Quantitative and trajectory analysis of movement trajectories in supplementary motor area seizures of frontal lobe epilepsy

Chen, Lei; Yang, Xiao; Liu, Yonghong; Zeng, Dong; Tang, Yufeng; Yan, Bo; Xu, Lin; Liu, Ling; Xu, Hongru; Zhou, Dong

doi:10.1016/j.yebeh.2008.11.007

Cited by 22 publications

(28 citation statements)

References 20 publications

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“…Because the quantification of movement trajectory in patients with TLE [55] based on the method developed by Li and colleagues [56] serves as another contemporary and appropriate tool for characterizing the details of behavioral manifestations of seizures, the technical developments and rationale behind movement trajectory quantification can be coupled to the current neuroethology tools to further improve semiological evaluation in FLE (see a comment in [57]). …”

Section: Neuroethology As a Reliable Quantitative Tool To Characterizmentioning

confidence: 99%

Looking for complexity in quantitative semiology of frontal and temporal lobe seizures using neuroethology and graph theory

Bertti

Tejada

Martins

et al. 2014

Epilepsy & Behavior

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Section: Neuroethology As a Reliable Quantitative Tool To Characterizmentioning

confidence: 99%

Looking for complexity in quantitative semiology of frontal and temporal lobe seizures using neuroethology and graph theory

Bertti

Tejada

Martins

et al. 2014

Epilepsy & Behavior

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“…Unfortunately, traditional naked-eye examination does not allow for quantitative analysis of the amplitude and frequency of movements in seizures [12]. Some researchers attempt to detect seizures through quantifying body movements with attached markers or sensors [12]- [14]. The works in [12] and [13] reflective markers or white foam markers to a patient's body for movement quantification.…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers attempt to detect seizures through quantifying body movements with attached markers or sensors [12]- [14]. The works in [12] and [13] reflective markers or white foam markers to a patient's body for movement quantification. However, these markers are often identified manually with mouse clicking and lead to correspondence difficulties.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Limb Movements in Epileptic Seizures Through Color-Based Video Analysis

Pan

Mandal

et al. 2013

IEEE Trans. Biomed. Eng.

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This paper proposes a color-based video analytic system for quantifying limb movements in epileptic seizure monitoring. The system utilizes colored pyjamas to facilitate limb segmentation and tracking. Thus, it is unobtrusive and requires no sensor/marker attached to patient's body. We employ Gaussian mixture models in background/foreground modeling and detect limbs through a coarse-to-fine paradigm with graph-cut-based segmentation. Next, we estimate limb parameters with domain knowledge guidance and extract displacement and oscillation features from movement trajectories for seizure detection/analysis. We report studies on sequences captured in an epilepsy monitoring unit. Experimental evaluations show that the proposed system has achieved comparable performance to EEG-based systems in detecting motor seizures.

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“…The multi-modal approach used by Conradsen et al [2] gives better results on patients with smaller seizures. Video based detection mainly focuses on the usage of markers [10] [1] or other ways to track limbs, like using colored pyjamas [4]. Karayiannis et al [6] didn't use any markers, but the moving limbs of the patients were clearly visible as they were monitored in the Neonatal Intensive Care Unit (NICU) of the hospital.…”

Section: Introductionmentioning

confidence: 99%

Using spatio-temporal interest points (STIP) for myoclonic jerk detection in nocturnal video

Cuppens

Chen

Wong

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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In this study we introduce a method for detecting myoclonic jerks during the night with video. Using video instead of the traditional method of using EEG-electrodes, permits patients to sleep without any attached sensors. This improves the comfort during sleep and it makes long term home monitoring possible. The algorithm for the detection of the seizures is based on spatio-temporal interest points (STIPs), proposed by Ivan Laptev, which is the state-of-the-art in action recognition.We applied this algorithm on a group of patients suffering from myoclonic jerks. With an optimal parameter setting this resulted in a sensitivity of over 75% and a PPV of over 85%, on the patients' combined data.

show abstract

Quantitative and trajectory analysis of movement trajectories in supplementary motor area seizures of frontal lobe epilepsy

Cited by 22 publications

References 20 publications

Looking for complexity in quantitative semiology of frontal and temporal lobe seizures using neuroethology and graph theory

Looking for complexity in quantitative semiology of frontal and temporal lobe seizures using neuroethology and graph theory

Quantifying Limb Movements in Epileptic Seizures Through Color-Based Video Analysis

Using spatio-temporal interest points (STIP) for myoclonic jerk detection in nocturnal video

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