فصلنامه مهندسی پزشکی زیستی
سال چهارم شماره 3 (پاییز 1389)

In this study a simple inverted pendulum model with PID controller and delayed feedback is used to model standing-still postural control system for the purpose of achieving useful information about its underlying control structure. Using the Genetic algorithm and an experimental study results, the model and the controller parameters were estimated in a way that the model mimics real experimental sway patterns. The controller parameters found meaningful interpretations and it is shown that degeneration of postural control system affects the values of the parameters. Our findings indicate that although the simple models are not able to describe complexities of postural control system and interactions between its components, they can help us to improve our understanding of postural control system, its performance, its features and the way that the features change.

This paper presents ICA analysis application for detection of autism disorder. In the first step, resources of EEG signals were extracted by ICA and then time domain and frequency domain processing were implemented. EEG signals of ten children with autism and ten healthy children aged 6 to 11 years have been obtained. The results have been compared statistically by T-test. Lower correlation levels between resources of the left hemisphere of the brain especially C3 channel region in autistic children compared with healthy subjects have been observed. Also the average energy of theta frequency band in C3 and F3 channels for children with autism were lower than that in healthy people and this criterion was higher in gamma frequency band.

There is a growing interest to improve seizure prediction by online analyzing of electroencephalogram (EEG) signals in epileptic patients. Seizure attack is occurred infrequently and unpredictably; hence, automatic detection of seizure during long-term is highly recommended. In this paper a novel Feature Reduction method namely AIS-RCA which adopted from the immunity system is proposed to improve the seizure detection rate. The automatic seizure detection can be performed in two successive stages: 1) The feature extraction/selection stage from EEG signals and 2) classifying the feature vectors by an efficient classifier. In this study, first, pseudo-Wigner-Ville distribution was applied to each window of the EEG signals and then the extracted features were transformed by AIS-RCA transform to represent the features in a more separable space. The AIS-RCA transformation matrix is estimated by using chunklets (a chunklet is defined as a subset of points that are known to be same). AIS-RCA using the proposed Artificial Immune System algorithm named Adaptive Distance-AIRS to discover the chunklets in the data space. Finally KNN classifier was applied to the transformed features to classify the seizure and non-seizure windows. The experimental results show that the proposed method yields epileptic detection accuracy rate up to 99.9% which is better than the results achieved by other types of features such as FFT, Wavelet transform, entropy and chaotic measures.

Today, the idea of photodynamic therapy (PDT) is considered as one of the fundamental basis of the new cancer treatment methods. One of the important issues in the application of this therapy is choosing the optimal dosimetry method. At best, PDT dosimetry should be done based on estimation of the accumulated singlet oxygen dose within the target tissue and comparison with the threshold value to ensure the efficacy of the treatment. In order to estimate the accumulated singlet oxygen level within the tissue, the most appropriate method is modeling the process of treatment. In this context, it is necessary to obtain enough information about the drug concentration within the target tissue, the amount of light absorbed by the drug, the amount of oxygen into the tissue, and the interactions between them that produce singlet oxygen. In this study modeling and simulation of the photobleaching has been investigated, considering the importance of the level of drug concentration in the target tissue which would be decreased by photobleaching. Simulation was done with Matlab software. A Comparison of simulation results with those of experimental methods showed that in the state of non-uniform drug distribution, simulation follows experimental results at the initial phase of rapid decline of drug concentration.

Nowadays, fast and accurate algorithms for signature verification are very attractive. In the area of dynamic signature verification, the features are classified into two groups: parametric and functional features. In parametric algorithms, although the speed of features extraction and classification process is faster than function based approaches but they are less accurate. The goal of this paper is modeling of the velocity signal that its pattern and properties are stable for a person. With using pole-zero models based on discrete cosine transform, a precise method is proposed for modeling and then features are extracted from strokes. These features are the deference of pole angles of strokes. Applying linear, parzen window and support vector machine classifiers, the proposed algorithm was tested on data set from Persian, Chinese, English and Turkish people and with common threshold, resulted equal error rates of 1.25% and 1.78% in the random and skilled forgeries, respectively.

Functional magnetic resonance imaging (fMRI) is widely used for investigation of brain neural activity. This imaging technique obtains signals and images from human brain’s response to prescheduled tasks. Several studies on blood oxygenation level-dependent (BOLD) signal responses demonstrate nonlinear behavior in response to a stimulus. In this paper we investigate nonlinear modeling of BOLD signal activity to model the nonlinear and time variant behaviors of this physiological system. For this purpose two categories of nonlinear methods are considered, first those one with emphasis on physiological parameters which affect BOLD response and methods model the input and output of system without any refer to all the hidden state variables (physiological parameters. Balloon model is analzyed and a new approach for activation detection based on this model is introduced. In addition, the Hammerstein-Wiener, NARMA and Volterra kernels are investigated as nonlinear and nonphysiological methods and their ability in detection of activation detection are compared. The Activation detection methods have been applied on the two data sets (real and synthetic). For synthetic data and threshold equal to 0.45, the Jaccard index for Wiener- Hammerstein, NARMA, and Volterra model was 0.9, 1.0, and 0.91, respectively. In real dataset and for optimal threshold (0.35, 0.4, and 0.45) the same index was 0.85, 0.90, and 0.87, respectively.

An otoacoustic emission is a low-level acoustic signal which is generated in cochlea. It could be recorded with a sensitive probe in the outer ear canal. OAEs are considered to be related to the amplification function of the cochlea. Outer hair cells are the elements that enhance cochlear sensitivity and frequency selectivity and hence act as the energy sources for amplification. Otoacoustic emission is transmitted through oval window to the outer ear canal, the distortion effects of middle ear and outer ear on the recorded signal are inevitable. Currently all clinical applications of otoacoustic emission are based on distorted measurement. For estimating the original otoacoustic emission produced in cochlea the middle ear and the outer ear effects must be compensated. The computational model of the auditory periphery is used to design a compensation filter for the estimation of the otoacoustic emission right after production and before entering the middle ear. Using Middle ear reverse transfer function and primary input signal Fourier transforms, OAE estimation before middle ear was obtained. The results of comparison of the estimated signal with the recorded one indicate that, due to the noise reduction and increase in reproducibility as a main criteria in hearing screening, the assessment based on the estimated otoacoustic emission is closer to the real response of the auditory system.