In this paper, a wavelet-based multimodal compression method is proposed. The method jointly compresses a medical image and an ECG signal within a single codec, i.e., JPEG-2000 in an effective and simple way. The multimodal scheme operates in two main stages: the first stage, consists of the encoder and involves a mixing function, aiming at inserting the samples of the signal in the image according to a predefined insertion pattern in the wavelet domain. The second stage represented by a separation function, consists of the extraction process of the ECG signal from the image after performing the decoding stage. Both the cubic spline and the median edge detection (MED) predictor have been adopted to conduct the interpolation process for estimating image pixels. Intensive experiments have been conducted to evaluate the performance of the multimodal scheme using objective distortion criteria. Results show clear superiority of the proposed scheme over the conventional separate compression approach involving two codecs: JPEG-2000 for images and ECG SPIHT-1D as well as other competing multimodal compression schemes in terms of both PRD and SNR at the signal decompression stage while maintaining good image quality and exhibiting a reduced computational complexity. Improvements in terms of average PRD and SNR values are as significant as 0.7 and 6 dB at low bit rates and 0.06 and 2 dB at higher bit rates on a number of test ECG signals and medical images.