Supporting ASD Diagnosis with EEG, ML and Swarm Intelligence Early Detection of Autism Spectrum Disorder Based on Electroencephalography Analysis by Machine Learning and Swarm Intelligence

1. Introduction

1.1. Motivation and Problem Characterization

Over the past half-century, our understanding of Autism Spectrum Disorder (ASD) has expanded significantly. ASD poses challenges in communication and social interaction, often accompanied by repetitive behaviors [1]. This increased knowledge has shed light on the complexities of autism.. Now, we see it as a spectrum with many different signs [2]. So, we now know autism is different for everyone. The word ’autism’ is used in different ways to describe different things. It used to be part of a bigger group called ’Pervasive Developmental Disorders’ [3]. The effects of ASD last a long time for individuals and their families. It also affects the kind of help they get in school and therapy. More people are being diagnosed with autism recently [4].

Until 2013, Autism had five categories: Asperger’s, Rett Syndrome, Childhood Disintegrative Disorder, Autistic Disorder, and Pervasive Developmental Disorder (PDD). Today, we use the DSM-5 for diagnosing ASD. We no longer use subtypes. Now, there are three levels based on support needs and abilities of autistic people, that can vary and evolve over time. While some people with autism can live independently, others have severe disabilities and require life-long care and support[5,6,7]. With this revision, diagnostic criteria have also changed. We now focus on just two main areas: social communication and repetitive behavior. To diagnose ASD, we check for at least three social communication symptoms and at least two repetitive behavior symptoms [8]. In 2022, WHO approved the ICD-11 for diagnosing ASD in the health system. (SUS). ICD-11 adopts the term "Autism Spectrum Disorder (ASD)" following DSM-5 standards. In the past, ICD-10 used ’childhood autism’ under Pervasive Developmental Disorders[9].

Signs of ASD usually show up early, sometimes before age two. But, at first, it might look like a general delay in movement and thinking, making diagnosis hard early on[10]. Right now, diagnosing ASD is mainly clinical. Teams of doctors use observations and questionnaires to identify it[11,12]. However, diagnosis is often inaccurate and delayed. ASD doesn’t have specific treatments, but there are therapies to help with symptoms. They aim to improve communication, social skills, and general life for the patient. We need better technologies to help diagnose ASD early, accurately, and easily.

Many studies used EEG signals to diagnose ASD by checking brain activity. These signals come from the small electrical currents in the human brain. These voltage changes happen because of the ions flow between the brain and neurons [13]. EEG signals have different rhythms: delta, theta, alpha, and beta waves [14]. To understand brain activity, we record EEG signals from different parts of the scalp. We cover both sides of the brain. Electrodes in the International System are placed on the head to cover the entire scalp. The most commonly used models are the 10-20 and 10-10 patterns [15]. EEG signals have significant importance in the early diagnosis of diseases. In recent years, EEG signals have been crucial for diagnosing diseases early. In recent years, researchers have linked EEG use with various pattern identification strategies [16]. EEG diagnoses conditions like brain edemas, Parkinson’s, and epilepsy. It also controls brain-machine interfaces and detects and classifies emotions [17].

Furthermore, researchers have investigated machine learning techniques to build diagnostic support tools [11,12]. This branch of artificial intelligence has two parts: supervised and unsupervised machine learning. Some studies suggest that ASD diagnosis can use supervised machine learning techniques. These try to predict a variable, like the diagnosis [18,19]. In supervised learning, the machine learns patterns from a set of examples [20]. This method helps create complex applications and make accurate predictions on different data. Machine learning has three main categories: SVM, ANN, and DL [21]. According to experts, most ASD diagnostic models used ADTree and SVM algorithms. ADTree is a machine learning algorithm for classification. It has decision nodes with conditions and prediction nodes with numbers. The Support Vector Machine (SVM) aims to find a hyperplane in N-dimensional space. This hyperplane should clearly separate data points [22,23].

This study aims to propose a new tool for diagnosing Autism Spectrum Disorder. We analyze EEG signals labeled by clinical diagnosis using machine learning and statistics. We also highlight the potential for EEG utilization not only for diagnosis but also for therapeutic interventions, as an area of interest within the larger project to which this work belongs. In this article, we’ll first cover the ’Related Works’ section. It explores studies using machine learning for diagnosis. In the "Methodology" section, we discuss the database utilized. We also explain key machine learning and statistical concepts. It’s essential to grasp these concepts for the work. Besides, we also describe data preprocessing and experiment execution. The Results and Discussions are presented in sections 3 and 4 respectively, followed by the Conclusion in section 5.

