In this paper we evaluate the suitability of handwriting patterns as potential biomarkers to model Parkinson's disease (PD). Although the study of PD is attracting the interest of many researchers around the world, databases to evaluate handwriting patterns are scarce and knowledge about patterns associated to PD is limited and biased to the existing datasets. This paper introduces a database with a total of 935 handwriting tasks collected from 55 PD patients and 94 healthy controls (45 young and 49 old). Three feature sets are extracted from the signals: neuromotor, kinematic, and nonlinear dynamic. Different classifiers are used to discriminate between PD and healthy subjects: support vector machines, k-nearest neighbors, and a multilayer perceptron. The proposed features and classifiers enable to detect PD with accuracies between 81% and 97%. Additionally, new insights are presented on the utility of the studied features for monitoring and detecting PD.

Today, airspace control has the challenge of merging information from independent and heterogeneous systems in order to minimize air safety risks and facilitate the decision-making process. One of the main risks for air operations is meteorology because convective formations like Torre cumulus or cumulonimbus could generate several dangerous phenomena such as icing, wind gusts, and thunderstorms, among others, that can affect the air operation safety. Based on previous works that allow the automatic identification of convective phenomena through the fusion of multispectral satellite images and other sources as winds and Meteorological Aerodrome Report (METAR), and establishing a common georeferenced coordinates system like WGS-84, for all sources, it can generate a system that could calculate early alerts about hazardous weather conditions in the aircrafts proximality for air traffic control system. For this, a meteorological analysis system can generate information about convective clouds calculating area, heights, temperatures, risk level and position of the meteorological formation. Parallelly the convective cloud is surrounded by optimal elliptical forms centered on the convective formation, generating a meteorological object. On the other hand, there is a system responsible for monitoring the information of the surveillance sensors. This system fused the air traffic sensors available like primary and secondary radar signals and ADS-B sensors in a unique WGS-84 coordinates system. Finally, in a georeferenced raster-type graphing system or in a Geographic Information System (GIS), the meteorological and surveillance information is correlated projecting the track routes generates by air traffic system and traces generated by meteorological objects in order to establish times and high-risk areas, early. With this information, the Air Traffic Controller (ATC) system users, could minimize risk areas and reorganize the air traffic flow. This methodology then, would contribute to the decision-making process of ATC, facilitating the air flow reorganization and minimizing meteorological risks. For the development of this project a cooperative experimental methodology by subsystems was used. It was based on an operational knowledge and normal operating procedures of the Colombian Air Force, integrated with radar tracking technologies that implement decision trees. These alerts allow the air traffic controller to assess the risk and in accordance with the evaluation, if necessary, reorganize the air traffic flow for a specific area before the aircraft enter areas of bad weather mitigating the risks.

In the recent past, estimating brain activity with magneto/electroencephalography (M/EEG) has been increasingly employed as a noninvasive technique for understanding the brain functions and neural dynamics. However, one of the main open problems when dealing with M/EEG data is its non-Gaussian and nonstationary structure. In this paper, we introduce a methodology for enhancing the data covariance estimation using a weighted combination of multiple Gaussian kernels, termed WM-MK, that relies on the Kullback-Leibler divergence for associating each kernel weight to its relevance. From the obtained results of validation on nonstationary and non-Gaussian brain activity (simulated and real-world EEG data), WM-MK proves that the accuracy of the source estimation raises by more effectively exploiting the measured nonlinear structures with high time and space complexity.

Automatic detection of Parkinson's disease (PD) from speech is a basic step towards computer-aided tools supporting the diagnosis and monitoring of the disease. Although several methods have been proposed, their applicability to real-world situations is still unclear. In particular, the effect of acoustic conditions is not well understood. In this paper, the effects on the accuracy of five different methods to detect PD from speech are evaluated. Among the considered conditions, background noise produces the worst effect, while dynamic compression or some speech codecs can even have a marginal positive impact. We also consider, for the first time in this context, the problem of mismatches, i.e., when train/test acoustic conditions are different, and observe a high negative impact on all considered methods. Overall, this study is a step forward in performing a continuous monitoring of the neurological state of the patients in non-controlled acoustic conditions.

The suitability of articulation measures and speech intelligibility is evaluated to estimate the neurological state of patients with Parkinson's disease (PD). A set of measures recently introduced to model the articulatory capability of PD patients is considered. Additionally, the speech intelligibility in terms of the word accuracy obtained from the Google® speech recognizer is included. Recordings of patients in three different languages are considered: Spanish, German, and Czech. Additionally, the proposed approach is tested on data recently used in the INTERSPEECH 2015 Computational Paralinguistics Challenge. According to the results, it is possible to estimate the neurological state of PD patients from speech with a Spearman's correlation of up to 0.72 with respect to the evaluations performed by neurologist experts.

