Gennaro Tartarisco

A mobile data collection platform for mental health research

Ubiquitous computing technologies offer exciting new possibilities for monitoring and analyzing user’s experience in real time. In this paper, we describe the design and development of Psychlog, a mobile phone platform designed to collect users’ psychological, physiological, and activity information for mental health research. The tool allows administering self-report questionnaires at specific times or randomly within a day. The system also permits to collect heart rate and activity information from a wireless electrocardiogram equipped with a three-axial accelerometer. By combining self-reports with heart rate and activity data, the application makes it possible to investigate the relationship between psychological, physiological, and behavioral variables, as well as to monitor their fluctuations over time. The software runs on Windows mobile operative system and is available as open source (http://sourceforge.net/projects/psychlog/).

Personal Health System architecture for stress monitoring and support to clinical decisions

Developments in computational techniques including clinical decision support systems, information processing, wireless communication and data mining hold new premises in Personal Health Systems. Pervasive Healthcare system architecture finds today an effective application and represents in perspective a real technological breakthrough promoting a paradigm shift from diagnosis and treatment of patients based on symptoms to diagnosis and treatment based on risk assessment. Such architectures must be able to collect and manage a large quantity of data supporting the physicians in their decision process through a continuous pervasive remote monitoring model aimed to enhance the understanding of the dynamic disease evolution and personal risk. In this work an automatic simple, compact, wireless, personalized and cost efficient pervasive architecture for the evaluation of the stress state of individual subjects suitable for prolonged stress monitoring during normal activity is described. A novel integrated processing approach based on an autoregressive model, artificial neural networks and fuzzy logic modeling allows stress conditions to be automatically identified with a mobile setting analysing features of the electrocardiographic signals and human motion. The performances of the reported architecture were assessed in terms of classification of stress conditions.

Autism and social robotics: A systematic review.

Social robotics could be a promising method for Autism Spectrum Disorders (ASD) treatment. The aim of this article is to carry out a systematic literature review of the studies on this topic that were published in the last 10 years. We tried to address the following questions: can social robots be a useful tool in autism therapy? We followed the PRISMA guidelines, and the protocol was registered within PROSPERO database (CRD42015016158). We found many positive implications in the use of social robots in therapy as for example: ASD subjects often performed better with a robot partner rather than a human partner; sometimes, ASD patients had, toward robots, behaviors that TD patients had toward human agents; ASDs had a lot of social behaviors toward robots; during robotic sessions, ASDs showed reduced repetitive and stereotyped behaviors and, social robots manage to improve spontaneous language during therapy sessions. Therefore, robots provide therapists and researchers a means to connect with autistic subjects in an easier way, but studies in this area are still insufficient. It is necessary to clarify whether sex, intelligence quotient, and age of participants affect the outcome of therapy and whether any beneficial effects only occur during the robotic session or if they are still observable outside the clinical/experimental context. Autism Res 2016, 9: 165–183.

An efficient unsupervised fetal QRS complex detection from abdominal maternal ECG.

Non-invasive fetal heart rate is of great relevance in clinical practice to monitor fetal health state during pregnancy. To date, however, despite significant advances in the field of electrocardiography, the analysis of abdominal fetal ECG is considered a challenging problem for biomedical and signal processing communities. This is mainly due to the low signal-to-noise ratio of fetal ECG and difficulties in cancellation of maternal QRS complexes, motion and electromyographic artefacts. In this paper we present an efficient unsupervised algorithm for fetal QRS complex detection from abdominal multichannel signal recordings combining ICA and maternal ECG cancelling, which outperforms each single method. The signal is first pre-processed to remove impulsive artefacts, baseline wandering and power line interference. The following steps are then applied: maternal ECG extraction through independent component analysis (ICA); maternal QRS detection; maternal ECG cancelling through weighted singular value decomposition; enhancing of fetal ECG through ICA and fetal QRS detection. We participated in the Physionet/Computing in Cardiology Challenge 2013, obtaining the top official scores of the challenge (among 53 teams of participants) of event 1 and event 2 concerning fetal heart rate and fetal interbeat intervals estimation section. The developed algorithms are released as open-source on the Physionet website.

