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PubMed1999

Otoneurological expert system for vertigo.

We have developed an otoneurological expert system (ONE) to aid the diagnostics of vertigo, to assist teaching and to implement a database for research. The ONE database is set to harvest data on patient history, signs and test results necessary for diagnostic work with vertiginous patients. A method based on pattern recognition was used in the reasoning process. Questions about symptoms, signs and test results are weighted and scored for each disease and the most likely disease is recognized from defined disease profiles. Missing information and uncertainties are solved with a method resembling fuzzy logic. ONE was validated by comparing diagnoses assessed by physicians with those provided by the system. It proved to be a valid decision-maker by solving 65% of the cases correctly, while the physicians' mean was 69%. To improve ONE further, a follow-up should be implemented for the patients, since diagnosing sudden deafness and Meniere's disease during the first visit is often impossible. We aim to obtain new information on diseases involving vertigo by applying adaptive computer applications, such as genetic algorithms, to the reasoning process.

Algorithms↗
PubMed2001

Integrating local and scientific knowledge: an example in fisheries science.

Attempting to predict the spatial dynamics fish stocks, as required for management, is an ominous task given our incomplete understanding of biological and ecological mechanisms underpinning behavioral responses of fish. Large gaps still exist in our basic scientific knowledge. Nonetheless, the knowledge of fishers and fishery managers is not incorporated into our scientific analyses, even though such information is rich in observation since knowledge of fish behavior and distribution is a prerequisite for their profession. Combining such observations with more conventional scientific studies and theoretical interpretations provides a means by which we may bridge some gaps in our knowledge. Presented here is an example of how both local and scientific knowledge can be integrated in a heuristic model. The model, CLUPEX, is developed in the framework of a fuzzy logic expert system and uses linguistic statements written in natural language to capture and combine knowledge sources in the form of IF... THEN rules. The rules are inferred from interviews with experts and fishery professionals including fishers, fishery managers, scientists, and First Nations people. The knowledge base, comprised of the set of rules, is flexible in the sense that it can easily be modified to add additional information or change current information. Using input pertaining to biotic and abiotic environmental conditions, CLUPEX uses the rules to provide quantitative and qualitative predictions on the structure, dynamics and mesoscale distribution of shoals of migratory adult herring during different life stages of their annual life cycle.

Animals↗
PubMed2006

Ecotoxicity prediction by adaptive fuzzy partitioning: comparing descriptors computed on 2D and 3D structures.

Classification models were established on four endpoints, i.e. trout, daphnia, quail and bee, including from 100 to 300 pesticides subdivided into 3 toxicity classes. For each species, five separate sets of molecular descriptors, computed by several software, were compared, including parameters related to 2D or 3D structures. The most relevant descriptors were selected with help of a procedure based on genetic algorithms. Then, structure-activity relationships were built by Adaptive Fuzzy Partition (AFP), a recursive partitioning method derived from Fuzzy Logic concepts.Globally, satisfactory results were obtained for each animal species. The best cross-validation and test set scores reached values of about 70-75%. More important, the relationships derived from the descriptors calculated from 2D structures were superior or similar to those computed from 3D structures. These results underline that the long computational time employed to compute 3D descriptors is often useless to improve the prediction ability of the ecotoxicity models. Finally, the differences in the prediction ability between the different software used were quite reduced and show the possibility to use different descriptor packages for obtaining similar satisfactory models.

Animals↗
PubMed2006

Risk of colon cancer in hereditary non-polyposis colorectal cancer patients as predicted by fuzzy modeling: Influence of smoking.

