Extraction of fuzzy rules using deterministic annealing integrated with ε-insensitive learning

A new method of parameter estimation for an artificial neural network inference system based on a logical interpretation of fuzzy if-then rules (ANBLIR) is presented. The novelty of the learning algorithm consists in the application of a deterministic annealing method integrated with ε-insensitive learning. In order to decrease the computational burden of the learning procedure, a deterministic annealing method with a “freezing” phase and ε-insensitive learning by solving a system of linear inequalities are applied. This method yields an improved neuro-fuzzy modeling quality in the sense of an increase in the generalization ability and robustness to outliers. To show the advantages of the proposed algorithm, two examples of its application concerning benchmark problems of identification and prediction are considered

Neuro-fuzzy modelling based on a deterministic annealing approach

This paper introduces a new learning algorithm for artificial neural networks, based on a fuzzy inference system ANBLIR. It is a computationally effective neuro-fuzzy system with parametrized fuzzy sets in the consequent parts of fuzzy if-then rules, which uses a conjunctive as well as a logical interpretation of those rules. In the original approach, the estimation of unknown system parameters was made by means of a combination of both gradient and least-squares methods. The novelty of the learning algorithm consists in the application of a deterministic annealing optimization method. It leads to an improvement in the neuro-fuzzy modelling performance. To show the validity of the introduced method, two examples of application concerning chaotic time series prediction and system identification problems are provided

Fetal state evaluation with fuzzy analysis of newborn attributes using CUDA architecture

Cardiotocography is a biophysical method of fetal state evaluation involving the recording and analysis of the fetal heart rate (FHR). Since a proper interpretation of the signal is relatively difficult, an automatic classification is often based on computational intelligence methods. The quality of classifiers based on supervised learning algorithms depends on a proper selection of learning data. In case of the fetal state evaluation, the learning is usually based on a set of quantitative parameters of FHR signal and the corresponding reference information determined on the basis of the retrospective analysis of newborn attributes. Values of the single attribute have been used so far as a reference. As a result, a part of information on the actual neonatal outcome has always been lost. The following paper presents a method of the fuzzy reasoning leading to an evaluation of neonatal outcome as a function of three newborn attributes. The fuzzy system was used in the process of a qualitative evaluation of the fetal state based on quantitative analysis of FHR signal using a support vector machine (SVM). In order to improve computational effectiveness, the learning algorithm was implemented in Compute Unified Device Architecture (CUDA). The results of these studies confirm the effectiveness of the proposed method and indicate the possibility of practical usage of the fuzzy system in supervised learning algorithms for the qualitative evaluation of the fetal state

An application of the Lp-norm in robust weighted averaging of biomedical signals

Averaging is one of the basic methods of statistical analysis of experimental data where the response of the system is periodic or quasi-periodic. As long as the noise are Gaussian, the standard averaging leads to good results and effective noise reduction. However, when the distortions have impulsive nature, then such an approach leads to a deterioration of the system. In this case the robust methods should be applied which are characterized by resistance to a statistical sample spoken. In this work a robust averaging method based on the minimization of a scalar criterion function using a Lp-norm functions are presented. The effectiveness of the proposed method was tested in an averaging periods aligned ECG signal cycles in the presence of impulse noise

Fuzzy clustering based methods for nystagmus movements detection in electronystagmography signal

The analysis of optokinetic nystagmus (OKN) provides valuable information about the condition of human vision system. One of the phenomena that is used in the medical diagnosis is optokinetic nystagmus. Nystagmus are voluntary or involuntarily eye movements being a response to a stimuli which activate the optokinetic systems. The electronystagmography (ENG) signal corresponding to the nystagmus has a form of a saw tooth waveform with fast components related to saccades. The accurate detection of the saccades in the ENG signal is the base for the further estimation of the nystagmus characteristic. The proposed algorithm is based on the proper filtering of the ENG signal providing a waveform with amplitude peaks corresponding the fast eyes rotation. The correct recognition of the local maxima of the signal is obtained by the means of fuzzy c-means clustering (FCM). The paper presents three variants of saccades detection algorithm based on the FCM. The performance of the procedures was investigated using the artificial as well as the real optokinetic nystagmus cycles. The proposed method provides high detection sensitivity and allows for the automatic and precise determination of the saccades location in the preprocessed ENG signal

