A new distance measure for hidden Markov models

Zeng, Jianping; Duan, Jing; Wu, Chengrong

doi:10.1016/j.eswa.2009.06.063

Cited by 20 publications

(16 citation statements)

References 13 publications

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“…To measure the similarity between the estimated HMMs, the Kullback-Leibler Distance (KLD) is measured between each pair of Markov models k 1 and k 2 . The KLD is widely used as a distance measure between HMMs [20]. The KLD is computed in the literature using the Monte-Carlo approach as follows:…”

Section: Resultsmentioning

confidence: 99%

Discovery of high-level tasks in the operating room

Bouarfa

Jonker

Dankelman

2011

Journal of Biomedical Informatics

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Recognizing and understanding surgical high-level tasks from sensor readings is important for surgical workflow analysis. Surgical high-level task recognition is also a challenging task in ubiquitous computing because of the inherent uncertainty of sensor data and the complexity of the operating room environment. In this paper, we present a framework for recognizing high-level tasks from low-level noisy sensor data. Specifically, we present a Markov-based approach for inferring high-level tasks from a set of low-level sensor data. We also propose to clean the noisy sensor data using a Bayesian approach. Preliminary results on a noise-free dataset of ten surgical procedures show that it is possible to recognize surgical high-level tasks with detection accuracies up to 90%. Introducing missed and ghost errors to the sensor data results in a significant decrease of the recognition accuracy. This supports our claim to use a cleaning algorithm before the training step. Finally, we highlight exciting research directions in this area.

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Section: Resultsmentioning

confidence: 99%

Discovery of high-level tasks in the operating room

Bouarfa

Jonker

Dankelman

2011

Journal of Biomedical Informatics

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“…Y lo que es más comprometedor, la divergencia KL no satisface la desigualdad triangular, ni las propiedades de simetría requeridas para la función de distancia que parametriza el clasicador [36]. Si bien la función de distribución acumulada estacionaria para los HMMs cumple con todas las propiedades de distancia [37], únicamente puede ser aplicada a observaciones unidimensionales y obvia las transiciones del modelo hasta llegar a su estado estacionario [38]. Recientemente, la conectividad cerebral funcional se ha convertido en uno de los conceptos más relevantes de la neurociencia cognitiva moderna ya que la función cerebral no sólo depende de las regiones activas, sino también de las interacciones funcionales entre los grupos neuronales distribuidas a lo largo de la corteza [39].…”

Section: Planteamiento Del Problemaunclassified

Herramienta de apoyo al diagnóstico del TDAH en niños a partir de múltiples características de actividad eléctrica cerebral desde registros EEG

Peña¹,

Noreña²,

Gómez³

et al. 2021

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Este libro propone un compendio de metodologías tendientes a generar biomarcadores que soporten el diagnóstico objetivo del trastorno de déficit de atención e hiperactividad, desarrolladas en el marco del proyecto de investigación “Herramienta de apoyo al diagnóstico del TDAH en niños a partir de múltiples características de actividad eléctrica cerebral desde registros EEG”, ejecutado por investigadores del Instituto del Sistema Nervioso de Risaralda, la Universidad Nacional de Colombia sede Manizales y la Universidad Tecnológica de Pereira. Dentro de los investigadores se encuentran doctores en ingeniería y psiquiatría, quienes en un trabajo aunado han generado nuevo conocimiento en las áreas de aprendizaje de máquina y de psiquiatría infantil, materializado en artículos de investigación y herramientas de software para soporte diagnóstico del TDAH. El libro presenta una variante del paradigma Stop Signal Task incluyendo recompensas de magnitud cambiante. Luego introduce tres propuestas ...

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“…The HSD distance [10] is used to compute the distance between two Gaussian-HMM. The distance between two , withΠ is the stationary probability of state .…”

Section: Hsdmentioning

confidence: 99%

Evolutions Detection Using Markov Models

Pâris¹,

Adnen²,

Cislo³

et al. 2017

IJIEE

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We consider a specific case of the anomaly detection problem where the considered system is unknown but observable. The aim is to detect if this system changes or not for a long period, and, if possible, to characterize these evolutions. The proposed method consists of two steps. The first one is to learn the system thanks to a Hidden Markov Model with explicit duration of states, using observations of the system at different moments. The second step is to compute distances between these models to detect the evolutions. Due to the constraints of the learning algorithm and computational time, we proposed a new method to compute distances between Markov models, based on the Kullback-Leibler divergences. The method is tested on a trial evolving model. These results are .

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A new distance measure for hidden Markov models

Cited by 20 publications

References 13 publications

Discovery of high-level tasks in the operating room

Discovery of high-level tasks in the operating room

Herramienta de apoyo al diagnóstico del TDAH en niños a partir de múltiples características de actividad eléctrica cerebral desde registros EEG

Evolutions Detection Using Markov Models

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