Accurate detection of characteristic points in electrocardiogram (ECG) signals is crucial for medical rehabilitation assistance devices, cardiac monitoring systems, and cardiovascular disease research. To address the issues of missed detections and false alarms in traditional methods, this paper proposes an optimized algorithm for ECG characteristic point detection based on ResNet-LSTM-Differential Threshold. In this study, adaptive thresholding is utilized to label the characteristic points of ECG signals, followed by training the ResNet-LSTM model on the annotated ECG signal data. Finally, the differential threshold method is integrated in the decision-making phase to detect R-waves in parallel. A detection is considered a true positive if either the neural network model or the threshold method successfully identifies an R-wave. Experimental results demonstrate that the proposed method achieves an R-wave detection accuracy of 99.4% on the MIT-BIH database, outperforming both single threshold methods and traditional deep learning approaches in terms of detection precision and computational efficiency. The proposed ResNet-LSTM-Differential Threshold method for ECG characteristic point detection effectively enhances the accuracy and robustness of detection. It enables efficient, precise, and real-time detection of characteristic points even when dealing with complex and variable ECG signals, offering broad application prospects for various medical devices and healthcare systems.