AIM: To investigate whether a fuzzy logic model could predict colorectal cancer (CRC) risk engendered by smoking in hereditary non-polyposis colorectal cancer (HNPCC) patients. METHODS: Three hundred and forty HNPCC mismatch repair (MMR) mutation carriers from the Creighton University Hereditary Cancer Institute Registry were selected for modeling. Age-dependent curves were generated to elucidate the joint effects between gene mutation (hMLH1 or hMSH2), gender, and smoking status on the probability of developing CRC. RESULTS: Smoking significantly increased CRC risk in male hMSH2 mutation carriers (P < 0.05). hMLH1 mutations augmented CRC risk relative to hMSH2 mutation carriers for males (P < 0.05). Males had a significantly higher risk of CRC than females for hMLH1 non smokers (P < 0.05), hMLH1 smokers (P < 0.1) and hMSH2 smokers (P < 0.1). Smoking promoted CRC in a dose-dependent manner in hMSH2 in males (P < 0.05). Females with hMSH2 mutations and both sexes with the hMLH1 groups only demonstrated a smoking effect after an extensive smoking history (P < 0.05). CONCLUSION: CRC promotion by smoking in HNPCC patients is dependent on gene mutation, gender and age. These data demonstrate that fuzzy modeling may enable formulation of clinical risk scores, thereby allowing individualization of CRC prevention strategies.

Adaptor Proteins, Signal Transducing↗
PubMed2005

Convenient intelligent cursor control web systems for Internet users with severe motor-impairments.

The objective of this research work is to simplify and improve the efficiency of cursor control and its interactions on the computer screen by applying fuzzy logic in its decision-making to make Internet users with disabilities use the networked computers conveniently and easily. "Point and click" interactions are one of the key features of operating systems based of graphical user interfaces (GUIs) [C.E. Steriadis, P. Constantinou, Designing human-computer interfaces for quadriplegic people, ACM Trans. Comput.-Hum.-Interact. (TOCHI) 10 (2) (2003) 87-118.]. People with severe motor-impairments due to spinal cord injury (SCI) or spinal cord dysfunction (SCD), however, often have difficulty with accurate and efficient control of pointing devices, limiting their integration to society and unassisted control over their environment through the use of such operating systems [S. Keates, F. Hwang, P. Langdon, P.J. Clarkson, P. Robinson, Cursor measures for motion-impaired computer users, in: Proceedings of ACM SIGCAPH Conference of Assistive Technologies - ASSETS 2002, pp. 135-142.]. The questions "How can someone with severe motor-impairments perform 'point and click' interactions as accurately and efficiently as an able-bodied person?" and "How can these interactions be advanced through use of Computational Intelligence (CI)?" are the driving forces behind the research described in this paper. Through this research, a novel fuzzy mouse cursor control system (FMCCS) is described. The FMCCS core consists of several fuzzy control functions, which define different user interactions with the system. Design and descriptions of these functions, as well first prototype implementation and testing with real users sustaining severe disabilities are presented.

Computer Systems↗
PubMed2006

Prediction of breast cancer using volatile biomarkers in the breath.

We evaluated a breath test for volatile organic compounds (VOCs) as a predictor of breast cancer. Breath VOCs were assayed in 51 asymptomatic women with abnormal mammograms and biopsy-proven breast cancer, and 42 age-matched healthy women. A fuzzy logic model predicted breast cancer with accuracy superior to previously reported findings. Following random assignment to a training set (64) or a prediction set (29), a model was constructed in the training set employing five breath VOCs that predicted breast cancer in the prediction set with 93.8% sensitivity and 84.6% specificity. The same model predicted no breast cancer in 16/50 (32.0%) women with abnormal mammograms and no cancer on biopsy. A two-minute breath test could potentially provide a safe, accurate and painless screening test for breast cancer, but prospective validation studies are required.

Age Factors↗
PubMed2000

On the place of fuzzy health in medical theory.

This commentary on Sadegh-Zadeh's article 'Fuzzy health, illness and disease,' has its focus on the philosophical background for applying fuzzy logic to medical theory. I concentrate on four issues. First, I contest some of Sadegh-Zadeh's statements on the present state of the theory of medicine, in particular with regard to assumptions ascribed to contemporary theorists. Second, I consider Sadegh-Zadeh's interesting idea that a person can have a disease to varying degrees, from not having it at all to having it completely. I argue that there are difficulties pertaining to the definition of particular diseases, which obstruct the application of this idea. The following two points concern medical semantics and principles for definition in general. I take issue with Sadegh-Zadeh's description of the correct procedure for definition. I also contest his unconditional proposal for a social definition of health, illness and disease.