Several studies have addressed the automatic classification of speakers with Parkinson’s disease (PD) and healthy controls (HC). Most of the studies are based on speech recordings of sustained vowels, isolated words, and single sentences. Only few investigations have considered read texts and/or sponta-neous speech. This paper addresses two main questions still open regarding the automatic analysis speech in patients with PD, (a) “Is it possible to classify PD patients and HC through running speech signals in multiple languages?”, and (b) “where is the information to discriminate between speech recordings of PD patients and HC? ” In this paper speech recordings of read texts and monologues spoken in three different languages are considered. The energy content of the borders between voiced and unvoiced sounds is modeled. According to the results with read texts it is possible to achieve accuracies ranging from 91% to 98 % depending on the language. With respect to the re-sults on monologues, the accuracies are above 98 % in all of the three languages. The presence of discriminant information in the voiced/unvoiced and unvoiced/voiced transitions is vali-dated here, evidencing the problems of PD patients to stop/start the vocal folds movement during the production of running speech. Index Terms: Parkinson’s disease, dysarthria, hesitation in speech, language and motor planning, energy content, voiced/unvoiced transitions. 1.

Abstract About 1% of people older than 65 years suffer from Parkinson's disease (PD) and 90% of them develop several speech impairments, affecting phonation, articulation, prosody and fluency. Computer‐aided tools for the automatic evaluation of speech can provide useful information to the medical experts to perform a more accurate and objective diagnosis and monitoring of PD patients and can help also to evaluate the correctness and progress of their therapy. Although there are several studies that consider spectral and cepstral information to perform automatic classification of speech of people with PD, so far it is not known which is the most discriminative, spectral or cepstral analysis. In this paper, the discriminant capability of six sets of spectral and cepstral coefficients is evaluated, considering speech recordings of the five Spanish vowels and a total of 24 isolated words. According to the results, linear predictive cepstral coefficients are the most robust and exhibit values of the area under the receiver operating characteristic curve above 0.85 in 6 of the 24 words.

This paper evaluates the accuracy of different characterization methods for the automatic detection of multiple speech disorders. The speech impairments considered include dysphonia in people with Parkinson's disease (PD), dysphonia diagnosed in patients with different laryngeal pathologies (LP), and hypernasality in children with cleft lip and palate (CLP). Four different methods are applied to analyze the voice signals including noise content measures, spectral-cepstral modeling, nonlinear features, and measurements to quantify the stability of the fundamental frequency. These measures are tested in six databases: three with recordings of PD patients, two with patients with LP, and one with children with CLP. The abnormal vibration of the vocal folds observed in PD patients and in people with LP is modeled using the stability measures with accuracies ranging from 81% to 99% depending on the pathology. The spectral-cepstral features are used in this paper to model the voice spectrum with special emphasis around the first two formants. These measures exhibit accuracies ranging from 95% to 99% in the automatic detection of hypernasal voices, which confirms the presence of changes in the speech spectrum due to hypernasality. Noise measures suitably discriminate between dysphonic and healthy voices in both databases with speakers suffering from LP. The results obtained in this study suggest that it is not suitable to use every kind of features to model all of the voice pathologies; conversely, it is necessary to study the physiology of each impairment to choose the most appropriate set of features.

About 90% of the people with Parkinson’s disease (PD) develop speech impairments such as monopitch, monoloudness, imprecise articulation, and other symptoms. There are several studies addressing the problem of the automatic detection of PD from speech signals in order to develop computer aided tools for the assessment and monitoring of the patients. Recent works have shown that it is possible to detect PD from speech with accuracies above 90%; however, it is still unclear whether it is possible to make the detection independent of the spoken language. This paper addresses the automatic detection of PD considering speech recordings of three languages: German, Spanish and Czech. According to the results it is possible to classify between speech of people with PD and healthy controls (HC) with accuracies ranging from 84% to 99%, depending on the utterance.

The automatic handwritten signature verification is an open problem for the scientific community. The most of the published studies examine a generic document trying to locate where the signature has been written, to segment the signature removing complex backgrounds containing lines and letter and to determining whether the signature was made by the owner. However, there are no studies to determine automatically the author of a fake. This paper presents a first approach to the identification of a static signature forger. The underlying hypothesis is the fact that a forger finds difficult to fight against their own free natural way of writing, leading to the second hypothesis that under several conditions it is possible to isolate these features to determine a fake within a population of known forgers. The experiments shown that gray level based features are a good start point to detect who has written the signatures.

Automatic hypernasality detection in children with Cleft Lip and Palate is classically performed by means of acoustic analysis; however, recent findings indicate that nonlinear dynamics features could be useful for this task. In order to continue deepening in this issue, in this paper the discriminant capability of 4 different nonlinear dynamics features along with a set of 6 entropy measurements is studied. The whole set of features is optimized using an automatic feature selection technique based on principal component analysis. The decision about the presence or absence of hypernasality is made by employing a support vector machine. The system is tested over two databases, one considers the five Spanish vowels and the words /coco/ and /gato/, and the other one considers different German words. The performance of the system is presented in terms of accuracy, sensitivity, specificity and receiver operating curves. According to the results, the accuracy of system increases when nonlinear and entropy measures are combined.

