On improvement of classification accuracy for stochastic discrimination.
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Algorithms↗
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Immaturity related lung hypoplasia: comment on current classification in neonatal death statistics.
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Algorithms↗
[Classification of chronic hepatitis].
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Algorithms↗
Quantitative morphology and shape classification of neurons by computerized image analysis.
We describe a new image processing method for semiautomatic quantitative analysis of neuronal morphology. It has been developed in a specific image analysis environment (IBAS 2.0), but the algorithms and the methods can be employed elsewhere. The program is versatile and allows the analysis of histological preparations of different quality on the basis of different levels of evaluation and image extraction. Some significant algorithms have been implemented (i.e. one for multiple focus image acquisition and one for automatic cell body shape recognition and classification). A wide set of specific morphological parameters has been defined to allow a better mathematical characterization of neuronal morphology as regards both dendrite trees and cell bodies. Cell bodies' shapes can be classified automatically, defining different neuronal populations. This is done by evaluating the number of main dendrites and perikarya shapes through a multi-valued-decision-tree based method, tested on somatostatin-positive cells in mouse brain. The methods presented have been applied to analysis of neurons, but they can well be used for any quantitative morphological study of other cell populations.
Algorithms↗
Adaptable fuzzy C-Means for improved classification as a preprocessing procedure of brain parcellation.
Parcellation, one of several brain analysis methods, is a procedure popular for subdividing the regions identified by segmentation into smaller topographically defined units. The fuzzy clustering algorithm is mainly used to preprocess parcellation into several segmentation methods, because it is very appropriate for the characteristics of magnetic resonance imaging (MRI), such as partial volume effect and intensity inhomogeneity. However, some gray matter, such as basal ganglia and thalamus, may be misclassified into the white matter class using the conventional fuzzy C-Means (FCM) algorithm. Parcellation has been nearly achieved through manual drawing, but it is a tedious and time-consuming process. We propose improved classification using successive fuzzy clustering and implementing the parcellation module with the modified graphic user interface (GUI) for the convenience of users.
Algorithms↗
BCI Competition 2003--Data set IV: an algorithm based on CSSD and FDA for classifying single-trial EEG.
This paper presents an algorithm for classifying single-trial electroencephalogram (EEG) during the preparation of self-paced tapping. It combines common spatial subspace decomposition with Fisher discriminant analysis to extract features from multichannel EEG. Three features are obtained based on Bereitschaftspotential and event-related desynchronization. Finally, a perceptron neural network is trained as the classifier. This algorithm was applied to the data set (self-paced 1s) of "BCI Competition 2003" with a classification accuracy of 84% on the test set.
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Independent component analysis-motivated approach to classificatory decomposition of cortical evoked potentials.
BACKGROUND: Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." RESULTS: The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. CONCLUSION: We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself.
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Functional imaging of vocal fold vibration: digital multislice high-speed kymography.
For the diagnosis of voice disorders, and especially for the classification of hoarseness, direct observation of vocal fold vibration is essential. Furthermore, a quantitative description of the movement of the vocal fold becomes increasingly necessary to document and compare findings as well as the progression of speech therapy. On the base of digital high-speed sequences of vocal fold vibration, multiple "functional images"-also called digital kymograms-are obtained using image- and signal-processing algorithms. Digital kymograms can serve as a powerful aid for visualization, description, and classification of vocal fold vibration and as an intermediate step for image interpretation by biomechanical modeling. This visualization technique will be discussed and compared to other techniques currently available: videokymography and videostroboscopy. The technique is applied to several clinical examples: aperiodic processes (phonation onset), irregular vocal fold vibration (paralysis of the recurrent nerve), particular vibration modes (anterior-posterior modes), and running speech.
Humans↗
Near-optimal region selection for feature space reduction: novel preprocessing methods for classifying MR spectra.
We introduce a global feature extraction method specifically designed to preprocess magnetic resonance spectra of biomedical origin. Such preprocessing is essential for the accurate and reliable classification of diseases or disease stages manifest in the spectra. The new method is genetic algorithm-guided. It is compared with our enhanced version of the standard forward selection algorithm. Both seek and select optimal spectral subregions. These subregions necessarily retain spectral information, thus aiding the eventual identification of the biochemistry of disease presence and progression. The power of the methods is demonstrated on two biomedical examples: the discrimination between meningioma and astrocytoma in brain tissue biopsies, and the classification of colorectal biopsies into normal and tumour classes. Both preprocessing methods lead to classification accuracies over 97% for the two examples.
