|Title: ||Neonatal EEG Signal Processing|
|Other Titles: ||Neonatale EEG signaal processing|
|Authors: ||Matic, Vladimir|
|Issue Date: ||26-Mar-2015 |
|Abstract: ||Within this thesis the automated algorithms for the EEG-based assessment of the brain f unctioning of asphyxiated infants have been develo ped. Their goal is to assess the severit y of the hypoxic brain injuries in the asphyxiated infants. This estimate will assist ¨clinicians to promptly diagnose and to guide furt her treatment decisions. Three main contribut ions are developed. First, an algor ithm that detects dynamic interburs t intervals has been extended with a post pro cessing step that removes dubious and uncerta in detections. In this way, a trustworthy alg orithm has been developed. Second, we explored qua ntification of long-range temporal behavior o f the neonatal EEG. We explored (Multifractal ) Detrended Fluctuation Analysis and, further on, we proposed four metrics that show¨ potential to differentiate the EEG background grad es. Third, an automated method for the backgr ound EEG classification has been developed. As the first step, it maps shorter, segme nted, EEG segments’ features into segments’ feature space, thereby creati ng a 3D distribution. Next, this 3D structure ¨is represented as a data tensor that is used ¨for further dimensionality reduction and rob ust classification. The algorithms and their¨ performances have been verified by expert EEG readers, demonstrating its potential. In add ition, the efficient visualization devel oped within our project NeoGuard will enable fast insight into the algorithms’ output and, hopefully, very soon be
implement ed in the NICUs.
The Neonatal Intensive Care Unit (NICU) is the very busy medical unit where preterm and critically ill full-term newborn babies are admitted. Worldwide, there are 15 million preterm deliveries (n=3642; Flanders, in 2008) from which one million neonates do not survive annually. In addition, another million of neonatal deaths are caused by hypoxia (n=449 (from mild to extremely severe hypoxia); Flanders, in 2008). Commonly there is a narrow window of opportunity that enables clinicians to intervene with neuroprotective therapies and medications. To promptly diagnose the level of brain injuries, continuous multi-channel electroencephalography (cEEG) can be used in NICUs as the optimal bed-side monitoring utility. However, a high level of expertise is required for the interpretation of the complex EEG patterns, and most NICUs, even the very large ones, are lacking this invaluable and expensive support. For instance, after the initial recruitment for hypothermia treatment, further cEEG monitoring generates approximately 100 hours of EEG data. Therefore, even when the expert’s support is available, visual interpretation of cEEG is very laborious, subjective and continuous expert support is required for several days/nights. Additionally, due to the high costs of this support, not every neonate that requires cEEG examination will receive this monitoring. Therefore, neonatologists often use a simplified alternative: amplitude integrated EEG (aEEG), which provides insight into brain functioning using compressed 2-channels EEG. Compared to cEEG, this utility cannot detect milder brain injuries neither the majority of relevant clinical patterns. In addition, the compressed aEEG form proves to be very vulnerable to artefacts, thereby resulting in potential erroneous interpretations. As a result, the latest clinical recommendations advise the use of multi-channel EEG, and express the need for automated software to support the interpretation of cEEG monitoring of critically ill babies. Within this thesis the automated algorithms for the EEG-based assessment of the brain functioning have been developed. Their goal is to assess the severity of the hypoxic brain injuries in the asphyxiated infants. This estimate will assist clinicians to promptly diagnose and to guide further treatment decisions. Three main contributions are developed. First, an algorithm that detects dynamic interburst intervals has been extended with a post processing step that removes dubious and uncertain detections. In this way, a trustworthy algorithm has been developed. Second, we explored quantification of long-range temporal behavior of the neonatal EEG. We explored (Multifractal) Detrended Fluctuation Analysis and, further on, we proposed four metrics that show potential to differentiate the EEG background grades. Third, an automated method for the background EEG classification has been developed. As the first step, it maps shorter, segmented, EEG segments’ features into segments’ feature space, thereby creating a 3D distribution. Next, this 3D structure is represented as a data tensor that is used for further dimensionality reduction and robust classification. The algorithms and their performances have been verified by expert EEG readers, demonstrating its potential. In addition, the efficient visualization developed within our project NeoGuard will enable fast insight into the algorithms’ output and, hopefully, very soon be implemented in the NICUs.
|Publication status: ||published|
|KU Leuven publication type: ||TH|
|Appears in Collections:||ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics|