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PubMed2001

Hierarchical categorization of coarticulated phonemes: a theoretical analysis.

This article is concerned with the question of how listeners recognize coarticulated phonemes. The problem is approached from a pattern classification perspective. First, the potential acoustical effects of coarticulation are defined in terms of the patterns that form the input to a classifier. Next, a categorization model called HICAT is introduced that incorporates hierarchical dependencies to optimally deal with this input. The model allows the position, orientation, and steepness of one phoneme boundary to depend on the perceived value of a neighboring phoneme. It is argued that, if listeners do behave like statistical pattern recognizers, they may use the categorization strategies incorporated in the model. The HICAT model is compared with existing categorization models, among which are the fuzzy-logical model of perception and Nearey's diphone-biased secondary-cue model. Finally, a method is presented by which categorization strategies that are likely to be used by listeners can be predicted from distributions of acoustical cues as they occur in natural speech.

Auditory Perception↗
PubMed2006

Quantitative evaluation of left ventricle performance from two dimensional echo images.

OBJECTIVES: We sought to quantify the left ventricle systolic dysfunction by a geometric index from two-dimensional (2D) echocardiography by implementing an automated fuzzy logic edge detection algorithm for the segmentation. BACKGROUND: The coronary injuries have repercussions on the left ventricle producing changes on wall contractility, the shape of the cavity, and as a whole changes on the ventricular function. METHODS: 2D echocardiogram and M-mode recordings were performed over the control group and those with the dysfunctions. From 2D recordings, individual frames were extracted for at least five cardiac cycles and then segmentation of left ventricle was done by automated fuzzy systems. In each frame, the volumes are measured and a geometric index, eccentricity ratio (ER), was derived. The endocardial fractional shortening (FS), midwall fractional shortening (mFS), and the relative wall thickness (RWT) were also measured in each case. RESULTS: Depressed value of endocardial FS (20.39 +/- 5.43 vs 34.28 +/- 9.36, P = 0.0046), mFS (33 +/- 8.3 vs 52.5 +/- 11.7, P = 0.0047), and the RWT (0.337 +/- 0.096 vs 0.525 +/- 0.119, P = 0.0002) was observed with dysfunction. ER measured at end-diastole (2.86 +/- 0.703 vs 4.14 +/- 0.38) and end-systole (3.14 +/- 0.79 vs 5.48 +/- 0.74) was found to be decreased in the dysfunction group and more significant at the end-systole (P = 0.00017 vs 6.6E-06). CONCLUSION: This work concludes that the regional and global left ventricle systolic dysfunction can be assessed by the ER measured at end-diastole and end-systole from 2D echocardiogram and may contribute to the high rate of cardiovascular disorders.

Adult↗
PubMed1998

Anesthesia control using midlatency auditory evoked potentials.

This paper shows the development of a system to control inhalation anesthetic concentration delivered to a patient based upon that patient's midlatency auditory evoked potentials (MLAEP's). It was developed and tested in dogs by determining response to the supramaximal stimulus of tail clamping. Prior to tail clamp, the MLAEP was recorded along with inhalational anesthetic concentration and classified as responders or nonresponders as determined by tail clamping. This was performed at a number of different anesthetic levels to obtain a data training set. The MLAEP's were compacted by means of discrete time wavelet transform (DTWT), and together with anesthetic concentration value, a stepwise discriminant analysis (SDA) was performed to determine those features which could separate responders from nonresponders. It was determined that only three features were necessary for this recognition. These features were then used to train a four-layer artificial neural network (ANN) to separate the responders from nonresponders. The network was tested using a separate set of data, resulting in a 93% recognition rate in the anesthetic transition zone between responders and nonresponders, and 100% recognition rate outside this zone. The anesthetic controller used this ANN combined with fuzzy logic and rule-based control. A set of ten animal experiments were performed to test the robustness of this controller. Acceptable clinical performance was obtained, showing the feasibility of this approach.

Anesthesia↗
PubMedDate not supplied

[Likeness: a system for rapid retrieval of similar conformation of proteins and their spatial structures].

We report a method for estimating the conformational likeness between any two proteins. Likeness is handled in terms of stochastic geometry, fuzzy logic and decision making theories. Using the method, a computer system LIKENESS was developed. The system does "real time" searching and spatial alignment of any PDB-available proteins. LIKENESS is accessible at URL: "http:@cl.sdsc.edu/".