Fuzzy prediction of fetal acidemia

Cardiotocography is the primary method for biophysical assessment of a fetal state. It is based mainly on the recording and analysis of fetal heart rate signal (FHR). Computer systems for fetal monitoring provide a quantitative description of FHR signals, however the effective methods for their qualitative assessment are still needed. The measurements of hydronium ions concentration (pH) in newborn cord blood is considered as the objective indicator of the fetal state. Improper pH level is a symptom of acidemia being the result of fetal hypoxia. The paper proposes a twostep analysis of signals allowing for effective prediction of the acidemia risk. The first step consists in the fuzzy classification of FHR signals. The task of fuzzy inference is to indicate signals that according to the FIGO guidelines represent the fetal wellbeing. These recordings are eliminated from the further classification with Lagrangian Support Vector Machines. The proposed procedure was evaluated using data collected with computerized fetal surveillance system. The classification results confirmed the high quality of the proposed fuzzy method of fetal state evaluation

An application of fuzzy clustering for saccade detection in ENG signal

Sygnał elektrynystagmograficzny (ENG) z oczopląsem ma postać fali o piłokształtnym kształcie składającym się z fazy wolnej oraz szybkiej. Faza szybka to ruch sakkadyczny gałki ocznej. Skuteczna i dokładna detekcja sakkad ma kluczowe znaczenie w określeniu charakteru oczopląsu. W celu prawidłowej detekcji położenia sakkad sygnał ENG jest filtrowany a maksima lokalne są wykrywane za pomocą rozmytej metody c-średnich. Proponowany algorytm charakteryzuje się dużą czułością i pozwala na automatyczną i precyzyjną lokalizację sakkad w sygnale ENG.The electronystagmography (ENG) signal corresponding to nystagmus has a form of a saw tooth waveform with fast components related to saccades. The accurate detection of saccades in ENG signal is the base for the further estimation of the nystagmus characteristic. The proposed algorithm is based on the proper filtering of the ENG signal providing a waveform with amplitude peaks corresponding the fast eyes rotation. The correct recognition of the local maxima of the signal is obtained by the means of fuzzy c-means clustering (FCM). The proposed algorithm is highly sensitive and allows for the automatic and precise localization of the saccades in ENG signal

Saccades detection in optokinetic nystagmus - a fuzzy approach

The analysis of eye movements is valuable in both clinical work and research. One of the characteristic type of eye movements is saccade. The accurate detection of saccadic eye movements is the base for further processing of saccade parameters such velocity, amplitude and duration. This paper presents an accurate saccade detection method which is supported by the fuzzy clustering. The proposed detection function is computationally efficient and precisely determines the time position of the saccadic eye movement event. The described method is characterized by low sensitivity for any kind of noise and can be applied in the analysis of the congenital nystagmus

The possibilities of optokinetic nystagmus cycles averaging

The analysis of eyes movements is a crucial part of eyes examination performed by clinicians. One of the characteristic type of eyes movements is a saccade. Its accurate detection is the base for further processing including the estimation of saccade parameters such as velocity, amplitude and duration. This paper presents averaging of optokinetic nystagmus (OKN) cycles that allows comparing and detecting different types of nystagmus phenomena. In order to average the OKN cycles the ENG signal needs to be processed. The saccade detection function is used to find the location of saccades in OKN waveform allowing the ENG signal to be divided into cycles. The resulting cycles are aligned using the Fourier shift method and then averaged providing the OKN cycle model, which can be used for evaluating the eyes at different movement conditions

Fuzzy system for evaluation of fetal heart rate signals using FIGO criteria

Cardiotocography is a biophysical method of fetal monitoring during pregnancy and labour. It is mainly based on recording and analysis of fetal heart activity. The computerized fetal monitoring systems provide the quantitative description of the recorded signals but the effective methods supporting the conclusion generation are still needed. The evaluation of the signal can be made using criteria recommended by FIGO. Nevertheless, the quantitative description of the traces is inconsistent with qualitative nature of the obstetric knowledge. Therefore, we applied the fuzzy system based on Takagi-Sugeno-Kang model to evaluate and classify signals. FIGO guidelines were used for developing a set of fuzzy conditional rules defining the system performance. The proposed system was evaluated using data collected with computerized fetal surveillance system – MONAKO. The classification results confirm the improvement of the fetal state evaluation quality while using the proposed fuzzy system support