Effective implementation of time-frequency matched filter with adapted pre and postprocessing for data-dependent detection of newborn seizures.

Neonatal EEG seizures often manifest as nonstationary and multicomponent signals, necessitating analysis in the time-frequency (TF) domain. This paper presents a novel neonatal seizure detector based on effective implementation of the TF matched filter. In the detection process, the TF signatures of EEG seizure are extracted to construct the TF templates used by the matched filter. Matching pursuit (MP) decomposition and narrowband filtering are proposed for the reduction of artifacts prior to seizure detection. Geometrical correlation is used to consolidate the multichannel detections and to reduce the number of false detections due to remnant artifacts. A data-dependent threshold is defined for the classification of EEG. Using 30 newborn EEG records with seizures, the classification process yielded an overall detection accuracy of 92.4% with good detection rate (GDR) of 84.8% and false detection rate of 0.36FD/h. Better detection performance (accuracy >95%) was recorded for relatively long EEG records with short seizure events.

Accelerometer-based fetal movement detection.

Monitoring fetal wellbeing is a compelling problem in modern obstetrics. Clinicians have become increasingly aware of the link between fetal activity (movement), well-being, and later developmental outcome. We have recently developed an ambulatory accelerometer-based fetal activity monitor (AFAM) to record 24-hour fetal movement. Using this system, we aim at developing signal processing methods to automatically detect and quantitatively characterize fetal movements. The first step in this direction is to test the performance of the accelerometer in detecting fetal movement against real-time ultrasound imaging (taken as the gold standard). This paper reports first results of this performance analysis.

Kalman filter-based time-varying cortical connectivity analysis of newborn EEG.

Multivariate Granger causality in the time-frequency domain as a representation of time-varying cortical connectivity in the brain has been investigated for the adult case. This is, however, not the case in newborns as the nature of the transient changes in the newborn EEG is different from that of adults. This paper aims to evaluate the performance of the time-varying versions of the two popular Granger causality measures, namely Partial Directed Coherence (PDC) and direct Directed Transfer Function (dDTF). The parameters of the time-varying AR, that models the inter-channel interactions, are estimated using Dual Extended Kalman Filter (DEKF) as it accounts for both non-stationarity and non-linearity behaviors of the EEG. Using simulated data, we show that fast changing cortical connectivity between channels can be measured more accurately using the time-varying PDC. The performance of the time-varying PDC is also tested on a neonatal EEG exhibiting seizure.

Detection of neonatal EEG seizure using multichannel matching pursuit.

It is unusual for a newborn to have the classic "tonic-clonic" seizure experienced by adults and older children. Signs of seizure in newborns are either subtle or may become clinically silent. Therefore, the electroencephalogram (EEG) is becoming the most reliable tool for detecting neonatal seizure. Being non-stationary and multicomponent, EEG signals are suitably analyzed using time-frequency (TF) based methods. In this paper, we present a seizure detection method using a new measure based on the matching pursuit (MP) decomposition of EEG data. Signals are represented in the TF domain where seizure structural characteristics are extracted to form a new coherent TF dictionary to be used in the MP decomposition. A new approach to set data-dependent thresholds, used in the seizure detection process, is proposed. To enhance the performance of the detector, the concept of areas of incidence is utilized to determine the geometrical correlation between EEG recording channels.

Multichannel-based newborn EEG seizure detection using time-frequency matched filter.

In recent years, much effort has been made toward developing computerized methods to detect seizures. In adults, the clinical signs of seizures are well defined and easily recognizable. But in newborns, these signs are either subtle or completely absent. For this reason, the electroencephalogram (EEG) has been the most dependable tool used for detecting seizures in newborns. Considering the non-stationary and multicomponent nature of the EEG signals, time-frequency (TF) based methods were found to be very suitable for the analysis of such signals. Using TF representation of EEG signals allows extracting TF signatures that are characteristic of EEG seizures. In this paper we present a TF method for newborn EEG seizure detection using a TF matched filter. The threshold used to distinguish between seizure and non-seizure is data-dependent and is set using the EEG background. Multichannel geometrical correlation, based on a concept of incidence matrix, was utilized to further enhance the performance of the detector.