Amino Acid Sequence↗
PubMed1993

Computer-assisted diagnosis of postmenopausal osteoporosis using a fuzzy expert system shell.

This paper shows how the AMDIS (Automated Medical Diagnosis with Intelligent Systems) integrated system can be employed to build a fuzzy medical expert system in the domain of postmenopausal osteoporosis. The fundamental aims of the expert system are to standardize knowledge and support physicians in the early detection of postmenopausal osteoporosis. A wide range of diagnostic situations has been considered for both categories of the disease, with judgments that range from disease is excluded to disease is definite. The salient aspects of the approach are the use of fuzzy logic as an analytic language for the representation and manipulation of knowledge and strategies and the integration of structured interview techniques and learning-by-example to address the knowledge acquisition task.

Adult↗
PubMed2005

Adaptive neuro-fuzzy based modelling for prediction of air pollution daily levels in city of Zonguldak.

Air pollution is a growing problem arising from domestic heating, high density of vehicle traffic, electricity production, and expanding commercial and industrial activities, all increasing in parallel with urban population. Monitoring and forecasting of air quality parameters in the urban area are important due to health impact. Artificial intelligent techniques are successfully used in modelling of highly complex and non-linear phenomena. In this study, adaptive neuro-fuzzy logic method has been proposed to estimate the impact of meteorological factors on SO2 and total suspended particular matter (TSP) pollution levels over an urban area. The model forecasts satisfactorily the trends in SO2 and TSP concentration levels, with performance between 75-90% and 69-80 %, respectively.

Air Pollutants↗
PubMedDate not supplied

Fuzzy sets in human anatomy.

Artificial intelligence will have a great impact on the rather traditional field of anatomy. Techniques of artificial intelligence can advance anatomical research in a wide range of applications. Fuzzy logic is especially useful in facilitating the use of natural language in the mathematical description of structures or functions. Examples presented demonstrate the broad use of information technology in anatomical applications, including description and classification, knowledge representation, image processing, and three-dimensional anatomical atlases.

Anatomy↗
PubMed2004

Development of a biomimetic robotic fish and its control algorithm.

This paper is concerned with the design of a robotic fish and its motion control algorithms. A radio-controlled, four-link biomimetic robotic fish is developed using a flexible posterior body and an oscillating foil as a propeller. The swimming speed of the robotic fish is adjusted by modulating joint's oscillating frequency, and its orientation is tuned by different joint's deflections. Since the motion control of a robotic fish involves both hydrodynamics of the fluid environment and dynamics of the robot, it is very difficult to establish a precise mathematical model employing purely analytical methods. Therefore, the fish's motion control task is decomposed into two control systems. The online speed control implements a hybrid control strategy and a proportional-integral-derivative (PID) control algorithm. The orientation control system is based on a fuzzy logic controller. In our experiments, a point-to-point (PTP) control algorithm is implemented and an overhead vision system is adopted to provide real-time visual feedback. The experimental results confirm the effectiveness of the proposed algorithms.

Algorithms↗
PubMed2003

Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue.

Microarray techniques have made it possible to observe the expression of thousands of genes simultaneously. They have recently been applied to study gene expression patterns in tissue samples. This may lead to highly desirable improvements in the diagnosis and treatment of human diseases. Statistical and machine learning methods have recently been used to classify cancer tissue based on gene expression data. Although some of these methods have achieved a high degree of accuracy, they generally lack transparency in their classification process. This, however, is crucial for the application in the medical field. In order to overcome this obstacle, we used knowledge-based neurocomputing (KBN), since KBN seeks to gain knowledge that is comprehensible to humans. In particular, we applied evolving fuzzy neural networks (EFuNNs) to classify cancer tissue, which is illustrated on the case studies of leukaemia and colon cancer. EFuNNs belong to the evolving connectionist system paradigm (ECOS) that has been recently introduced. They are well suited for adaptive learning and knowledge discovery. Fuzzy logic rules can be extracted from the trained networks and offer knowledge about the classification process in an easily accessible form. These rules point to genes that are strongly associated with specific types of cancer and may be used for the development of new tests and treatment discoveries.

Algorithms↗
PubMed1995

Advanced clinical monitoring: similar scenario discrimination.

