A novel method based on matching pursuit decomposition of gait signals for Parkinson's disease, Amyotrophic lateral sclerosis and Huntington's disease detection

BACKGROUND AND OBJECTIVE

An accurate detection of neurodegenerative diseases (NDDs) definitely improves the life of patients and has attracted growing attention.

METHODS

In this paper, a general automatic method for detection of Parkinson's disease (PD), Amyotrophic lateral sclerosis (ALS) and Huntington's disease (HD) has been proposed based on the localized time-frequency information of gait signals. The new main part of the detection method is to obtain a small set of sparse coefficients for the local representation of gait signals with appropriate time and frequency resolution. For this purpose, a hybrid feature set based on sparse matching pursuit decomposition and two sets of nonlinear and linear features has been developed. Then, principal components of the proposed feature have been analyzed using a sparse coding classifier. Results The proposed approach has achieved high average accuracy rates of 93%, 94%, and 97% for PD, ALS, and HD detection, respectively.

CONCLUSIONS

The obtained results have indicated that combination of time and frequency information of the gait signals through adaptive localized window length in MP makes it more efficient in comparison with the existing time, frequency or other time-frequency gait parameters. The great potential of nonlinear sparse features for PD and HD detection and linear ones for ALS detection has also been shown.

Comparison of computer-key-hold-time and alternating-finger-tapping tests for early-stage Parkinson's disease

Giancardo et al. recently introduced the neuroQWERTY index (nQi), which is a novel motor index derived from computer-key-hold-time data using an ensemble regression algorithm, to detect early-stage Parkinson's disease. Here, we derive a much simpler motor index from their hold-time data, which is the standard deviation (SD) of the hold-time fluctuations, where fluctuation is defined as the difference between successive natural-log of hold time. Our results show the performance of the SD and nQi tests in discriminating early-stage subjects from controls do not differ, although the SD index is much simpler. There is also no difference in performance between the SD and alternating-finger-tapping tests.

SPECTRAL ANALYSIS OF GAIT DISORDERS IN HUNTINGTON'S DISEASE: A NEW HORIZON TO EARLY DIAGNOSIS

Huntington's disease (HD) is one of the neural diseases with movement disorders like chorea, ballism, and athetosis. These symptoms play an important role on gait asymmetry, thus gait recordings are considered important resources for studying this disease. Diagnosing HD in its early stages is important and critical. We aimed to discover differences between HD and normal behavior. As the gait is semi-periodic, analyzing frequency could reveal disorders. We made an attempt to extract some proper features using power spectra density. Studies revealed that HD adds high frequencies to the spectrum on all gait phases. Statistical analysis of the gait features showed significant differences between normal and HD groups. At the end, we tried to separate the patients and healthy individuals using a new intelligent mathematical system. An artificial neural network classifier was used for this reason,and our best separation accuracy was 96.6%. This study could be the basis of designing a practical decision support system. This system can diagnose patients at the first stages of the disease, and it also can recommend suspected persons to the specialist.

SEPARATING PARKINSONIAN PATIENTS FROM NORMAL PERSONS USING HANDWRITING FEATURES

Parkinson's disease (PD) is a widespread progressive neural degenerative disease. Patients encounter problems in carrying out ordinary voluntary movements such as gaiting and writing. Effects of tremor, rigidity, and bradykinesia are seen in writing. Since the diagnosis of PD is based on the presence of cardinal symptoms, therefore voluntary movements like writing could be helpful as an indicator in this process. Handwriting analysis can be a suitable method to separate patients from normal individuals. We recorded handwriting data from 17 normal and 13 PD subjects, and then used mathematical analysis in order to extract proper features. At the end of the study, we used these selected features in a classifier. We achieved 93.89% accuracy in the test phase. Hence, this tool may be aid in the diagnosing of both PD and suspected PD individuals in the early stages of the disease.