decision-making process, which involves recognizing specific waveforms like sleep spindles in N2 or high-amplitude delta waves in N3. This is achieved by constructing a multi-stage computational pipeline that systematically transforms raw, noisy signals into a clear sleep stage label for each epoch [10].

Table 1: Dataset and Acquisition Parameters

The pipeline typically involves data acquisition, preprocessing to remove artifacts, extraction of quantifiable features that distinguish one stage from another, and finally, a classification decision. Such systems can be based on deterministic rules (emulating official scoring manuals) or data-driven machine learning models. The development of these algorithms not only requires engineering expertise but also a deep understanding of the underlying physiology to ensure clinical validity. This project demonstrates the implementation of a complete, transparent rule-based pipeline in MATLAB, designed for clarity and educational value while establishing a foundation for more complex systems.

1.3 System Overview and Implementation Rationale

This article presents a complete, modular framework for automated sleep stage classification, implemented from the ground up in the MATLAB programming environment. The system begins by simulating realistic multi-channel PSG data, generating synthetic EEG, EOG, and EMG signals that embody the key characteristics of each sleep stage over a 30-minute period [11]. This simulation provides a controlled, ground-truth dataset for development and validation. The core of the framework is a sequential pipeline: first, raw signals are cleansed using digital filters to isolate relevant frequency bands and remove electrical interference; next, a comprehensive set of twelve features spanning spectral power, statistical measures, and signal complexity is computed from 30-second data epochs [12]. A rule-based classifier then analyzes these features, applying thresholds derived from established physiological knowledge to assign one of the five sleep stages. The implementation prioritizes transparency and educational insight, featuring six detailed visualizations that illustrate every step from raw signals to final hypnogram. By building a fully functional system that integrates simulation, processing, analysis, and visualization, this work serves as a practical bridge between theoretical sleep physiology and applied computational engineering, offering a versatile template for research and development in automated sleep analysis.

1.4 Signal Simulation and Realistic Data Synthesis

A critical first step in developing and validating any biosignal processing system is the creation of a realistic, controlled dataset. This framework simulates a 30-minute polysomnography recording at a sampling frequency of 256 Hz, generating four synchronous channels of data. The simulation models the defining electrophysiological hallmarks of each sleep stage for instance, it produces alpha rhythm (8-12 Hz) during the simulated Wake period, theta waves (4-8 Hz) and slow eye movements for N1, distinct sleep spindles (~14 Hz) and K-complexes for N2, high-amplitude, slow delta waves (0.5-4 Hz) for N3, and a low-voltage, mixed-frequency EEG combined with rapid eye movements for REM. Each channel two EEGs, one EOG, and one EMG is modeled with stage-appropriate amplitude, frequency, and noise characteristics, ensuring the synthetic data reflects the complex, non-stationary nature of real physiological signals. Controlled Gaussian noise and 50 Hz line noise are added to mimic real-world recording artifacts [13]. This approach provides a perfect ground truth for algorithm development, allowing for precise performance evaluation without the ambiguities of manual scoring. The simulated data serves as a consistent and shareable benchmark for testing each component of the processing pipeline [14].

1.5 Raw Data to Clean Signals

Raw physiological recordings are invariably contaminated with artifacts that can obscure meaningful information and degrade classification performance. This step implements a crucial preprocessing chain to isolate the signal of interest [15]. First, application-specific bandpass filters are applied: a 0.5-35 Hz filter cleans the EEG to focus on relevant neural oscillations, a 0.1-10 Hz filter is used for the EOG channel to capture eye movements, and a 10-100 Hz filter isolates the EMG’s myographic activity [16]. The use of bidirectional `filtfilt` filtering ensures zero-phase distortion, preserving the exact temporal alignment of waveform features essential for accurate event detection. Subsequently, a narrow notch filter centered at 50 Hz (or 60 Hz, region-dependent) is applied to mitigate power line interference, a common source of contamination in clinical settings. This preprocessing stage transforms the noisy, multi-component raw signals into cleaned versions where the salient features of each sleep stage are markedly enhanced. The effectiveness of this cleaning process is visually verified in comparative plots, demonstrating the removal of high-frequency noise and interference while maintaining the integrity of the underlying physiological waveforms [17].

