Mutual information between heart rate variability and respiration for emotion characterization.

OBJECTIVE: Interest in emotion recognition has increased in recent years as a useful tool for diagnosing psycho-neural illnesses. In this study, the auto-mutual and the cross-mutual information function, AMIF and CMIF respectively, are used for human emotion recognition. APPROACH: The AMIF technique was applied to heart rate variability (HRV) signals to study complex interdependencies, and the CMIF technique was considered to quantify the complex coupling between HRV and respiratory signals. Both algorithms were adapted to short-term RR time series. Traditional band pass filtering was applied to the RR series at low frequency (LF) and high frequency (HF) bands, and a respiration-based filter bandwidth was also investigated ([Formula: see text]). Both the AMIF and the CMIF algorithms were calculated with regard to different time scales as specific complexity measures. The ability of the parameters derived from the AMIF and the CMIF to discriminate emotions was evaluated on a database of video-induced emotion elicitation. Five elicited states i.e. relax (neutral), joy (positive valence), as well as fear, sadness and anger (negative valences) were considered. MAIN RESULTS: The results revealed that the AMIF applied to the RR time series filtered in the [Formula: see text] band was able to discriminate between the following: relax and joy and fear, joy and each negative valence conditions, and finally fear and sadness and anger, all with a statistical significance level p -value [Formula: see text] 0.05, sensitivity, specificity and accuracy higher than 70% and area under the receiver operating characteristic curve index AUC [Formula: see text]0.70. Furthermore, the parameters derived from the AMIF and the CMIF allowed the low signal complexity presented during fear to be characterized in front of any of the studied elicited states. SIGNIFICANCE: Based on these results, human emotion manifested in the HRV and respiratory signal responses could be characterized by means of the information-content complexity.

Human emotion recognition using heart rate variability analysis with spectral bands based on respiration.

The work presented in this paper aims at assessing human emotion recognition by means of the analysis of the heart rate variability (HRV) with varying spectral bands based on respiratory frequency (RF). Three specific emotional states are compared corresponding to calm-neutral state (Relax), positive elicitation (Joy) and negative elicitation (Fear). Standard HRV analysis in time and frequency domain is performed. In order to better characterize the HRV component related to respiratory sinus arrhythmia, the high frequency (HF) band is centered on RF. Results reveal that the power content in low band (PLF), the normalized power content in HF band (PHFn) and the sympathovagal ratio (LF/HF) can be suitable indices to distinguish Relax and Joy. Mean heart rate and RF are significantly different between Relax and Fear. Different HRV indices show significant differences between Joy and Fear, such as pNN50, PLF, PHFn and LF/HF. Statistical analysis of HRV indices with HF centered in the RF results in a lower p-value than the ones with a HF standard band.

Assessment of protein entrapment in hydroxyapatite scaffolds by size exclusion chromatography.

Although it is well known that the textural properties of scaffolds play an important role in the process of tissue regeneration, the investigation of such effects remain difficult especially at the micro/nano level. Texture confers the material the additional ability to entrap/concentrate molecules circulating in the body fluid regardless of their binding affinity to the material. The goal of the present work is to isolate protein entrapment from protein adsorption phenomena in two macroporous hydroxyapatite scaffolds with identical chemical structure, similar macroporosity but different micro/nanoporosity using proteins of different sizes. This was achieved implementing size exclusion chromatography and using the scaffolds as chromatographic columns. The results showed that the larger the crystal size and the lower the packing density of the crystals composing the scaffold increased protein retention but decreased the protein dwelling time in the column. Differences in the amount of protein retained depended on the protein type.