Elsevier

Neurocomputing

Volume 121, 9 December 2013, Pages 53-63
Neurocomputing

Identification of saccadic components in spinocerebellar ataxia applying an independent component analysis algorithm

https://doi.org/10.1016/j.neucom.2012.11.048Get rights and content

Abstract

Anomalies in the oculomotor system are well known symptoms in different neurodegenerative diseases. It has been found that patients suffering from severe spino cerebellar ataxia type 2 show deterioration in the main parameters used to describe saccadic movements, specifically the slowing of horizontal saccadic eye movements. Besides, a combination of two components, named pulse and step, constitutes an accepted model of the saccadic generation system. In the present work, independent component analysis is applied in order to separate both pulse and step components, revealing significant differences in several parameters related to the morphology of these components between patients and control responses. Ten electro-oculographic records of spino cerebellar ataxia type 2 patients and ten control subjects were processed with the proposed algorithm with the aim of obtaining a correct diagnosis. The results obtained from these real experiments reveal the validity of the proposed approach as a classification tool for the diagnosis of this disease.

Introduction

There are almost 800 patients and 8000 presymptomatic relatives in Cuba at risk of developing some autosomal dominant cerebellar ataxia (ADCAs), in the next few years. This is a heterogeneous group of dominantly inherited neurological disorders characterized by progressive ataxia that results from degeneration of the cerebellum and its afferent connections [1], [2].

Worldwide prevalence of SCAs is estimated at about 6 cases per 100,000 people, although much higher figures have been reported in particular populations [3]. The variant of SCA2 is between the three most prevalent subtype worldwide among the almost 30 SCAs, together with SCA3 and SCA6 [3], [4]. However, exact global prevalence of SCA2 is undetermined as the few existing epidemiological studies have been performed in isolated geographical regions [4], [5]. SCA2 is the most predominant SCA variant in Italy, England, Spain, India, South Korea, and especially in Cuba [4], [6].

Cuba is the country that presents the highest concentration of patients with hereditary ataxias over the world. Particularly, the spinocerebellar ataxia type 2 (SCA2) molecular form is predominant. SCA2 reaches the highest prevalence in the province of Holguin, located in the north-east of Cuba, getting to a rate of 40 patients for each 100,000 inhabitants [7]. Reported high prevalence is probably the result of a founder effect, but might be due to an interaction between a mutant gene and an unidentified environmental neurotoxin [8], [9]. The stabilization of the prevalence along time suggests the existence of mutated chromosomes, acting as reservoir for further expansions [10].

Ocular saccades are the rapid eyes movements followed by a stable fixation that allow animals with an area of high acuity in the retina to inspect peripheral stimuli. Saccade trajectories tend to be remarkably stereotyped both within and across the different species. The saccade duration and its peak velocity increase monotonically with the amplitude of the movement in a regular way, which has been called the “main sequence” as a resemblance to the astronomical term [11], [12].

A commonly accepted model of the saccadic system states that the programming of the saccades consists of a pulse and a step component. The pulse consists in a high frequency burst that creates the muscle forces which rapidly move the eye to a new position, whereas the step component keeps the eyes stable at the new position [13], [14].

The ocular movement records have been widely used in processing and classification of biological signals and pathological conditions. Specifically, saccadic movements in response to a visual stimulation are considered amongst the most useful tools in the study of neurological pathologies [15], [16], [17], [18], [19], [20], [21], [22], [23]. Several studies have reported oculomotor abnormalities in ADCA [3], [24]; among them, slowness of saccades has been suggested as a relatively characteristic finding in SCA2 patients [5], [25], [26].

In order to quantify and to establish the possibility of comparing clinical findings, a variety of clinical testing scales were developed. The most frequently used scale is the International Cooperative Ataxia Rating Scales (ICARS) [27], which involves a quantification of postural and stance disorders, limb ataxia, dysarthria and oculomotor disorders. The Scale for the Assessment and Rating of Ataxia (SARA) [28], [29] is proposed as an alternative to ICARS, claiming to be easier and faster to assess and eliminating redundant items form ICARS. However, the utilization of the results from these scales in computer-aided diagnosis of SCA2 is difficult, since the obtained data is semiquantitative and based on subjective estimates made by medical experts.