Several papers have recently appeared in the literature which propose pseudo-dynamic features for automatic static handwritten signature verification based on the use of gray level values from signature stroke pixels. Good results have been obtained using rotation invariant uniform local binary patterns LBP8,1riu2plus LBP16,2riu2and statistical measures from gray level co-occurrence matrices (GLCM) with MCYT and GPDS offline signature corpuses. In these studies the corpuses contain signatures written on a uniform white “nondistorting” background, however the gray level distribution of signature strokes changes when it is written on a complex background, such as a check or an invoice. The aim of this paper is to measure gray level features robustness when it is distorted by a complex background and also to propose more stable features. A set of different checks and invoices with varying background complexity is blended with the MCYT and GPDS signatures. The blending model is based on multiplication. The signature models are trained with genuine signatures on white background and tested with other genuine and forgeries mixed with different backgrounds. Results show that a basic version of local binary patterns (LBP) or local derivative and directional patterns are more robust than rotation invariant uniform LBP or GLCM features to the gray level distortion when using a support vector machine with histogram oriented kernels as a classifier.

This paper presents a study of biometric identification using a methodology based on complexity measures. The identification system designed, implemented and evaluated uses nonlinear dynamic techniques such as Lempel-Ziv Complexity, the Largest Lyapunov Exponent, Hurst Exponent, Correlation Dimension, Shannon Entropy and Kolmogorov Entropy to characterize the process and capture the intrinsic dynamics of the user's signature. In the validation process 3 databases were used SVC, MCYT and our own (ITMMS-01) obtaining closed-set identification performances of 98.12%, 97.38% and 99.50% accordingly. Satisfactory results were achieved with a conventional linear classifier spending a minimum computational cost.

Automatic detection of hypernasality in voices of children with Cleft Lip and Palate (CLP) is made considering two charcaterization techniques, one based on acoustic, noise and cepstral analysis and other based on nonlinear dynamic features. Besides characterization, two automatic feature selection techniques are implemented in order to find optimal sub-spaces to better discriminate between healthy and hypernasal voices. Results indicate that nonlinear dynamic features are valuable tool for automatic detection of hypernasality; addtionally both feature selection techniques show stable and consistent results, achieving accuracy levels of up to 93.73%. Index Terms: Hypernasality, Cleft Lip and Palate, acoustic, cepstral, nonlinear dynamics.

The objective of this competition (4NSigComp2010) is to ascertain the performance of automatic off-line signature verifiers to evaluate recent technology developments in the areas of document analysis and machine learning. The current paper focuses on the second scenario, which aims at performance evaluation of off-line signature verification systems on a newly-created large dataset that comprises genuine, simulated signatures produced by unskilled imitators or random signatures (genuine signatures from other writers). Ten systems were evaluated, and some interesting results are presented in terms of accuracy and execution time. The top ranking system attained an overall error of 8.94%. This result interestingly correlates with the top ranking accuracy achieved in a previous signature verification competition at ICDAR 2009.

A method for Off-line Handwritten Signature Verification is described. It works at the global and local image level, measuring the stroke gray-level variations by means of wavelet analysys and statistical texture features. This method begins with a proposed background removal. Then Wavelet Analysis allows to estimate and alleviate the global influence of ink-type, and finally, properties of the Co-occurrence Matrix are used as features representing individual characteristics at local level. Genuine samples have been used for train an SVM model, random and skilled forgeries have been used for testing it. Experiments were conducted on three differente databases (MCYT75, GPDS100, and GPDS750). Results are reasonable according to the state of the art and approaches that use the same public available database (MCYT75) and prove the feasibility of the proposed methodology.

Features representing information about pressure distribution from a static image of a handwritten signature are analyzed for an offline verification system. From gray-scale images, its histogram is calculated and used as "spectrum'' for calculation of pseudo-cepstral coefficients. Finally, the unique minimum-phase sequence is estimated and used as feature vector for signature verification. The optimal number of pseudo-coefficients is estimated for best system performance. Experiments were carried out using a database containing signatures from 100 individuals. The robustness of the analyzed system for simple forgeries is tested out with a LS-SVM model. For the sake of completeness, a comparison of the results obtained by the proposed approach with similar works published using pseudo-dynamic feature for offline signature verification is presented.

The current need for large databases to evaluate automatic biometric recognition systems has motivated the developing of the GPDS-960 corpus, an off-line handwritten signature database which contains 24 genuine signatures and 30 forgeries of 960 individuals. This paper describes the GPDS signature corpus, gives details about the acquisition protocols and presents preliminary verification results obtained using the GPDS data.

The effect of changing the image resolution over an off-line signature verification system performance is analyzed. The geometrical features used for the system analyzed in this paper are based on two vectors which represent the envelope and the interior stroke distribution in polar and Cartesian coordinates. Image resolution is progressively diminished from an initial 600 ppp resolution till 45 ppp. The robustness of the analyzed system for random and simple forgeries is tested out with a hidden Markov model. The results show that 150 ppp offers a good trade-off between performance and image resolution for static features.