Radar-based respiration monitoring has been increasingly popular among researchers in biomedical fields during the last decades since it is a contactless monitoring technique. It is very convenient for subjects because it does not impose any restrictions on subjects or require their cooperation. Meanwhile, recognizing alternations in respiratory patterns is an important early clue of the diagnosis of several cardiorespiratory diseases. Thus, a study of biomedical radar-based respiration monitoring and respiratory pattern classification is carried out in this thesis. Radar-based respiration monitoring technology has a shortcoming that the collected respiratory signal will be easily distorted by the body movement of the monitoring subjects or disturbed by environment noise because of the contactless measurement attribute. This shortcoming limits the application of the respiratory pattern classification model, that is, the existing models cannot be applied automatically since the distorted respiratory signal needs to be manually filtered out ahead of the classification. In this study, a new respiratory pattern classification strategy, which can be implemented full-automatic, is proposed. In this strategy, a class “moving” is introduced to classify the distorted signal, and the sampling window length is shortened to reduce the effect caused by the signal distortion. A performance requirement for the continuous respiratory pattern classification is also proposed based on its expected function that can alert the occurrence of the abnormal breathing patterns. Several models which can meet the proposed performance requirement are developed in this thesis based on the state-of-the-art pattern classification technique and the time-series-based shapelet transform algorithm. The proposed models can classify four breathing patterns including eupnea, Cheyne Stokes respiration, Kussmaul breathing and apnea. A radar-collected respiratory signal database is built in this study, and a respiration simulation model which can generate breath samples for pattern classification is developed in this thesis. The proposed models were tested and validated in batch and stream processing manner with independently collected data and continuously collected data, respectively.