Mathematical Methods in Biomedical Optics

This paper presents a review of the phenomena regarding light-tissue interactions, especially absorption and scattering. The most important mathematical approaches for modeling the light transport in tissues and their domain of application: “first-order scattering,” “Kubelka-Munk theory,” “diffusion approximation,” “Monte Carlo simulation,” “inverse adding-doubling” and “finite element method” are briefly described.

Teager Energy Based Filter-Bank Cepstra in EEG Classification for Seizure Detection Using Radial Basis Function Neural Network

About 1–3% of the world population suffers from epilepsy. Epileptic seizures are abnormal sudden discharges in the brain with signatures manifesting in the electroencephalograph (EEG) recordings by frequency changes and increased amplitudes. These changes, in this work, are captured through static and dynamic features derived from three Teager energy based filter-bank cepstra (TE-FB-CEPs). We compared the performance of linear, logarithmic, and Mel frequency scale TE-FB-CEPs using radial basis function neural network in general epileptic seizure detection. The comparison is tried on eight different classification problems which encompass all the possible discriminations in the medical field related to epilepsy. In a previous study, using traditional cepstrum on the same database, we had found that the composite vectors showed a degraded performance in seizure detection. In this study, however, irrespective of frequency scaling used, it is found that the composite vectors of TE-FB-CEPs maintain excellent overall accuracy in all the eight classification problems.

Optical Measurement of Blood Oxygen Saturation of Dental Pulp

The applicability of arterial pulse oximetry to dental pulp was demonstrated using in vitro and in vivo measurements. First, porcine blood of known oxygen saturation (SO2) was circulated through extracted human upper incisors, while transmitted-light plethysmography was performed using three different visible wavelengths. From the light intensity waveforms measured in vitro, a parameter that is statistically correlated to SO2 was calculated using the pulsatile/nonpulsatile component ratios of two wavelengths for different SO2. Then, values were measured in vivo for living incisors, and the corresponding SO2 values were calculated using the results of in vitro measurements. The estimated SO2 values of the upper central incisors measured in vivo were from 71.0 to 92.7%. This study showed the potential to measure the oxygen saturation changes to identify the sign of pulpal inflammation.

Comparison of Baseline Cepstral Vector and Composite Vectors in the Automatic Seizure Detection Using Probabilistic Neural Networks

Epileptic seizures are abnormal sudden discharges in the brain with signatures manifesting in the electroencephalogram (EEG) recordings by frequency changes and increased amplitudes. These changes, in this work, are captured through traditional cepstrum and the cepstrum-derived dynamic features. We compared the performance of the traditional baseline cepstral vector with that of the two composite vectors, the first including velocity cepstral coefficients and the second including velocity and acceleration cepstral coefficients, using probabilistic neural network in general epileptic seizure detection. The comparison is tried on seven different classification problems which encompass all the possible discriminations in the medical field related to epilepsy. In this study, it is found that the overall performance of both the composite vectors deteriorates compared to that of baseline cepstral vector.

Slip Effects on Pulsatile Flow of Blood through a Stenosed Arterial Segment under Periodic Body Acceleration

A theoretical investigation concerning the influence of externally imposed periodic body acceleration on the flow of blood through a time-dependent stenosed arterial segment by taking into account the slip velocity at the wall of the artery has been carried out. A mathematical model is developed by treating blood as a non-Newtonian fluid obeying the Casson fluid model. The pulsatile flow is analyzed by considering a periodic pressure gradient and the inertial effects as negligibly small. A suitable generalized geometry for time-dependent stenosis is taken into account. Perturbation method is used to solve the coupled implicit system of nonlinear differential equations that govern the flow of blood. Analytical expressions for the velocity profile, volumetric flow rate, and wall shear stress are obtained. A thorough quantitative analysis has been made through numerical computations of the variables involved in the analysis that are of special interest in this study. The computational results are presented graphically. The results for different values of the parameters involved in the problem under consideration presented here show that the flow is appreciably influenced by slip velocity in the presence of periodic body acceleration.