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Fishing for biomarkers: analyzing mass spectrometry data with the new ClinProTools software.
Recently, applications of mass spectrometry in the field of clinical proteomics have gained tremendous visibility in the scientific and clinical community. One major objective is the search for potential biomarkers in complex body fluids like serum, plasma, urine, saliva, or cerebral spinal fluid. For this purpose, efficient visualization of large data sets derived from patient cohorts is crucial to provide clinical experts an interactive impression of the data quality. Additionally, it is necessary to apply statistical analysis and pattern matching algorithms to attain validated signal patterns that may allow for later applications in sample classification. We introduce the new ClinProTools bioinformatics software, which performs all major steps of profiling, screening, and monitoring applications in clinical proteomics. ClinProTools is the data interpretation software of the mass spectrometry-based ClinProt solutions for biomarker analysis. ClinProTools performs data pretreatment, visualization, statistics, pattern determination, pattern evaluation, and classification of spectra. This article will focus on ClinProTool's powerful and intuitive visualization options for clinical proteomics applications.
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Artificial intelligence in pediatrics: important clinical signs in newborn syndromes.
New methods are warranted in the field of syndromology. This study is an exploration into whether an artificial intelligence method (ID3) could provide a new angle for approaching syndromes. Diagnosing syndromes in the newborn is difficult. The accepted approach is to look for individual clinical signs that add up to a syndrome diagnosis. Of all possible clinical signs, one would want to extract the signs with the strongest predictive power. I used the ID3 algorithm to extract predictive clinical signs from a catalogue of syndromes (Birth Defects Encyclopedia Online; BDEO). Using information from BDEO, files of randomly generated "patients" were created. The signs consistently high in the identification tree were long philtrum, short palpebral fissures, low-set ears, and hepatosplenomegaly. The program used featured a crude "expert system" based on the ID3 algorithm. When using one-half of the data set as a training set and the other half as a testbed, a correct classification rate of 92.1-98.1% was attained. When the ID3 expert system was tested against cases from a clinical database (Pictures of Standard Syndromes and Undiagnosed Malformations), the correct classification rate was less than 20%. This may not necessarily reflect faults with the ID3 approach, but possibly biases in the clinical database. In syndromology no "criterion standards" exist that can confirm a diagnosis. The statistical method of cluster analysis does not require prior knowledge of diagnoses and will make a tree of syndromes based upon clinical signs. A cluster analysis was performed as a validity check to provide a tree for comparison with the ID3 tree. There was a reasonable degree of agreement between the two. Applying artificial intelligence methods to this field highlights problems with basic assumptions and philosophical aspects of syndrome diagnosis.
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Application of phylogenetic algorithms to assess Rab functional relationships.
Researchers looking to solve biological problems have access to enormous amounts of sequence information and the desktop computational infrastructure to personally interrogate and analyze large datasets. Many powerful bioinformatics tools are available online; however, this discourages the customized analysis of data that is necessary for the experimental scientist to make maximally effective use of the information. In addition, a customized environment facilitates the critical evaluation of bioinformatic methods. This chapter presents a protocol developed to aid in classification of subfamilies and subclasses of a superfamily using the personal desktop computer. The visual representation of the qualitative and quantitative results of data analyses is also considered. The examples are focused on Rab GTPases but are more widely applicable to the classification of any given protein family.
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On the classification of experimental data modeled via a stochastic leaky integrate and fire model through boundary values.
We present a computational algorithm aimed to classify single unit spike trains on the basis of observed interspikes intervals (ISI). The neuronal activity is modeled with a stochastic leaky integrate and fire model and the inverse first passage time method is extended to the Ornstein-Uhlenbeck (OU) process. Differences between spike trains are detected in terms of the boundary shape. The proposed classification method is applied to the analysis of multiple single units recorded simultaneously in the thalamus and in the cerebral cortex of unanesthetized rats during spontaneous activity. We show the existence of at least three different firing patterns that could not be classified using the usual statistical indices.
Action Potentials↗
Digitized cervical images: problems, solutions, and potential medical impact.