By creating a monitoring system to detect potentially fatal physiologic trends, we can provide physicians with an automatically-generated differential diagnosis based upon a patient's recent history. In order to make this diagnosis clinically reliable, the system must be sensitive enough to discriminate between physiologic events that can share nearly identical trends. In an effort to refine our discriminatory techniques, we have created a software "monitor" that uses fuzzy logic to analyze physiologic signals to make a clear distinction between an arrhythmic cardiac arrest (ACA) and the onset of cardiopulmonary bypass (CPB). The differentiation capabilities of this monitor will form the foundation for a comprehensive automated diagnostic system.

Adult↗
PubMed2004

Development of an early diagnostic system using fuzzy theory for postoperative infections in patients with gastric cancer.

BACKGROUND: Early prediction of infection following surgery for gastric cancer may permit earlier intervention. AIM: The aim was to develop an early diagnostic system for postoperative infection. METHODS: Clinical and laboratory data were analyzed in 180 patients who had surgery for gastric cancer at the Wakayama Medical University Hospital (January 1992 to December 1994). Of these, 60 patients developed a postoperative infection. A predictive system was then devised using fuzzy theory and evaluated in a second set of 137 patients who underwent surgery for gastric cancer at Wakayama Medical University and 10 other associated hospitals (August 1995 to January 1997). RESULTS: The system identified seven parameters grouped into six rules and entered into a fuzzy logic system to predict either infection or non-infection. (1) Blood loss, and extent of resection; (2) the febrile pattern during days 2 to 4; (3) differential leukocyte count on day 4; (4) C-reactive protein level on day 4; (5) time sequential vectors during days 1-4 for the band cell neutrophil count, and (6) leukocyte count. The sensitivity was 86% (24/28), the specificity was 90% (98/109), the negative predictive value was 96% (98/102), the positive predictive value was 69% (24/35) and the overall accuracy was 80% (122/137). CONCLUSION: Fuzzy theory can provide early prediction of postoperative infection using standard clinical and biochemical parameters. The clinical utility of this system needs to be determined in future studies.

Diagnosis, Computer-Assisted↗
PubMed1992

Selectivity, scope, and simplicity of models: a lesson from fitting judgments of perceived depth.

When comparing psychological models a researcher should assess their relative selectivity, scope, and simplicity. The third of these considerations can be measured by the models' parameter counts or equation length, the second by their ability to fit random data, and the first by their differential ability to fit patterned data over random data. These conclusions are based on exploration of integration models reflecting depth judgments. Replication of Massaro's (1988a) results revealed an additive model (Bruno & Cutting, 1988), and Massaro's fuzzy-logical model of perception (FLMP) fit data equally well, but further exploration showed that the FLMP fit random data better. The FLMP's successes may reflect not its sensitivity in capturing psychological process but its scope in fitting any data and its complexity as measured by equation length.

Adult↗
PubMed2002

Protein motif extraction with neuro-fuzzy optimization.

MOTIVATION: It is attempted to improve the speed and flexibility of protein motif identification. The proposed algorithm is able to extract both rigid and flexible protein motifs. RESULTS: In this work, we present a new algorithm for extracting the consensus pattern, or motif, from a group of related protein sequences. This algorithm involves a statistical method to find short patterns with high frequency and then neural network training to optimize the final classification accuracies. Fuzzy logic is used to increase the flexibility of protein motifs. C2H2 Zinc Finger Protein and epidermal growth factor protein sequences are used to demonstrate the capability of the proposed algorithm in finding motifs. AVAILABILITY: This program is freely available for academic use by request.

Algorithms↗
PubMed2006

Knowledge-based fuzzy system for diagnosis and control of an integrated biological wastewater treatment process.

A supervisory expert system based on fuzzy logic rules was developed for diagnosis and control of a laboratory- scale plant comprising anaerobic digestion and anoxic/aerobic modules for combined high rate biological N and C removal. The design and implementation of a computational environment in LabVIEW for data acquisition, plant operation and distributed equipment control is described. A step increase in ammonia concentration from 20 to 60 mg N/L was applied during a trial period of 73 h. Recycle flow rate from the aerobic to the anoxic module and bypass flow rate from the influent directly to the anoxic reactor were the output variables of the fuzzy system. They were automatically changed (from 34 to 111 L/day and from 8 to 13 L/day, respectively), when new plant conditions were recognised by the expert system. Denitrification efficiency higher than 85% was achieved 30 h after the disturbance and 15 h after the system response at an HRT as low as 1.5 h. Nitrification efficiency gradually increased from 12 to 50% at an HRT of 3 h. The system proved to react properly in order to set adequate operating conditions that led to timely and efficient recovery of N and C removal rates.

