Comput. Biol. Med. - Detection of artifacts from high energy bursts in neonatal EEG.


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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.

Resumo Limpo

detect noncerebr activ artifact intermix within background eeg essenti discard subsequ pattern analysi problem much harder neonat eeg background eeg contain spike wave rapid fluctuat amplitud frequenc exist artifact detect method most limit detect subset artifact ocular muscl power line artifact method integr differ modul detect one specif categori artifact furthermor refer approach implement test adult eeg record direct applic method neonat eeg caus perform deterior due greater pattern variat inher complex method detect wide rang artifact categori neonat eeg thus requir time method specif enough preserv background eeg inform current studi describ featur base classif approach detect repetit generat ecg emg puls respir etc transient generat eye blink eye movement patient movement etc artifact focus artifact detect within high energi burst pattern instead detect artifact within complet background eeg wide pattern variat object find true burst pattern can later use identifi burstsuppress bs pattern common observ newborn seizur select artifact detect proven sensit artifact specif burst compar exist artifact detect approach appli complet background eeg sever time domain frequenc domain statist featur featur generat wavelet decomposit analyz model propos biclassif burst artifact segment featur select method also appli select featur subset produc highest classif accuraci suggest featur base classif method execut use record neonat eeg dataset consist burst artifact segment obtain sensit specif accuraci measur accuraci obtain use propos method found higher refer approach joint use propos method previous work burst detect outperform refer method simultan burst artifact detect propos method support detect wide rang artifact pattern can improv incorpor detect artifact within seizur pattern background eeg inform well

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