OBJECTIVE: To demonstrate compression, illumination enhancement, registration, segmentation, automated classification and steganography using digitized cervical images. MATERIALS AND METHODS: The Hybrid Multi-Scale Vector Quantization algorithm developed at Texas Technological University and other automated systems were used to improve digitized cervical images. RESULTS: We demonstrated high levels of image compression, illumination enhancement, registration, automated segmentation and classification and steganography of digitized cervical images. CONCLUSIONS: Digitized cervical images can be altered to facilitate research of cervical neoplasia.
Cervix Uteri↗
[Application of the rough sets theory to the analysis of structure-activity relationships of pyridinium antifungal compounds].
Relationships between chemical structure and antifungal activity of 72 quaternary pyridinium chlorides were analysed using the method of rough sets. In the information system the compounds are described by eight condition attributes and divided into three classes of activity. Using the rough sets approach a smallest set of four condition attributes significant for a high quality of classification and accuracy of classes has been found. The resulting decision algorithm describes relations between structure and antifungal activity in terms of significant condition attributes. It may be helpful in predicting structures of new antifungal compounds to be synthesized.
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Revealing the metabolome of animal tissues using 1H nuclear magnetic resonance spectroscopy.
The measurement of tissue-specific metabolic fingerprints can be of particular interest when investigating disease processes, mechanisms of toxicity, or when knowledge of the metabolic interactions between different organs is required. This chapter presents several optimized protocols for the extraction of metabolites from animal tissues, their analysis by 1H nuclear magnetic resonance (NMR) spectroscopy, and the subsequent spectral preprocessing required for an NMR-based metabolomics experiment. First, the three critical steps in the preparation of tissue extracts for NMR analysis are described, including both a perchloric acid protocol for the extraction of polar metabolites, and a methanol:chloroform protocol for extraction of polar and lipophilic metabolites. Then a series of NMR experiments are described including a standard one-dimensional (1D) 1H NMR study, a 1D 1H Carr-Purcell-Meiboom-Gill spin-echo experiment, and a two-dimensional 1H-1H J-resolved NMR experiment. The advantages and limitations of each experiment for metabolomics research are discussed. Analysis of the resulting NMR datasets is typically conducted in two phases comprising "low level" spectral preprocessing and "high level" multivariate analysis. NMR spectral preprocessing is a critical step that converts raw NMR spectra into an appropriate data format for multivariate analysis. A detailed protocol for preprocessing NMR data, using ProMetab software, is presented. Because a plethora of algorithms exist for multivariate analyses, which can be used to construct classification models or for biomarker discovery, this is beyond the scope of the current chapter.
Animals↗
[Radiologic evaluation of acetabular defects on acetabular loosening of hip alloarthroplasty].
In 18 human pelvis specimens we produced different acetabular defects according to Paprosky. Standardized X-rays of these specimens were taken, and these X-rays were evaluated by six orthopaedic surgeons with different levels of surgical experience. A total of 350 evaluation sheets were analyzed. The X-rays of the acetabular rim were correctly interpreted in only 66% of cases. The interpretation of plates showing bone loss was even worse. A medial wall defect was correctly assessed in just 49% of cases, giving a correlation (r) or 0.60 between the real defect and the radiological estimation. While massive defects were recognized in most cases, moderate defects were correctly estimated in about 50%. Moderate defects included migration of the cup, which was overlooked in between 46% and 69% of cases. Similar results were achieved in the judgement of bone loss. Only in 25% was direct classification according to Paprosky successful; it was better in small defects and worse in massive defects, with 16%. The interobserver correlation was only 0.54. In general, in most cases the defects were underestimated. This study raises the question of whether classification systems can provide the information needed for clinical useful therapeutic algorithms. The fact that the real bone loss is generally underestimated must receive due considerations when operations are being planned.
Acetabulum↗
Speech recognition and control system for the severely disabled.
The availability of microprocessors opens many applications for speech recognition systems where constraints on price, weight, volume and power consumption are imposed, such as in the case of a voice control system for the severely disabled. A microprocessor based speech recognition system for the voice control of wheelchair, touch-tone phone, typewriter and environmental control unit, is described. The finite word length, fixed point arithmetics and relatively slow execution time of the microprocessor introduce several difficulties in the implementation of the recognition process. Algorithms for feature extraction (mainly the autoregressive, or linear prediction coefficients), classification and training are presented. The hierarchical memory organization is discussed. Results with the ten digit set, used to activate a touch-tone phone, are given. With this set the system exhibits less than one percent substitutions and eleven percent rejections.
Computers↗