There are other symptoms of SCA2 and similar ataxias apart from the cited abnormalities in ocular saccades which have been studied and quantified for SCA2 diagnosis. In particular, deficits in ocular and manual tracking [24], postural stability [30], and stair-climbing [31] have been studied. Comparison and reproduction of the results are complicated since the experiment equipment is not easily accessible and the number of tested subjects is small due to the low prevalence of SCA2 world-wide.

In the present work, the application of an independent component analysis (ICA) based approach is proposed in order to separate both pulse and step components from the original saccadic responses to a given visual stimuli. Results reveal significant differences between patients and control responses. A k-means classification algorithm will be applied in order to identify a given experiment according to the values of the parameters related to its pulse and step components.

Through the use of our ICA based methodology, we expect to obtain a diagnosis process which is meant to be easy to apply, independent from the medical experts' subjective opinion, and adequate to the Cuban sanitary system resources and, specifically, to the Centre for the Research and Rehabilitation of Hereditary Ataxias (CIRAH) at Holguín (Cuba). ICA eliminates noise components, focusing only in the pulse and step components that form an ocular saccade. Section 2 of this paper describes the materials and methods needed for the application of the diagnosis algorithm, starting from an outline of the algorithm itself. Section 3 shows the results over 20 experiments, where 10 of them belong to a group of control subjects and the 10 remaining are SCA2 patients. 4 Discussion, 5 Conclusions present the discussion and conclusions, respectively.

Section snippets

Algorithm description

The proposed algorithm for SCA-2 diagnosis goes along the steps described below.

  • 1.

    Extract the m saccadic segments from the subject record. This process implies the preprocessing of data described in more detail in Section 2.3: data filtering and determination of the start and end points of each saccade. The number of m saccadic segments is bounded by the natural numbers interval [15], [24], as it is explained in Section 2.2. All saccades are set to start at the 0 value for proper alignment, as it

Results

According to the experimental setup described in Section 2.2., Fig. 4 shows a signal fragment of several saccades for a control subject (left) and a severe SCA2 patient (right). As it can be drawn from the figure, the behavior of the SCA2 patient is much more erratic and less accurate to the stimulus (dashed line).

After applying the first step of the algorithm, the different saccades were extracted and preprocessed. The ensemble of saccades forms the input x to the ICA algorithm (Fig. 1).

Discussion

Application of ICA to the EOG records obtained in all cases two components corresponding to the pulse and step saccade model. These components were analyzed in order to perform their evaluation with parameters for the description of its most relevant characteristics and differentiation between control subjects and SCA2 patients for classification purposes.

While PCA eliminates correlation between the observations (PCA is based in second order statistics only), ICA uses the much richer concept of

Conclusions

This contribution discusses a satisfactory approach for SCA2 classification and diagnosis using independent component analysis. The proposed method starts from the assumption that the saccadic response to a visual stimulus is different in a healthy individual when compared to the response of an individual with SCA2. The independent component analysis approach extracts the pulse and a step component that form a saccade and consequently a k-means algorithm classified into two classes (healthy and

Acknowledgments

This work has been supported by the Genil Start-up Project for Young Researchers (http://genil.ugr.es) “Processing and Classification of Electro-oculography (EOG) Data for Ataxia SCA-2 Diagnosis” (PYR-2010-23) from the CEI BioTIC GENIL (CEB09-0010) of the CEI Program from the MICINN. The authors would like to thank the rest of the personnel in the Centre for the Research and Rehabilitation of Hereditary Ataxias “Carlos J. Finlay”, Holguín, (Cuba) for their support and collaboration.