1.2. Related Works

This subsection presents studies that explore EEG and machine learning for ASD diagnosis, highlighting different techniques and classification models.

Kang et al. (2020) [24] combined EEG and eye-tracking data, achieving 85.44% accuracy and an AUC of 0.93, demonstrating the potential of multimodal approaches. Similarly, Mareeswaran and Selvarajan (2023) [25] explored AI tools for early ASD screening in infants, reaching 96% accuracy by categorizing data based on age, gender, and jaundice. Chung et al. (2024) [26] linked EEG patterns to Restricted and Repetitive Behaviors (RRBs) in infants, showing that EEG traits may help detect ASD-related behaviors early.

Deep learning approaches also showed promising results. Ali et al. (2020) [27] trained deep neural networks on EEG data, achieving 80% accuracy. Radhakrishnan et al. (2021) [28] explored deep convolutional networks, with ResNet50 reaching 81% accuracy. Baygin et al. (2021) [29] used 1D LBP and STFT with MobileNetV2 and SqueezeNet, obtaining 96.44% accuracy. Meanwhile, Shu Lih Oh et al. (2021) [30] applied polynomial SVMs, with degree 2 achieving 98.70% accuracy, surpassing other algorithms.

Other studies focused on feature extraction and classification techniques. Jayawardana et al. (2019) [31] used Frequency Band Decomposition and Wavelet Transform with Random Forest and CNN, achieving over 90% accuracy. Alotaibi and Maharatna (2021) [32] analyzed brain connectivity with Phase Locking Value (PLV) methods, reaching 95.8% accuracy. Abdolzadegan et al. (2020) [33] incorporated linear and non-linear EEG features, using KNN and SVM with DBSCAN artifact removal, leading to 94.68% accuracy.

These studies reinforce the effectiveness of EEG-based machine learning for ASD detection, with various models demonstrating high accuracy and potential for clinical applications.

2. Materials and Methods

This section presents the materials and methods used in this study. We begin by detailing the selected database, followed by the preprocessing steps applied to the data. Next, we describe the classifiers used for training and testing, as well as the evaluation metrics chosen to assess model performance.

2.1. Database

This study utilized Dataset 1 from the Sheffield database, containing EEG signals from 56 participants aged 18 to 68. Data was recorded using the Biosemi Active Two EEG system for 150 seconds, with participants at rest and under visual stimulation. A bandpass filter (0.01 to 140 Hz) was applied, with Cz as the reference channel. EEG recordings primarily used a 64-sensor montage, while 128-sensor setups were adjusted for consistency [34,35]. Preprocessing with EEGLAB [36] included filtering, removal of corrupted data, and downsampling to 512 Hz. Several challenges were noted in database analysis, including missing electrodes (Figure 1), which can disrupt data completeness and affect interpretation. Variability in electrode placement due to non-standardized positioning also influenced measurement accuracy. Additionally, high-density electrode systems, while improving resolution, increased data complexity, requiring significant computational resources for processing and analysis.

2.2. Preprocessing

The data preprocessing stage followed the flow depicted in the Figure 2.

The original files in .set and .fdt formats were converted to .edf, a widely used format for storing biomedical signals such as EEG and PSG due to its ability to handle multiple channels of temporal data [37].

To facilitate analysis, the .edf files were converted to .csv using Python 3.11.1 [38], making them compatible with various processing tools. Missing electrode data was addressed using Inverse Distance Weighting (IDW) interpolation, modified with a negative exponential function to reduce computational cost and avoid division by zero. The 128-electrode system was visually compared to the 64-electrode system, and missing channels were identified. Empty columns were added in the correct order, ensuring alignment with the electrode layout shown in Figure 3, based on the ASD 113 file, the only one with complete electrode signals.

The software GIMP (version 2.10.34) [39] was used to obtain the coordinates needed for distance calculations. Using this, the distance between each point was determined using Equation 1, which is based on the Pythagorean theorem [40]. In the formula, "a" and "b" are the points, "x1" and "y1" are the coordinates of point "a", and "x2" and "y2" are the coordinates of point "b". Equation 2 below was used to fill in the missing channels:

$$d_{ab}=\sqrt{{(X_2-X_1)}^2+{(Y_2-Y_1)}^2}$$

(1)

$$p_j={\displaystyle \frac{{\sum }_{i=1}^m{\left(pi{x}^e\right)}^{-d_{ij}}}{{\sum }_{i=1}^m{\left(e\right)}^{-d_{ij}}}}$$

(2)

The missing channel "Pj" represents the final interpolated signal, while "Pi" corresponds to the existing non-missing channels, ensuring that $i\ne j$. Once all missing data was processed, a complete dataset was generated and converted into a CSV file.

