Objective ADHD diagnosis using convolutional neural networks over daily-life activity records

Attention Deficit/Hyperactivity Disorder (ADHD) is the most common neurobehavioral disorder in children and adolescents. However, its etiology is still unknown, and this hinders the existence of reliable, fast and inexpensive standard diagnostic methods. Objective: This paper proposes an end-to-end methodology for automatic diagnosis of the combined type of ADHD. Methods: Diagnosis is based on the analysis of 24 hour-long activity records using Convolutional Neural Networks to classify spectrograms of activity windows. Results: We achieve up to 97.62% average sensitivity, 99.52% specificity and AUC values over 99%. Overall, our figures overcome those obtained by actigraphy-based methods reported in the literature as well as others based on more expensive (and not so convenient) acquisition methods. Conclusion: These results reinforce the idea that combining deep learning techniques together with actimetry can lead to a robust and efficient system for objective ADHD diagnosis. Significance: Reliance on simple activity measurements leads to an inexpensive and non-invasive objective diagnostic method, which can be easily implemented with daily devices

Texture and color segmentation based on the combined use of the structure tensor and the image components

International audienceIn this paper, we propose a novel segmentation scheme for textured gray-level and color images based on the combined use of the local structure tensor and the original image components. The structure tensor is a well-established tool for image segmentation and has been successfully employed for unsupervised segmentation of textured gray-level and color images. The original image components can also provide very useful information. Therefore, a combined segmentation approach has been designed that combines both elements within a common energy minimization framework. Besides, an original method is proposed to dynamically adapt the relative weight of these two pieces of information. Quantitative experimental results on a large number of gray-level and color images show the improved performance of the proposed approach, in comparison to several related approaches in recent studies. Experiments have also been carried out on real world images in order to validate the proposed method. r 2007 Elsevier B.V. All rights reserved

A Fuzzy MHT Algorithm Applied to Text-Based Information Tracking

In this paper, we carry out a detailed analysis of a fuzzy version of Reid's classical multiple hypothesis tracking (MHT) algorithm. Our fuzzy version is based on well-known fuzzy feedback systems, but the fact that the system we describe is specialized for likelihood discrimination makes this study particularly novel. We discuss several techniques for rule activation. One of them, namely, the sum--product, seems particularly useful for likelihood management and its linearity makes it tractable for further analysis. Our analysis is performed in two stages. First, we demonstrate that, with appropriately chosen rules, our system can discriminate the correct hypothesis. Second, the steady-state behavior with constant input is characterized analytically. This enables us to establish the optimality of the sum--product method and it also gives a simple procedure to predict the system's behavior as a function of the rule base. We believe this fact can be used to devise a simple procedure for fine-tuning the rule base according to the system designer needs. The application driving our fuzzy MHT implementation and analysis is the tracking of natural language text-based messages. That application is used as an example throughout the paper

PPG Beat Reconstruction Based on Shape Models and Probabilistic Templates for Signals Acquired with Conventional Smartphones

Ubiquitous monitoring has become a useful tool for the pre- vention and early diagnosis of some disorders. This kind of monitoring has been promoted last years thanks to the irruption of the smartphones, which make easier the collection and delivery of the patient's data. How- ever, there are some problems with the quality of the acquired data. This paper presents a novel methodology in which the shape of PPG beats is recovered through a multistage full-automatic pipeline including shape modeling stage and template{based (template estimated from the sur- rounding beats) shape recovery through a level{set approach. To validate the proposal, a registry acquired using the camera of a Motorola MotoG has been used. Results can be quali ed as promising, since the shape of damaged beats is recovered, however a more comprehensive validation must be addressed

A computational TW3 classifier for skeletal maturity assessment. A Computing with Words approach

This paper proposes a fuzzy methodology to translate the natural language descriptions of the TW3 method for bone age assessment into an automatic classifier. The classifier is built upon a modified version of a fuzzy ID3 decision tree. No large data records are needed to train the classifier, i.e., to find out the classification rules, since the classifier is built upon rules given by the TW3 method. Only small data records are needed to fine-tune the fuzzy sets used to implement the rulebase