Automated detection of videotaped neonatal seizures based on motion tracking methods.

This study was carried out during the second phase of the project "Video Technologies for Neonatal Seizures" and aimed at the development of a seizure detection system by training neural networks, using quantitative motion information extracted by motion tracking methods from short video segments of infants monitored for seizures. The motion of the infants' body parts 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 autonomously adjust to illumination and contrast changes that may occur during the video frame sequence. The computational tools and procedures developed for automated seizure detection were evaluated on short video segments selected and labeled by physicians from a set of 240 video recordings of 54 patients exhibiting myoclonic seizures (80 segments), focal clonic seizures (80 segments), and random infant movements (80 segments). This evaluation provided the basis for selecting the most effective strategy for training neural networks to detect neonatal seizures as well as the decision scheme used for interpreting the responses of the trained neural networks. The best neural networks exhibited sensitivity and specificity above 90%. The best among the motion trackers developed in this study produced quantitative features that constitute a reliable basis for detecting myoclonic and focal clonic neonatal seizures. The performance targets of the second phase of the project may be achieved by combining the quantitative features described in this paper with those obtained by analyzing motion strength signals produced by motion segmentation methods.

Training reformulated radial basis function neural networks capable of identifying uncertainty in data classification.

This paper introduces a learning algorithm that can be used for training reformulated radial basis function neural networks (RBFNNs) capable of identifying uncertainty in data classification. This learning algorithm trains a special class of reformulated RBFNNs, known as cosine RBFNNs, by updating selected adjustable parameters to minimize the class-conditional variances at the outputs of their radial basis functions (RBFs). The experiments verify that quantum neural networks (QNNs) and cosine RBFNNs trained by the proposed learning algorithm are capable of identifying uncertainty in data classification, a property that is not shared by cosine RBFNNs trained by the original learning algorithm and conventional feed-forward neural networks (FFNNs). Finally, this study leads to a simple classification strategy that can be used to improve the classification accuracy of QNNs and cosine RBFNNs by rejecting ambiguous feature vectors based on their responses.

Improving the accuracy and reliability of motion tracking methods used for extracting temporal motor activity signals from video recordings of neonatal seizures.

This paper presents an approach for improving the accuracy and reliability of motion tracking methods developed for video based on block motion models. This approach estimates the displacement of a block of pixels between two successive frames by minimizing an error function defined in terms of the pixel intensities at these frames. The minimization problem is made analytically tractable by approximating the error function using a second-order Taylor expansion. The improved reliability of the proposed method is illustrated by its application in the extraction of temporal motor activity signals from video recordings of neonatal seizures.

Quantifying motion in video recordings of neonatal seizures by robust motion trackers based on block motion models.

This paper introduces a methodology for the development of robust motion trackers for video based on block motion models. According to this methodology, the motion of a site between two successive frames is estimated by minimizing an error function defined in terms of the intensities at these frames. The proposed methodology is used to develop robust motion trackers that rely on fractional block motion models. The motion trackers developed in this paper are utilized to extract motor activity signals from video recordings of neonatal seizures. The experimental results reveal that the proposed motion trackers are more accurate and reliable than existing motion tracking methods relying on pure translation and affine block motion models.

Automated detection of videotaped neonatal seizures of epileptic origin.

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 feed-forward 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.

Computerized motion analysis of videotaped neonatal seizures of epileptic origin.

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 optical- flow methods are promising computational tools for quantifying neonatal seizures from video recordings in the form of motion-strength 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 >or=80% and the specificity goal being >or=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.