The application of wavelet and feature vectors to ECG signals

A joint application of optimal threshold based discrete cosine transform and ASCII encoding for ECG data compression with its inherent encryption

In this paper, a joint use of the discrete cosine transform (DCT), and differential pulse code modulation (DPCM) based quantization is presented for predefined quality controlled electrocardiogram (ECG) data compression. The formulated approach exploits the energy compaction property in transformed domain. The DPCM quantization has been applied to zero-sequence grouped DCT coefficients that were optimally thresholded via Regula-Falsi method. The generated sequence is encoded using Huffman coding. This encoded series is further converted to a valid ASCII code using the standard codebook for transmission purpose. Such a coded series possesses inherent encryption capability. The proposed technique is validated on all 48 records of standard MIT-BIH database using different measures for compression and encryption. The acquisition time has been taken in accordance to that existed in literature for the fair comparison with contemporary state-of-art approaches. The chosen measures are (1) compression ratio (CR), (2) percent root mean square difference (PRD), (3) percent root mean square difference without base (PRD1), (4) percent root mean square difference normalized (PRDN), (5) root mean square (RMS) error, (6) signal to noise ratio (SNR), (7) quality score (QS), (8) entropy, (9) Entropy score (ES) and (10) correlation coefficient (r _x,y ). Prominently the average values of CR, PRD and QS were equal to 18.03, 1.06, and 17.57 respectively. Similarly, the mean encryption metrics i.e. entropy, ES and r _x,y were 7.9692, 0.9962 and 0.0113 respectively. The novelty in combining the approaches is well justified by the values of these metrics that are significantly higher than the comparison counterparts.

Extended Kalman smoother with differential evolution technique for denoising of ECG signal

Electrocardiogram (ECG) signal gives a lot of information on the physiology of heart. In reality, noise from various sources interfere with the ECG signal. To get the correct information on physiology of the heart, noise cancellation of the ECG signal is required. In this paper, the effectiveness of extended Kalman smoother (EKS) with the differential evolution (DE) technique for noise cancellation of the ECG signal is investigated. DE is used as an automatic parameter selection method for the selection of ten optimized components of the ECG signal, and those are used to create the ECG signal according to the real ECG signal. These parameters are used by the EKS for the development of the state equation and also for initialization of the parameters of EKS. EKS framework is used for denoising the ECG signal from the single channel. The effectiveness of proposed noise cancellation technique has been evaluated by adding white, colored Gaussian noise and real muscle artifact noise at different SNR to some visually clean ECG signals from the MIT-BIH arrhythmia database. The proposed noise cancellation technique of ECG signal shows better signal to noise ratio (SNR) improvement, lesser mean square error (MSE) and percent of distortion (PRD) compared to other well-known methods.