1.6 Feature Extraction: Quantifying the Signature of Sleep

The cleaned, continuous time-series data must be converted into a set of numerical descriptors, or features, that a classifier can use to discriminate between sleep stages. This process is performed on sequential 30-second epochs, the standard scoring window [18]. For each epoch, twelve distinct features are computed. Spectral features are derived from the power spectral density (estimated via Welch’s method) and include the absolute or relative power in the delta, theta, alpha, sigma, and beta frequency bands. These are directly tied to AASM scoring rules. Additional features capture complementary information: the 90% spectral edge frequency indicates the dominant frequency content; the variance of the EOG channel quantifies eye movement activity; the mean amplitude of the EMG signals reflects muscle atonia. Finally, Hjorth parameters—activity, mobility, and complexity are computed from the EEG to describe the signal’s statistical properties in the time domain, such as its amplitude variance and waveform complexity [19]. This multi-domain feature set creates a comprehensive numerical “fingerprint” for each 30-second epoch, encapsulating the key neurophysiological phenomena that a human scorer would visually assess.

1.7 Rule-Based Classification and Translating Physiology into Logic

With features extracted, the system must now replicate the decision logic of a sleep technician. This implementation employs an interpretable, rule-based classifier that directly encodes established scoring guidelines into a series of conditional statements [20].

Table 2: Sleep Stage Distribution (Predicted)

The classifier analyzes the computed feature vector for each epoch, applying hierarchical thresholds: for example, if delta power is very high and sigma power is low, the epoch is classified as N3 (slow-wave sleep). If sigma power is prominent, it indicates the presence of sleep spindles, leading to an N2 classification.

Table 3: Classification Performance Summary

A combination of high alpha power and high EMG amplitude suggests Wakefulness, while low EMG amplitude coupled with high EOG variance points to REM sleep. Epochs not meeting these more specific criteria default to an N1 classification [21]. This transparent, “if-then” logic structure makes the system’s decisions fully auditable, allowing users to understand exactly why a particular stage was assigned. While potentially less flexible than machine learning models, this approach is robust, requires no training data, and faithfully mirrors the clinical reasoning process, making it an excellent tool for education and foundational system design.

1.8 Visualization and Interpretability

A major strength of this framework is its extensive suite of six integrated visualizations, which transform abstract computations into intuitive, clinical-grade outputs. The plots include a multi-panel display of raw and filtered signals, allowing for direct inspection of data quality. A hypnogram the fundamental chart in sleep medicine plots the progression of scored stages over time, comparing predicted stages against the ground truth [22]. A confusion matrix provides a statistical summary of classification performance across all stages. Feature distribution boxplots reveal how key metrics like delta power systematically vary between stages. Time-frequency spectrograms illustrate the dynamic spectral evolution within an epoch, highlighting transient events like spindles. Finally, a Principal Component Analysis (PCA) plot projects the high-dimensional feature data into a 2D space, visually demonstrating the separability of different sleep stages [23]. These visualizations serve multiple purposes: they are essential for debugging and validating the algorithm, they offer deep insight into the data and the model’s behavior, and they produce publication-ready figures that communicate results effectively to both engineering and clinical audiences.

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1.9 Performance Evaluation and Sleep Architecture Summary

Following classification, the system conducts a comprehensive quantitative analysis. The overall classification accuracy is calculated by comparing the algorithm’s output to the known ground-truth labels from the simulation. More granular performance is assessed via a stage-by-stage confusion matrix, which identifies if the system has specific biases, such as confusing N1 and Wake. Beyond mere accuracy, the framework generates a detailed sleep architecture report, a primary output of clinical PSG [24]. This report summarizes the absolute number and percentage of epochs spent in each stage, the total sleep time (TST), and the sleep efficiency (the ratio of TST to total time in bed). These metrics are vital for diagnosing sleep disorders; for instance, a low percentage of N3 sleep or fragmented REM can indicate specific pathologies. By automating this summary, the system replicates a core, time-consuming task of the sleep technician. This step closes the loop, demonstrating that the pipeline does not just assign labels but delivers actionable, clinical insights derived from those labels.

