Objective ADHD Diagnosis Using Convolutional Neural Networks Over Daily-Life Activity Records

Amado-Caballero, Patricia; Casaseca‐de‐la‐Higuera, Pablo; Alberola-López, Susana; Andrés-de-Llano, Jesús María; Villalobos, José Antonio López; Leiza, José Ramón Garmendia; Alberola-López, Carlos

doi:10.1109/jbhi.2020.2964072

Cited by 26 publications

(7 citation statements)

References 35 publications

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“…In addition, only body parts equipped with sensors can be recorded. The difference between accelerometers and actigraphy is that accelerometer analyzes the subject’s movements during normal daily activities and the recording time is limited by the power of battery [ 29 ], whereas actigraphy studies the subject’s sleep efficiency and the recording is limited by low sampling rate [ 30 ]. Regarding to infrared, the strength of infrared is noncontact without placing any type of sensor in the body of the subjects [ 10 ].…”

Section: Discussionmentioning

confidence: 99%

Objective and automatic assessment approach for diagnosing attention-deficit/hyperactivity disorder based on skeleton detection and classification analysis in outpatient videos

Ouyang,

Yang,

et al. 2024

Child Adolesc Psychiatry Ment Health

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Background Attention-deficit/hyperactivity disorder (ADHD) is diagnosed in accordance with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria by using subjective observations and information provided by parents and teachers. However, subjective analysis often leads to overdiagnosis or underdiagnosis. There are two types of motor abnormalities in patients with ADHD. First, hyperactivity with fidgeting and restlessness is the major diagnostic criterium for ADHD. Second, developmental coordination disorder characterized by deficits in the acquisition and execution of coordinated motor skills is not the major criterium for ADHD. In this study, a machine learning-based approach was proposed to evaluate and classify 96 patients into ADHD (48 patients, 26 males and 22 females, with mean age: 7y6m) and non-ADHD (48 patients, 26 males and 22 females, with mean age: 7y8m) objectively and automatically by quantifying their movements and evaluating the restlessness scales. Methods This approach is mainly based on movement quantization through analysis of variance in patients’ skeletons detected in outpatient videos. The patients’ skeleton sequence in the video was detected using OpenPose and then characterized using 11 values of feature descriptors. A classification analysis based on six machine learning classifiers was performed to evaluate and compare the discriminating power of different feature combinations. Results The results revealed that compared with the non-ADHD group, the ADHD group had significantly larger means in all cases of single feature descriptors. The single feature descriptor “thigh angle”, with the values of 157.89 ± 32.81 and 15.37 ± 6.62 in ADHD and non-ADHD groups (p < 0.0001), achieved the best result (optimal cutoff, 42.39; accuracy, 91.03%; sensitivity, 90.25%; specificity, 91.86%; and AUC, 94.00%). Conclusions The proposed approach can be used to evaluate and classify patients into ADHD and non-ADHD objectively and automatically and can assist physicians in diagnosing ADHD.

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Section: Discussionmentioning

confidence: 99%

Objective and automatic assessment approach for diagnosing attention-deficit/hyperactivity disorder based on skeleton detection and classification analysis in outpatient videos

Ouyang,

Yang,

et al. 2024

Child Adolesc Psychiatry Ment Health

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“…However, using Recurrent Neural Networks, they analyzed the data that collected similarly and failed to find a significant difference between the ADHD group and the control group [ 18 ]. In the latest study, Amado-Caballero et al placed a triaxial accelerometer on the wrist and used CNN to analyze 24-h activity data [ 21 ]. The results obtained a mean sensitivity of up to 97.62%, a specificity of 99.52%, and an AUC value of over 99%.…”

Section: Discussionmentioning

confidence: 99%

“…This study followed the STARD statement (Standards for Reporting of Diagnostic Accuracy Studies [ 19 ]. The area under the receiver operating curve (ROC) of previous similar wearable devices was 0.9 or more at α = 0.05 (unilateral), β = 0.1, and a 1:1 ratio between groups [ 20 , 21 ]. The sample size was estimated using PASS15 software (NCSS LLC.,Kaysville, U.T., USA) and a minimum of 55 participants with ADHD and 55 controls were needed after considering a 10% of lost to follow-up.…”

