Why Spiking Neural Networks Are Efficient: A Theorem

Current artificial neural networks are very successful in many machine learning applications, but in some cases they still lag behind human abilities. To improve their performance, a natural idea is to simulate features of biological neurons which are not yet implemented in machine learning. One of such features is the fact that in biological neural networks, signals are represented by a train of spikes. Researchers have tried adding this spikiness to machine learning and indeed got very good results, especially when processing time series (and, more generally, spatio-temporal data). In this paper, we provide a theoretical explanation for this empirical success

Developing an algorithm for pulse oximetry derived respiratory rate (RRoxi): a healthy volunteer study

Objective The presence of respiratory information within the pulse oximeter signal (PPG) is a well-documented phenomenon. However, extracting this information for the purpose of continuously monitoring respiratory rate requires: (1) the recognition of the multi-faceted manifestations of respiratory modulation components within the PPG and the complex interactions among them; (2) the implementation of appropriate advanced signal processing techniques to take full advantage of this information; and (3) the post-processing infrastructure to deliver a clinically useful reported respiratory rate to the end user. A holistic algorithmic approach to the problem is therefore required. We have developed the RROXI algorithm based on this principle and its performance on healthy subject trial data is described herein

Exploration of key stakeholders' preferences for pre-hospital physiologic monitoring by emergency rescue services

Peer reviewedPostprintPostprin

WAVOS: a MATLAB toolkit for wavelet analysis and visualization of oscillatory systems

<p>Abstract</p> <p>Background</p> <p>Wavelets have proven to be a powerful technique for the analysis of periodic data, such as those that arise in the analysis of circadian oscillators. While many implementations of both continuous and discrete wavelet transforms are available, we are aware of no software that has been designed with the nontechnical end-user in mind. By developing a toolkit that makes these analyses accessible to end users without significant programming experience, we hope to promote the more widespread use of wavelet analysis.</p> <p>Findings</p> <p>We have developed the WAVOS toolkit for wavelet analysis and visualization of oscillatory systems. WAVOS features both the continuous (Morlet) and discrete (Daubechies) wavelet transforms, with a simple, user-friendly graphical user interface within MATLAB. The interface allows for data to be imported from a number of standard file formats, visualized, processed and analyzed, and exported without use of the command line. Our work has been motivated by the challenges of circadian data, thus default settings appropriate to the analysis of such data have been pre-selected in order to minimize the need for fine-tuning. The toolkit is flexible enough to deal with a wide range of oscillatory signals, however, and may be used in more general contexts.</p> <p>Conclusions</p> <p>We have presented WAVOS: a comprehensive wavelet-based MATLAB toolkit that allows for easy visualization, exploration, and analysis of oscillatory data. WAVOS includes both the Morlet continuous wavelet transform and the Daubechies discrete wavelet transform. We have illustrated the use of WAVOS, and demonstrated its utility for the analysis of circadian data on both bioluminesence and wheel-running data. WAVOS is freely available at <url>http://sourceforge.net/projects/wavos/files/</url></p

Analysis of cardiac signals using spatial filling index and time-frequency domain

BACKGROUND: Analysis of heart rate variation (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. METHODS: This paper presents the spatial filling index and time-frequency analysis of heart rate variability signal for disease identification. Renyi's entropy is evaluated for the signal in the Wigner-Ville and Continuous Wavelet Transformation (CWT) domain. RESULTS: This Renyi's entropy gives lower 'p' value for scalogram than Wigner-Ville distribution and also, the contours of scalogram visually show the features of the diseases. And in the time-frequency analysis, the Renyi's entropy gives better result for scalogram than the Wigner-Ville distribution. CONCLUSION: Spatial filling index and Renyi's entropy has distinct regions for various diseases with an accuracy of more than 95%

Particle release from implantoplasty of dental implants and impact on cells

Abstract: Background: With increasing numbers of dental implants placed annually, complications such as peri-implantitis and the subsequent periprosthetic osteolysis are becoming a major concern. Implantoplasty, a commonly used treatment of peri-implantitis, aims to remove plaque from exposed implants and reduce future microbial adhesion and colonisation by mechanically modifying the implant surface topography, delaying re-infection/colonisation of the site. This in vitro study aims to investigate the release of particles from dental implants and their effects on human gingival fibroblasts (HGFs), following an in vitro mock implantoplasty procedure with a diamond burr. Materials and methods: Commercially available implants made from grade 4 (commercially pure, CP) titanium (G4) and grade 5 Ti-6Al-4 V titanium (G5) alloy implants were investigated. Implant particle compositions were quantified by inductively coupled plasma optical emission spectrometer (ICP-OES) following acid digestion. HGFs were cultured in presence of implant particles, and viability was determined using a metabolic activity assay. Results: Microparticles and nanoparticles were released from both G4 and G5 implants following the mock implantoplasty procedure. A small amount of vanadium ions were released from G5 particles following immersion in both simulated body fluid and cell culture medium, resulting in significantly reduced viability of HGFs after 10 days of culture. Conclusion: There is a need for careful evaluation of the materials used in dental implants and the potential risks of the individual constituents of any alloy. The potential cytotoxicity of G5 titanium alloy particles should be considered when choosing a device for dental implants. Additionally, regardless of implant material, the implantoplasty procedure can release nanometre-sized particles, the full systemic effect of which is not fully understood. As such, authors do not recommend implantoplasty for the treatment of peri-implantitis