1.10 Limitations, and Future Directions

While the presented framework successfully demonstrates a complete automated scoring pipeline, it is important to contextualize its capabilities and potential evolution. The primary limitation is its reliance on simulated data and a deterministic rule set, which may not capture the full heterogeneity and artifact contamination seen in real patient recordings. Future iterations would necessitate validation on extensive, expert-annotated clinical databases [25]. The logical progression is to replace or augment the rule-based classifier with a machine learning model such as a Support Vector Machine (SVM), Random Forest, or deep learning convolutional neural network (CNN) trained on real data, which could learn more complex, nuanced patterns. Other enhancements could include real-time processing capabilities, the addition of more channels (like ECG for heart rate variability), and the detection of micro-events like arousals or apnea. Ultimately, this system serves as a powerful educational prototype and a modular foundation. It effectively deconstructs the complex problem of sleep scoring into manageable engineering components, providing a clear roadmap for researchers and developers to build upon, aiming toward robust, clinically deployable tools that can make sleep analysis more accessible and efficient.

2. Problem Statement

Manual sleep stage scoring from polysomnography (PSG) is the gold standard in sleep medicine but suffers from significant inefficiencies and inconsistencies. This labor-intensive process requires expert technicians to visually analyze hours of multi-channel data, including electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) signals, according to complex rules. The methodology is inherently time-consuming, prone to inter-rater and intra-rater variability, and creates a bottleneck in sleep clinics, delaying diagnoses for conditions like sleep apnea, insomnia, and narcolepsy. The subjectivity involved can affect the reliability of the sleep architecture metrics that are critical for treatment decisions. Consequently, there is a pressing need for an automated, objective, and computationally efficient system that can accurately classify sleep stages. This project addresses this need by developing a comprehensive MATLAB-based framework that replicates the clinical scoring pipeline through signal processing and transparent rule-based logic. The system aims to provide consistent, rapid analysis while offering detailed visualization and validation tools, thereby augmenting clinical workflows and establishing a foundation for advanced diagnostic technologies.

3. Mathematical Approach

The mathematical approach is built upon digital signal processing and feature-based pattern recognition. It applies finite impulse response (FIR) and infinite impulse response (IIR) filters, specifically Butterworth bandpass and notch filters, to isolate physiologically relevant frequency bands and remove noise. Spectral feature extraction is performed via Welch’s method for power spectral density estimation, calculating integrals over defined bands (e.g., Delta: 0.5-4 Hz). Time-domain features, including variance and Hjorth parameters (Activity = Var(x), Mobility = √(Var(dx) Var(x))), are computed to characterize signal morphology. Finally, a deterministic, multi-threshold classifier implements a piecewise function where stage assignment is a logical mapping of feature vectors based on physiological. The mathematical approach integrates digital filtering, spectral analysis, and rule-based logic. Filtering employs a 4th-order Butterworth bandpass design, to isolate EEG (0.5–35 Hz), EOG (0.1–10 Hz), and EMG (10–100 Hz) bands.

Spectral decompositionmuses Welch’s method to compute the power spectral density with band power defined as:

Feature engineeringmcalculates Hjorth parameters:

Classification follows a piecewise rule and physiologically derived thresholds:e.g

4. Methodology

The methodology for this automated sleep stage classification system is structured as a sequential, modular pipeline implemented in MATLAB. The process initiates with the synthesis of a controlled dataset, simulating 30 minutes of multi-channel polysomnography (PSG) data at 256 Hz to include the electrophysiological signatures of all five AASM sleep stages across EEG, EOG, and EMG channels, complete with added noise for realism. Preprocessing follows, where raw signals are cleansed using a 4th-order Butterworth bandpass filter (H(z) ) with application-specific cutoffs (EEG: 0.5–35 Hz, EOG: 0.1–10 Hz, EMG: 10–100 Hz) to isolate relevant biopotentials, and a 50 Hz notch filter is applied to suppress line interference, utilizing zero-phase `filtfilt` to prevent temporal distortion [26]. The continuous data is then segmented into standard 30-second epochs, from which a suite of twelve features is systematically extracted. This includes calculating band-limited power for Delta, Theta, Alpha, Sigma, and Beta ranges from the Welch power spectral density (P_{xx}(f) ), determining the 90% spectral edge frequency, and computing time-domain metrics such as EOG variance, EMG mean amplitude, and the Hjorth parameters (Activity, Mobility, Complexity).

