Unusual localization of Dirofilaria repens (Spirurida: Onchocercidae) infection in the testicle of a dog.

This report describes an uncommon localization of Dirofilaria repens in the testicle of a nine-year-old dog from southern Italy. The dog underwent neutring and three adult nematodes were found in the tunica vaginalis. At gross anatomy, no pathological changes were observed on the tunica, in the testicle or epididymis. The parasites, one female and two males, were morphologically and molecularly identified as D. repens. This is the first report of D. repens in the canine testicle in Italy. This case report highlights the asymptomatic nature of D. repens infection, suggesting that dogs living in endemic areas may act as silent carriers. Careful screening and an effective chemoprophylaxis should be ensured for all animals potentially exposed to D. repens infection in order to reduce the risk of infection to humans in areas where the parasite is endemic.

Low-complexity hardware design methodology for reliable and automated removal of ocular and muscular artifact from EEG.

BACKGROUND AND OBJECTIVE: EEG is a non-invasive tool for neuro-developmental disorder diagnosis and treatment. However, EEG signal is mixed with other biological signals including Ocular and Muscular artifacts making it difficult to extract the diagnostic features. Therefore, the contaminated EEG channels are often discarded by the medical practitioners which may result in less accurate diagnosis. Many existing methods require reference electrodes, which will create discomfort to the patient/children and cause hindrance to the diagnosis of the neuro-developmental disorder and Brain Computer Interface in the pervasive environment. Therefore, it would be ideal if these artifacts can be removed real time on the hardware platform in an automated fashion and then the denoised EEG can be used for online diagnosis in a pervasive personalized healthcare environment without the need of any reference electrode. METHODS: In this paper we propose a reliable, robust and automated methodology to solve the aforementioned problem. The proposed methodology is based on the Haar function based Wavelet decompositions with simple threshold based wavelet domain denoising and artifacts removal schemes. Subsequently hardware implementation results are also presented. 100 EEG data from Physionet, Klinik für Epileptologie, Universität Bonn, Germany, Caltech EEG databases and 7 EEG data from 3 subjects from University of Southampton, UK have been studied and nine exhaustive case studies comprising of real and simulated data have been formulated and tested. The proposed methodology is prototyped and validated using FPGA platform. RESULTS: Like existing literature, the performance of the proposed methodology is also measured in terms of correlation, regression and R-square statistics and the respective values lie above 80%, 79% and 65% with the gain in hardware complexity of 64.28% and improvement in hardware delay of 53.58% compared to state-of-the art approaches. Hardware design based on the proposed methodology consumes 75 micro-Watt power. CONCLUSIONS: The automated methodology proposed in this paper, unlike the state of the art methods, can remove blink and muscular artifacts real time without the need of any extra electrode. Its reliability and robustness is also established after exhaustive simulation study and analysis on both simulated and real data. We believe the proposed methodology would be useful in next generation personalized pervasive healthcare for Brain Computer Interface and neuro-developmental disorder diagnosis and treatment.

GOLIAH (Gaming Open Library for Intervention in Autism at Home): a 6-month single blind matched controlled exploratory study.

BACKGROUND: To meet the required hours of intensive intervention for treating children with autism spectrum disorder (ASD), we developed an automated serious gaming platform (11 games) to deliver intervention at home (GOLIAH) by mapping the imitation and joint attention (JA) subset of age-adapted stimuli from the Early Start Denver Model (ESDM) intervention. Here, we report the results of a 6-month matched controlled exploratory study. METHODS: From two specialized clinics, we included 14 children (age range 5-8 years) with ASD and 10 controls matched for gender, age, sites, and treatment as usual (TAU). Participants from the experimental group received in addition to TAU four 30-min sessions with GOLIAH per week at home and one at hospital for 6 months. Statistics were performed using Linear Mixed Models. RESULTS: Children and parents participated in 40% of the planned sessions. They were able to use the 11 games, and participants trained with GOLIAH improved time to perform the task in most JA games and imitation scores in most imitation games. GOLIAH intervention did not affect Parental Stress Index scores. At end-point, we found in both groups a significant improvement for Autism Diagnostic Observation Schedule scores, Vineland socialization score, Parental Stress Index total score, and Child Behavior Checklist internalizing, externalizing and total problems. However, we found no significant change for by time × group interaction. CONCLUSIONS: Despite the lack of superiority of TAU + GOLIAH versus TAU, the results are interesting both in terms of changes by using the gaming platform and lack of parental stress increase. A large randomized controlled trial with younger participants (who are the core target of ESDM model) is now discussed. This should be facilitated by computing GOLIAH for a web platform. Trial registration Clinicaltrials.gov NCT02560415.

