Epileptic seizures show a certain degree of rhythmicity, a feature of heuristic and practical interest. In this paper, we introduce a simple model of this type of behavior, and suggest a measure for detecting and quantifying it. To evaluate our method, we develop a set of test segments that incorporate rhythmicity features, and present results from the application of this measure to test segments. We then analyze electrocorticogram segments containing seizures, and present two examples. Finally, we discuss the similarity of our method to techniques for detecting unstable periodic orbits in chaotic time series. © 2008 American Institute of Physics. ͓DOI: 10.1063/1.2973817͔Epilepsy is the second most common neurological disorder, second only to stroke. Epileptic seizures often occur without warning, may be associated with loss of consciousness and violent tremors, and significantly degrade quality of life for those suffering from epilepsy. The brain activity that gives rise to seizures can be monitored through electrodes on the scalp or in direct contact with the brain. This activity shows certain patient-specific stereotypical features, which may be detectable before the onset of behavioral manifestations, and this activity frequently appears more "rhythmic" than background brain activity. These rhythmic signals frequently consist of repetitions of similar waveform patterns. In this paper, we describe a technique for detecting this type of rhythmic signal, which is derived from a time series analysis method for detecting unstable periodic orbits. Accurate detection of rhythmic signals, a subset of the vast variety of anomalous waveforms associated with epilepsy, may provide valuable information to benefit and improve implantable medical devices being developed to detect and disrupt epileptic signals.