This paper presents a novel algorithm for the estimation of heart rate variability (HRV) features using 24-GHz continuous-wave Doppler radar with quadrature architecture. The proposed algorithm combines frequency and time domain analysis for high-accuracy estimation of beat-to-beat intervals (BBIs). Initially, band pass filtered in-phase (I) and quadrature (Q) radar components are fused into a single combined signal that contains information on the heartbeats. Its frequency domain analysis is used for coarse heart rate estimation. At the same time, the combined signal is processed using a filter bank containing narrowband band pass filters with different center frequencies. One of the band pass filter outputs is selected as the valid output based on the coarse heart rate estimation. Zero crossings in the resulting filter bank output signal represent heartbeats that are used to extract the BBIs. Finally, four HRV features are calculated from the BBIs. The algorithm is tested on real data obtained from recordings on ten human subjects. The mean relative error of extracted BBIs compared to electrocardiogram (ECG) measurement is in the 1.02−2.07 % range. Furthermore, two time-domain and two frequency domain HRV features were calculated from the BBIs. The obtained results show a high level of agreement between radar-extracted and ECG-extracted HRV features. Low computation complexity makes this algorithm suitable for real-time monitoring. INDEX TERMS Band pass filters, beat-to-beat intervals (BBI), chirp Z-transform, Doppler radar, frequency domain analysis, heart rate variability (HRV), noncontact vital signs monitoring, real-time processing.

The measurement of human vital signs is a highly important task in a variety of environments and applications. Most notably, the electrocardiogram (ECG) is a versatile signal that could indicate various physical and psychological conditions, from signs of life to complex mental states. The measurement of the ECG relies on electrodes attached to the skin to acquire the electrical activity of the heart, which imposes certain limitations. Recently, due to the advancement of wireless technology, it has become possible to pick up heart activity in a contactless manner. Among the possible ways to wirelessly obtain information related to heart activity, methods based on mm-wave radars proved to be the most accurate in detecting the small mechanical oscillations of the human chest resulting from heartbeats. In this paper, we presented a method based on a continuous-wave Doppler radar coupled with an artificial neural network (ANN) to detect heartbeats as individual events. To keep the method computationally simple, the ANN took the raw radar signal as input, while the output was minimally processed, ensuring low latency operation (<1 s). The performance of the proposed method was evaluated with respect to an ECG reference (“ground truth”) in an experiment involving 21 healthy volunteers, who were sitting on a cushioned seat and were refrained from making excessive body movements. The results indicated that the presented approach is viable for the fast detection of individual heartbeats without heavy signal preprocessing.

This study investigated the influence of white vs. 12 background and overlay colors on the reading process in twenty-four school-age children. Previous research reported that colors could affect reading skills as an important factor in the emotional and physiological state of the body. The aim of the study was to assess developmental differences between second and third grade students of an elementary school, and to evaluate differences in electroencephalography (EEG), ocular, electrodermal activities (EDA) and heart rate variability (HRV). Our findings showed a decreasing trend with age regarding EEG power bands (Alpha, Beta, Delta, Theta) and lower scores of reading duration and eye-tracking measures in younger children compared to older children. As shown in the results, HRV parameters showed higher scores in 12 background and overlay colors among second than third grade students, which is linearly correlated to the level of stress and is readable from EDA measures as well. Our study showed the calming effect on second graders of turquoise and blue background colors. Considering other colors separately for each parameter, we assumed that there are no systematic differences in reading duration, EEG power band, eye-tracking and EDA measures.

Reading is one of the essential processes during the maturation of an individual. It is estimated that 5–10% of school-age children are affected by dyslexia, the reading disorder characterised by difficulties in the accuracy or fluency of word recognition. There are many studies which have reported that coloured overlays and background could improve the reading process, especially in children with reading disorders. As dyslexia has neurobiological origins, the aim of the present research was to understand the relationship between physiological parameters and colour modifications in the text and background during reading in children with and without dyslexia. We have measured differences in electroencephalography (EEG), heart rate variability (HRV), electrodermal activities (EDA) and eye movements of the 36 school-age (from 8 to 12 years old) children (18 with dyslexia and 18 of control group) during the reading task in 13 combinations of background and overlay colours. Our findings showed that the dyslexic children have longer reading duration, fixation count, fixation duration average, fixation duration total, and longer saccade count, saccade duration total, and saccade duration average while reading on white and coloured background/overlay. It was found that the turquoise background, turquoise overlay, and yellow background colours are beneficial for dyslexic readers, as they achieved the shortest time duration of the reading tasks when these colours were used. Additionally, dyslexic children have higher values of beta (15–40 Hz) and the broadband EEG (0.5–40 Hz) power while reading in one particular colour (purple), as well as increasing theta range power while reading with the purple overlay. We have observed no significant differences between HRV parameters on white colour, except for single colours (purple, turquoise overlay, and yellow overlay) where the control group showed higher values for mean HR, while dyslexic children scored higher with mean RR. Regarding EDA measure, we found systematically lower values in children with dyslexia in comparison to the control group. Based on the present results, we can conclude that both pastel and intense background/overlays are beneficial for reading of both groups and all sensor modalities could be used to better understand the neurophysiological origins in dyslexic children.

Case studies of unusual traits can provide unique snapshots of the effects of modified systems. In this study, we report on an individual from a Serbian family with the ability to rapidly, accurately and voluntarily speak backwards. We consider psychological, neural and genetic correlates of this trait to identify specific relevant neural mechanisms and new molecular pathways for working memory and speech-related tasks. EEG data suggest that the effect of word reversal precedes semantic integration of visually presented backward-words, and that event-related potentials above the frontal lobe are affected by both word reversal and the maintenance of backward-words in working memory. fMRI revealed that the left fusiform gyrus may facilitate the production of backward-speech. Exome sequencing identified three novel coding variants of potential significance in the RIC3, RIPK1 and ZBED5 genes. Taken together, our data suggest that, in this individual, the ability to speak backwards is afforded by an extraordinary working memory capacity. We hypothesise that this is served by cholinergic projections from the basal forebrain to the frontal cortex and supported by visual semantic loops within the left fusiform gyrus and that these neural processes may be mediated by a genetic mutation in RIC3; a chaperone for nicotinic acetylcholine receptors.

Considering the detrimental effects of dyslexia on academic performance and its common occurrence, developing tools for dyslexia detection, monitoring, and treatment poses a task of significant priority. The research performed in this paper was focused on detecting and analyzing dyslexic tendencies in Serbian children based on eye-tracking measures. The group of 30 children (ages 7–13, 15 dyslexic and 15 non-dyslexic) read 13 different text segments on 13 different color configurations. For each text segment, the corresponding eye-tracking trail was recorded and then processed offline and represented by nine conventional features and five newly proposed features. The features were used for dyslexia recognition using several machine learning algorithms: logistic regression, support vector machine, k-nearest neighbor, and random forest. The highest accuracy of 94% was achieved using all the implemented features and leave-one-out subject cross-validation. Afterwards, the most important features for dyslexia detection (representing the complexity of fixation gaze) were used in a statistical analysis of the individual color effects on dyslexic tendencies within the dyslexic group. The statistical analysis has shown that the influence of color has high inter-subject variability. This paper is the first to introduce features that provide clear separability between a dyslexic and control group in the Serbian language (a language with a shallow orthographic system). Furthermore, the proposed features could be used for diagnosing and tracking dyslexia as biomarkers for objective quantification.

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