Higher order spectra based support vector machine for arrhythmia classification

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. HRV analysis is an important tool to observe the heart’s ability to respond to normal regulatory impulses that affect its rhythm. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. A computer-based arrhythmia detection system of cardiac states is very useful in diagnostics and disease management. In this work, we studied the identification of the HRV signals using features derived from HOS. These features were fed to the support vector machine (SVM) for classification. Our proposed system can classify the normal and other four classes of arrhythmia with an average accuracy of more than 85%.

Differential effects of refractive blur on day and night-time driving performance

Purpose To investigate the effect of different levels of refractive blur on real-world driving performance measured under day and nighttime conditions. Methods Participants included 12 visually normal, young adults (mean age = 25.8 ± 5.2 years) who drove an instrumented research vehicle around a 4 km closed road circuit with three different levels of binocular spherical refractive blur (+0.50 diopter sphere [DS], +1.00 DS, +2.00 DS) compared with a baseline condition. The subjects wore optimal spherocylinder correction and the additional blur lenses were mounted in modified full-field goggles; the order of testing of the blur conditions was randomized. Driving performance was assessed in two different sessions under day and nighttime conditions and included measures of road signs recognized, hazard detection and avoidance, gap detection, lane-keeping, sign recognition distance, speed, and time to complete the course. Results Refractive blur and time of day had significant effects on driving performance (P < 0.05), where increasing blur and nighttime driving reduced performance on all driving tasks except gap judgment and lane keeping. There was also a significant interaction between blur and time of day (P < 0.05), such that the effects of blur were exacerbated under nighttime driving conditions; performance differences were evident even for +0.50 DS blur relative to baseline for some measures. Conclusions The effects of blur were greatest under nighttime conditions, even for levels of binocular refractive blur as low as +0.50 DS. These results emphasize the importance of accurate and up-to-date refractive correction of even low levels of refractive error when driving at night.

Combining opinions for use in Bayesian networks: A measurement error approach

Bayesian networks (BNs) are graphical probabilistic models used for reasoning under uncertainty. These models are becoming increasing popular in a range of fields including ecology, computational biology, medical diagnosis, and forensics. In most of these cases, the BNs are quantified using information from experts, or from user opinions. An interest therefore lies in the way in which multiple opinions can be represented and used in a BN. This paper proposes the use of a measurement error model to combine opinions for use in the quantification of a BN. The multiple opinions are treated as a realisation of measurement error and the model uses the posterior probabilities ascribed to each node in the BN which are computed from the prior information given by each expert. The proposed model addresses the issues associated with current methods of combining opinions such as the absence of a coherent probability model, the lack of the conditional independence structure of the BN being maintained, and the provision of only a point estimate for the consensus. The proposed model is applied an existing Bayesian Network and performed well when compared to existing methods of combining opinions.

Growing rod analysis for the fusionless correction of early onset scoliosis (EOS)

Managing spinal deformities in young children is challenging, particularly early onset scoliosis (EOS). Surgical intervention is often required if EOS has been unresponsive to conservative treatment particularly with rapidly progressive curves. An emerging treatment option for EOS is fusionless scoliosis surgery. Similar to bracing, this surgical option potentially harnesses growth, motion and function of the spine along with correcting spinal deformity. Dual growing rods are one such fusionless treatment, which aims to modulate growth of the vertebrae. The aim of this study was to ascertain the extent to which semi-constrained growing rods (Medtronic Sofamor Danek Memphis, TN, USA) with a telescopic sleeve component, reduce rotational constraint on the spine compared with standard rigid rods and hence potentially provide a more physiological mechanical environment for the growing spine. This study found that semi-constrained growing rods would be expected to allow growth via the telescopic rod components while maintaining the axial flexibility of the spine and the improved capacity for final correction.

Classification of echolocation calls from 14 species of bat by Support Vector Machines and Ensembles of Neural Networks

Calls from 14 species of bat were classified to genus and species using discriminant function analysis (DFA), support vector machines (SVM) and ensembles of neural networks (ENN). Both SVMs and ENNs outperformed DFA for every species while ENNs (mean identification rate – 97%) consistently outperformed SVMs (mean identification rate – 87%). Correct classification rates produced by the ENNs varied from 91% to 100%; calls from six species were correctly identified with 100% accuracy. Calls from the five species of Myotis, a genus whose species are considered difficult to distinguish acoustically, had correct identification rates that varied from 91 – 100%. Five parameters were most important for classifying calls correctly while seven others contributed little to classification performance.

Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis

This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposedGA- based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

Error and variance bounds on sigmoidal neurons with weight and input errors

Bounds on the expectation and variance of errors at the output of a multilayer feedforward neural network with perturbed weights and inputs are derived. It is assumed that errors in weights and inputs to the network are statistically independent and small. The bounds obtained are applicable to both digital and analogue network implementations and are shown to be of practical value.

