After cardiac arrest and resuscitation, part of the patients will still be in a coma and treated at an intensive care unit. Their prospects are uncertain. What is needed to get an outcome prediction that is reliable? Researchers of the University of Twente and the ‘Medisch Spectrum Twente’ hospital developed a learning network that is capable of interpreting EEG-patterns. Artificial Intelligence (AI) can give a reliable outcome prediction, and thus forms a valuable extra source of information for decision-making.
In The Netherlands, about one third of the people that had a cardiac arrest followed by resuscitation, will have to be treated at the ICU. These patients, about 7000 each year, are in a coma. More than half of them will not regain consciousness. The family will want to know what the prospects are and, if their relative regains consciousness, what will be the quality of life. The question ‘does further treatment make sense?’ can only be answered after careful analysis of the situation. One of the options, now, is the SEPP-test; if an electrical signal applied to the wrist does not reach the brain, this is no good news. The electrical signals of the brain, the EEG pattern measured via electrodes on the head, give a lot of information as well. Analysis of EEG using artificial intelligence gives a very accurate outcome predicition, as the researchers now show in their paper. Twelve hours after resuscitation, the learning network is capable of predicting a good outcome with 58 percent accuracy and a bad outcome with 48 percent. This is a better performance than the trained eye of a neurologist. Both computer and human, however, still have a category ‘I don’t know’, in situations the EEG data are not specific enough.
The first author, Marleen Tjepkema, already made a plea for using EEG in the outcome prediction, in her PhD thesis in 2014 as a UT Technical Medicine graduate. She and her colleagues now take this an important step ahead by introducing automated interpretation of the EEG scan. The deep learning network has been trained using 600 EEG patterns, it did not get any hints on what to look at. After that, it was fed with 300 EEG patterns to see how it performed in giving a prediction. Neurologists have to look at hundreds of EEG’s as well, as part of their training. An experienced neurologist will guide them and point out what they have to look at. Still, the EEG-patterns are so information-rich that the computer outperforms the human judgment.
Once trained, the network will be capable of judging the EEG very fast, well within a second. The researchers expect that this adds valuable information to human judgment. One of the other advantages is its flexibility, an analysis can be made any time of the day. Using the new technology at ICU’s will have to make clear if the ‘intensivist’ also sees as a valuable tool. One of the next steps in this research is having a closer look at the learning strategy of the network, making it more transparent than a black box approach. For this, the neurophysiologists collaborate with computer scientists and mathematicians of the University of Twente.
Source: University of Twente