Detection of Discontinuous Patterns in Spontaneous Brain Activity of Neonates and Fetuses

Eswaran, Hari; Haddad, Naim; Rose, Douglas F.; Preißl, Hubert; Wilson, James D.; Lowery, Curtis L.; Govindan, Rathinaswamy B.

doi:10.1109/tbme.2009.2028875

Cited by 15 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The discontinuous patterns detected by the algorithm in the ICs with the highest SNR in neonates and fetuses were also confirmed by the neurophysiologists by visual inspection. In addition, the burst duration (BD) and IBI computed by the HP approach [8] for the discontinuous patterns are well within the range of the reported values [12]. …”

Section: Resultssupporting

confidence: 63%

“…In our approach, ICA has been performed in the data corresponding to sensors of a small subset (approximately the size of fetal head) for 1-min duration. The independent components (ICs) obtained are further investigated for the presence of discontinuous brain patterns based on the Hilbert phase (HP) analysis [8]. The ICs identified as discontinuous brain patterns are populated in the sensors around the centroid of the sensor subset.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Localization of spontaneous magnetoencephalographic activity of neonates and fetuses using independent component and Hilbert phase analysis

Eswaran

Preißl

Wilson

et al. 2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

Self Cite

View full text Add to dashboard Cite

The fetal magnetoencephalogram (fMEG) is measured in the presence of large interference from maternal and fetal magnetocardiograms (mMCG and fMCG). These cardiac interferences can be attenuated by orthogonal projection (OP) technique of the corresponding spatial vectors. However, the OP technique redistributes the fMEG signal among the channels and also leaves some cardiac residuals (partially attenuated mMCG and fMCG) due to loss of stationarity in the signal. In this paper, we propose a novel way to extract and localize the neonatal and fetal spontaneous brain activity by using independent component analysis (ICA) technique. In this approach, we perform ICA on a small subset of sensors for 1-min duration. The independent components obtained are further investigated for the presence of discontinuous patterns as identified by the Hilbert phase analysis and are used as decision criteria for localizing the spontaneous brain activity. In order to locate the region of highest spontaneous brain activity content, this analysis is performed on the sensor subsets, which are traversed across the entire sensor space. The region of the spontaneous brain activity as identified by the proposed approach correlated well with the neonatal and fetal head location. In addition, the burst duration and the inter-burst interval computed for the identified discontinuous brain patterns are in agreement with the reported values.

show abstract

Section: Resultssupporting

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Localization of spontaneous magnetoencephalographic activity of neonates and fetuses using independent component and Hilbert phase analysis

Eswaran

Preißl

Wilson

et al. 2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

Self Cite

View full text Add to dashboard Cite

show abstract

“…In our earlier works, we have shown that the phase slip metric can be used to distinguish the sensors containing the fetal brain signalsembedded in 151-SQUID sensors [15, 17]. Further, using this metric we could successfully identify the spontaneous brain patterns of the fetuses and newborns [18]. Based on these two preliminary works, we believe that this approach can be used as an equivalent to SNR as it distinguishes the signal of interest from the background activity.…”

Section: Resultsmentioning

confidence: 99%

Decrement of uterine myometrial burst duration as a correlate to active labor: A Hilbert phase approach

Govindan

Furdea²,

Murphy

et al. 2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

Self Cite

View full text Add to dashboard Cite

We propose a novel approach based on Hilbert phase to identify the burst in the uterine myometrial activity. We apply this approach to 24 serial magnetomyographic signals recorded from four pregnant women using a 151 SQUID array system. The bursts identified with this approach are evaluated for duration and are correlated with the gestational age. In all four subjects, we find a decrease in the duration of burst as the subject approaches active labor. As was shown in animal studies, this result indicates a faster conduction time between the muscle cells which activate a larger number of muscle units in a synchronous manner.

show abstract

“…On the other hand, the smoothness assumption (A6) might not be appropriate in some cases. For example, discontinuity of the brain activity is common in certain applications (Vairavan et al, 2009), leading to discontinuous µ(u) and Σ(u) which are usually modeled as piecewise-continuous functions. This gives rise to a similar problem as ours where the aim is to identify the number of the discontinuous points and their locations.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Dynamic linear discriminant analysis in high dimensional space

Jiang¹,

Chen²,

Leng³

2020

Bernoulli

View full text Add to dashboard Cite

High-dimensional data that evolve dynamically feature predominantly in the modern data era. As a partial response to this, recent years have seen increasing emphasis to address the dimensionality challenge. However, the non-static nature of these datasets is largely ignored. This paper addresses both challenges by proposing a novel yet simple dynamic linear programming discriminant (DLPD) rule for binary classification. Different from the usual static linear discriminant analysis, the new method is able to capture the changing distributions of the underlying populations by modeling their means and covariances as smooth functions of covariates of interest. Under an approximate sparse condition, we show that the conditional misclassification rate of the DLPD rule converges to the Bayes risk in probability uniformly over the range of the variables used for modeling the dynamics, when the dimensionality is allowed to grow exponentially with the sample size. The minimax lower bound of the estimation of the Bayes risk is also established, implying that the misclassification rate of our proposed rule is minimax-rate optimal. The promising performance of the DLPD rule is illustrated via extensive simulation studies and the analysis of a breast cancer dataset.

show abstract

Detection of Discontinuous Patterns in Spontaneous Brain Activity of Neonates and Fetuses

Cited by 15 publications

References 9 publications

Localization of spontaneous magnetoencephalographic activity of neonates and fetuses using independent component and Hilbert phase analysis

Localization of spontaneous magnetoencephalographic activity of neonates and fetuses using independent component and Hilbert phase analysis

Decrement of uterine myometrial burst duration as a correlate to active labor: A Hilbert phase approach

Dynamic linear discriminant analysis in high dimensional space

Contact Info

Product

Resources

About