Artificial neural network: A new diagnostic posturographic tool for disorders of stance
Introduction
Static posturography is used to analyze the body sway of unsteady patients with various neurological disorders. Until now clinicians have accepted this method as a tool for following-up balance disorders but not for reliably establishing a specific diagnosis. Their main reason has been that there are no specific analysis criteria of pathological sway patterns. So far diagnoses of only 3 conditions have been established on the basis of a routine analysis of body sway: (i) the 3 Hz sway in anterior lobe cerebellar atrophy (Dichgans et al., 1976, Diener et al., 1984); (ii) increased sway activity in the higher power spectra frequency band with a typical peak between 12 and 19 Hz in primary orthostatic tremor patients (Bronstein and Guerraz, 1999, Yarrow et al., 2001); and (iii) increased sway activity in the 3.5–8 Hz frequency band in patients with somatoform phobic postural vertigo (PPV) (Krafczyk et al., 1999). It is much more difficult to determine posturographic criteria for vestibular disorders like vestibular neuritis. The body sway of these patients increases in darkness during the first days after disease onset; however, because of the large variation in balance performance it cannot be differentiated from other balance disorders by body sway alone (Strupp and Brandt, 1999, Strupp et al., 1998).
Basically, however, it is possible to use a single or a combination of multiple parameters to characterize a certain balance disorder. In the following study we improved the discriminatory power of posturography by using multiple parameters obtained from the original data. These included sway path (SP) (for details see Hufschmidt et al., 1980); root mean square (RMS) (Brandt et al., 1981); and the sum activity of body sway in different frequency ranges after Fourier transformation analysis (FFT). Our goal was to determine the typical patterns of pathological sway for 4 disorders of upright stance: anterior lobe cerebellar atrophy, PPV, primary orthostatic tremor, and acute vestibular neuritis.
Since, this approach increases the complexity of the data comparison to such an extent that it is impractical for routine clinical use, an artificial neural network (ANNW), as described by Duda et al. (2001), was applied for computational analysis of the sway patterns in the patients and in normal controls. The advantage of ANNW is that all posturographic parameters, as well as many pre-selected sway patterns, which have been identified by experienced examiners, can be included. The effectiveness of an ANNW model was newly shown by classifying the risk of falls in the elderly on the basis of an analysis of balance control during gait (Hahn and Chou, 2005).
We recorded the postural sway characteristics of 4 neurological and vestibular disorders of upright stance and fed an ANNW with multiple parameters extracted from the raw data. Initially, the ANNW was trained with the sway parameters of 60 training cases (TCs) and was then validated with 60 validation cases (VCs). The VCs included 5 categories (4 balance disorders and normal sway); the diagnoses had been previously established clinically. The goal was to test the validity of the ANNW when used to determine the probable diagnosis of a balance disorder by comparing the data of new patients with the stored experience of the ANNW but without any access to further information.
Section snippets
Patients
A database of 676 adult patients, who had been assessed according to the same protocol and had undergone static posturography between 1998 and 2005, served as the basis for selecting 421 patients in the categories under investigation. The TCs and VCs (normal subjects included) were selected after the identification of congruent sway patterns with the help of confidence plots (see Section 2.3) and the verification of the diagnosis by clinical methods on the basis of the patients' data sheet
Results
In order to keep all TCs or VCs in the study, the parameters of only the first 7 measurements were fed into the ANNW. This was necessary, because the 10 conditions made increasing balance demands that required the experimenter to intervene in some cases of impending risk of falls in conditions 8 and 10, especially in patients with cerebellar atrophy, vestibular neuritis, or orthostatic tremor. In some of these TCs and VCs, the experimenter had to actively hold the patients to prevent them from
Discussion
This study demonstrates that artificial neural network techniques can be used to differentiate postural sway patterns typical of several distinct clinical balance disorders with sufficiently high sensitivity and specificity. To the best of our knowledge this is the first report on an attempt to use ANNW for the analysis of posturographic data.
As to the methods, up to now pattern classification has been typically used to compare specific patterns established by ‘learning processes’ with
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