Disease↗
PubMed2002

A supervisory control system for optimising nitrogen removal and aeration energy consumption in wastewater treatment plants.

A fuzzy logic supervisory control system for optimising nitrogen removal and aeration energy consumption has been developed. This control system allows optimising and controlling the dissolved oxygen (DO) concentration in the aerobic reactors, the blowers discharge pressure and the effluent ammonia and nitrate concentrations. DO is controlled by adjusting control valve opening and blower discharge pressure is controlled by modifying rotational speed of the blowers. Optimum nitrification/denitrification is achieved by modifying the DO set point in the last aerobic reactor and the internal recirculation. This system has been tested by simulation in a Bardenpho process using the Activated Sludge Model No2. A significant improvement in stability on the activated sludge process is achieved. Moreover, significant energy saving has been achieved with this control strategy.

Aerobiosis↗
PubMed1999

Clinical application of a blood pressure autoregulation system during hypotensive anesthesia.

A blood pressure autoregulation system based on the state-predictive control method was developed to minimize intraoperative blood loss and hence avoid blood transfusion. In this report, the system is further improved by incorporating fuzzy logic with a fail-safe function and an individual parameter-identifying function. The safety and stability of this system had been confirmed by preclinical experiments with dogs. Thereafter clinical application with 17 patients was conducted to maintain their mean arterial pressure at around 60 mmHg during major surgery. The use of this system resulted in decreased blood loss and more speedy and accurate surgery due to a clearer surgical field. Unwanted effects of hypotension were not observed clinically or in laboratory tests. This system is therefore safe, stable, and effective in reducing the blood loss during major surgery that otherwise might cause substantial blood loss.

Anesthesia↗
PubMed2005

Adaptive dynamic range optimization (ADRO): a digital amplification strategy for hearing aids and cochlear implants.

Adaptive dynamic range optimization (ADRO) is an amplification strategy that uses digital signal processing techniques to improve the audibility, comfort, and intelligibility of sounds for people who use cochlear implants and/or hearing aids. The strategy uses statistical analysis to select the most information-rich section of the input dynamic range in multiple-frequency channels. Fuzzy logic rules control the gain in each frequency channel so that the selected section of the dynamic range is presented at an audible and comfortable level. The ADRO processing thus adaptively optimizes the dynamic range of the signal in multiple-frequency channels. Clinical studies show that ADRO can be fitted easily to all degrees of hearing loss for hearing aids and cochlear implants in a direct and intuitive manner, taking the preferences of the listener into account. The result is high acceptance by new and experienced hearing aid users and strong preferences for ADRO compared with alternative amplification strategies. The ADRO processing is particularly well suited to bimodal and hybrid stimulation which combine electric and acoustic stimulation in opposite ears or in the same ear, respectively.

Algorithms↗
PubMed1995

Continuous versus discrete information processing in pattern recognition.

A discrete feature model (DFM) and the fuzzy logical model (FLMP) were formulated to predict the distribution of rating judgments in a pattern recognition task. The distinction was between the spoken vowels /i/ and /I/, as in beet and bit. Subjects were instructed to rate the vowel on a nine-point scale from /i/ to /I/. Two features, the first formant frequency (F1) and the vowel duration, were orthogonally varied: The vowel /i/ has a lower (F1) and a longer duration compared to a somewhat higher (F1) and shorter duration for /I/. The DFM predicts that the separate features are recognized discretely, whereas the FLMP assumes that continuous information is available about each feature. Tests of these models on the observed data indicated that the continuous information assumption of the FLMP gave a significantly better description of the distribution of rating judgments.

Adult↗
PubMed2006

Evolving fuzzy rules to model gene expression.