Experiential Virtual Scenarios With Real-Time Monitoring (Interreality) for the Management of Psychological Stress: A Block Randomized Controlled Trial

Background The recent convergence between technology and medicine is offering innovative methods and tools for behavioral health care. Among these, an emerging approach is the use of virtual reality (VR) within exposure-based protocols for anxiety disorders, and in particular posttraumatic stress disorder. However, no systematically tested VR protocols are available for the management of psychological stress. Objective Our goal was to evaluate the efficacy of a new technological paradigm, Interreality, for the management and prevention of psychological stress. The main feature of Interreality is a twofold link between the virtual and the real world achieved through experiential virtual scenarios (fully controlled by the therapist, used to learn coping skills and improve self-efficacy) with real-time monitoring and support (identifying critical situations and assessing clinical change) using advanced technologies (virtual worlds, wearable biosensors, and smartphones). Methods The study was designed as a block randomized controlled trial involving 121 participants recruited from two different worker populations—teachers and nurses—that are highly exposed to psychological stress. Participants were a sample of teachers recruited in Milan (Block 1: n=61) and a sample of nurses recruited in Messina, Italy (Block 2: n=60). Participants within each block were randomly assigned to the (1) Experimental Group (EG): n=40; B1=20, B2=20, which received a 5-week treatment based on the Interreality paradigm; (2) Control Group (CG): n=42; B1=22, B2=20, which received a 5-week traditional stress management training based on cognitive behavioral therapy (CBT); and (3) the Wait-List group (WL): n=39, B1=19, B2=20, which was reassessed and compared with the two other groups 5 weeks after the initial evaluation. Results Although both treatments were able to significantly reduce perceived stress better than WL, only EG participants reported a significant reduction (EG=12% vs CG=0.5%) in chronic “trait” anxiety. A similar pattern was found for coping skills: both treatments were able to significantly increase most coping skills, but only EG participants reported a significant increase (EG=14% vs CG=0.3%) in the Emotional Support skill. Conclusions Our findings provide initial evidence that the Interreality protocol yields better outcomes than the traditionally accepted gold standard for psychological stress treatment: CBT. Consequently, these findings constitute a sound foundation and rationale for the importance of continuing future research in technology-enhanced protocols for psychological stress management. Trial Registration ClinicalTrials.gov: NCT01683617; http://clinicaltrials.gov/show/NCT01683617 (Archived by WebCite at http://www.webcitation.org/6QnziHv3h).

Evaluation of pulmonary disease using static lung volumes in primary ciliary dyskinesia

Background In primary ciliary dyskinesia (PCD) lung damage is usually evaluated by high-resolution CT (HRCT). Objective To evaluate whether HRCT abnormalities and Pseudomonas aeruginosa infection were better predicted by spirometry or plethysmography. Methods A cross-sectional study performed in consecutive patients with PCD who underwent sputum culture, spirometry, plethysmography and HRCT within 48 h. Principal component analysis and soft computing were used for data evaluation. Results Fifty patients (26 children) were studied. P aeruginosa infection was found in 40% of the patients and bronchiectasis in 88%. There was a correlation between infection with P aeruginosa and extent of bronchiectasis (p=0.009; r =0.367) and air-trapping (p=0.03; r =0.315). Moreover, there was an association between infection with P aeruginosa and residual volume (RV) values >150% (p=0.04) and RV/total lung capacity (TLC) ratio >140% (p=0.001), but not between infection with P aeruginosa and forced expiratory volume in 1 s (FEV1)<80%, or forced expiratory flow between 25% and 75% of forced vital capacity (FVC) (FEF25–75%)<70% or FEV1/FVC<70% (<80% in children). Severity of the total lung impairment on chest HRCT directly correlated with RV when expressed as per cent predicted (p=0.003; r =0.423), and RV/TLC (p<0.001; r =0.513) or when expressed as z scores (p=0.002, r =0.451 and p<0.001, r =0.536 respectively). Principal component analysis on plethysmographic but not on spirometry data allowed recognition of different severities of focal air trapping, atelectasis and extent of bronchiectasis. Conclusions Plethysmography better predicts HRCT abnormalities than spirometry. Whether it might be a useful test to define populations of patients with PCD who should or should not have HRCT scans requires further longitudinal studies.

