In the Netherlands the preterm birth rate is around 7%. The problematic part is that these preterm infants make up for 72% of all deaths during or shortly after birth. To provide a way to aid these infants the following question was stated by the doctor: "turn on a light in the Neonatal Intensive Care Unit when the preterm infant’s development is not as expected". This question was interpreted as following: first of all, find an informative signal present for preterm infants which can provide knowledge on the state of development. Secondly, find a way to quantify this signal through measurement and provide a way to automate the detection. Based on literature the delta brush was selected as a promising waveform in the neonatal EEG, which can be used to follow the maturation of the brain. The spectral behavior of the EEG is used to identify the specific waveforms, but this analysis of the EEG is still mainly based on visual analysis, which is a subjective and time-consuming task. A way to quantify and automate this process was found in the Continuous Wavelet Transform (CWT). Using the Morlet mother wavelet, a time-frequency distribution of the EEG was calculated. The CWT’s redundant behavior was found to provide a finer sampling which could be used to reinforce specific waveform behavior. A third goal was to investigate the troubles that new innovations face in being accepted by the medical field. This was found to be related to the building of trust. The key elements for building trust in this work were identified as: interpretability of the results, transparency of the process, and ease of operation. These elements will be taken into account in the proposed solution. A set of high-density EEG measurements of preterm infants was acquired through collaboration with the the Academic Hospital Antwerp and the Erasmus Medical Center. This high-density consists of 19 electrode channels instead of the more commonly used 11 electrode channels. The measurement was done at 30 weeks postmenstrual age (PMA). These measurements were accompanied with annotation for the waveform of interest. An interactive application for visualization of the EEG data and wavelet coefficients was designed in MATLAB. This application can run standalone and can be used to visualize the detector’s results for the end user. Based on the inherent characteristics of the target waveform two detection features were chosen. The first feature is a measure of the ratio between peak in the high-frequency region (3.3-40 Hz) and the low-frequency region (0.1-3.3 Hz). The second feature is an energy calculation based on a windowed Squared Energy Operator (SEO). A triple-threshold multi-channel detector, based on these features, was initialized and performance was tested over the full threshold range. A Precision-Recall (PRC) curve was provided showing the total solution space for both feature thresholds. Based on the point closest to (100,100) in the training set PRC, a single threshold was selected for validation with the validation datasets. This has resulted in a detector with a sensitivity of 73.66% and a precision of 21.71%. This performance is expected to increase with cross-validation, due to lower quality EEG datasets in the validation set. The differences in performance can be explained by the uncertainties in the exact delta brush behavior and the incomplete annotation set, which causes possible detected delta brush activity to be labeled as false positives. In the end an informative waveform was found and a suitable quantification method has been proposed. A delta brush detector has been designed based on the inherent characteristics of the waveform. A standalone interactive application has been developed to visualize the EEG signal in combination with its time-frequency behavior. The application can also be used in combination with the detector to provide insight into the decision-making process and to visualize the detector’s output.