Fractal analysis of acceleration signals from patients with CPPD, rheumatoid arthritis, and spondyloarthroparthy of the finger joint

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Summary

Arthritis is one of the leading causes of disability and affects a major segment of the population. Consequently, accurate diagnosis of arthritis is important. Arthritis due to calcium pyrophosphate deposition disease (CPPD), rheumatoid arthritis, and spondyloarthropathy, induce complex changes in the cartilage and the articular surface. The fractal dimension provides a measure of the complexity of a signal. Recently, we have developed non-invasive acceleration measurements to characterize the arthritic patients. The question remains if the fractal dimension of the acceleration signal is different for different arthritis conditions. The purpose of this study was to distinguish between different types of arthritis of the finger joint using the fractal dimension of the acceleration signal obtained from the finger joint of the arthritic patients. Acceleration signals were obtained from the finger joint of arthritis patients with rheumatoid arthritis, spondyloarthropathy, and calcium pyrophosphate deposition disease of the finger joint. ANOVA results showed that there were significant differences between the fractal dimension of acceleration signals from patients having calcium pyrophosphate deposition disease and rheumatoid arthritis and spondyloarthropathy. Fractal dimension of acceleration signals, in concert with other clinical symptoms, can be used to classify different types of arthritis.

Introduction

Arthritis is one of the leading causes of disability across the globe. Arthritis and its allied disorders affect almost 43 million Americans. It is the cause of limiting the daily activities of about 7 million Americans. Arthritis creates a huge financial burden on individuals and their families and leads to a total medical cost of about 15 billion dollars in America alone, which is an enormous expense for any nation [1].

Arthritis affects people of all ages and ethnic groups. Arthritis is a combination of many different diseases. The common symptoms are pain, stiffness, ache, and swelling around the joints [2], [3]. Arthritis includes rheumatoid arthritis, osteoarthritis, ankylosing spondylitis, gout, infectious arthritis, psoriatic arthritis, pseudogout, Reiter's syndrome, lupus, lyme disease, and scleroderma.

Early diagnosis is one of the most important aspects in the treatment of arthritis. Many techniques are currently used for detection of arthritis. The invasive techniques include arthroscopy [4]. The non-invasive diagnostic techniques include radiography, magnetic resonance imaging (MRI), ultrasonography, scintigraphy, etc. [5]. Knee auscultation and thermography have been suggested for clinical use. Reddy et al. [6] have used non-invasive acceleration measurements from the knee joint to distinguish osteoarthritis, rheumatoid arthritis, and chondromalacia patients from normal subjects.

Clinically, there are a number of types of arthritis like spondyloarthropathy (SpA), rheumatoid arthritis (RA), and calcium pyrophosphate deposition disease (CPPD) that are difficult for differential diagnosis. An extensive investigation is necessary to differentiate between CPPD, SpA and RA. Recently, Reddy et al. [7] have found significant differences in the parameters extracted from the acceleration signal measured from knee joints of subjects with SpA and RA.

Fractal analysis is a nonlinear technique and can be used for the analysis of complex signals. One of the most important characteristics of a fractal is that it exhibits ‘self-similarity’, which means that an object, signal or a pattern is made up of structures that resemble the larger structure in complexity and shape. The fractal dimension can be used to quantify a fractal process [8]. The fractal dimension gives the ratio of the number of units that composes the structure to the minimum number of units required to make the simplest structure of the same spatial filling capacity. The fractal dimension is a feature of the fractal that measures the complexity of the signal, the space filling capacity or the spatial extent of the signal and is related to the shape of the signal [9]. Therefore, the fractal dimension can be used to characterize physiological signals that possess the property of self-similarity over the time scale and have broadband frequency spectrum. The fractal dimension has already been used to distinguish between normal and physiological ECG waveforms, and for quantification of EEG signals during Alzheimer's disease and epileptic seizures. Fractals have been used to characterize EMG signals. Significant differences were found in the fractal dimension of the EMG signals obtained during different loading conditions and different rates of flexion and extension, and there was a high correlation coefficient between the fractal dimension and the load and fractal dimension and rate of flexion–extension [8]. Fractals have also been used to analyze the texture of macroradiographs of the osteoarthritis knees. There were significant differences between the fractal signatures of the macroradiographs of the normal and arthritic knees [10].

The purpose of this study was to determine the fractal dimension of acceleration signals from the finger joint of the arthritis patients, and use this fractal dimension to differentiate between CPPD, SpA and RA of the metacarpo–phalyngeal joint. Normal individuals do not experience any joint pain. On the other hand, the patients with different arthritis experience pain. Since the overall goal of the investigation was to develop a non-invasive diagnostic tool to differentiate between CPPD, SpA, and RA, normal subjects were not included in the present study.

Section snippets

Subject groups

The study consisted of three groups of subjects. Group I consisted of 11 subjects diagnosed to have CPPD of the finger. Group II consisted of 11 subjects having of the rheumatoid arthritis (RA) of the finger. Group III consisted 13 subjects with Spondyloarthropathy (SPA) of the finger joint.

The subjects for rheumatoid arthritis were selected on the basis fulfilling the criteria of the American College of Rheumatology and presence of marginal erosions and periarticular osteopenia [11], [12].

Results

Acceleration measurements were obtained from RA, SpA, and CPPD patients. Single cycles were manually extracted in each case by selecting the start and the end points at the zero crossing of the cycles in the signal. These single cycles were then used to determine the fractal dimension of the acceleration signals. Fig. 2 shows the single cycle extracted from the acceleration signals from patients with CPPD of the finger joint. Fig. 3 represents the single cycle extracted from the acceleration

Discussion

The present study represents the first analysis of the fractal dimension of the acceleration signals to classify different types of arthritis. In this study, the fractal dimension of the acceleration signal of the patients with different types of finger arthritis was successfully determined. The results indicated that the fractal dimension of the patients with CPPD of the finger joint is significantly different from patients with RA and SpA of the finger joint. The fractal dimension of the

Conclusion

The fractal dimension of the acceleration signals from the finger joint of RA, SpA, and CPPD patients was determined. The fractal dimension of the CPPD patients was higher as compared to that of RA and SpA patients (p < 0.05). Thus, fractal dimension can be used to characterize CPPD, RA, and SpA of the finger joint.

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1

Present address: Philips Medical Systems, 595 Miner Road, Highland Heights, OH 44143, USA.

2

Biomedical Engineering Department, University of Akron, Akron OH, USA, Northeastern Ohio College of Medicine and Carnegie Museum of Natural History.

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