An electrocardiogram (ECG) is a simple, painless test that records the heart's electrical activity. To understand this test, it helps to understand how the heart works. With each heartbeat, an electrical signal spreads from the top of the heart to the bottom. As it travels, the signal causes the heart to contract and pump blood. The process repeats with each new heartbeat. The heart's electrical signals set the rhythm of the heartbeat. An ECG shows:
How fast your heart is beating
Whether the rhythm of your heartbeat is steady or irregular
The strength and timing of electrical signals as they pass through each part of your hear
Doctors use ECGs to detect and study many heart problems, such as heart attacks, arrhythmias, and heart failure. The test's results also can suggest other disorders that affect heart function.
The role of compressing an ECG signal data plays a momentous role in examining a patient’s overall health status because a large amount of signal data needs to be stored and transmitted. The ECG compression methods can be broadly classified as
Direct methods
Parameter Extraction Method
Transform method
Here in this paper a transform based methodology is presented for compression of ECG. This method deploys following methods such as FFT, DCT, DST, DWT and Walsh-Hadamard transform among which, Discrete Wavelet Transform (DWT) technique is used here since it gives better compression ratio than others. For performing DWT the records from MIT-BIH arrhythmia database are tested. The parameters like Compression Ratio (CR), Percent Root Mean Square (PRM) Difference are used for performance evaluation. The effectiveness of transform in biomedical signal processing is embellished in the simulation result.
The Challenge
Compression of electrocardiography (ECG) is necessary for efficient storage and transmission of the digitized ECG signals. A typical ECG monitoring device generates a large amount of data in the continuous long-term (24-48 hours) ambulatory monitoring tasks. For good diagnostic quality, up to 12 different streams of data may be obtained from various sensors placed on the patient’s body. The data rate from 12 different sensors totally will generate 12 times amount of data and it is enormously big. Besides, recording over a period of time as long as 24 hours maybe needed for a patient with irregular heart rhythms . The monitor device such as Holter must have a memory capacity of about 400-800 MB for a 12 lead recording, but such a big memory cost may render a solid-state commercial Holter device impossible. Thus, efficient ECG data compression to dramatically reduce the data storage capacity.
The Solution
The recorded ECG signal of which is containing the low frequency and high frequency samples. The high frequency samples are having negligible values which also occupying the time domain. So by neglecting these values or by making high frequency to zero. The removal of these values reduces the ECG signal. This leads to the compression of the ECG .we intend to implement a discrete cosine transform (DCT)/ Fast Fourier transform(FFT) based ECG compression. In this, signal is first transform to frequency domain. A nonlinear transformation is applied on frequency coefficients and then inverse transformations is computed to obtain the reconstructed ECG signal. The quality of the reconstructed signal is compared to that of the original signal subjectively by using plots and objectively by using mean square error(MSE) criterion, compression ratio etc
A Paper Presented by
N. SURENDRANATH
And
A.R. RAJESH KUMAR
Student Details:
N. Surendranath,
Department of ECE,
Prathyusha Institute of Technology and Management.