Fernando Rojas received the B.Sc. degree in Business Computing from the Institute of Technology of Sligo (Ireland) in 1999, the M.Sc. degree in Computer Science in 2000 from the University of Granada (Spain) and the Ph.D. degree in 2003, from the same University. He works at the Department of Computer Architecture and Computer Technology at the University of Granada as an Associate Professor. His main research interests comprise signal processing, neural networks, evolutionary computation and

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  • Cited by (2)

    Fernando Rojas received the B.Sc. degree in Business Computing from the Institute of Technology of Sligo (Ireland) in 1999, the M.Sc. degree in Computer Science in 2000 from the University of Granada (Spain) and the Ph.D. degree in 2003, from the same University. He works at the Department of Computer Architecture and Computer Technology at the University of Granada as an Associate Professor. His main research interests comprise signal processing, neural networks, evolutionary computation and biomedical engineering.

    Rodolfo García Bermúdez received the B.Sc. degree in Electronic Engineering from the Universidad Central de Las Villas (Cuba, 1985), and the M.S. degree in Mathematics and Computer Science for Management from the Universidad de Holguín “Oscar Lucero Moya” (Cuba, 2005); and the Ph.D. degree in Computer Science from the Universidad de Granada (Spain, 2010), focused in independent component analysis applied to ocular movements. Currently, he is professor in the Department of Networks, in the University of Holguín, and his research interests are related to adaptive processing of biomedical signals.

    Jesús González received the M.Sc. degree with honors in Computer Science in 1997 and the Ph.D. degree also with honors in 2001, both from the University of Granada, Spain. He is currently an Associate Professor in the Department of Computer Architecture and Computer Technology at the same University. He has published over 30 research papers and 3 book chapters. His current areas of research interest are in the fields of multi-objective optimization, function approximation, neural networks, fuzzy systems, evolutionary computation and embedded systems.

    Luis Velázquez is a Doctor in Medical Sciences (Ph.D.). He is First and Second-degree Specialist in Normal and Pathologic Physiology (confined to Clinical Neurophysiology). He is the coordinator for Cuba of the Multidisciplinary American Network for the Study of Movement Disorders (RIBERMOV), member of the Cuban Neuroscience and Clinical Neurophysiology Society. He is the Director of the Center for Research and Rehabilitation of Hereditary Ataxia (CIRAH). His research has been dedicated to the hereditary ataxias for over 20 years.

    Roberto Becerra works at the Networks Department of the University of Holguin (Cuba) and he has a B.Sc. degree in Computer Science. His main research interests are in the area of signal processing of biological data, more specifically in electrooculographic (EOG) records. As a result of this research, he completed an application for processing EOG saccadic signals and he is the main author and coauthor of several contributions in this field.

    Olga Valenzuela received the M.Sc. degree in Mathematics in 1995 and the Ph.D. degree in 2003, both from the University of Granada, Spain. She was working at the Department of Statistic of the University of Jaen (Spain), and also with the Department of Computer and Information Science of the University of Genoa (Italy) as an invited researcher. Her current research interests include optimization theory and applications, neural networks, time series forecasting using linear and non-linear methods, and evolutionary computation.

    Belén San Román received the M.Sc. degree in Biology in 2003 and the M. Sc. degree in Food Science and Technology in 2005 both from the “Universidad Autónoma de Madrid”. In 2011, she received the Ph.D. degree in Molecular Biology and Biochemistry with honors from the University of Granada (Spain). Her research interests are in the area of health promotion and quality of life, eating disorders, understanding of the genetics and molecular pathogenesis of many diseases that impact on health, and the use of biotechnology for specific applications.

    This contribution shows a novel approach for classification of Spino Cerebellar Ataxia Type 2 (SCA2) using independent component analysis (ICA). Although research in SCA2 diagnosis and treatment is extensive, there are no precedent works in SCA2 classification using ICA. Our proposed method has shown to be effective in the differentiation of SCA2 patients and control individuals.

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