Individual identification via electrocardiogram analysis

Background: During last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research. Methods: We searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents. Results: 100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %. Conclusions: Biometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations.

Element characteristic tolerance for semi-batch fixed bed biomass pyrolysis

Biomass pyrolysis to bio-oil is one of the promising sustainable fuels. In this work, relation between biomass feedstock element characteristic and crude bio-oil production yield and lower heating value was explored. The element characteristics considered in this study include moisture, ash, fix carbon, volatile matter, C, H, N, O, S, cellulose, hemicellulose, and lignin content. A semi-batch fixed bed reactor was used for biomass pyrolysis with heating rate of 30 °C/min from room temperature to 600 °C and the reactor was held at 600 °C for 1 h before cooling down. Constant nitrogen flow (1bar) was provided for anaerobic condition. Sago and Napier glass were used in the study to create different element characteristic of feedstock by altering mixing ratio. Comparison between each element characteristic to crude bio-oil yield and low heating value was conducted. The result suggested potential key element characteristic for pyrolysis and provide a platform to access the feedstock element acceptance range.

Novel input-output prediction approach for biomass pyrolysis

Biomass pyrolysis to bio-oil is one of the promising sustainable fuels. In this work, relation between biomass feedstock element characteristic and pyrolysis process outputs was explored. The element characteristics considered in this study include moisture, ash, fix carbon, volatile matter, carbon, hydrogen, nitrogen, oxygen, and sulphur. A semi-batch fixed bed reactor was used for biomass pyrolysis with heating rate of 30 °C/min from room temperature to 600 °C and the reactor was held at 600 °C for 1 h before cooling down. Constant nitrogen flow rate of 5 L/min was provided for anaerobic condition. Rice husk, Sago biomass and Napier grass were used in the study to form different element characteristic of feedstock by altering mixing ratio. Comparison between each element characteristic to total produced bio-oil yield, aqueous phase bio-oil yield, organic phase bio-oil yield, higher heating value of organic phase bio-oil, and organic bio-oil compounds was conducted. The results demonstrate that process performance is associated with feedstock properties, which can be used as a platform to access the process feedstock element acceptance range to estimate the process outputs. Ultimately, this work evaluated the element acceptance range for proposed biomass pyrolysis technology to integrate alternative biomass species feedstock based on element characteristic to enhance the flexibility of feedstock selection.