Sleep apnea syndrome (SAS) is regarded as one of the most common sleep-related breathing disorders, which can severely affect sleep quality. Since SAS is usually accompanied with the cyclical heart rate variation (HRV), many studies have been conducted on heart rate (HR) to identify it at an earlier stage. While most related work mainly based on clinical devices or signals (e.g., polysomnography (PSG), electrocardiography (ECG)), in this paper we focus on the ballistocardiographic (BCG) signal which is obtained in a non-invasive way. Moreover, as the precision and reliability of BCG signal are not so good as PSG or ECG, we propose a fine-grained feature extraction and analysis approach in SAS recognition. Our analysis takes both the basic HRV features and the breathing effort variation into consideration during different sleep stages rather than the whole night. The breathing effort refers to the mechanical interaction between respiration and BCG signal when SAS events occur, which is independent from autonomous nervous system (ANS) modulations. Specifically, a novel method named STC-Min is presented to extract the breathing effort variation feature. The basic HRV features depict the ANS modulations on HR and Sample Entropy and Detrended Fluctuation Analysis are applied for the evaluations. All the extracted features along with personal factors are fed into the knowledge-based support vector machine (KSVM) classification model, and the prior knowledge is based on dataset distribution and domain knowledge. Experimental results on 42 subjects in 3 nights validate the effectiveness of the methods and features in identifying SAS (90.46% precision rate and 88.89% recall rate).