Analysis of lossless compression techniques time-frequency-based in ECG signal compression

Technological advances introduce different methods for telecardiology. Telecardiology includes many applications and is one of the long-standing medical fields that have grown very well. In telecardiology, a very high amount of ECG data is recorded. Therefore, to compress electrocardiogram data (ECG), there is a need for an efficient technique and lossless compression. The compression of ECG data reduces the storage needs for a more efficient cardiological system and for analyzing and diagnosing the condition of the heart. In this paper, ECG signal data compression techniques are analyzed using the MIT-BIH database and then compared with the Apnea-ECG and Challenge 2017 Training bases. During the study, some of the various techniques of frequency analysis, range and time are widely used, such as run-time coding, AZTEC, Spin-Coding, FFT, DCT, DST, SAPA/FAN and DCT-II, where DCT and SAPA/FAN have the best compression performance compared to other methods.

Biological signals are derived from biological processes. This process is very complex and dynamic. Biomedical signals are usually a function of time. To describe the biological signals, Rick's solution is quasi-periodic, depending on how we need it and how accurate it is. In the alternating pseudo-signal, the form is repeated approximately at specified intervals. Transient signals occur only once over time. The waveform of such signals is indefinite and can only be described using statistical concepts. Depending on the biological process, random signals are divided into static and non-stationary types. In static random signals, the statistical characteristics of the signal do not change over time. If the biological process generating a random signal in a given condition is the accidental generated signal will be invalid. For example, an ECG is a non-stop random signal.

Compression on the basis of run length encoding, with proper operation in Compression Ratio (CR) and Compression Rate (CRC) parameters, indicates that it is smaller than the other methods of measuring the size of the compressed file and the greater the compression ratio. Compression methods by Amplitude Zone Time Epoch Coding (AZTEC) and compression based on the spline coding did not have a good performance in almost any of them and are not suitable for compressing the ECG signal. Compression methods based on Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) proper operation in the parameters of the Percent Mean Square Difference (PRD), Quality Score (QS), Root Square Error (RMS), indicating that the transformation-based methods have a low distortion between the original and the reconstructed signal and the high compression ratio and proper stability in the signal reconstructed with the initial signal.

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