Automatic detection of developmental coordination disorder from power spectral densities of electroencephalography (EEG) signals using deep learning model

Year 2025, Volume: 40 Issue: 1, 401 - 412, 16.08.2024

Hanife Göker

https://doi.org/10.17341/gazimmfd.1109475

Abstract

Developmental coordination disorder is a neurodevelopmental disorder characterized by a marked deterioration in the development of motor and coordination skills that significantly impairs daily activity and academic performance. Early diagnosis is very important for medical intervention. Accurate diagnosis of the disease requires extensive testing and long-term observations. These tests and observations can be time consuming, expensive, incomplete, inaccurate and subjective. EEG signals are a method used to monitor brain activity used in early diagnosis. EEG is widely used in the diagnosis of diseases due to its advantages such as being non-invasive, being based on findings, being less costly and getting results in a short time. In this study, an EEG-based deep learning model is presented to support experts in detecting developmental coordination disorder in children from EEG signals. The dataset consists of EEG signals recorded from 16 children without developmental coordination disorder and 16 children with developmental coordination disorder. First of all, power spectral density values of frequencies between 1-49 hertz of EEG signals were calculated separately by using periodogram, welch and multitaper spectral analysis methods. For each of the three different spectral analysis methods, 49 feature vectors were extracted. Then, the performances of support vector machine (SVM), random forest (RF), k-nearest neighbor (kNN) and long-short-term memory (LSTM) algorithms are compared using extracted feature vectors. After the comparison, the model integrating the welch spectral analysis and the LSTM deep learning algorithm showed the highest performance as a result of the experiments. The proposed deep learning model achieved promising performance with 97.20% accuracy, 0.984 sensitivity, 0.959 specificity, 0.962 precision, 0.973 f1-score and 0.944 Matthews correlation coefficient (MCC). The study is a rare attempt in which a deep learning model is used in the effective diagnosis of automatic developmental coordination disorder by analyzing EEG signals and provides evidence of the superiority of deep learning algorithms over traditional machine learning algorithms.

Keywords

Deep learning, developmental coordination disorder, EEG signal processing, spectral analysis, long-short-term memory networks

Elektroensefalografi (EEG) sinyallerinin güç spektral yoğunlukları kullanılarak gelişimsel koordinasyon bozukluğunun derin öğrenme modeli ile otomatik tespiti

Abstract

Gelişimsel koordinasyon bozukluğu, günlük aktivite ve akademik performansı önemli ölçüde engelleyen motor ve koordinasyon becerilerinin gelişiminde belirgin bir bozulma ile karakterize nöro-gelişimsel bir hastalıktır. Tıbbi müdahale için erken tanı çok önemlidir. Hastalığın doğru teşhisi kapsamlı testler ve uzun vadeli gözlemler gerektirir. Bu testler ve gözlemler zaman alıcı, pahalı, eksik, yanlış ve sübjektif olabilir. EEG sinyalleri, erken tanıda kullanılan beyin aktivitesini izlemek için kullanılan bir yöntemdir. EEG invaziv olmaması, bulgulara dayalı olması, daha az maliyetli olması ve kısa sürede sonuç alabilmesi gibi avantajları nedeniyle hastalıkların tanısında yaygın olarak kullanılmaktadır. Bu çalışmada EEG sinyallerinden çocuklarda gelişimsel koordinasyon bozukluğunun tespitinde uzmanlara destek olmak amacıyla EEG tabanlı bir derin öğrenme modeli sunulmaktadır. Veriseti 16 gelişimsel koordinasyon bozukluğu olmayan ve 16 gelişimsel koordinasyon bozukluğu olan çocuktan kaydedilen EEG sinyallerinden oluşmaktadır. Öncelikle periodogram, welch ve multitaper spektral analiz yöntemleri kullanılarak EEG sinyallerinin 1-49 hertz arasındaki frekanslarının güç spektral yoğunluk değerleri ayrı ayrı hesaplanmıştır. Üç farklı spektral analiz yöntemlerinin her biri için 49 özellik vektörü çıkarılmıştır. Daha sonra, çıkarılan öznitelik vektörleri kullanılarak destek vektör makinesi (SVM), rastgele orman (RF), k-en yakın komşu (kNN) ve uzun-kısa süreli bellek (LSTM) algoritmalarının performansları karşılaştırılmıştır. Karşılaştırma sonrasında welch spektral analizi ile LSTM derin öğrenme algoritmasını bütünleştiren model, deneyler sonucunda en yüksek performansı göstermiştir. Önerilen derin öğrenme modeli, %97,20 doğruluk, 0,984 duyarlılık, 0.959 özgüllük, 0,962 kesinlik, 0,973 f1-skoru ve 0,944 Matthews korelasyon katsayısı (MCC) değerleri ile umut verici bir performans elde etmiştir. Çalışma EEG sinyallerini analiz ederek otomatik gelişimsel koordinasyon bozukluğunun efektif tanısında derin öğrenme modelinin kullanıldığı nadir bir girişimdir ve geleneksel makine öğrenmesi algoritmalarına göre derin öğrenme algoritmalarının üstünlüğüne dair kanıt sağlamaktadır.

Keywords

Derin öğrenme, gelişimsel koordinasyon bozukluğu, EEG sinyal işleme, spektral analiz, uzun-kısa süreli bellek ağları

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