Section: Methodsmentioning

confidence: 99%

A Wearable Diagnostic Assessment System vs. SNAP-IV for the auxiliary diagnosis of ADHD: a diagnostic test

et al. 2022

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Objective We design a diagnostic test to evaluate the effectiveness and accuracy of A non-intrusive Wearable Diagnostic Assessment System versus SNAP-IV for auxiliary diagnosis of children with ADHD. Methods This study included 55 children aged 6–16 years who were clinically diagnosed with ADHD by DSM-5, and 55 healthy children (typically developing). Each subject completes 10 tasks on the WeDA system (Wearable Diagnostic Assessment System) and Parents of each subject complete the SNAP-IV scale. We will calculate the validity indexes, including sensitivity, specificity, Youden's index, likelihood ratio, and other indexes including predictive value, diagnostic odds ratio, diagnostic accuracy and area under the curve [AUC] to assess the effectiveness of the WeDA system as well as the SNAP-IV. Results The sensitivity (94.55% vs. 76.36%) and the specificity (98.18% vs. 80.36%) of the WeDA system were significantly higher than the SNAP-IV. The AUC of the WeDA system (0.964) was higher than the SNAP-IV (0.907). There is non-statistically significant difference between groups (p = 0.068), and both of them have high diagnostic accuracy. In addition, the diagnostic efficacy of the WeDA system was higher than that of SNAP-IV in terms of the Youden index, diagnostic accuracy, likelihood ratio, diagnostic odds ratio and predictive value. Conclusion The advantages of the WeDA system in terms of diagnostic objectivity, scientific design and ease of operation make it a promising system for widespread use in clinical practice.

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“…Similarly, using the ABIDE dataset, which was based on rs-fMRI and deep neural network, ASD was distinguished from typically developing subjects [71]. Wireless Communications and Mobile Computing In addition, it was found that CNN algorithm was most efficient among all applied ML algorithms, and several studies have reported the rise in accuracy for ADHD diagnosis and examination by utilizing CNN with an accuracy range of between 90 ± 10 percent [55,[72][73][74][75][76][77][78]. Similarly, numerous studies were also conducted using CNN for ASD diagnosis and analyses showing a high accuracy rate > 70-90% [44,[79][80][81][82].…”

Section: Recent Machine Learning and Deep Learning Softwarementioning

confidence: 99%

Investigation of Machine Learning Methods for Early Prediction of Neurodevelopmental Disorders in Children

Alam

Raja

Gulzar

2022

Wireless Communications and Mobile Computing

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Several variables, for instance, inheritance and surroundings, influence the growth of neurodevelopmental disorders, e.g., autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) during the first 36 months of life. ADHD and ASD diagnosis mainly rely heavily on traditional clinical assessments from the last few decades. These traditional methods are based on massive data collection from multiple respondents’ responses and the extent of various behavioral descriptors, which are then recognized by the researcher while forming a diagnostic criterion. However, opting for traditional diagnostic methods, there is a high possibility of being misdiagnosed, which may lead to the administration of unnecessary long-term pharmaceutical treatment. That may lead to reduction in functioning and an increase in the risk of developing additional social and clinical issues. Moreover, such diagnostic procedures are also time-consuming and costly. In this sense, rapid and advanced criteria are required to be accurate and cost-effective. Consequently, this study emphasizes the application of machine learning (ML) tools and deep learning (DL) techniques such as convolutional neural network (CNN) and Deep Learning APIs (Application Programming Interface), for the early diagnosis and treatment of ADHD and ASD symptoms. From this investigation, it can be concluded that diagnostic techniques based on ML reduce the intervention time and increase the accuracy with simultaneous understanding of the techniques and algorithms applied to different types of Image data. Numerous studies have been done on ASD and ADHD separately, but our investigation also focuses on cooccurrences of these disorders in one individual.

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Objective ADHD Diagnosis Using Convolutional Neural Networks Over Daily-Life Activity Records

Cited by 26 publications

References 35 publications

Objective and automatic assessment approach for diagnosing attention-deficit/hyperactivity disorder based on skeleton detection and classification analysis in outpatient videos

Objective and automatic assessment approach for diagnosing attention-deficit/hyperactivity disorder based on skeleton detection and classification analysis in outpatient videos

A Wearable Diagnostic Assessment System vs. SNAP-IV for the auxiliary diagnosis of ADHD: a diagnostic test

Investigation of Machine Learning Methods for Early Prediction of Neurodevelopmental Disorders in Children

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