For further analysis, GNU Octave (version 8.2.0) [41] was used to segment the EEG signal and extract attributes. A total of 34 features were obtained from each window, as listed in Table 1. Prior studies confirm their effectiveness in EEG analysis [42] and audio analysis [43,44,45]. These features include statistical measures (mean, variance, standard deviation), time-domain metrics (waveform length, zero crossing, Hjorth parameters), and frequency-based attributes (mean power, peak frequency, Shannon entropy). The signal was windowed into 2-second segments with 0.5-second overlap at a sampling rate of 512Hz. Finally, the most relevant attributes were selected using Particle Swarm Optimization (PSO) and evolutionary search methods [46]. Thus, three final files were generated: the original dataset, the optimized dataset using PSO, and the optimized dataset using evolutionary search, all properly processed.

2.3. Classification

We acquired three distinct databases using the methods described before. Then, we trained, validated, and tested various classifiers. This process is shown in the diagram in Figure 4. We used the following algorithms, chosen according to previous methodologies to represent distribution-based and decision tree-base classifiers [43,44,45,47,48,49,50,51,52,53]: Bayes Net [54], Naive Bayes [55], Random Tree [56], and Random Forest [57]. The Random Forest had 10, 100, and 500 trees. We also used the Support Vector Machine [58] with various kernels. These were Linear, Polynomial of Degree 2, and Polynomial of Degree 3. We also tried Rbf with Gamma 0.1, 0.2, and 0.3.

We used the Waikato Environment for Knowledge Analysis (WEKA) software, version 3.9.6 [59,60], to split each dataset into 80% training and 20% testing sets. The selected models and configurations were then applied. To ensure statistical robustness, each experiment was repeated 30 times in addition to performing 10-fold cross-validation.

In this method, the dataset is divided into 10 equal parts, with the model trained on 9 parts and tested on the remaining one. This process repeats until all parts serve as a test set once, averaging the performance metrics across iterations. This technique enhances model reliability, reducing variance and bias while providing a more accurate estimate of generalization ability [61].

3. Results

This section presents and analyzes the research results. We first examined the complete dataset as a baseline, followed by evaluating the Particle Swarm Optimization (PSO) and evolutionary search methods. Tests were conducted on the best models from each dataset to determine the most effective techniques for future studies.

Table 2 displays the results using the full dataset, including all channels and features. The accuracy difference between the best and worst models reached 40.55%, highlighting the variation in performance.

The results of the training and validation stage after feature selection with PSO are presented in Table 3. In this approach, the interval between the average accuracies of the best and worst models was 39.74%.

The results of the evolutionary search-based feature selection are presented in Table 4. In this approach, the accuracy gap between the best and worst models was 38.18%.

Figure 5 and Figure 6 display boxplots illustrating the dispersion of accuracy and kappa index values for the best model in each approach. Some dispersion patterns and outliers were identified, which will be analyzed in the next section.

The best models in each approach (Complete, PSO, and Evolutionary Search) were finally tested. They were tested with separate samples from the dataset in a single round. Table 5 shows the results of this test. The same testing stage generated the confusion matrices for each classifier. They are in Figure 7. We can use them to analyze true and false positive and negative rates from each model. This analysis will be done in the results discussion section.

4. Discussion

From Table 2, Naive Bayes had the weakest performance, with an accuracy of 58.58%, a Kappa Index of 0.17, sensitivity of 0.25, and AUC of 0.60. Its specificity (0.92) was an exception but indicated near-random ASD classification. Bayes Net performed slightly better, with 74.2% accuracy and improved sensitivity.

Tree-based models performed significantly better, with Decision Trees and Random Forests achieving over 90% in accuracy, sensitivity, and specificity. The Decision Tree had a lower Kappa Index (80%), suggesting weaker agreement between training and validation. Random Forest with 100 and 500 trees excelled, reaching over 98% accuracy with well-balanced sensitivity and specificity.

Among Support Vector Machines (SVM), polynomial kernels (degrees 1, 2, and 3) reached 96%, 97%, and 98% accuracy, maintaining balanced true positive and true negative rates. The best-performing model was the SVM with an RBF kernel (degree 0.1), achieving 99.13% accuracy with a standard deviation of 0.44, highlighting potential refinements for classifier optimization.

