- Publikační typ
- abstrakt z konference MeSH
The electroencephalogram (EEG) is a cornerstone of neurophysiological research and clinical neurology. Historically, the classification of EEG as showing normal physiological or abnormal pathological activity has been performed by expert visual review. The potential value of unbiased, automated EEG classification has long been recognized, and in recent years the application of machine learning methods has received significant attention. A variety of solutions using convolutional neural networks (CNN) for EEG classification have emerged with impressive results. However, interpretation of CNN results and their connection with underlying basic electrophysiology has been unclear. This paper proposes a CNN architecture, which enables interpretation of intracranial EEG (iEEG) transients driving classification of brain activity as normal, pathological or artifactual. The goal is accomplished using CNN with long short-term memory (LSTM). We show that the method allows the visualization of iEEG graphoelements with the highest contribution to the final classification result using a classification heatmap and thus enables review of the raw iEEG data and interpret the decision of the model by electrophysiology means.
- MeSH
- artefakty MeSH
- datové soubory jako téma MeSH
- deep learning * MeSH
- elektroencefalografie klasifikace přístrojové vybavení metody MeSH
- lidé MeSH
- ROC křivka MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- pozorovací studie MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
- validační studie MeSH
False alarms in intensive care units represent a serious threat to patients. We propose a method for detection of five live-threatening arrhythmias. It is designed to work with multimodal data containing electrocardiograph and arterial blood pressure or photoplethysmograph signals. The presented method is based on descriptive statistics and Fourier and Hilbert transforms. It was trained using 750 records. The method was validated during the follow-up phase of the CinC/Physionet Challenge 2015 on a hidden dataset with 500 records, achieving a sensitivity of 93% (95%) and a specificity of 87% (88%) for real-time (retrospective) files. The given sensitivity and specificity resulted in score of 81.62 (84.96) for real-time (retrospective) records. The presented method is an improved version of the original algorithm awarded the first and the second prize in CinC/Physionet Challenge 2015.
- MeSH
- algoritmy * MeSH
- elektrokardiografie přístrojové vybavení MeSH
- falešně pozitivní reakce MeSH
- fotopletysmografie přístrojové vybavení MeSH
- jednotky intenzivní péče * MeSH
- klinické alarmy * MeSH
- krevní tlak MeSH
- lidé MeSH
- monitorování fyziologických funkcí přístrojové vybavení MeSH
- počítačové zpracování signálu * MeSH
- srdeční arytmie diagnóza patofyziologie MeSH
- strojové učení MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