All features are subsequently Z-score normalized per feature dimension to ensure equal weighting prior to classification. The core rule-based classifier then analyzes each epoch’s feature vector, applying a hierarchical set of conditional thresholds derived from established scoring guidelines; for example, an epoch is classified as N3 if its normalized delta power exceeds 0.3 and sigma power is below 0.1. The system’s output is validated  against the simulation’s ground-truth labels to compute overall accuracy and generate a confusion matrix. Finally, a comprehensive visualization and reporting stage produces six integrated plots including signal comparisons, hypnograms, and feature analyses and a summary sleep report detailing architecture metrics like sleep efficiency, providing both diagnostic insight and transparent validation of the entire computational process.

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5. Design Matlab Simulation and Analysis

The simulation module generates a controlled, synthetic polysomnography dataset that replicates the fundamental electrophysiological signatures of all five AASM-defined sleep stages over a continuous 30-minute recording sampled at 256 Hz. It constructs four synchronized signal channels: two electroencephalogram (EEG) leads (Fpz-Cz and Pz-Oz), one electrooculogram (EOG), and one electromyogram (EMG), modeling the temporal progression from Wake through N1, N2, N3, and REM sleep. For the wake stage, the simulation produces low-amplitude, mixed-frequency EEG with prominent alpha activity (8-12 Hz) and adds substantial, random EMG tone to represent muscle activity. Transitioning to (N1), the signals introduce slower theta waves (4-8 Hz) in the EEG and slow, rolling eye movements in the EOG channel. The (N2) stage is characterized by simulating the hallmark sleep spindles (~14 Hz) through a sinusoid modulated by a slower envelope, alongside K-complex-like waveforms and a reduction in background EMG. N3 or slow-wave sleep is synthesized using high-amplitude, low-frequency delta oscillations (0.5-4 Hz), creating the characteristic slow, synchronized waves with minimal higher-frequency content. Finally, REM sleep is modeled with a desynchronized, low-voltage EEG featuring theta rhythms, coupled with rapid, episodic eye movements in the EOG and a near-complete absence of muscle tone (atonia) in the EMG. Throughout all stages, controlled Gaussian white noise and 50 Hz line noise are systematically added to each channel, introducing realistic recording artifacts and variability. This carefully engineered simulation provides a vital ground-truth dataset with perfectly known stage labels, enabling the precise development, validation, and transparent demonstration of the entire classification pipeline without the ambiguity inherent in manually scored clinical data.

Raw simulated signals from four polysomnography channels (two EEG, one EOG, one EMG) and initial sleep stage classification results. Top panels display 60 seconds of raw data showing characteristic waveforms for each biosignal type. The spectrogram reveals the time-frequency distribution of EEG Channel 1, illustrating the transition between different sleep stages. The bottom panel presents the predicted sleep stages across all epochs as colored scatter points, providing an initial overview of the sleep architecture throughout the 30-minute recording period. This comprehensive visualization establishes the baseline data quality and demonstrates the system’s preliminary classification output before detailed analysis, serving as both a data quality check and an introductory hypnogram preview.

Comparison of raw versus processed EEG signals and their corresponding power spectral densities. The top panel juxtaposes 10 seconds of unfiltered EEG data (blue) with the cleaned, filtered output (red), demonstrating the effective removal of high-frequency noise and baseline wander while preserving physiological waveforms. The bottom panel displays logarithmic power spectral density plots for both signal conditions, highlighting how preprocessing eliminates broadband noise and attenuates the 50 Hz power line interference while maintaining the integrity of clinically relevant frequency bands. This figure validates the preprocessing pipeline’s efficacy in enhancing signal-to-noise ratio and preparing the data for subsequent feature extraction, crucial for accurate sleep stage classification.

Box plot visualization of four key classification features delta power, alpha power, sigma power, and EMG amplitude stratified by predicted sleep stage. Each subplot displays the distribution (median, quartiles, and outliers) of a specific feature across all five sleep stages, revealing characteristic patterns: elevated delta power in N3, increased alpha during Wake, prominent sigma in N2, and reduced EMG amplitude during REM sleep. These distributions demonstrate the discriminative power of selected features and validate their physiological relevance for sleep stage differentiation, while also highlighting potential feature overlap between adjacent stages that presents classification challenges.

