Objectives. To accurately identify potentially torsadogenic compounds in an earlier stage of drug development, innovative preclinical strategies including label-free impedance and extracellular field potential recordings of stem cell-derived cardiomyocytes have been recently proposed. Unfortunately, they pro-duce high-content signals and size of their data files may exceed 10GB, which prevents any web data transfer for remote data analysis. Therefore, our objec-tive is to compare the performances of several compression algorithms applied to those signals. Methods. The general plan of a lossy compression algorithm consists of three main stages. The first one transforms the signal in a mathematical space where the signal is sparser in order to decorrelate the data and to minimize their amount (one can use for example Discrete Cosine or Fourier Transforms). The se-cond step is the only lossy step of the compression chain. The quantization of the transform coefficients, which can be associated to thresholding discards the non significant coefficients. The last step optimally encodes the quantized coefficients by using an entropy code (e.g. Huffman algorithm, RLE, arithmetic coding…) to exploit residual redundancies of the quantized data. Several combinations of methods mentioned above have been tested and evaluated on two types of one-dimensional signals obtained from impedimetric contractility and extra cellular potential recordings from cardiac myocytes. Results. Results clearly present the ability and reliability of the different methods to compress sensitive data coming from cardiomyocytes with a compression ratio of up to 10:1 while preserving the relevant information content of the recorded data. Conclusion. The proposed method enables biologists to use web-based remote analysis tools for generated cardiomyocyte impedance and field potential data by reducing the size of their files without distortion of their data.