Detection of artifacts from high energy bursts in neonatal EEG.

Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the feature subset producing highest classification accuracy. The suggested feature based classification method is executed using our recorded neonatal EEG dataset, consisting of burst and artifact segments. We obtain 78% sensitivity and 72% specificity as the accuracy measures. The accuracy obtained using the proposed method is found to be about 20% higher than that of the reference approaches. Joint use of the proposed method with our previous work on burst detection outperforms reference methods on simultaneous burst and artifact detection. As the proposed method supports detection of a wide range of artifact patterns, it can be improved to incorporate the detection of artifacts within other seizure patterns and background EEG information as well.

A robust method for ventriculomegaly detection from neonatal brain ultrasound images.

Ventriculomegaly is the most commonly detected abnormality in neonatal brain. It can be defined as a condition when the human brain ventricle system becomes dilated. This in turn increases the intracranial pressure inside the skull resulting in progressive enlargement of the head. Sometimes it may also cause mental disability or death. For these reasons early detection of ventriculomegaly has become an important task. In order to identify ventriculomegaly from neonatal brain ultrasound images, we propose an automated image processing based approach that measures the anterior horn width as the distance between medial wall and floor of the lateral ventricle at the widest point. Measurement is done in the plane of the scan at the level of the intraventricular foramina. Our study is based on neonatal brain ultrasound images in the midline coronal view. In addition to ventriculomegaly detection, this work also includes both cross sectional and longitudinal study of anterior horn width of lateral ventricles. Experiments were carried out on brain ultrasound images of 96 neonates with gestational age ranging from 26 to 39 weeks and results have been verified with the ground truth provided by doctors. Accuracy of the proposed scheme is quite promising.