Automated tracking of multiple body parts in video recordings of neonatal seizures

Sami, A.; Karayiannis, N.B.; Frost, James D.; Wise, Merrill S.; Mizrahi, Eli M.

doi:10.1109/isbi.2004.1398537

Cited by 7 publications

(12 citation statements)

References 8 publications

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“…Motion tracking in video also requires an automated procedure capable of selecting anatomical sites on the moving body part, which also was developed in this study (30). This automated procedure was developed even further to perform tracking of multiple anatomical sites located on moving body parts.…”

Section: Methodsmentioning

confidence: 99%

“…The results of our ongoing project, “Video Technologies for Neonatal Seizures,” provided evidence suggesting that the analysis of motion in video can facilitate the recognition and characterization of the types of neonatal seizures (23–30) and revealed the potential of computerized video as a relatively inexpensive and noninvasive health‐monitoring tool. Computerized video processing and analysis of video recordings of neonatal seizures can generate novel methods for extracting quantitative information that is relevant only to the seizure.…”

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confidence: 99%

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Computerized Motion Analysis of Videotaped Neonatal Seizures of Epileptic Origin

et al. 2005

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Summary: Purpose:The main objective of this research is the development of automated video processing and analysis procedures aimed at the recognition and characterization of the types of neonatal seizures. The long-term goal of this research is the integration of these computational procedures into the development of a stand-alone automated system that could be used as a supplement in the neonatal intensive care unit (NICU) to provide 24-h per day noninvasive monitoring of infants at risk for seizures.Methods: We developed and evaluated a variety of computational tools and procedures that may be used to carry out the three essential tasks involved in the development of a seizure recognition and characterization system: the extraction of quantitative motion information from video recordings of neonatal seizures in the form of motion-strength and motor-activity signals, the selection of quantitative features that convey some unique behavioral characteristics of neonatal seizures, and the training of artificial neural networks to distinguish neonatal seizures from random infant behaviors and to differentiate between myoclonic and focal clonic seizures.Results: The methods were tested on a set of 240 video recordings of 43 patients exhibiting myoclonic seizures (80 cases), focal clonic seizures (80 cases), and random infant movements (80 cases). The outcome of the experiments verified that opticalflow methods are promising computational tools for quantifying neonatal seizures from video recordings in the form of motionstrength signals. The experimental results also verified that the robust motion trackers developed in this study outperformed considerably the motion trackers based on predictive block matching in terms of both reliability and accuracy. The quantitative features selected from motion-strength and motor-activity signals constitute a satisfactory representation of neonatal seizures and random infant movements and seem to be complementary. Such features lead to trained neural networks that exhibit performance levels exceeding the initial goals of this study, the sensitivity goal being ≥80% and the specificity goal being ≥90%.Conclusions: The outcome of this experimental study provides strong evidence that it is feasible to develop an automated system for the recognition and characterization of the types of neonatal seizures based on video recordings. This will be accomplished by enhancing the accuracy and improving the reliability of the computational tools and methods developed during the course of the study outlined here.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

Computerized Motion Analysis of Videotaped Neonatal Seizures of Epileptic Origin

et al. 2005

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“…During the first phase of this project, we built on the results of our preliminary study (16) by developing new computational tools and procedures that would provide the building blocks of an automated seizure-detection system (17)(18)(19)(20)(21)(22)(23)(24). Motion was quantified by motion-strength signals extracted by using motion-segmentation methods (19,20) and by motion-trajectory signals extracted by using motion-tracking methods (21)(22)(23)(24).…”

mentioning

confidence: 99%

Automated Detection of Videotaped Neonatal Seizures of Epileptic Origin

Karayiannis

Xiong²,

Tao³

et al. 2006

Epilepsia

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Summary:Purpose: This study aimed at the development of a seizure-detection system by training neural networks with quantitative motion information extracted from short video segments of neonatal seizures of the myoclonic and focal clonic types and random infant movements.Methods: The motion of the infants' body parts was quantified by temporal motion-strength signals extracted from video segments by motion-segmentation methods based on optical flow computation. The area of each frame occupied by the infants' moving body parts was segmented by clustering the motion parameters obtained by fitting an affine model to the pixel velocities. The motion of the infants' body parts also was quantified by temporal motion-trajectory signals extracted from video recordings by robust motion trackers based on block-motion models. These motion trackers were developed to adjust autonomously to illumination and contrast changes that may occur during the video-frame sequence. Video segments were represented by quantitative features obtained by analyzing motion-strength and motion-trajectory signals in both the time and frequency domains. Seizure recognition was performed by conventional feedforward neural networks, quantum neural networks, and cosine radial basis function neural networks, which were trained to detect neonatal seizures of the myoclonic and focal clonic types and to distinguish them from random infant movements.Results: The computational tools and procedures developed for automated seizure detection were evaluated on a set of 240 video segments of 54 patients exhibiting myoclonic seizures (80 segments), focal clonic seizures (80 segments), and random infant movements (80 segments). Regardless of the decision scheme used for interpreting the responses of the trained neural networks, all the neural network models exhibited sensitivity and specificity >90%. For one of the decision schemes proposed for interpreting the responses of the trained neural networks, the majority of the trained neural-network models exhibited sensitivity >90% and specificity >95%. In particular, cosine radial basis function neural networks achieved the performance targets of this phase of the project (i.e., sensitivity >95% and specificity >95%).Conclusions: The best among the motion segmentation and tracking methods developed in this study produced quantitative features that constitute a reliable basis for detecting neonatal seizures. The performance targets of this phase of the project were achieved by combining the quantitative features obtained by analyzing motion-strength signals with those produced by analyzing motion-trajectory signals. The computational procedures and tools developed in this study to perform off-line analysis of short video segments will be used in the next phase of this project, which involves the integration of these procedures and tools into a system that can process and analyze long video recordings of infants monitored for seizures in real time.

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“…Robust motion tracking relied on two admissible functions, namely 2 ( ) ln cosh , x x and 2 ( ) tanh (2 ).…”

Section: Robust Motion Trackingmentioning

confidence: 99%

“…As the seizure progresses in time, the projection X of the anatomical site of interest to the horizontal axis and its projection Y to the vertical axis produce temporal signals ( ) X t and recording motor activity of the body part of interest. Motion tracking in video can be initialized by an automated procedure capable of selecting anatomical sites on the moving body part [2]. The anatomical sites of interest can be tracked by motion trackers based on a variety of block motion models [3].…”

Section: Introductionmentioning

confidence: 99%

Automated Recognition of Videotaped Neonatal Seizures Using Robust Motion Tracking Methods That Adjust to Illumination and Contrast Changes

Xiong

Karayiannis

3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006.

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This paper presents motion trackers developed to quantify motion in video recordings of infants monitored for seizures. The proposed formulation relies on a variety of block motion models and can be used to develop robust motion trackers that adjust to illumination and contrast changes. The resulting motion trackers are utilized to extract motion trajectory signals, which provide the basis for selecting quantitative features that convey some unique behavioral characteristics of neonatal seizures. Such quantitative features provide the basis for training feedforward neural networks to recognize neonatal seizures.

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Automated tracking of multiple body parts in video recordings of neonatal seizures

Cited by 7 publications

References 8 publications

Computerized Motion Analysis of Videotaped Neonatal Seizures of Epileptic Origin

Computerized Motion Analysis of Videotaped Neonatal Seizures of Epileptic Origin

Automated Detection of Videotaped Neonatal Seizures of Epileptic Origin

Automated Recognition of Videotaped Neonatal Seizures Using Robust Motion Tracking Methods That Adjust to Illumination and Contrast Changes

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