The utility of wearable electroencephalography combined with behavioral measures to establish a practical multi-domain model for facilitating the diagnosis of young children with attention-deficit/hyperactivity disorder - PubMed (original) (raw)

The utility of wearable electroencephalography combined with behavioral measures to establish a practical multi-domain model for facilitating the diagnosis of young children with attention-deficit/hyperactivity disorder

I-Chun Chen et al. J Neurodev Disord. 2024.

Abstract

Background: A multi-method, multi-informant approach is crucial for evaluating attention-deficit/hyperactivity disorders (ADHD) in preschool children due to the diagnostic complexities and challenges at this developmental stage. However, most artificial intelligence (AI) studies on the automated detection of ADHD have relied on using a single datatype. This study aims to develop a reliable multimodal AI-detection system to facilitate the diagnosis of ADHD in young children.

Methods: 78 young children were recruited, including 43 diagnosed with ADHD (mean age: 68.07 ± 6.19 months) and 35 with typical development (mean age: 67.40 ± 5.44 months). Machine learning and deep learning methods were adopted to develop three individual predictive models using electroencephalography (EEG) data recorded with a wearable wireless device, scores from the computerized attention assessment via Conners' Kiddie Continuous Performance Test Second Edition (K-CPT-2), and ratings from ADHD-related symptom scales. Finally, these models were combined to form a single ensemble model.

Results: The ensemble model achieved an accuracy of 0.974. While individual modality provided the optimal classification with an accuracy rate of 0.909, 0.922, and 0.950 using the ADHD-related symptom rating scale, the K-CPT-2 score, and the EEG measure, respectively. Moreover, the findings suggest that teacher ratings, K-CPT-2 reaction time, and occipital high-frequency EEG band power values are significant features in identifying young children with ADHD.

Conclusions: This study addresses three common issues in ADHD-related AI research: the utility of wearable technologies, integrating databases from diverse ADHD diagnostic instruments, and appropriately interpreting the models. This established multimodal system is potentially reliable and practical for distinguishing ADHD from TD, thus further facilitating the clinical diagnosis of ADHD in preschool young children.

Keywords: Artificial intelligence (AI); Attention-deficit/hyperactivity disorders (ADHD); Conners’ kiddie continuous performance test second edition (K-CPT-2); Deep learning; Electroencephalography (EEG); Machine learning; Preschool children; Rating scales; Wearable technology.

© 2024. The Author(s).

PubMed Disclaimer

Conflict of interest statement

Declarations Ethics approval and consent to participate This study was approved by the Research Ethics Committee of the National Health Research Institutes in Taiwan (EC1070401-F). Written informed consent from parents and assent from children were obtained prior to study entry. Consent for publication Written informed consents from participating parents and assents from children were obtained. Competing interests The authors declare no competing interests.

Figures

Fig. 1

Fig. 1

Graphical display of wireless EEG data collection and dataset generation

Fig. 2

Fig. 2

The ensemble model proposed in this research. The ensemble model consists of 3 basic classifiers: decision tree, random forest, and bidirectional LSTM models

Fig. 3

Fig. 3

The ranks of important features in predictive model #1 - trained by decision tree (a), in predictive model #2 - trained by random forest (b), in predictive model #3 - trained by bidirectional LSTM

References

    1. Gupta C, Chandrashekar P, Jin T, He C, Khullar S, Chang Q, Wang D. Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases. J Neurodev Disord. 2022;14(1):28. - PMC - PubMed
    1. Jeste SS, Frohlich J, Loo SK. Electrophysiological biomarkers of diagnosis and outcome in neurodevelopmental disorders. Curr Opin Neurol. 2015;28(2):110–6. - PMC - PubMed
    1. Alba G, Pereda E, Mañas S, Méndez LD, González A, González JJ. Electroencephalography signatures of attention-deficit/hyperactivity disorder: clinical utility. Neuropsychiatr Dis Treat. 2015;11:2755–69. - PMC - PubMed
    1. McVoy M, Lytle S, Fulchiero E, Aebi ME, Adeleye O, Sajatovic M. A systematic review of quantitative EEG as a possible biomarker in child psychiatric disorders. Psychiatry Res. 2019;279:331–44. - PubMed
    1. Loo SK, Makeig S. Clinical utility of EEG in attention-deficit/hyperactivity disorder: a research update. Neurotherapeutics: J Am Soc Experimental Neurother. 2012;9(3):569–87. - PMC - PubMed

MeSH terms

LinkOut - more resources