ECG Signal Compression for Diverse Transforms

Biological signal compression and especially ECG has an important role in the diagnosis, prognosis and survival analysis of heart diseases. Various techniques have been proposed over the years addressing the signal compression. Compression of digital electrocardiogram (ECG) signals is desirable for three reasons- economic use of storage data, reduction of the data transmission rate and transmission bandwidth conversation. ECG signal. In this paper a comparative study of Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT), Discrete Cosine compression is used for telemedicine field and re Transform (DCT) and Wavelet Transform (WT) transform based approach is carried out. Different ECG signals are tested from MIT-BIH arrhythmia database using MATLAB software. The experimental results are obtained for Percent Root Mean Square Difference (PRD), Signal to Noise ratio (SNR) and Compression ratio (CR). The result of ECG signal compression shows better compression performance in DWT compared to DFT, FFT and DCT. Keywords: Electrocardiogram (ECG), Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT), Wavelet Transform (WT)

A unified methodology for heartbeats detection in seismocardiogram and ballistocardiogram signals

This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance assessment is conducted in terms of sensitivity, precision, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of annotation. The methodology is tested on four independent datasets, covering different measurement setups and time resolutions. A wide application range is therefore explored, which better characterizes the robustness and generality of the method with respect to a single dataset. Overall, sensitivity and precision scores are uniform across all datasets (p > 0.05 from the Kruskal–Wallis test): the average sensitivity among datasets is 98.7%, with 98.2% precision. On the other hand, a slight yet significant difference in RMSE and MAE scores was found (p < 0.01) in favor of datasets with higher sampling frequency. The best RMSE scores for SCG and BCG are 4.5 and 4.8 ms, respectively; similarly, the best MAE scores are 3.3 and 3.6 ms. The results were compared to relevant recent literature and are found to improve both detection performance and temporal annotation errors