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Performance evaluation through hypnogram comparison and confusion matrix analysis. The top panel presents traditional hypnograms comparing true (blue solid line) versus predicted (red dashed line) sleep stages across all epochs, visually assessing temporal alignment and transition accuracy. The bottom panel displays a heatmap-style confusion matrix quantifying classification performance, with diagonal elements indicating correct predictions and off-diagonal elements representing misclassifications between specific stages. This dual visualization provides both temporal and statistical perspectives on system performance, identifying patterns in classification errors and stage-specific accuracy that inform potential algorithmic refinements.

Detailed time-frequency analysis of a representative epoch and comparative power spectra across sleep stages. The left panel shows a spectrogram of a single 30-second epoch, illustrating the dynamic frequency content and transient events like sleep spindles within that time window. The right panel overlays power spectra from representative epochs of each sleep stage, clearly demonstrating characteristic spectral profiles: Wake shows prominent alpha peak, N2 displays sigma activity, N3 exhibits dominant delta power, and REM presents mixed theta with minimal muscle artifact. This figure provides insight into both the temporal evolution within epochs and the distinctive spectral fingerprints that enable stage discrimination.

Principal Component Analysis (PCA) of the 12-dimensional feature space for dimensionality reduction and pattern visualization. The left scatter plot projects all epochs onto the first two principal components, colored by predicted sleep stage, revealing natural clustering and separation between stages in reduced dimensions. The middle scree plot displays the variance explained by successive principal components, indicating the intrinsic dimensionality of the feature space. The right loading matrix visualization shows how original features contribute to each principal component, identifying which features (e.g., delta power, EMG amplitude) drive the most variance and thus contribute most significantly to stage separation. This comprehensive analysis validates the feature set’s discriminative capacity and provides geometric intuition about the classification problem’s complexity.

6. Results and Discussion

The developed sleep stage classification system achieved an overall accuracy of approximately 80-85% on the simulated dataset, successfully distinguishing between the five primary sleep stages with a rule-based approach. Analysis of the confusion matrix revealed particularly strong performance in identifying N3 (slow-wave sleep) and Wake stages, where distinct physiological features like high delta power and combined alpha-EMG activity provided clear discrimination [27]. However, the system exhibited expected confusion between transitional stages, notably between N1 and Wake, and occasionally between REM and N1, reflecting the inherent ambiguity in these stages even for human scorers.

Table 4: Extracted Features per Epoch

The extracted features demonstrated strong physiological validity, with box plots clearly showing elevated delta power in N3, prominent sigma activity in N2, suppressed EMG in REM, and alpha dominance during Wake, aligning perfectly with established polysomnography principles. The hypnogram comparison showed accurate capture of macro-sleep architecture, including the progression from Wake to NREM and REM cycles, though some minor temporal misalignments in stage transitions were observed. The visualization suite effectively bridged the gap between raw signal processing and clinical interpretation, with spectrograms confirming the presence of sleep spindles in N2 and slow-wave activity in N3. Principal Component Analysis further validated the feature set, showing distinct clustering of stages in the reduced dimensional space, with the first two principal components explaining over 70% of the total variance [28]. These results demonstrate that a transparent, physiology-driven rule-based system can serve as a robust foundation for automated scoring, though its performance on heterogeneous clinical data would require further validation and potential integration with adaptive machine learning techniques to handle real-world variability and artifacts.

7. Conclusion

In conclusion, this work successfully demonstrates a comprehensive MATLAB-based framework for automated sleep stage classification, integrating physiological simulation, digital signal processing, and rule-based decision logic into a cohesive pipeline [29]. The system effectively translates clinical scoring guidelines into computational operations, achieving reliable performance on simulated data while maintaining full transparency in its classification process. The six complementary visualizations provide crucial interpretability, bridging the technical analysis with clinical sleep medicine concepts. While the rule-based approach offers an excellent educational foundation and validation benchmark, real-world deployment would benefit from augmenting it with machine learning models trained on diverse, annotated clinical datasets [30]. This project establishes a modular and extensible platform that can accelerate sleep research, serve as an educational tool in biomedical engineering, and form the basis for developing more advanced diagnostic systems aimed at making sleep analysis more objective, efficient, and accessible.

8. References

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