Deficits in the interpretation of others' intentions from gaze-direction or other social attention cues are well-recognized in ASD. Here we investigated whether an EEG brain computer interface (BCI) can be used to train social cognition skills in ASD patients. We performed a single-arm feasibility clinical trial and enrolled 15 participants (mean age 22y 2m) with high-functioning ASD (mean full-scale IQ 103). Participants were submitted to a BCI training paradigm using a virtual reality interface over seven sessions spread over 4 months. The first four sessions occurred weekly, and the remainder monthly. In each session, the subject was asked to identify objects of interest based on the gaze direction of an avatar. Attentional responses were extracted from the EEG P300 component. A final follow-up assessment was performed 6-months after the last session. To analyze responses to joint attention cues participants were assessed pre and post intervention and in the follow-up, using an ecologic “Joint-attention task.” We used eye-tracking to identify the number of social attention items that a patient could accurately identify from an avatar's action cues (e.g., looking, pointing at). As secondary outcome measures we used the Autism Treatment Evaluation Checklist (ATEC) and the Vineland Adaptive Behavior Scale (VABS). Neuropsychological measures related to mood and depression were also assessed. In sum, we observed a decrease in total ATEC and rated autism symptoms (Sociability; Sensory/Cognitive Awareness; Health/Physical/Behavior); an evident improvement in Adapted Behavior Composite and in the DLS subarea from VABS; a decrease in Depression (from POMS) and in mood disturbance/depression (BDI). BCI online performance and tolerance were stable along the intervention. Average P300 amplitude and alpha power were also preserved across sessions. We have demonstrated the feasibility of BCI in this kind of intervention in ASD. Participants engage successfully and consistently in the task. Although the primary outcome (rate of automatic responses to joint attention cues) did not show changes, most secondary neuropsychological outcome measures showed improvement, yielding promise for a future efficacy trial.(clinical-trial ID: NCT02445625—clinicaltrials.gov).

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The Arterial Pressure Waveform (APW) can provide essential information about arterial wall integrity and arterial stiffness. Most of APW analysis frameworks individually process each hemodynamic parameter and do not evaluate inter-dependencies in the overall pulse morphology. The key contribution of this work is the use of machine learning algorithms to deal with vectorized features extracted from APW. With this purpose, we follow a five-step evaluation methodology: (1) a custom-designed, non-invasive, electromechanical device was used in the data collection from 50 subjects; (2) the acquired position and amplitude of onset, Systolic Peak (SP), Point of Inflection (Pi) and Dicrotic Wave (DW) were used for the computation of some morphological attributes; (3) pre-processing work on the datasets was performed in order to reduce the number of input features and increase the model accuracy by selecting the most relevant ones; (4) classification of the dataset was carried out using four different machine learning algorithms: Random Forest, BayesNet (probabilistic), J48 (decision tree) and RIPPER (rule-based induction); and (5) we evaluate the trained models, using the majority-voting system, comparatively to the respective calculated Augmentation Index (AIx). Classification algorithms have been proved to be efficient, in particular Random Forest has shown good accuracy (96.95%) and high area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve (0.961). Finally, during validation tests, a correlation between high risk labels, retrieved from the multi-parametric approach, and positive AIx values was verified. This approach gives allowance for designing new hemodynamic morphology vectors and techniques for multiple APW analysis, thus improving the arterial pulse understanding, especially when compared to traditional single-parameter analysis, where the failure in one parameter measurement component, such as Pi, can jeopardize the whole evaluation.