GOLIAH: A Gaming Platform for Home-Based Intervention in Autism - Principles and Design.

Children with Autism need intensive intervention and this is challenging in terms of manpower, costs, and time. Advances in Information Communication Technology and computer gaming may help in this respect by creating a nomadically deployable closed-loop intervention system involving the child and active participation of parents and therapists. An automated serious gaming platform enabling intensive intervention in nomadic settings has been developed by mapping two pivotal skills in autism spectrum disorder: Imitation and Joint Attention (JA). Eleven games - seven Imitations and four JA - were derived from the Early Start Denver Model. The games involved application of visual and audio stimuli with multiple difficulty levels and a wide variety of tasks and actions pertaining to the Imitation and JA. The platform runs on mobile devices and allows the therapist to (1) characterize the child's initial difficulties/strengths, ensuring tailored and adapted intervention by choosing appropriate games and (2) investigate and track the temporal evolution of the child's progress through a set of automatically extracted quantitative performance metrics. The platform allows the therapist to change the game or its difficulty levels during the intervention depending on the child's progress. Performance of the platform was assessed in a 3-month open trial with 10 children with autism (Trial ID: NCT02560415, Clinicaltrials.gov). The children and the parents participated in 80% of the sessions both at home (77.5%) and at the hospital (90%). All children went through all the games but, given the diversity of the games and the heterogeneity of children profiles and abilities, for a given game the number of sessions dedicated to the game varied and could be tailored through automatic scoring. Parents (N = 10) highlighted enhancement in the child's concentration, flexibility, and self-esteem in 78, 89, and 44% of the cases, respectively, and 56% observed an enhanced parents-child relationship. This pilot study shows the feasibility of using the developed gaming platform for home-based intensive intervention. However, the overall capability of the platform in delivering intervention needs to be assessed in a bigger open trial.

Hybrid wavelet and EMD/ICA approach for artifact suppression in pervasive EEG.

BACKGROUND: Electroencephalogram (EEG) signals are often corrupted with unintended artifacts which need to be removed for extracting meaningful clinical information from them. Typically a priori knowledge of the nature of the artifacts is needed for such purpose. Artifact contamination of EEG is even more prominent for pervasive EEG systems where the subjects are free to move and thereby introducing a wide variety of motion-related artifacts. This makes hard to get a priori knowledge about their characteristics rendering conventional artifact removal techniques often ineffective. NEW METHOD: In this paper, we explore the performance of two hybrid artifact removal algorithms: Wavelet Packet Transform followed by Independent Component Analysis (WPTICA) and Wavelet Packet Transform followed by Empirical Mode Decomposition (WPTEMD) in pervasive EEG recording scenario, assuming existence of no a priori knowledge about the artifacts and compare their performance with two existing artifact removal algorithms. RESULTS: Artifact cleaning performance has been measured using Root Mean Square Error (RMSE) and Artifact to Signal Ratio (ASR)-an index similar to traditional Signal to Noise Ratio (SNR), and also by observing normalized power distribution topography over the scalp. COMPARISON WITH EXISTING METHOD(S): Comparison has been made first using semi-simulated signals and then with real experimentally acquired EEG data with commercially available 19-channel pervasive EEG system Enobio corrupted by eight types of artifact. CONCLUSIONS: Our explorations show that WPTEMD consistently gives best artifact cleaning performance not only in semi-simulated scenario but also in the case of real EEG data containing artifacts.

Development of an automated updated Selvester QRS scoring system using SWT-based QRS fractionation detection and classification.

The Selvester score is an effective means for estimating the extent of myocardial scar in a patient from low-cost ECG recordings. Automation of such a system is deemed to help implementing low-cost high-volume screening mechanisms of scar in the primary care. This paper describes, for the first time to the best of our knowledge, an automated implementation of the updated Selvester scoring system for that purpose, where fractionated QRS morphologies and patterns are identified and classified using a novel stationary wavelet transform (SWT)-based fractionation detection algorithm. This stage informs the two principal steps of the updated Selvester scoring scheme--the confounder classification and the point awarding rules. The complete system is validated on 51 ECG records of patients detected with ischemic heart disease. Validation has been carried out using manually detected confounder classes and computation of the actual score by expert cardiologists as the ground truth. Our results show that as a stand-alone system it is able to classify different confounders with 94.1% accuracy whereas it exhibits 94% accuracy in computing the actual score. When coupled with our previously proposed automated ECG delineation algorithm, that provides the input ECG parameters, the overall system shows 90% accuracy in confounder classification and 92% accuracy in computing the actual score and thereby showing comparable performance to the stand-alone system proposed here, with the added advantage of complete automated analysis without any human intervention.