Phonocardiogram signal compression using sound repetition and vector quantization.

BACKGROUND: A phonocardiogram (PCG) signal can be recorded for long-term heart monitoring. A huge amount of data is produced if the time of a recording is as long as days or weeks. It is necessary to compress the PCG signal to reduce storage space in a record and play system. In another situation, the PCG signal is transmitted to a remote health care center for automatic analysis in telemedicine. Compression of the PCG signal in that situation is necessary as a means for reducing the amount of data to be transmitted. Since heart beats are of a cyclical nature, compression can make use of the similarities in adjacent cycles by eliminating repetitive elements as redundant. This study proposes a new compression method that takes advantage of these repetitions.

METHODS: Data compression proceeds in two stages, a training stage followed by the compression as such. In the training stage, a section of the PCG signal is selected and its sounds and murmurs (if any) decomposed into time-frequency components. Basic components are extracted from these by clustering and collected to form a dictionary that allows the generative reconstruction and retrieval of any heart sound or murmur. In the compression stage, the heart sounds and murmurs are reconstructed from the basic components stored in the dictionary. Compression is made possible because only the times of occurrence and the dictionary indices of the basic components need to be stored, which greatly reduces the number of bits required to represent heart sounds and murmurs. The residual that cannot be reconstructed in this manner appears as a random sequence and is further compressed by vector quantization. What we propose are quick search parameters for this vector quantization.

RESULTS: For normal PCG signals the compression ratio ranges from 20 to 149, for signals with median murmurs it ranges from 14 to 35, and for those with heavy murmurs, from 8 to 20, subject to a degree of distortion of ~5% (in percent root-mean-square difference) and a sampling frequency of 4kHz.

DISCUSSION: We discuss the selection of the training signal and the contribution of vector quantization. Performance comparisons between the method proposed in this study and existing methods are conducted by computer simulations.

CONCLUSIONS: When recording and compressing cyclical sounds, any repetitive components can be removed as redundant. The redundancies in the residual can be reduced by vector quantization. The method proposed in this study achieves a better performance than existing methods.