This paper develops an algorithm that extracts explanatory rules from microarray data, which we treat as time series, using genetic programming (GP) and fuzzy logic. Reverse polish notation is used (RPN) to describe the rules and to facilitate the GP approach. The algorithm also allows for the insertion of prior knowledge, making it possible to find sets of rules that include the relationships between genes already known. The algorithm proposed is applied to problems arising in the construction of gene regulatory networks, using two different sets of real data from biological experiments on the Arabidopsis thaliana cold response and the rat central nervous system, respectively. The results show that the proposed technique can fit data to a pre-defined precision even in situations where the data set has thousands of features but only a limited number of points in time are available, a situation in which traditional statistical alternatives encounter difficulties, due to the scarcity of time points.

Algorithms↗
PubMed2000

Segmentation of suspicious clustered microcalcifications in mammograms.

We have developed a multistage computer-aided diagnosis (CAD) scheme for the automated segmentation of suspicious microcalcification clusters in digital mammograms. The scheme consisted of three main processing steps. First, the breast region was segmented and its high-frequency content was enhanced using unsharp masking. In the second step, individual microcalcifications were segmented using local histogram analysis on overlapping subimages. For this step, eight histogram features were extracted for each subimage and were used as input to a fuzzy rule-based classifier that identified subimages containing microcalcifications and assigned the appropriate thresholds to segment any microcalcifications within them. The final step clustered the segmented microcalcifications and extracted the following features for each cluster: the number of microcalcifications, the average distance between microcalcifications, and the average number of times pixels in the cluster were segmented in the second step. Fuzzy logic rules incorporating the cluster features were designed to remove nonsuspicious clusters, defined as those with typically benign characteristics. A database of 98 images, with 48 images containing one or more microcalcification clusters, provided training and testing sets to optimize the parameters and evaluate the CAD scheme, respectively. The results showed a true positive rate of 93.2% and an average of 0.73 false positive clusters per image. A comparison of our results with other reported segmentation results on the same database showed comparable sensitivity and at the same time an improved false positive rate. The performance of the CAD scheme is encouraging for its use as an automatic tool for efficient and accurate diagnosis of breast cancer.

Biophysical Phenomena↗
PubMed2001

Advanced monitoring and control of anaerobic wastewater treatment plants: diagnosis and supervision by a fuzzy-based expert system.

A fuzzy-based expert system (ES) for the diagnosis and supervision for anaerobic digesters is presented. The system was developed in a Microsoft Windows support using fuzzy logic inference together with a rule base for the implementation of expert knowledge. The ES runs on-line through three main modules, which determine the state and trend of the process, and the best set points for the actuation on the final control elements of the plant. Two further modules run in parallel, when they are required by the operator, using off-line and on-line information for the detection of inhibition due to toxic compounds in the process and for the validation of the on-line diagnosis. The diagnosis and supervision ES was tuned up in order to adjust the membership functions describing the process, and lately tested, running on-line, to study the response of the rule base.

Anaerobiosis↗
PubMed1994

A pattern recognition account of decision making.

In the domain of pattern recognition, experiments have shown that perceivers integrate multiple sources of information in an optimal manner. In contrast, other research has been interpreted to mean that decision making is nonoptimal. As an example, Tversky and Kahneman (1983) have shown that subjects commit a conjunction fallacy because they judge it more likely that a fictitious person named Linda is a bank teller and a feminist than just a bank teller. This judgment supposedly violates probability theory, because the probability of two events can never be greater than the probability of either event alone. The present research tests the hypothesis that subjects interpret this judgment task as a pattern recognition task. If this hypothesis is correct, subjects' judgments should be described accurately by the fuzzy logical model of perception (FLMP)--a successful model of pattern recognition. In the first experiment, the Linda task was extended to an expanded factorial design with five vocations and five avocations. The probability ratings were described well by the FLMP and described poorly by a simple probability model. The second experiment included (1) two fictitious people, Linda and Joan, as response alternatives and (2) both ratings and categorization judgments. Although the ratings were accurately described by both the FLMP and an averaging of the sources of information, the categorization judgments were described better by the FLMP. These results reveal important similarities in recognizing patterns and in decision making. Given that the FLMP is an optimal method for combining multiple sources of information, the probability judgments appear to be optimal in the same manner as pattern-recognition judgments.