From Table 3, after applying PSO, the dataset’s dimensionality significantly decreased from 2142 features (34 extracted from each of the 63 electrodes) to 686. This reduction improved processing efficiency while maintaining classifier effectiveness, with performance levels similar to the previous approach.

Naive Bayes remained the weakest model in training, though it showed slight improvements across key metrics. Sensitivity for ASD classification remained its main limitation. Bayes Net and Random Tree also continued to rank among the lowest-performing models.

Among the classifiers, the SVM with an RBF kernel and gamma of 0.2 stood out, achieving 99.23% accuracy, a Kappa Index of 0.98, and 99% across sensitivity, specificity, and AUC. Despite the significant reduction in features, these metrics remained close to 100%. Other SVM models, except those with a linear polynomial kernel, delivered results similar to the best-performing classifier.

The configurations of Random Forest, both the simplest one with 10 trees and the one with 500 trees, also achieved metrics above 90%. As models that require less computational processing, they become a viable alternative to the solution.

Finally, an evolutionary search was applied to select the most relevant features, reducing the dataset from 2142 to 20 key features. Unlike previous approaches where SVM with an RBF kernel performed best, this method yielded the best results with Random Forest. As shown in Table 4, both the 100-tree and 500-tree configurations achieved over 93% accuracy, sensitivity, and specificity, along with a kappa index of 0.87 and an AUC of 0.98.

Performance among other classifiers varied significantly. Naive Bayes had the weakest results, with 55.73% accuracy, a kappa index of 0.11, and low sensitivity (0.18), but high specificity (0.92), indicating poor suitability for this dataset. Bayes Net performed similarly to SVM but showed weaker results compared to its performance in previous approaches, with 68.61% accuracy. SVM models displayed inconsistency, with the polynomial kernel (degree 3) performing best at 72.59% accuracy and a kappa index of 0.45, but still below Random Forest. Random Tree showed strong performance, reaching 87.43% accuracy and kappa values above 0.70. In the end, Random Forest with 500 trees was selected as the best classifier, reinforcing its effectiveness for this classification problem.

Figure 5 presents the boxplots of the best models from each approach. The Random Forest with 500 trees showed a wider range of values compared to SVM models, with greater variability in the evolutionary search-based dataset, despite having the lowest median. In contrast, the SVM models in the original and PSO-based datasets had similar distributions, with values closer to their medians, suggesting greater precision.

Figure 6 illustrates the dispersion of Kappa index values. The SVM models exhibited more consistent classification agreement, with a smaller spread between maximum and minimum values. The Random Forest with 500 trees in the evolutionary search approach had more dispersed values, with 75% of observations below 0.9, and even its highest values were lower than the worst SVM results in the other approaches.

The test results in Table 5 show that all three models performed well, with similar outcomes. However, the SVM models for the complete and PSO-reduced datasets had slight decreases across all five metrics. The Random Forest with the evolutionary search dataset performed best in testing, which was expected due to lower statistical rigor at this stage. The top-performing classifier in this single test round was the SVM with RBF kernel (degree 0.2) after PSO, reinforcing the effectiveness of these models when applied to new data.

Figure 7 a) shows that the SVM with the complete dataset correctly classified 562 ASD windows and 561 control windows, maintaining high sensitivity and specificity. In Figure 7 b), the SVM after PSO correctly identified 570 ASD and 576 control windows. Lastly, Figure 7 c) presents the Random Forest with evolutionary search, classifying 569 ASD windows correctly, with 26 false positives, and 562 control windows correctly, with 28 misclassifications.

5. Conclusions

This study proposed a machine learning-based method for diagnosing Autism Spectrum Disorder (ASD) using EEG signals. By applying interpolation to estimate missing signals and using particle swarm optimization for feature selection, the approach demonstrated strong classification performance with Random Forest and Support Vector Machine (SVM) models. These results highlight the effectiveness of combining advanced machine learning techniques with robust data preprocessing to identify EEG patterns associated with ASD.

Despite its strengths, some limitations must be considered, such as variability in EEG recording protocols and the need for validation with independent samples. Using a public dataset may also introduce biases, affecting result generalization. Addressing these challenges is crucial to refining the method and enhancing its clinical applicability.

Future work includes developing a proprietary EEG database with standardized data collection and designing software for automated EEG analysis and diagnostic support. This will optimize healthcare workflows and contribute to broader access to ASD diagnosis. By improving diagnostic methods, this study can help facilitate early interventions, enhance patient outcomes, and reduce healthcare costs, representing a significant step toward more efficient and accessible ASD detection.