Cardiovascular diseases are a growing epidemiological burden in today's society. A great deal of effort has been made to find solutions able to perform non-invasive monitoring and early diagnosis of such pathologies. The pulse wave velocity and certain waveform characteristics constitute some of the most important cardiovascular risk indicators. Optical sensors are an attractive instrumental solution in this kind of time assessment applications due to their truly non-contact operation capability and better resolution than commercial devices. This study consisted on the experimental validation and a clinical feasibility for a non-invasive and multi-parametric optical system for evaluation of the cardiovascular condition. Two prototypes, based on two different types of photodetectors (planar and avalanche photodiode) were tested in a small group of volunteers, and the main hemodynamic parameters were measured, such as pulse wave velocity and indexes of pulse waveform analysis: the Augmentation Index, Subendocardial Viability Ratio and Ejection Time Index. The probes under study proved to be able to measure the pulse pressure wave in a reliable manner at the carotid site, and demonstrated the consistency of the parameters determined using dedicated algorithms. This study represents a preliminary evaluation of an optical system devoted to the clinical evaluation environment. Further development to take this system to a higher level of clinical significance, by incorporating it in a multicenter study, is currently underway.

The pulse pressure waveform has, for long, been known as a fundamental biomedical signal and its analysis is recognized as a non-invasive, simple, and resourceful technique for the assessment of arterial vessels condition observed in several diseases. In the current paper, waveforms from non-invasive optical probe that measures carotid artery distension profiles are compared with the waveforms of the pulse pressure acquired by intra-arterial catheter invasive measurement in the ascending aorta. Measurements were performed in a study population of 16 patients who had undergone cardiac catheterization. The hemodynamic parameters: area under the curve (AUC), the area during systole (AS) and the area during diastole (AD), their ratio (AD/AS) and the ejection time index (ETI), from invasive and non-invasive measurements were compared. The results show that the pressure waveforms obtained by the two methods are similar, with 13% of mean value of the root mean square error (RMSE). Moreover, the correlation coefficient demonstrates the strong correlation. The comparison between the AUCs allows the assessment of the differences between the phases of the cardiac cycle. In the systolic period the waveforms are almost equal, evidencing greatest clinical relevance during this period. Slight differences are found in diastole, probably due to the structural arterial differences. The optical probe has lower variability than the invasive system (13% vs 16%). This study validates the capability of acquiring the arterial pulse waveform with a non-invasive method, using a non-contact optical probe at the carotid site with residual differences from the aortic invasive measurements.

Local pulse-wave velocity (PWV) is an accurate indicator of the degree of arteriosclerosis (stiffness) in an artery, providing a direct characterization of the properties of its wall. Devices currently available for local PWV measurement are mainly based on ultrasound systems and have not yet been generalized to clinical practice since they require high technical expertise and most of them are limited in precision, due to the lack of reliable signal processing methods. The present work describes a new type of probe, based on a double-headed piezoelectric (PZ) sensor. The principle of PWV measurement involves determination of the pulse transit time between the signals acquired simultaneously by both PZs, placed 23 mm apart. The double probe (DP) characterization is accomplished in three main studies, carried out in a dedicated test bench system, capable of reproducing a range of clinically relevant properties of the cardiovascular system. The first study refers to determination of the impulse response (IR) for each PZ sensor, whereas the second one explores the existence of crosstalk between both transducers. In the last one, DP time resolution is inferred from a set of three different algorithms based on (a) the maximum of cross-correlation function, (b) the maximum amplitude detection and (c) the zero-crossing point identification. These values were compared with those obtained by the reference method, which consists of the simultaneous acquisition of pressure waves by means of two pressure sensors. The new probe demonstrates good performance on the test bench system and results show that the signals do not exhibit crosstalk. A good agreement was also verified between the PWV obtained from the DP signals (19.55 ± 2.02  ms(-1)) and the PWV determined using the reference method (19.26 ± 0.04  ms(-1)). Although additional studies are still required, this probe seems to be a valid alternative to local PWV stand-alone devices.

A new type of optical probe based on laser Doppler self-mixing technology, for a truly non-contact measurement in a single location, and extraction of the temporal features of the distension wave in the arterial wall, was developed. The monitoring of temporal features allows the assessment of cardiovascular function when measurement is carried out at the carotid artery. An algorithm based on the short-time Fourier transform and empirical mode decomposition was applied to the test setup self-mixing signals for the determination of waveform features, with an accuracy of a few milliseconds and a root mean square error less than 3 ms. In vivo testing signals show great consistency in the measured pulse pressure waveform.