Rapid diagnosis of primary ciliary dyskinesia: cell culture and soft computing analysis

Diagnosis of primary ciliary dyskinesia (PCD) sometimes requires repeated nasal brushing to exclude secondary ciliary alterations. Our aim was to evaluate whether the use of a new method of nasal epithelial cell culture can speed PCD diagnosis in doubtful cases and to identify which are the most informative parameters by means of a multilayer artificial neural network (ANN). A cross-sectional study was performed in patients with suspected PCD. All patients underwent nasal brushing for ciliary motion analysis, ultrastructural assessment and evaluation of ciliary function after ciliogenesis in culture by ANN. 151 subjects were studied. A diagnostic suspension cell culture was obtained in 117 nasal brushings. A diagnosis of PCD was made in 36 subjects (29 of whom were children). In nine out of the 36 patients the diagnosis was made only after a second brushing, because of equivocal results of both tests at first examination. In each of these subjects diagnosis of PCD was confirmed by cell culture results. Cell culture in suspension evaluated by means of ANN allows the separation of PCD from secondary ciliary dyskinesia patients after only 5 days of culture and allows diagnosis to be reached in doubtful cases, thus avoiding the necessity of a second sample.

Olfaction in autism spectrum disorders: A systematic review

Olfactory function is a well-known early biomarker for neurodegeneration and neural functioning in the adult population, being supported by a number of brain structures that could be dysfunctioning in neurodegenerative processes. Evidence has suggested that atypical sensory and, particularly, olfactory processing is present in several neurodevelopmental conditions, including autism spectrum disorders (ASDs). In this paper, we present data obtained by a systematic literature review, conducted according to PRISMA guidelines, regarding the possible association between olfaction and ASDs, and analyze them critically in order to evaluate the occurrence of olfactory impairment in ASDs, as well as the possible usefulness of olfactory evaluation in such conditions. The results obtained in this analysis suggested a possible involvement of olfactory impairment in ASDs, underlining the importance of olfactory evaluation in the clinical assessment of ASDs. This assessment could be potentially included as a complementary evaluation in the diagnostic protocol of the condition. Methods for study selection and inclusion criteria were specified in advance and documented in PROSPERO protocol #CRD42014013939.

A system for automatic detection of momentary stress in naturalistic settings.

Prolonged exposure to stressful environments can lead to serious health problems. Therefore, measuring stress in daily life situations through non-invasive procedures has become a significant research challenge. In this paper, we describe a system for the automatic detection of momentary stress from behavioral and physiological measures collected through wearable sensors. The systems architecture consists of two key components: a) a mobile acquisition module; b) an analysis and decision module. The mobile acquisition module is a smartphone application coupled with a newly developed sensor platform (Personal Biomonitoring System, PBS). The PBS acquires behavioral (motion activity, posture) and physiological (hearth rate) variables, performs low-level, real-time signal preprocessing, and wirelessly communicates with the smartphone application, which in turn connects to a remote server for further signal processing and storage. The decision module is realized on a knowledge basis, using neural network and fuzzy logic algorithms able to combine as input the physiological and behavioral features extracted by the PBS and to classify the level of stress, after previous knowledge acquired during a training phase. The training is based on labeling of physiological and behavioral data through self-reports of stress collected via the smartphone application. After training, the smartphone application can be configured to poll the stress analysis report at fixed time steps or at the request of the user. Preliminary testing of the system is ongoing.

An open source mobile platform for psychophysiological self tracking.

Self tracking is a recent trend in e-health that refers to the collection, elaboration and visualization of personal health data through ubiquitous computing tools such as mobile devices and wearable sensors. Here, we describe the design of a mobile self-tracking platform that has been specifically designed for clinical and research applications in the field of mental health. The smartphone-based application allows collecting a) self-reported feelings and activities from pre-programmed questionnaires; b) electrocardiographic (ECG) data from a wireless sensor platform worn by the user; c) movement activity information obtained from a tri-axis accelerometer embedded in the wearable platform. Physiological signals are further processed by the application and stored on the smartphones memory. The mobile data collection platform is free and released under an open source licence to allow wider adoption by the research community (download at: http://sourceforge.net/projects/psychlog/).