Development of a Neonatal EEG Monitor for Automated Brain Analysis
A neonatal intensive care unit (NICU) is a specialized unit for intensive care of the critically ill or premature newborn babies. This unit is equipped with incubators and different bedside systems to monitor and nurse the infants for several days or weeks. In the last decade, the electroencephalogram (EEG) has been considered as an important monitoring system in the NICUs. Different types of rhythms and patterns which manifest in the EEG signals can be related to brain development, sleep stages, and several brain abnormalities such as epileptic seizures. However, to detect these patterns, to differentiate them from artifacts, and to diagnose abnormalities, special expertise is required, which is not available around the clock. Furthermore, this analysis is very expensive, labor-intensive, and time-consuming. Thus, many NICUs only monitor limited number of neonates from whom brain monitoring is urgently needed. Therefore, developing a brain monitor equipped with automated algorithms and alarms alleviates the workload, decreases the costs, and gives the possibility to monitor more neonates simultaneously.
In order to develop such a neonatal brain monitor, this project focuses on two objectives: 1) developing new algorithms, or improving existing ones, for automated EEG analysis, 2) practical implementation of the algorithms for clinical use. For the former one, the research focuses on improving neonatal seizure detection, as well as neonatal sleep stage classification. To this end, first, the available recordings are characterized and the inter-rater agreement of several human EEG readers is studied. Then, a multi-stage seizure detector, which is an extension of a previously developed heuristic algorithm, is proposed. This multi-stage detector uses a machine learning technique as a post-processor and a novel set of features to discriminate seizures from artifacts. Next, an adaptive learning method is added to the detector in order to increase the reliability and accuracy of the algorithm through the use of an alarming system. Furthermore, two deep convolutional neural networks are proposed and applied to detect neonatal seizures and classify sleep stages. These networks are able to learn the required features automatically and consequently can classify raw multi-channel EEG segments. Besides, a new framework for measuring the performance of algorithms with a multi-rated database and various degrees of high inter-rater disagreement is proposed. In this framework, 6 commonly used performance metrics - 3 epoch-based and 3 event-based metrics - are fuzzily extended and tested on the multi-rated seizure database and the results are compared with a majority voting technique. Finally, a learning platform is also developed based on the multi-rated seizure database annotated by 4 independent expert EEG readers.
For the second objective, several algorithms, developed by different researchers from our group, are implemented in a real-time and cloud-based monitor. These monitors can facilitate the clinical validation of the developed algorithms. The core of the real-time monitor includes different algorithms translated into a low-level programming language. On one end, it connects to an EEG recording machine wirelessly, and on the other end, it communicates with the developed graphical user interface to monitor the signals and show the alarms. The cloud-based monitor has also different modules for uploading the EEG recordings, processing and storing the data on the server, and displaying the signals and alarms on a secure platform on the web. In the cloud-based system, the algorithms can run in their native languages which facilitates validation of newly developed algorithms in several centers.