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Biomedical subjects

Richard A Hrachovy

Publications and source records attributed to Richard A Hrachovy.

8 recordsLinked to original sources
PubMed2006

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

OBJECTIVE: This study was aimed at the development of a seizure detection system by training neural networks using quantitative motion information extracted by motion segmentation methods from short video recordings of infants monitored for seizures. METHODS: The motion of the infants' body parts was quantified by temporal motion strength signals extracted from video recordings by motion segmentation methods based on optical flow computation. The area of each frame occupied by the infants' moving body parts was segmented by direct thresholding, by clustering of the pixel velocities, and by clustering the motion parameters obtained by fitting an affine model to the pixel velocities. The computational tools and procedures developed for automated seizure detection were tested and evaluated on 240 short video segments selected and labeled by physicians from a set of video recordings of 54 patients exhibiting myoclonic seizures (80 segments), focal clonic seizures (80 segments), and random infant movements (80 segments). RESULTS: The experimental study described in this paper 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. Depending on the decision scheme used for interpreting the responses of the trained neural networks, the best neural networks exhibited sensitivity above 90% or specificity above 90%. CONCLUSIONS: The best among the motion segmentation methods developed in this study produced quantitative features that constitute a reliable basis for detecting myoclonic and focal clonic neonatal seizures. The performance targets of this phase of the project may be achieved by combining the quantitative features described in this paper with those obtained by analyzing motion trajectory signals produced by motion tracking methods. SIGNIFICANCE: A video system based upon automated analysis potentially offers a number of advantages. Infants who are at risk for seizures could be monitored continuously using relatively inexpensive and non-invasive video techniques that supplement direct observation by nursery personnel. This would represent a major advance in seizure surveillance and offers the possibility for earlier identification of potential neurological problems and subsequent intervention.

Cluster Analysis↗
PubMed2006

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.

Diagnosis, Computer-Assisted↗
PubMed2006

The EEG in selected generalized seizures.

This article reviews the ictal and interictal EEG findings associated with a select group of generalized seizures. These include absence seizures, myoclonic seizures seen in juvenile myoclonic epilepsy, idiopathic generalized tonic clonic seizures, infantile spasms, and atypical absence, tonic, and atonic seizures associated with the Lennox Gastaut syndrome.

Diagnosis, Differential↗
PubMed2006

Detection of pseudosinusoidal epileptic seizure segments in the neonatal EEG by cascading a rule-based algorithm with a neural network.

This paper presents an approach to detect epileptic seizure segments in the neonatal electroencephalogram (EEG) by characterizing the spectral features of the EEG waveform using a rule-based algorithm cascaded with a neural network. A rule-based algorithm screens out short segments of pseudosinusoidal EEG patterns as epileptic based on features in the power spectrum. The output of the rule-based algorithm is used to train and compare the performance of conventional feedforward neural networks and quantum neural networks. The results indicate that the trained neural networks, cascaded with the rule-based algorithm, improved the performance of the rule-based algorithm acting by itself. The evaluation of the proposed cascaded scheme for the detection of pseudosinusoidal seizure segments reveals its potential as a building block of the automated seizure detection system under development.

Algorithms↗
PubMed2006

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.

Automation↗
PubMedDate not supplied

Infantile epileptic encephalopathy with hypsarrhythmia (infantile spasms/West syndrome).

Infantile spasms is a unique disorder peculiar to infancy and early childhood. In this article, the clinical manifestations and electroencephalographic features of the disorder are described. The possible pathophysiologic mechanisms underlying infantile spasms and the relation of this disorder to other childhood encephalopathies are discussed. Finally, the treatment of patients with infantile spasms and their long-term outcome are briefly reviewed.

Brain↗
PubMedDate not supplied

Pathogenesis of infantile spasms: a model based on developmental desynchronization.

Infantile spasms is a severe epileptic encephalopathy of infancy. The fundamental cause is unknown, although a number of predisposing conditions are recognized. In this article, the authors critically review current knowledge concerning the pathophysiologic basis of infantile spasms and propose a new model based on developmental desynchronization. It is suggested that infantile spasms may result from a particular temporal desynchronization of two or more central nervous system developmental processes, resulting in a specific disturbance of brain function. The disturbance of function is postulated to be crucially dependent on an unbalanced maturational pattern, in which certain brain systems become dysfunctional owing to divergent developmental status. An important aspect of this model is the idea that disturbed function of a specific kind can result from multiple causative factors, and so can be associated with a variety of different anatomic and/or biochemical abnormalities. Thus, this concept is compatible with the observed diversity of pathologic findings and multiplicity of etiological associations observed in infantile spasms patients.

Age Factors↗