Adult↗
PubMed2001

A fuzzy system for detecting distorted plethysmogram pulses in neonates and paediatric patients.

Pulse oximetry is a useful, quick, non-invasive and widely used technology for monitoring oxygen saturation (SaO2) for neonates and paediatric patients. However, pulse oximetry is fraught with artefacts, causing false alarms resulting from patient or probe movement. The shape of the plethysmogram is a useful visual indicator for determining the reliability of SaO2 numerical readings. If certain features could be defined that tag valid plethysmogram pulses, then automatic recognition of valid SaO2 values can be attained. We observed that the systolic upstroke time (t1), the diastolic time (t2) and heart rate (HR) extracted from the plethysmogram pulse constitute features which can be used for detecting normal and distorted plethysmogram pulses. Therefore, we developed a knowledge-based system using fuzzy logic for classifying plethysmogram pulses into two categories: valid and artefact. A total of 22,497 pulse waveforms were used to define the system parameters. These were obtained from 13 patients with heart rates ranging between 62 and 209 beats min-1. A further 1420 waveforms obtained from another four patients were used for testing the system, and visually classified into 833 (59%) valid and 587 (41%) distorted segments. The system was able to classify 679 (82%) valid segments and 543 (93%) distorted segments correctly. The calculations of the system's performance showed 82% sensitivity, 86% accuracy and 93% specificity. We, therefore, conclude that the algorithm used in this system can be implemented in its present from for real-time SaO2 monitoring in intensive care for detecting valid and distorted plethysmogram pulses.

Adolescent↗
PubMed2001

Real-time gait event detection for paraplegic FES walking.

A real-time method for the detection of gait events that occur during the electrically stimulated locomotion of paraplegic subjects is described. It consists of a two-level algorithm for the processing of sensor signals and the determination of gait event times. Sensor signals and information about the progression of the stimulator though its pre-specified stimulation "pattern" are processed by a machine intelligence (fuzzy logic) algorithm to determine an initial estimate of the patient's current phase of gait. This is then reviewed and modified by a second algorithm that removes spurious gait estimates, and determines gait event times. These gait event times are known to the system within approximately one-half of a gait cycle. The resulting gait event detection system was successfully evaluated on three subjects. Detection accuracy is not adversely affected by day-to-day gait variability. This work resolved technical and practical issues that previously limited the real time application of these methods. In particular, cosmetically acceptable insole force transducers were used. This gait event detector is designed for use in a real time controller for the automatic adjustment of the intensity and timing of stimulation while the subject is walking using functional electrical stimulation (FES).

Adult↗
PubMed2001

Automatic segmentation of subcortical brain structures in MR images using information fusion.

This paper reports a new automated method for the segmentation of internal cerebral structures using an information fusion technique. The information is provided both by images and expert knowledge, and consists in morphological, topological, and tissue constitution data. All this ambiguous, complementary and redundant information is managed using a three-step fusion scheme based on fuzzy logic. The information is first modeled into a common theoretical frame managing its imprecision and incertitude. The models are then fused and a decision is taken in order to reduce the imprecision and to increase the certainty in the location of the structures. The whole process is illustrated on the segmentation of thalamus, putamen, and head of the caudate nucleus from expert knowledge and magnetic resonance images, in a protocol involving 14 healthy volunteers. The quantitative validation is achieved by comparing computed, manually segmented structures and published data by means of indexes assessing the accuracy of volume estimation and spatial location. Results suggest a consistent volume estimation with respect to the expert quantification and published data, and a high spatial similarity of the segmented and computed structures. This method is generic and applicable to any structure that can be defined by expert knowledge and morphological images.

Algorithms↗