High-Fidelity Visualization of Large Medical Datasets on Commodity Hardware

Recent advances in CT and MRI static and dynamic scanning techniques have led to great improvements in the resolution and size of volumetric medical datasets, and this trend is still ongoing. However, the explosion of dataset size prevents clinicians from taking advantage of an interactive, high-resolution exploration of volumetric medical data on commodity hardware, due to the memory constraints of modern graphics cards. This paper presents a hybrid CPU-GPU volume ray-casting method and some hybrid-based inspection tools aimed at providing interactive, medical-quality visualization using an ordinary desktop PC. Experimental results show that the hybrid method provides a near-interactive high-fidelity visualization of large medical datasets even if only limited hardware resources are available.

0.5 V Cardiac Sense Amplifier Realization Using Log-Domain Filtering

A novel configuration of a cardiac sense amplifier for pacemakers, realized using the concept of Log-Domain filtering, is introduced in this paper. The analog part of the amplifier operates under a single 0.5 V power supply voltage. Compared to the corresponding already published configuration, the proposed scheme offers the benefits of reduced operating voltage and dc power dissipation. The performance of the intermediate stages, as well as of the whole system, has been evaluated through the utilization of the Analog Design Environment of the Cadence software and, also, the design kit provided by the AMS 0.35 μm CMOS process.

Companding Realizations of the Nonlinear Energy Operator

Realizations of the nonlinear energy operator (NEO), using the concept of companding filtering, are introduced and compared in this work. For this purpose, the Log-Domain and Sinh-Domain filtering techniques have been followed. Both topologies are constructed from differentiator and multiplier blocks which have been realized through the utilization of nonlinear transconductor cells. Both of the proposed topologies offer the capability of ultra-low voltage operation, thanks to the employment of MOS transistors in the weak inversion. Considering a single power supply voltage of 0.5 V, the behavior of the proposed NEO realizations has been simulated using the Analog Design Environment of the Cadence software and the design kit of the TSMC 130 nm process. Comparison results show that the Sinh-Domain realization offers a more power efficient design than that offered by the Log-Domain realization.

A New Approach to Detect Epileptic Seizures in Electroencephalograms Using Teager Energy

A Teager energy (TE) based approach to discriminate electroencephalogram signals corresponding to nonseizure (eyes open, eyes closed, or interictal) and seizure (ictal) intervals is proposed. Though a good number of contributions have been made for seizure detection, the challenges of unbalanced data (nonseizure and seizure events) and system computational efficiency still remain a challenge. It is reported in the literature that the seizures are characterized by abnormal sudden discharges in the brain which get manifested in the EEG recordings by frequency changes and increased amplitudes. Teager energy (TE) is capable of tracking such rapid changes in frequency as well as amplitude in the time domain. An important finding of this study is that the mean TE quantifier is largely independent of the window length and exhibits relative consistency when used as a relative measure for comparison. We compared the diagnostic capability of TE quantifier with those of Higuchi’s fractal dimension and sample entropy in discriminating nonseizure and seizure states in the EEGs and found that TE outperforms the other two nonlinear quantifiers. The result shows that the application of this method compares favorably with conventional classification methods in terms of performance and is well suited for real-time automatic epileptic seizure detection.

Artificial Neural Network-Based Automated ECG Signal Classifier

The ECG signal is well known for its nonlinear dynamic behavior and a key characteristic that is utilized in this research; the nonlinear component of its dynamics changes more significantly between normal and abnormal conditions than does the linear one. As the higher-order statistics (HOS) preserve phase information, this study makes use of one-dimensional slices from the higher-order spectral domain of normal and ischemic subjects. A feedforward multilayer neural network (NN) with error back-propagation (BP) learning algorithm was used as an automated ECG classifier to investigate the possibility of recognizing ischemic heart disease from normal ECG signals. Different NN structures are tested using two data sets extracted from polyspectrum slices and polycoherence indices of the ECG signals. ECG signals from the MIT/BIH CD-ROM, the Normal Sinus Rhythm Database (NSR-DB), and European ST-T database have been utilized in this paper. The best classification rates obtained are 93% and 91.9% using EDBD learning rule with two hidden layers for the first structure and one hidden layer for the second structure, respectively. The results successfully showed that the presented NN-based classifier can be used for diagnosis of ischemic heart disease.