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Biomedical Signal Processing and Control
IF
2.94
Papers
1627
Papers 1728
1 page of 173 pages (1,728 results)
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#1Mimma Nardelli (UniPi: University of Pisa)H-Index: 9
#2Antonio Lanata (UniPi: University of Pisa)H-Index: 25
Last.Enzo Pasquale Scilingo (UniPi: University of Pisa)H-Index: 33
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Abstract This work describes a novel signal quality index (SQI), i.e. higher-order-statistics-SQI (hosSQI), for the real-time evaluation of electrocardiogram (ECG) recording quality. The hosSQI formula combines two already known SQIs, kurtosis (kSQI) and skewness (sSQI), exploiting the related properties to improve their performance. We validated hosSQI using 1000 human pre-labelled twelve-lead ECGs and compared its performance with the state-of-the-art indexes in the literature. Our index outpe...
#1Pankaj Kandhway (National Institute of Technology, Patna)H-Index: 1
#2Ashish Kumar Bhandari (National Institute of Technology, Patna)H-Index: 15
Last.Anurag Singh (International Institute of Information Technology)
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Abstract In this paper, a novel krill herd (KH) based optimized contrast and sharp edge enhancement framework is introduced for medical images. Plateau limit and fitness function are proposed in this paper to achieve the best-enhanced image. A new plateau limit is applied to clip the histogram using minimum, maximum, mean, and median of the histogram with a tunable parameter. The residue pixels are reallocated to the relative vacancy available on histogram bins. This method explores KH meta-heur...
#1Jan WerthH-Index: 1
#2Mustafa Radha (Philips)H-Index: 4
Last.X Xi Long (Philips)H-Index: 11
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Abstract Preterm infant neuronal development is related to the distribution of their sleep states. The distribution changes throughout development. Automated sleep state monitoring can become a powerful aid for development monitoring in preterm infants. Three datasets including 34 preterm infants and a total of 18,018 30 s manually annotated sleep intervals (sleep-epochs) were analyzed in this study. The annotation of sleep states includes active sleep, quiet sleep, intermediate sleep, wake, and...
#1Bei Wang (ECUST: East China University of Science and Technology)H-Index: 12
#2Yudong Sun (ECUST: East China University of Science and Technology)
Last.Xingyu Wang (ECUST: East China University of Science and Technology)H-Index: 28
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Abstract In this study, a Bayesian classifier with the multivariate distribution based on the D-vine copula model is developed and evaluated for the awake/drowsiness interpretation during the power nap. The objective is to consider the correlation among the features into the automatic classification algorithm. A power nap is a short sleep process, which is commonly considered as a supplement to the insufficient overnight sleep. It may involve the states of awake and drowsiness. Neurophysiologica...
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