Key result
A comparative study of turning point and fan compression techniques for ECG signals was performed based on compression ratio, percentage root mean square difference, and quality score.
Why the study?
How does the turning point compression technique compare to the fan compression technique for ECG signal compression?
How does the turning point compression technique compare to the fan compression technique for ECG signal compression?
This study compares turning point and fan data compression techniques for ECG signals to optimize data storage and transmission in telemedicine.
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May enhance ECG telemedicine efficiency; leaves open clinical validation of signal fidelity.
Electrocardiogram (ECG) is utilized in finding and treatment of various heart diseases. ECG data is compressed so that it can be effectively used in telemedicine. For telemedicine huge quantity of data signals are to be stored and sent to different places. So it is very essential to compress the ECG signal data in a resourceful way. Electrocardiogram (ECG) signals are mainly compressed for two reasons, on-line data transmission and effective and economical data storage. In the last five decades several data compression techniques has been developed for the compression of ECG signals. These techniques can be classified into three categories: direct data compression (DDC), transformation compression (TC), parameter extraction compression (PEC). In this paper a comparative study of turning point (TP) compression technique and fan compression technique is done. The comparison has been on parameters like compression ratio (CR), percentage root mean square difference (PRD) and quality score (QS). The main comparison has been made morphologically.
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singh et al. (2017) studied ECG signal compression. Turning point (TP) compression technique vs. Fan compression technique was evaluated on Compression ratio (CR), percentage root mean square difference (PRD), and quality score (QS). A comparative study of turning point and fan compression techniques for ECG signals was performed based on compression ratio, percentage root mean square difference, and quality score.
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