On The Automation of Medical Knowledge and Medical Decision Support Systems

Moret‐Bonillo, Vicente; Fernández-Varela, Isaac; Hernández-Pereira, Elena; Álvarez-Estévez, Diego; Perlitz, Volker

doi:10.1007/978-3-319-67513-8_9

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Experiments conducted on 26 patients reported a sensitivity of 0.81 and a specificity of 0.88 with an error of 0.13. More recently, Isaac Fernández-Varela et al [8] combined various signal analysis solutions to identify relevant arousal patterns with special emphasis on robustness and artifact tolerance. Experiments conducted on 22 patients reported precision of 0.86 and F1 score of 0.79.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Target Regions of Respiratory Effort-Related Arousals Using Recurrent Neural Networks

Prainsson

Ragnarsdóttir

Kristjansson

et al. 2018

2018 Computing in Cardiology Conference (CinC)

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We present a method for classifying target sleep arousal regions of polysomnographies. Time-and frequencydomain features of clinical and statistical origins were derived from the polysomnography signals and the features fed into a Bidirectional Recurrent Neural Network, using Long Short-Term Memory units (BRNN-LSTM). The predictions of five recurrent neural networks, trained using different features and training sets, were averaged for each sample, to yield a more robust classifier. The proposed method was developed and validated on the PhysioNet Challenge dataset which consisted of a training set of 994 subjects and a hidden test set of 989 subjects. Five-fold cross-validation on the training set resulted in an area under precision-recall curve (AUPRC) score of 0.452, an area under receiver operating characteristic curve (AUROC) score of 0.901 and intraclass correlation ICC(2,1) of 0.59. The classifier was further validated on the PhysioNet Challenge test set, resulting in an AUPRC score of 0.45.

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Section: Introductionmentioning

confidence: 99%