Biometric Applications Related to Human Beings: There Is Life beyond Security

Faundez-Zanuy, Marcos; Hussain, Amir; Mekyska, Jiří; Sesa-Nogueras, Enric; Monte-Moreno, Enric; Esposito, Anna; Chétouani, Mohamed; Garré-Olmo, Josep; Abel, Andrew; Smékal, Zdeněk; López-de-Ipiña, Karmele

doi:10.1007/s12559-012-9169-9

Cited by 54 publications

(28 citation statements)

References 72 publications

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“…For instance, handwriting skill degradation and Alzheimer's disease (AD) appear to be significantly correlated [17,18], and some aspects of handwriting can be good indicators for its diagnosis [6,17,18] or help to differentiate between mild Alzheimer's disease and mild cognitive impairment [17,18]. Additionally, the analysis of handwriting has proved useful to assess the effects of substances such as alcohol [19], marijuana [20] or caffeine [21].…”

Section: Online Drawing Applied To Health Analysismentioning

confidence: 99%

Selection of Entropy Based Features for Automatic Analysis of Essential Tremor

Ipiña

Solé-Casals

Faundez-Zanuy

et al. 2016

Entropy

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Biomedical systems produce biosignals that arise from interaction mechanisms. In a general form, those mechanisms occur across multiple scales, both spatial and temporal, and contain linear and non-linear information. In this framework, entropy measures are good candidates in order provide useful evidence about disorder in the system, lack of information in time-series and/or irregularity of the signals. The most common movement disorder is essential tremor (ET), which occurs 20 times more than Parkinson's disease. Interestingly, about 50%-70% of the cases of ET have a genetic origin. One of the most used standard tests for clinical diagnosis of ET is Archimedes' spiral drawing. This work focuses on the selection of non-linear biomarkers from such drawings and handwriting, and it is part of a wider cross study on the diagnosis of essential tremor, where our piece of research presents the selection of entropy features for early ET diagnosis. Classic entropy features are compared with features based on permutation entropy. Automatic analysis system settled on several Machine Learning paradigms is performed, while automatic features selection is implemented by means of ANOVA (analysis of variance) test. The obtained results for early detection are promising and appear applicable to real environments.

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Section: Online Drawing Applied To Health Analysismentioning

confidence: 99%

Selection of Entropy Based Features for Automatic Analysis of Essential Tremor

Ipiña

Solé-Casals

Faundez-Zanuy

et al. 2016

Entropy

Self Cite

View full text Add to dashboard Cite

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“…Moreover, these techniques are very low-cost and do not require extensive infrastructure or the availability of medical equipment. They are thus capable of yielding information easily, quickly, and inexpensively [11][12][13][14][15]. In addition to the loss of memory, one of the major problems caused by AD is the loss of language skills, which is reflected in difficulty with both speaking and comprehension and complicates natural communication with the environment (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

“…Spoken language is the most spontaneous, natural, intuitive, and efficient method of communication among people. Therefore, the analysis by automated methods of Spontaneous Speech (SS), which is the freest and more natural expression of communication, possibly combined with other methodologies, has the potential to become a useful non-invasive method for early AD diagnosis [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

On Automatic Diagnosis of Alzheimer’s Disease Based on Spontaneous Speech Analysis and Emotional Temperature

et al. 2013

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Abstract:Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socioeconomic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer's disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer's disease patients.

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“…On the other hand, hypoxemic COPD seems to have a cognitive impairment profile different from that of normal and demented subjects, with verbal memory and praxic/executive function being the most affected, as shown by Antonelli et al [7,1]. The aim of this study was to propose a method that can be done in the patient's home, does not require specialized health technical support and reflects the fine motor brain control [11].…”

Section: Introductionmentioning

confidence: 95%