This work deals with computer aided monitoring of electrical brain signals to detect disease-related patterns. Severe neurological disorders can trigger unusually strong firing of brain cells that distinguishes clearly from normal brain activity. A well-known example of such a disease is epilepsy. During an epileptic seizure, fast repeating electrical discharges on the head surface are often measurable. Epileptic seizures usually occur rarely or unnoticed by patients during night wherefore considerable effort is needed to properly evaluate and treat patients. Besides epilepsy, inflammatory brain diseases such as encephalopathies or traumatic brain injuries can trigger different types of patterns with repetitive discharges and seizures. The severity of these diseases and injuries often require intensive medical treatment and continuous monitoring of neurological activity. ^The automatic detection of epileptic seizures and repetitive patterns in measurements of electrical brain signals is a central part of this work. Currently, diagnostic in neurological patients involves a wide spectrum of methods and tools. In addition to clinical observations the objective quantification of the brain status is the primary step in diagnosis. Imaging methods such as magnetic resonance imaging (MRI) are able to generate a snapshot of the brain morphology. To evaluate the brain activity over time the electroencephalography (EEG) is used. The EEG is able to continuously record the electrical activity of the cortex which makes this method to a central element in the diagnosis of neurological patients. Manual evaluation of EEG is done by visual inspection of a graphical representation of these signals. Specially trained medical staff interpret the EEG in 10 to 15 second sections throughout the whole recording period. ^With a usual recording time of 4 days more than 34,000 EEG pages need to be evaluated, which requires a considerably amount of time for EEG interpretation. The correct evaluation of the EEG curves requires a high degree of experience in order to avoid misinterpretations. Despite intensive training, different interpretations of an EEG by different reviewers and differences in the evaluation of an EEG by the same reviewer at different time points are fundamental problems. Further, distortions from electrical signals of muscles (electromyography, EMG) can overlay with the EEG, which may lead to misinterpretations. In order to make medical findings comparable in the context of international studies, standardized descriptions of EEG patterns have been developed which are, however, require experienced staff to be assigned correctly. ^If the EEG is used for real-time monitoring of patients with serious illnesses, the evaluation of the EEGs must be carried out promptly in order to achieve an improved treatment. However, this places extremely high demands on the staff. Many of the mentioned problems in manual EEG analysis can be addressed by using computer-aided evaluation methods. A computer algorithm is able to reduce the cost and time of analysis and provides perfect repeatability of the result. Despite these obvious advantages, correct automatic analysis of the highly complex EEG signal is an unsolved problem that prevents the widespread use of such computer algorithms. In this work novel computer algorithms for the automatic interpretation and monitoring of EEG signals are presented that were published in eight papers of highly-ranked peer reviewed journals. The overall aim is to make EEG evaluation considerably easier by automatically marking important time points in real-time. ^The focus is on the detection of epileptic seizures and patterns with repetitive discharges. Although the EEG is the primary data source for the algorithms, EMG interferences have to be treated adequately in order to achieve the highest possible precision. To raise sensitivity of the automatic seizure detection algorithm even further the electrocardiography (ECG) signal was additionally evaluated to find seizure related activity. The use of computer algorithms for real-time monitoring of EEG activity is intended to improve treatment of patients and to increase patient safety. A fundamentally new approach for the detection of EEG discharges was developed in this work that can be applied to a wide range of pathological patterns. By combining individual discharges into groups that are extended spatially and over time, different types of patterns are modelled. Important measures such as frequency and amplitude can then be found by simply averaging the group elements. ^Furthermore, the temporal progression of patterns is used to quantify changes. The timedomain algorithm therefore creates the basis for analysis of seizures and other EEG patterns. The 7 classification algorithms that utilize this information then allow the detection of seizures as well as the quantification of EEG patterns in intensive care patients. The results of the computer algorithms can be read and interpreted efficiently by means of a newly developed graphical visualization. The clinical validation of computer algorithms is an essential part of this work. The quality of the algorithms can only be determined with statistical significance by diagnostic studies including a high number of patients. Results of the algorithms were compared to manual annotations from experts to measure sensitivity and specificity. In this work, four multi-center studies and some smaller preliminary investigations were carried out for different medical questions and algorithms. ^In total, EEGs of 621 patients from 6 centers in Europe and the USA were used for validation. The results show that seizure alarming is possible with a sensitivity of 81% and a false alarm rate of 7 false alarms per day. A time delay of only 3 seconds was measured from the seizure pattern to the alarm. In the detection of seizures based on existing EEG files, the algorithms achieved a high sensitivity of 86% which is required for efficient evaluation. Special epilepsy types such as temporal lobe epilepsy showed a sensitivity of 94%. The detection of different patterns in the EEG of intensive care patients yielded in sensitivities between 85% and 93% and specificities in the range of 90% and 96%. Improved treatment of patients as well as a reduction in workload for medical staff are thus possible. In the future, mobile EEG systems in the outpatient setting can represent a further significant improvement in diagnostic. ^Patients with rarely occurring seizures can save themselves from protracted hospital stays. Moreover, the use of mobile sleep diagnostic including EEG can increase the quality of life and save costs. At present, mobile EEG systems are still suffering from problems such as high time expenditure for the attachment of the electrodes and complex wiring. This results in low patient acceptance and prevents their widespread use. As soon as these difficulties are solved computer algorithms can be utilized to evaluate such mobile EEG systems. A large number of medical applications are then conceivable in which the quality of the algorithms will play a central role.