Quantifying motion in video recordings of neonatal seizures by feature trackers based on predictive block matching

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

doi:10.1109/iembs.2004.1403447

Cited by 3 publications

(6 citation statements)

References 8 publications

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“…In the latter, the velocity vectors associated with each pixel of the frame were defined, and the area containing all the pixels with a speed greater than a defined threshold was computed. Similarly, to define the motor activity signal, the predictive block matching technique [55], [56] was compared to methods involving other models of blocks ("robust motion trackers") [57]. In these studies, a block of pixels of predefined dimension ("reference block") was defined in the first frame of the considered sequence around the anatomical sites of interest.…”

Section: Nsd Video-based Systemsmentioning

confidence: 99%

Automatic Detection of Epileptic Seizures in Neonatal Intensive Care Units Through EEG, ECG and Video Recordings: A Survey

Olmi¹,

Frassineti

Lanatà

et al. 2021

IEEE Access

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In Neonatal Intensive Care Units (NICUs), the early detection of neonatal seizures is of utmost importance for a timely, effective and efficient clinical intervention. The continuous video electroencephalogram (v-EEG) is the gold standard for monitoring neonatal seizures, but it requires specialized equipment and expert staff available 24/24h. The purpose of this study is to present an overview of the main Neonatal Seizure Detection (NSD) systems developed during the last ten years that implement Artificial Intelligence techniques to detect and report the temporal occurrence of neonatal seizures. Expert systems based on the analysis of EEG, ECG and video recordings are investigated, and their usefulness as support tools for the medical staff in detecting and diagnosing neonatal seizures in NICUs is evaluated. EEGbased NSD systems show better performance than systems based on other signals. Recently ECG analysis, particularly the related HRV analysis, seems to be a promising marker of brain damage. Moreover, video analysis could be helpful to identify inconspicuous but pathological movements. This study highlights possible future developments of the NSD systems: a multimodal approach that exploits and combines the results of the EEG, ECG and video approaches and a system able to automatically characterize etiologies might provide additional support to clinicians in seizures diagnosis.

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Section: Nsd Video-based Systemsmentioning

confidence: 99%

Automatic Detection of Epileptic Seizures in Neonatal Intensive Care Units Through EEG, ECG and Video Recordings: A Survey

Olmi¹,

Frassineti

Lanatà

et al. 2021

IEEE Access

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“…A large part of studies using video recordings was dedicated to the analysis of neonatal seizures. Whereas first studies focused on observational classifications of seizures from video recordings (Mizrahi and Kellaway, 1987;Tharp, 2002), later, thanks to the development of video processing, several approaches were proposed to automatically detect or classify seizures from motion descriptors (Karayiannis et al, 2001;Sami et al, 2004;Karayiannis et al, 2004Karayiannis et al, , 2005aNtonfo et al, 2012). For their part, Cuppens et al focused on the specific case of the epilepsy (Cuppens et al, 2009(Cuppens et al, , 2010.…”

Section: Video Analysismentioning

confidence: 99%

“…Two database types can be identified regarding camera/patient positioning. The more common one contained video recordings performed in a controlled environment where cameras were located above a mattress and infants were placed in a supine position with no blanket and fully visible (Karayiannis et al, 2001;Sami et al, 2004;Karayiannis et al, 2004Karayiannis et al, , 2005aAdde et al, 2009Adde et al, , 2010Stahl et al, 2012;Rahmati et al, 2014Rahmati et al, , 2015Orlandi et al, 2015). A marginal positioning has also been used in (Zaker et al, 2013(Zaker et al, , 2014 where infants were placed in a baby seat with only the head and shoulders visible.…”

Section: Video Acquisitionmentioning

confidence: 99%

Video and audio processing in paediatrics: a review

Cabon¹,

Porée²,

Simon³

et al. 2019

Physiol. Meas.

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Objective: Video and sound acquisition and processing technologies have seen great improvements in recent decades, with many applications in the biomedical area. The aim of this paper is to review the overall state of the art of advances within these topics in paediatrics and to evaluate their potential application for monitoring in the neonatal intensive care unit (NICU). Approach: For this purpose, more than 150 papers dealing with video and audio processing were reviewed. For both topics, clinical applications are described according to the considered cohorts—full-term newborns, infants and toddlers or preterm newborns. Then, processing methods are presented, in terms of data acquisition, feature extraction and characterization. Main results: The paper first focuses on the exploitation of video recordings; these began to be automatically processed in the 2000s and we show that they have mainly been used to characterize infant motion. Other applications, including respiration and heart rate estimation and facial analysis, are also presented. Audio processing is then reviewed, with a focus on the analysis of crying. The first studies in this field focused on induced-pain cries and the newest ones deal with spontaneous cries; the analyses are mainly based on frequency features. Then, some papers dealing with non-cry signals are also discussed. Significance: Finally, we show that even if recent improvements in digital video and signal processing allow for increased automation of processing, the context of the NICU makes a fully automated analysis of long recordings problematic. A few proposals for overcoming some of the limitations are given.

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“…Although adaptive block matching was generally successful, it was not always reliable, because it attempts to find the best match of the block of interest within a large search window in the next frame. Our investigation led to the development of predictive block matching, a method developed to track motion by exploiting the advantages of block‐motion estimation and adaptive block matching (27).…”

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.…”

mentioning

confidence: 99%

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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Quantifying motion in video recordings of neonatal seizures by feature trackers based on predictive block matching

Cited by 3 publications

References 8 publications

Automatic Detection of Epileptic Seizures in Neonatal Intensive Care Units Through EEG, ECG and Video Recordings: A Survey

Automatic Detection of Epileptic Seizures in Neonatal Intensive Care Units Through EEG, ECG and Video Recordings: A Survey

Video and audio processing in paediatrics: a review

Computerized Motion Analysis of Videotaped Neonatal Seizures of Epileptic Origin

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