Automatic Diagnosis of Attention Deficit Hyperactivity Disorder with Continuous Wavelet Transform and Convolutional Neural Network

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참고문헌 (36)

1. "Attention deficit hyperactivity disorder: diagnosis and management" (2018)[URL]
2. Guze SB, "Book forum. Diagnostic and statistical manual of mental disorders, 4th ed", Am J Psychiatry, 152, pp.1228~ (1995)
3. Polanczyk G, "The worldwide prevalence of ADHD : a systematic review and metaregression analysis", Am J Psychiatry, 164, pp.942~948 (2007)
4. Willcutt EG, "The prevalence of DSM-IV attention-deficit", hyperactivity disorder : a meta-analytic review, 2012, pp.9, 490~499
5. Biederman J, "Young adult outcome of attention deficit hyperactivity disorder : a controlled 10-year follow-up study", Psychol Med, 36, pp.167~179 (2006)
6. Caye A, "Attention-deficit", hyperactivity disorder trajectories from childhood to young adulthood : evidence from a Birth Cohort supporting a late-onset syndrome, 2016, pp.73, 705~712
7. 김설기, "Deficits in Verbal Working Memory among College Students with Attention- Deficit", Hyperactivity Disorder Traits: An Event-related Potential Study, 2016, pp.14(1), 64~73[DOI]
8. 김준원, "Differences in Resting-state Quantitative Electroencephalography Patterns in Attention Deficit", Hyperactivity Disorder with or without Comorbid Symptoms, 2017, pp.15(2), 138~145[DOI]
9. Rettew DC, "Temperament and its role in developmental psychopathology", Harv Rev Psychiatry, 13, pp.14~27 (2005)
10. Muris P, "The role of temperament in the etiology of child psychopathology", Clin Child Fam Psychol Rev, 8, pp.271~289 (2005)
11. De Pauw SS, "Temperament, personality and developmental psychopathology : a review based on the conceptual dimensions underlying childhood traits", Child Psychiatry Hum Dev, 41, pp.313~329 (2010)
12. Martel MM, "Child ADHD and personality", temperament traits of reactive and effortful control, resiliency, and emotionality, 2006, pp.47, 1175~1183
13. Faraone SV, "Early response to SPN-812(viloxazine extended-release)can predict efficacy outcome in pediatric subjects with ADHD : a machine learning post-hoc analysis of four randomized clinical trials", Psychiatry Res, 296, pp.113664~ (2021)
14. Faraone S, "Predicting comorbid disorders in ADHD using machine learning", Biol Psychiatry, 85(S10), pp.S6~ (2019)
15. Ahmadi A, "Computer aided diagnosis system using deep convolutional neural networks for ADHD subtypes", Biomed Signal Process Control, 63, pp.102227~ (2021)
16. Narin A, "Detection of focal and non-focal epileptic seizure using continuous wavelet transform-based scalogram images and pre-trained deep neural networks", IRBM (2020)[DOI]
17. Alchalabi AE, "FOCUS : detecting ADHD patients by an EEG-based serious game", IEEE Trans Instrum Meas, 67, pp.1512~1520 (2018)
18. Mercado-Aguirre IM, "EEG feature selection for ADHD detection in children", The 16th International Symposium on Medical Information Processing and Analysis (2020)
19. Mohammad Reza Mohammadi, "EEG Classification of ADHD and Normal Children Using Non-linear Features and Neural Network", Biomedical Engineering Letters (BMEL), 6(2), pp.66~73 (2016), 대한의용생체공학회[DOI]
20. Faust O, "Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis", Seizure, 26, pp.56~64 (2015)
21. Yochum M, "Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT", Biomed Signal Process Control, 25, pp.46~52 (2016)
22. Yochum M, "Estimation of muscular fatigue under electromyostimulation using CWT", IEEE Trans Biomed Eng, 59, pp.3372~3378 (2012)
23. Garg D, "Emotion recognition in valence-arousal space from multi-channel EEG data and wavelet based deep learning framework", Proced Comput Sci, 171, pp.857~867 (2020)
24. Krizhevsky A, "ImageNet classification with deep convolutional neural networks", Commun ACM, 60, pp.84~90 (2017)
25. Jiang B, "Fusion of machine vision technology and AlexNet-CNNs deep learning network for the detection of postharvest apple pesticide residues", Artif Intell Agric, 1, pp.1~8 (2019)
26. Chen J, "Medical image segmentation and reconstruction of prostate tumor based on 3D AlexNet", Comput Methods Programs Biomed, 200, pp.105878~ (2021)
27. Szegedy C, "Going deeper with convolutions", 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
28. Ritesh, "Word representations for gender classification using deep learning", Proced Comput Sci, 132, pp.614~622 (2018)
29. Jahandad, "Offline signature verification using deep learning convolutional neural network(CNN)architectures GoogLeNet inception-v1 and inception-v3", Proced Comput Sci, 161, pp.475~483 (2019)
30. Ucar F, "COVIDiagnosis-Net : Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019(COVID-19)from X-ray images", Med Hypotheses, 140, pp.109761~ (2020)
31. Wang A, "A dual neural architecture combined SqueezeNet with OctConv for LiDAR data classification", Sensors(Basel), 19, pp.4927~ (2019)
32. Chiesa-Estomba CM, "Machine learning models for predicting facial nerve palsy in parotid gland surgery for benign tumors", J Surg Res, 262, pp.57~64 (2021)
33. Tenev A, "Application of machine learning techniques for classification of ADHD and control adults", Neurosci Lett, 500(Suppl), pp.e31~e32 (2011)
34. Kautzky A, "Machine learning classification of ADHD and HC by multimodal serotonergic data", Transl Psychiatry, 10, pp.104~ (2020)
35. Riaz A, "DeepFMRI : End-to-end deep learning for functional connectivity and classification of ADHD using fMRI", J Neurosci Methods, 335, pp.108506~ (2020)
36. Mao Z, "Spatiotemporal deep learning method for ADHD fMRI classification", Inf Sci, 499, pp.1~11 (2019)

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