Ammonia↗
PubMed2005

Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients.

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of EEG signals were used as input patterns of the five ANFIS classifiers. To improve diagnostic accuracy, the sixth ANFIS classifier (combining ANFIS) was trained using the outputs of the five ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals.

Artificial Intelligence↗
PubMedDate not supplied

Computerised anaesthesia monitoring using fuzzy trend templates.

The task of administering anaesthesia requires the clinician to be vigilant for long periods of time to detect the onset of adverse conditions. Large amounts of data must be analysed in real-time and, if a problem is detected, it must be diagnosed as a matter of urgency, this being done while other management protocols are being carried out. For these reasons it would be of benefit if automated decision support could be provided for anaesthesia monitoring, to lighten the cognitive load on the anaesthetist. The Sentinel anaesthesia monitor has been developed with this objective in mind. It uses a fuzzy time-domain pattern matching technique, termed fuzzy trend templates, to detect vaguely specified patterns in multiple physiological data streams. These patterns are representative of symptoms associated with undesirable patient states. The system is capable of detecting trends and states such as 'significant rise' and 'high', and associating vague duration and temporal intervals with individual trends. Fuzzy trend templates have proven to be quite intuitive to specify, given linguistic (anaesthetists') knowledge about the problem domain. Sentinel's implementation of fuzzy trend templates also uses an extension to fuzzy logic based on the theory of evidence, to handle situations where desired information is not available, for example, when sensors are not being used. In off-line testing, Sentinel has achieved sensitivity and specificity of above 90% in the diagnosis of seven common or serious conditions that can arise during anaesthesia.

Anesthesia, General↗
PubMed1996

Perceiving speech from inverted faces.

We examined whether the orientation of the face influences speech perception in face-to-face communication. Participants identified auditory syllables, visible syllables, and bimodal syllables presented in an expanded factorial design. The syllables were /ba/, /va/, /oa/, or /da/. The auditory syllables were taken from natural speech whereas the visible syllables were produced by computer animation of a realistic talking face. The animated face was presented either as viewed in normal upright orientation or inverted orientation (180 degrees frontal rotation). The central intent of the study was to determine if an inverted view of the face would change the nature of processing bimodal speech or simply influence the information available in visible speech. The results with both the upright and inverted face views were adequately described by the fuzzy logical model of perception (FLMP). The observed differences in the FLMP's parameter values corresponding to the visual information indicate that inverting the view of the face influences the amount of visible information but does not change the nature of the information processing in bimodal speech perception.

Adult↗
PubMed2001

A new method to incorporate age and gender into the criteria for the detection of acute inferior myocardial infarction.

Recent studies have shown that younger women are more likely to die during and after hospitalization for acute myocardial infarction (MI) than older women and men of all ages. This may be partly due to incorrect diagnosis or late detection of acute MI in younger women. At high specificity levels (>98%), the sensitivity of the initial ECG to detect acute MI may be as low as 30% when using traditional criteria by both physicians and computerized interpretation programs. This study examines if women of different age groups have a similar ECG presentation to men during acute inferior MI and if the diagnostic accuracies of the initial ECG are comparable. We analyzed chest pain ECGs from Mayo Clinic and Medical College of Wisconsin, which included 1,339 patients with acute inferior MI and 1,169 age-matched controls with noncardiac chest pain. We subdivided all groups by age (below and above 60 years) and compared ECG parameters (ST elevation, ST depression, QRS duration, R-wave amplitude, Q-wave duration and amplitude, QT interval) between genders. For inferior MI patients under age 60, women had lower ST elevations at the J point in lead II than men (57 +/- 91 microV vs. 86 +/- 117 microV, P < .02). This trend was reversed for patients over age 60 (lead a VF: 102 +/- 126 microV vs. 84+/-117 microV, P < .04; Lead III: 130+/-146 microV vs. 103+/-131 microV, P < .007). A neural network method was used to identify the most significant group of ECG parameters for detecting acute MI. An adaptive fuzzy logic method was developed for adapting to the threshold differences among the different gender and age groups. The new algorithm improved the sensitivity of acute inferior MI detection by more than 25% relative to old algorithm, while maintaining the high specificity around 98% for noncardiac chest pain patients.

Age Factors↗