A biomechanical investigation of dual growing rods used for fusionless scoliosis correction

Background The use of dual growing rods is a fusionless surgical approach to the treatment of early onset scoliosis (EOS), which aims of harness potential growth in order to correct spinal deformity. The purpose of this study was to compare the in-vitro biomechanical response of two different dual rod designs under axial rotation loading. Methods Six porcine spines were dissected into seven level thoracolumbar multi-segmental units. Each specimen was mounted and tested in a biaxial Instron machine, undergoing nondestructive left/right axial rotation to peak moments of 4Nm at a constant rotation rate of 8deg.s-1. A motion tracking system (Optotrak) measured 3D displacements of individual vertebrae. Each spine was tested in an un-instrumented state first and then with appropriately sized semi-constrained growing rods and ‘rigid’ rods in alternating sequence. Range of motion, neutral zone size and stiffness were calculated from the moment-rotation curves and intervertebral ranges of motion were calculated from Optotrak data. Findings Irrespective of test sequence, rigid rods showed significantly reduction of total rotation across all instrumented levels (with increased stiffness) whilst semi-constrained rods exhibited similar rotation behavior to the un-instrumented (P<0.05). An 11% and 8% increase in stiffness for left and right axial rotation respectively and 15% reduction in total range of motion was recorded with dual rigid rods compared with semi-constrained rods. Interpretation Based on these findings, the semi-constrained growing rods do not increase axial rotation stiffness compared with un-instrumented spines. This is thought to provide a more physiological environment for the growing spine compared to dual rigid rod constructs.

A deformable Brodmann area atlas

Functional MRI studies commonly refer to activation patterns as being localized in specific Brodmann areas, referring to Brodmann’s divisions of the human cortex based on cytoarchitectonic boundaries [3]. Typically, Brodmann areas that match regions in the group averaged functional maps are estimated by eye, leading to inaccurate parcellations and significant error. To avoid this limitation, we developed a method using high-dimensional nonlinear registration to project the Brodmann areas onto individual 3D co-registered structural and functional MRI datasets, using an elastic deformation vector field in the cortical parameter space. Based on a sulcal pattern matching approach [11], an N=27 scan single subject atlas (the Colin Holmes atlas [15]) with associated Brodmann areas labeled on its surface, was deformed to match 3D cortical surface models generated from individual subjects’ structural MRIs (sMRIs). The deformed Brodmann areas were used to quantify and localize functional MRI (fMRI) BOLD activation during the performance of the Tower of London task [7].

DS-VBF : dual sink vector-based routing protocol for underwater wireless sensor network

Underwater wireless sensor networks (UWSNs) have become the seat of researchers' attention recently due to their proficiency to explore underwater areas and design different applications for marine discovery and oceanic surveillance. One of the main objectives of each deployed underwater network is discovering the optimized path over sensor nodes to transmit the monitored data to onshore station. The process of transmitting data consumes energy of each node, while energy is limited in UWSNs. So energy efficiency is a challenge in underwater wireless sensor network. Dual sinks vector based forwarding (DS-VBF) takes both residual energy and location information into consideration as priority factors to discover an optimized routing path to save energy in underwater networks. The modified routing protocol employs dual sinks on the water surface which improves network lifetime. According to deployment of dual sinks, packet delivery ratio and the average end to end delay are enhanced. Based on our simulation results in comparison with VBF, average end to end delay reduced more than 80%, remaining energy increased 10%, and the increment of packet reception ratio was about 70%.

Bi-modal biometric authentication on mobile phones in challenging conditions

This paper examines the issue of face, speaker and bi-modal authentication in mobile environments when there is significant condition mismatch. We introduce this mismatch by enrolling client models on high quality biometric samples obtained on a laptop computer and authenticating them on lower quality biometric samples acquired with a mobile phone. To perform these experiments we develop three novel authentication protocols for the large publicly available MOBIO database. We evaluate state-of-the-art face, speaker and bi-modal authentication techniques and show that inter-session variability modelling using Gaussian mixture models provides a consistently robust system for face, speaker and bi-modal authentication. It is also shown that multi-algorithm fusion provides a consistent performance improvement for face, speaker and bi-modal authentication. Using this bi-modal multi-algorithm system we derive a state-of-the-art authentication system that obtains a half total error rate of 6.3% and 1.9% for Female and Male trials, respectively.

Reliability and reproducibility of a handheld videorefractor

PURPOSE To assess the performance of the 2Win eccentric videorefractor in relation to subjective refraction and table-mounted autorefraction. METHODS Eighty-six eyes of 86 adults (46 male and 40 female subjects) aged between 20 and 25 years were examined. Subjective refraction and autorefraction using the table-mounted Topcon KR8800 and the handheld 2Win videorefractor were carried out in a randomized fashion by three different masked examiners. Measurements were repeated about 1 week after to assess instrument reproducibility, and the intertest variability was compared between techniques. Agreement of the 2Win videorefractor with subjective refraction and autorefraction was assessed for sphere and for cylindrical vectors at 0 degrees (J0) and 45 degrees (J45). RESULTS Reproducibility coefficients for sphere values measured by subjective refraction, Topcon KR8800, and 2Win (±0.42, ±0.70, and ±1.18, respectively) were better than their corresponding J0 (±1.0, ±0.85, and ±1.66) and J45 (±1.01, ±0.87, and ±1.31) vector components. The Topcon KR8800 showed the most reproducible values for mean spherical equivalent refraction and the J0 and J45 vector components, whereas reproducibility of spherical component was best for subjective refraction. The 2Win videorefractor measurements were the least reproducible for all measures. All refractive components measured by the 2Win videorefractor did not differ significantly from those of subjective refraction, in both sessions (p > 0.05). The Topcon KR8800 autorefractometer and the 2Win videorefractor measured significantly more positive spheres and mean spherical equivalent refraction (p < 0.0001), but the J0 and J45 vector components were similar (p > 0.05), in both sessions. CONCLUSIONS The 2Win videorefractor compares well, on average, with subjective refraction. The reproducibility values for the 2Win videorefractor were considerably worse than either subjective refraction or autorefraction. The wide limits of reproducibility of the 2Win videorefractor probably limit its usefulness as a primary screening device.