Fractal dynamics of body motion in patients with Parkinson's disease

Shimizu, Masaki; Akay, Metin; Tamura, Toshiyo; Higashi, Yuji; Fujimoto, Toshiro

doi:10.1088/1741-2560/1/1/002

Cited by 53 publications

(34 citation statements)

References 32 publications

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“…Many other types of features have been used and proposed for the analysis of accelerometer data. Large sets of heterogeneous features [19] and other instances inspired on frequency domain [20] or time-scale analysis [21]. Such applications focus on short-term monitoring.…”

Section: Discussionmentioning

confidence: 99%

Assessing physical activity in the daily life of cystic fibrosis patients

Dias

Gorzelniak

Jörres³

et al. 2012

Pervasive and Mobile Computing

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a b s t r a c tPhysical Activity (PA) plays an important role in the health and quality of life of cystic fibrosis (CF) patients, but little is known about their PA in daily living. With the use of accelerometers it is now possible to monitor activity profiles in detail. The goals of this study are to assess feasibility and acceptance of a longer-term use of accelerometers in daily living in CF patients, study the possibility of detecting changes in PA in relation to the patients' clinical state and compare the findings between a CF and an age-matched healthy control group. We asked 15 CF patients to wear two accelerometers for 21 days and fill in a diary. Ten of them (age 21 to 40, mean 29.5 years) participated and delivered data that could be evaluated. We also recruited 10 age-matched control subjects. Data was processed for calculating usage time and features extracted to construct models of activity. The younger patients, particularly females, were concerned with fashion and style and considered wearing the sensors a challenge. Overall, the compliance of patients with CF seemed to be lower than reported for elder subjects in the literature. Time-series analysis of the data indicated characteristic patterns of PA over time, provided that data preprocessing and noise-filtering had been optimized. Further studies have to assess whether the continuous recording of PA yields additional clinical information in CF and in particular, whether it is possible to detect or even predict exacerbations in patients with CF or other diseases.

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Section: Discussionmentioning

confidence: 99%

Assessing physical activity in the daily life of cystic fibrosis patients

Dias

Gorzelniak

Jörres³

et al. 2012

Pervasive and Mobile Computing

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“…Therefore, reliable analytical methods of the body acceleration signal free from the level of activity are required to describe the characteristics of body activity during daily living. Recently, fractal analysis was shown to be a robust tool to 226 disclose hidden auto-correlation patterns in biological data, such as heartbeat and limb movement (Ohashi et al, 2003;Pan et al, 2007;Peng et al, 1995;Sekine et al, 2004;Struzik et al, 2006). Power-law auto-correlation exponents for local maxima and minima of fluctuations of locomotor activity would be the most useful for our purpose, as they represent the level of persistency of movement patterns (Ohashi et al, 2003;Pan et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Objective Evaluation of the Severity of Parkinsonism Using Power-Law Temporal Auto-Correlation of Activity

Pan¹,

Yamamoto²,

Kwak³

2011

Diagnostics and Rehabilitation of Parkinson's Disease

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“…The second set (Set 2) included frequency and time-scale features from each sensor. These are features commonly used in signal processing problems, and have been previously used in the classification of accelerometer data [22], namely in the scope of activity detection through accelerometers. They are based on the frequency domain analysis of the data, an important tool for signal processing.…”

Section: Feature Extractionmentioning

confidence: 99%

Classification of Exacerbation Episodes in Chronic Obstructive Pulmonary Disease Patients

Dias

Gorzelniak²,

Schultz³

et al. 2014

Methods Inf Med

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Summary Background: Chronic obstructive pulmonary disease (COPD) is a progressive disease affecting the airways, which constitutes a major cause of chronic morbidity and a significant economic and social burden throughout the world. Despite the fact that in COPD patients exacerbations are common acute events causing significant and often fatal worsening of symptoms, an accurate prognostication continues to be difficult. Objectives: To build computational models capable of distinguishing between normal life days from exacerbation days in COPD patients, based on physical activity measured by accelerometers. Methods: We recruited 58 patients suffering from COPD and measured their physical activity with accelerometers for 10 days or more, from August 2009 to March 2010. During this period we recorded six exacerbation episodes in the patients, accounting for 37 days. We were able to analyse data for 52 patients (369 patient days), and extracted three distinct sets of features from the data, one set of basic features such as average, one set based on the frequency domain and the last exploring the cross-information among sensors pairs. These were used by three machine-learning techniques (logarith mic regression, neural networks, support vector machines) to distinguish days with exacerbation events from normal days. Results: The support vector machine clas -sifier achieved an AUC of 90% ± 9, when supplied with a set of features resulting from sequential feature selection method. Neural networks achieved an AUC of 83% ± 16 and the logarithmic regression an AUC of 67% ± 15. Conclusions: None of the individual feature sets provided robust for reasonable classi -fication of PA recording days. Our results indicate that this approach has the potential to extract useful information for, but are not robust enough for medical application of the system.

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Fractal dynamics of body motion in patients with Parkinson's disease

Cited by 53 publications

References 32 publications

Assessing physical activity in the daily life of cystic fibrosis patients

Assessing physical activity in the daily life of cystic fibrosis patients

Objective Evaluation of the Severity of Parkinsonism Using Power-Law Temporal Auto-Correlation of Activity

Classification of Exacerbation Episodes in Chronic Obstructive Pulmonary Disease Patients

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