Wyniki wyszukiwania - BazTech

1

The method of neuron weight vector initial values selection in Kohonen network

Chandzlik S.

Journal of Medical Informatics & Technologies

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2006

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Vol. 10

189--197

EN

Diagnosing of morbid conditions by means of automatic tools supported by computers is a significant and often used element in modern medicine. Some examples of these tools are automatic conclusion-making units of Parotec System for Windows (PSW). In the initial period of PSW system implementation, the units were used for recognition of orthopaedic diseases on the basis of the patient's walk and posture [15,17]. Subsequently, many additional options have been implemented, which have been used for purposes of diagnosing neurological diseases [1,2,3,9,12]. During automatic classification of diseases the additional units use elements of neural networks. The vectors based on normalised diagnostic measures [3] are inputs of the units. The measurements describe a patient's posture condition, his walk and overloads occurring on his feet. The Counter-Propagation (CP), two-layer network has been used in one of the automatic conclusion-making units. During CP network activity, we can see not only supervised but unsupervised learning processes as well. This is a characteristic feature of the CP network. The initial steps of the CP network learning process are very important, because the success of the network training process depends on them to a great extent. Therefore, a new method of weight vector initial values selection was proposed. The efficiency of the method was compared with classical methods. The results were very satisfactory. Owing to the proposed method, the time of the network training process as well as the mean-square error and the classification error was reduced. The research has been carried out using clinical cases of some neurological diseases: Parkinson's Disease, left-lateral hemiparesis and right-lateral hemiparesis after ischemic stroke. The measurements, which were made on a control group of patients without any neurological diseases, were the reference for these diagnostic classes.

2

The diseases classification method on gait abnormalities characteristic contributions

Chandzlik S., Piecha J.

Journal of Medical Informatics & Technologies

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2005

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Vol. 9

187--194

EN

Present medicine uses computers in various applications, especially in a field of a diseases level classification and diagnosis. In many cases an automatic conclusion making units are the main goal of the computer systems usage. The software units are developed for the diseases classification or for monitoring of the disease medical treatment. An example application was described in this paper. It concerns a gait abnormalities level analysis that is described by a data records gathered by insoles of Parotec System for Windows (PSW) [17,18]. The PSW software package is used for visualisation of the gait characteristic static and dynamic characteristic features. In the authors' works many additional data components were distinguished. The field of the applications is located within the neurological gait characteristics also the source applications concern orthopaedics [16,18]. Careful analysis of the data provided the developers with new areas the PSW applications [4,11,13]. For conclusion making units the artificial networks theory was implemented [2,4,11,13]. For more effective training of the neural networks specific characteristic measures were introduced [4,5]. They allow controlling the training process more precisely, avoiding mistakes in current records classification.

3

The interference spectrum extraction of a gait characteristics data record

Chandzlik S., Piecha J.

Journal of Medical Informatics & Technologies

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2004

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Vol. 7

KB23--32

EN

The paper shows several aspects of the gait data record analysis describing neurological diseases. The diagnosis of the gait abnormalities concerns interferences level of the patient physiological records. The disease source and level can be classified by the relevant interference functions. These functions were used for artificial records creation to multiply the necessary set of data needed for neural network training.

4

The body balance measures for neurological disease estimation and classification

Chandzlik S., Piecha J.

Journal of Medical Informatics & Technologies

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2003

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Vol. 6

IT87--94

EN

The paper describes body balance characteristics needed for neurological diseases classification and for rehabilitation processes controlling during patient recovery processes. These diagnosis factors allow simplify the PSW (Parotec System for Windows) records recognition [1, 2] then a walk-motor disturbances level estimation. The discussed clinical experiments illustrate new methods for Parkinson disease and stroke progress monitoring. This study was based on many observations of patient walk disturbances recorded in PSW files describing the pressure distribution on an insole set of sensors [1, 2, 8]. The gait regular asymmetry in a data spectrum has been noticed as an independent factor from the disease duration and its severity. In majority of analysed cases for Parkinson disease a gravity centre of the body moved into a heel region. Trajectories of foot gravity centre elongation, their irregularities, a floor-contact time and paresis limb loading values increase also were observed. The PSW system has successfully been used for recognition and quantification of walk-motor disturbances, marking the neurological diseases level. Options available in PSW [1, 2] give the user many aims in putting proper diagnosis anyhow, due to simplify the training process of conclusion making unit several methods for data records modifications and the diagnosis factors extraction were also considered.

5

Modelling the data record of a patient walk by langrange-polynomial method

Chandzlik S., Piecha J.

Journal of Medical Informatics & Technologies

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2002

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Vol. 3

MI143--152

EN

Various options available in PSW footprint and walking characteristics measuring equipment [6], [7], give the user many aims in putting diagnosis. A Conclusion-Making Unit (CMU) that has been described in this paper supports the diagnosis automation procedures. Due to simplifying the CMU training process some affords in a field of the input record length reduction have been undertaken. The paper describes an analytical method of the data record description that allows converting discrete data samples into continuous function. This way a redigitalisation of the record can be done, where sampling period is matched with the walk length. This normalization allows reducing the data record length used for fast training of the CMU.

6

A patient walk-data-record modelling using a spline interpolation method

Chandzlik S., Piecha J.

Journal of Medical Informatics & Technologies

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2002

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Vol. 3

MI153--159

EN

The paper describes an analytical method of data record description that allows converting samples of discrete data record into continuous function. This operation allows re-sampling the data record with a sampling rate that is adequate to step duration. The record length is limited to an efficient size for training the Conclusion-Making Unit (CMU). Various options available in the PSW equipment [6], [7] give the user many aims in putting diagnosis anyhow, due to simplification of the CMU training process several methods for data records modifications are considered.

7

Experiments with network parameters-selection for foot abnormalities recognition

Chandzlik S., Kopicera K.

Journal of Medical Informatics & Technologies

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2000

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Vol. 1

CS 71--78

EN

The paper describes experiments done with the automatic diagnosis module for the PSW System. The experiments have been done in spite of insufficient data records used for learning the neural network. Taking advantage of the artificial data making data making system there was possible to examine some neural network models: Back-Propagation, ART and Counter-Propagation. The paper presents analysis of obtained results and compares efficiency of the neural network algorithms.

PL

Praca prezentuje opis eksperymentów wykonanych przy wykorzystaniu modułu automatycznego wspomagania diagnozy dla systemu PSW (Parotec System for Windows). Eksperymenty zostały wykonane w warunkach niedoboru ilości danych uczących sieć neuronową, stanowiącą trzon całości systemu wnioskowania. Korzystając z metody sztucznej generacji nowych danych pomiarowych na bazie danych rzeczywistych zakłócanych zbiorem wybranych funkcji przetestowano użyteczność dla systemu PSW trzech różnych konfiguracji sieci neuronowej: Back-Propagation, Counter-Propagation oraz ART. Przedstawiona została analiza uzyskanych wyników obejmująca m.in. porównanie efektywności badanych algorytmów oraz zaproponowany został kierunek dalszych badań.