Automatic extraction and detection of characteristic movement patterns in children with ADHD Based on a convolutional neural network (CNN) and acceleration images

Attention deficit and hyperactivity disorder (ADHD) is a neurodevelopmental disorder, which is characterized by inattention, hyperactivity and impulsive behaviors. In particular, children have difficulty keeping still exhibiting increased fine and gross motor activity. This paper focuses on analyzing the data obtained from two tri-axial accelerometers (one on the wrist of the dominant arm and the other on the ankle of the dominant leg) worn during school hours by a group of 22 children (11 children with ADHD and 11 paired controls). Five of the 11 ADHD diagnosed children were not on medication during the study. The children were not explicitly instructed to perform any particular activity but followed a normal session at school alternating classes of little or moderate physical activity with intermediate breaks of more prominent physical activity. The tri-axial acceleration signals were converted into 2D acceleration images and a Convolutional Neural Network (CNN) was trained to recognize the differences between non-medicated ADHD children and their paired controls. The results show that there were statistically significant differences in the way the two groups moved for the wrist accelerometer (t-test p-value <0.05). For the ankle accelerometer statistical significance was only achieved between data from the non-medicated children in the experimental group and the control group. Using a Convolutional Neural Network (CNN) to automatically extract embedded acceleration patterns and provide an objective measure to help in the diagnosis of ADHD, an accuracy of 0.875 for the wrist sensor and an accuracy of 0.9375 for the ankle sensor was achieved

Using recurrent neural networks to compare movement patterns in ADHD and normally developing children based on acceleration signals from the wrist and ankle

Attention deficit and hyperactivity disorder (ADHD) is a neurodevelopmental condition that affects, among other things, the movement patterns of children suffering it. Inattention, hyperactivity and impulsive behaviors, major symptoms characterizing ADHD, result not only in differences in the activity levels but also in the activity patterns themselves. This paper proposes and trains a Recurrent Neural Network (RNN) to characterize the moment patterns for normally developing children and uses the trained RNN in order to assess differences in the movement patterns from children with ADHD. Each child is monitored for 24 consecutive hours, in a normal school day, wearing 4 tri-axial accelerometers (one at each wrist and ankle). The results for both medicated and non-medicated children with ADHD, and for different activity levels are presented. While the movement patterns for non-medicated ADHD diagnosed participants showed higher differences as compared to those of normally developing participants, those differences were only statistically significant for medium intensity movements. On the other hand, the medicated ADHD participants showed statistically different behavior for low intensity movements

Differences between children and adolescents in treatment response to atomoxetine and the correlation between health-related quality of life and Attention Deficit/Hyperactivity Disorder core symptoms: Meta-analysis of five atomoxetine trials

<p>Abstract</p> <p>Objectives</p> <p>To explore the influence of age on treatment responses to atomoxetine and to assess the relationship between core symptoms of attention deficit/hyperactivity disorder (ADHD) and health-related quality of life (HR-QoL) outcomes.</p> <p>Data Sources</p> <p>Data from five similar clinical trials of atomoxetine in the treatment of children and adolescents with ADHD were included in this meta-analysis.</p> <p>Study Selection</p> <p>Atomoxetine studies that used the ADHD Rating Scale (ADHD-RS) and the Child Health and Illness Profile Child Edition (CHIP-CE) as outcome measures were selected.</p> <p>Interventions</p> <p>Treatment with atomoxetine.</p> <p>Main Outcome Measures</p> <p>Treatment group differences (atomoxetine vs placebo) in terms of total score, domains, and subdomains of the CHIP-CE were compared across age groups, and correlations between ADHD-RS scores and CHIP-CE scores were calculated by age.</p> <p>Results</p> <p>Data of 794 subjects (611 children, 183 adolescents) were pooled. At baseline, adolescents showed significantly (p < 0.05) greater impairment compared with children in the Family Involvement, Satisfaction with Self, and Academic Performance subdomains of the CHIP-CE. Treatment effect of atomoxetine was significant in both age groups for the Risk Avoidance domain and its subdomains. There was a significant age-treatment interaction with greater efficacy seen in adolescents in both the Risk Avoidance domain and the Threats to Achievement subdomain. Correlations between ADHD-RS and CHIP-CE scores were generally low at baseline and moderate in change from baseline and were overall similar in adolescents and children.</p> <p>Conclusions</p> <p>Atomoxetine was effective in improving some aspects of HR-QoL in both age groups. Correlations between core symptoms of ADHD and HR-QoL were low to moderate.</p

The impact of ADHD on the health and well-being of ADHD children and their siblings

Childhood attention-deficit/hyperactivity disorder (ADHD) has been associated with reduced health and well-being of patients and their families. The authors undertook a large UK survey-based observational study of the burden associated with childhood ADHD. The impact of ADHD on both the patient (N = 476) and their siblings (N = 337) on health-related quality of life (HRQoL) and happiness was quantified using multiple standard measures [e.g. child health utility-9D (CHU-9D), EuroQol-5D-Youth]. In the analysis, careful statistical adjustments were made to ensure a like-for-like comparison of ADHD families with two different control groups. We controlled for carers' ADHD symptoms, their employment and relationship status and siblings' ADHD symptoms. ADHD was associated with a significant deficit in the patient's HRQoL (with a CHU-9D score of around 6 % lower). Children with ADHD also have less sleep and were less happy with their family and their lives overall. No consistent decrement to the HRQoL of the siblings was identified across the models, except that related to their own conduct problems. The siblings do, however, report lower happiness with life overall and with their family, even when controlling for the siblings own ADHD symptoms. We also find evidence of elevated bullying between siblings in families with a child with ADHD. Overall, the current results suggest that the reduction in quality of life caused by ADHD is experienced both by the child with ADHD and their siblings