This study presents CGB-Net, a novel deep learning architecture specifically developed for classifying twelve distinct sleep positions using a single abdominal accelerometer, with direct applicability to gastroesophageal reflux disease (GERD) monitoring. Unlike conventional approaches limited to four basic postures, CGB-Net enables fine-grained classification of twelve clinically relevant sleep positions, providing enhanced resolution for personalized health assessment. The architecture introduces a unique integration of three complementary components: 1D Convolutional Neural Networks (1D-CNN) for efficient local spatial feature extraction, Gated Recurrent Units (GRU) to capture short-term temporal dependencies with reduced computational complexity, and Bidirectional Long Short-Term Memory (Bi-LSTM) networks for modeling long-term temporal context in both forward and backward directions. This complementary integration allows the model to better represent dynamic and contextual information inherent in the sensor data, surpassing the performance of simpler or previously published hybrid models. Experiments were conducted on a benchmark dataset consisting of 18 volunteers (age range: 19–24 years, mean 20.56 $\pm$ 1.1 years; height 164.78 $\pm$ 8.18 cm; weight 55.39 $\pm$ 8.30 kg; BMI 20.24 $\pm$ 2.04), monitored via a single abdominal accelerometer. A subject-independent evaluation protocol with multiple random splits was employed to ensure robustness and generalizability. The proposed model achieves an average Accuracy of 87.60% and F1-score of 83.38%, both reported with standard deviations over multiple runs, outperforming several baseline and state-of-the-art methods. By releasing the dataset publicly and detailing the model design, this work aims to facilitate reproducibility and advance research in sleep posture classification for clinical applications.