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

Altun, Sinan; Alkan, Ahmet; Altun, Hatice

doi:10.9758/cpn.2022.20.4.715

Cited by 3 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…True Positives (TP) are the number of correctly predicted samples, False Negatives (FN) are the number of incorrectly predicted samples, True Negatives (TN) are the number of correctly predicted negative samples, and False Positives (FP) represent incorrectly predicted negative samples [36].…”

Section: Resultsmentioning

confidence: 99%

Biometric Authentication Based on EMG Hand Gestures Signals Using CNN

Gursoy

2024

ELEKTRON ELEKTROTECH

View full text Add to dashboard Cite

Biometric identification systems are increasingly important today compared to traditional recognition/classification systems. Electromyography (EMG) signals and person identification/classification systems are preferred for high-security systems as they include physiological and behavioural movements. This study investigates biometric EMG signals based on convolutional neural networks (CNNs) and personal identification/classification systems. Bioelectric signals were recorded at six different wrist movements from five volunteer participants with a four-channel EMG device. To determine the spectrum characteristics of EMG signals, the frequency subbands of the signals were found using the discrete wavelet transform (DWT), empirical wavelet transform (EWT), and empirical mode decomposition (EMD) methods. In addition, statistical methods are used to improve the effectiveness of the feature vector. The CNN model was used to define or classify people. The performance of the developed system was evaluated using Accuracy, Precision, Sensitivity, F-score parameters. As a result, a classification success of 95.66 % was achieved with the developed EMD-CNN method, 94.10 % with the DWT-CNN method, and 93.33 % with the EWT-CNN method. The artificial intelligence model presented in this study explains the effectiveness of EMG signals in person recognition or classification as a biometric identification system. Furthermore, the developed model shows promise for the development and design of future biometric recognition systems.

show abstract

Section: Resultsmentioning

confidence: 99%

Biometric Authentication Based on EMG Hand Gestures Signals Using CNN

Gursoy

2024

ELEKTRON ELEKTROTECH

View full text Add to dashboard Cite

show abstract

“…Process of evaluating the extent to which the predictions fit the derivation data after controlling for overfitting and optimism 92], sociodemographic, clinical and cognitive (K = 3) [93][94][95], cognitive, sociodemographic and neuroimaging (K = 1) [96], clinical, sociodemographic and neuroimaging (k = 1) [97], clinical, cognitive and neuroimaging (k = 1) [98] and sociodemographic, clinical, cognitive and physical health (K = 2) [99,100] predictors.…”

Section: Internal Validationmentioning

confidence: 99%

Individualized prediction models in ADHD: a systematic review and meta-regression

Salazar de Pablo,

Iniesta,

Bellato

et al. 2024

Mol Psychiatry

View full text Add to dashboard Cite

There have been increasing efforts to develop prediction models supporting personalised detection, prediction, or treatment of ADHD. We overviewed the current status of prediction science in ADHD by: (1) systematically reviewing and appraising available prediction models; (2) quantitatively assessing factors impacting the performance of published models. We did a PRISMA/CHARMS/TRIPOD-compliant systematic review (PROSPERO: CRD42023387502), searching, until 20/12/2023, studies reporting internally and/or externally validated diagnostic/prognostic/treatment-response prediction models in ADHD. Using meta-regressions, we explored the impact of factors affecting the area under the curve (AUC) of the models. We assessed the study risk of bias with the Prediction Model Risk of Bias Assessment Tool (PROBAST). From 7764 identified records, 100 prediction models were included (88% diagnostic, 5% prognostic, and 7% treatment-response). Of these, 96% and 7% were internally and externally validated, respectively. None was implemented in clinical practice. Only 8% of the models were deemed at low risk of bias; 67% were considered at high risk of bias. Clinical, neuroimaging, and cognitive predictors were used in 35%, 31%, and 27% of the studies, respectively. The performance of ADHD prediction models was increased in those models including, compared to those models not including, clinical predictors (β = 6.54, p = 0.007). Type of validation, age range, type of model, number of predictors, study quality, and other type of predictors did not alter the AUC. Several prediction models have been developed to support the diagnosis of ADHD. However, efforts to predict outcomes or treatment response have been limited, and none of the available models is ready for implementation into clinical practice. The use of clinical predictors, which may be combined with other type of predictors, seems to improve the performance of the models. A new generation of research should address these gaps by conducting high quality, replicable, and externally validated models, followed by implementation research.

show abstract

Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review

Madububambachu,

Ukpebor,

Ihezue

2024

CPEMH

View full text Add to dashboard Cite

Introduction This study aims to investigate the potential of machine learning in predicting mental health conditions among college students by analyzing existing literature on mental health diagnoses using various machine learning algorithms. Methods The research employed a systematic literature review methodology to investigate the application of deep learning techniques in predicting mental health diagnoses among students from 2011 to 2024. The search strategy involved key terms, such as “deep learning,” “mental health,” and related terms, conducted on reputable repositories like IEEE, Xplore, ScienceDirect, SpringerLink, PLOS, and Elsevier. Papers published between January, 2011, and May, 2024, specifically focusing on deep learning models for mental health diagnoses, were considered. The selection process adhered to PRISMA guidelines and resulted in 30 relevant studies. Results The study highlights Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine (SVM), Deep Neural Networks, and Extreme Learning Machine (ELM) as prominent models for predicting mental health conditions. Among these, CNN demonstrated exceptional accuracy compared to other models in diagnosing bipolar disorder. However, challenges persist, including the need for more extensive and diverse datasets, consideration of heterogeneity in mental health condition, and inclusion of longitudinal data to capture temporal dynamics. Conclusion This study offers valuable insights into the potential and challenges of machine learning in predicting mental health conditions among college students. While deep learning models like CNN show promise, addressing data limitations and incorporating temporal dynamics are crucial for further advancements.

show abstract

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

Cited by 3 publications

References 36 publications

Biometric Authentication Based on EMG Hand Gestures Signals Using CNN

Biometric Authentication Based on EMG Hand Gestures Signals Using CNN

Individualized prediction models in ADHD: a systematic review and meta-regression

Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review

Contact Info

Product

Resources

About