KR20100076648A - Method and apparatus of compressing / restoring signal of electrocardiogram - Google Patents

Abstract

PURPOSE: An electrocardiogram signal compression method and a device thereof are provided to remarkably reduce the size of image by executing an encoding process for divided blocks. CONSTITUTION: An electrocardiogram signal compressor is composed of a processor(100) and a memory device(102). The processor receives the electrocardiogram signal. The processor executes a compression operation according to the electrocardiogram signal. The processor restores the compressed electrocardiogram signal. The processor decodes the compressed electrocardiogram signal with the JPEG 2000. The memory device stores the processing program of the processor. The memory device offers necessary memory area for processing a program process step by the processor.

Description

Electrocardiogram signal compression method and apparatus {Method and Apparatus of compressing / restoring signal of electrocardiogram}

The present invention relates to a biosignal processing technique, and more particularly, to a method and apparatus for compressing an electrocardiogram signal.

The biosignal measuring apparatus is a device for measuring physiological signals generated in a living body such as EEG, electrocardiogram, safety, blood pressure, induction potential, and electromyography, and is used to measure and store a patient's biosignals and apply them to medical care.

Since a general biosignal measuring apparatus performs measurement, storage, reading and reading of a biosignal on a single independent machine, an independent biosignal measuring apparatus must be accessed for measuring, reading and reading a biosignal.

In order to overcome this limitation, the measurement function of the biological signal and the reading and reading function are separated, and the separated function is connected to a local area network (LAN), so that the reading and viewing can be performed at a place other than the laboratory where the biological signal measuring device is located. Attempts have been made to enable reading.

Moreover, the separation of measurement and reading and reading of biological signals through the network can be extended beyond the scope of the hospital, and the concept of "remote care" is used when patients and caregivers are connected beyond the hospital.

As described above, with the development of medical technology, biological signals are measured and stored or transmitted, but the biological signals have a large amount of problems.

Therefore, there is an urgent need for the development of a technology that can efficiently compress the bio-signals.

It is an object of the present invention to provide a method and apparatus for compressing an electrocardiogram signal for remote diagnosis and storage.

In the ECG signal compression method of the present invention for achieving the above object, by dividing the ECG signal for a plurality of beats by a beat unit, having a line corresponding to the beat period, the points forming the line ECG at each position Converting to have a luminance corresponding to a vertical height of a signal to generate one segment, and vertically stacking the segments to two-dimensionally convert the ECG signal; Horizontally moving the segments with respect to a starting point having a minimum vertical change amount (Tn) between the vertically stacked segments; Calculating a complexity for the horizontally shifted segments and sorting the segments according to the calculated complexity; Detecting an edge for the aligned segments and dividing the edge into the plurality of groups based on a point at which the detected edge is greater than or equal to a predetermined value; For each of the plurality of groups, performing segments-by-length alignment; For each of the plurality of groups, a first block and a remainder are set as a first block from the end position of the shortest segment of the length-aligned segments to the end position of the longest segment, and within the first block. Implementing interpolation in the; And performing encoding for each of the plurality of blocks making up the plurality of groups.

The present invention described above has the effect of efficiently compressing the ECG signal for remote diagnosis or storage.

1 is a block diagram of an ECG signal compression apparatus according to a preferred embodiment of the present invention. The ECG signal compression device includes a processor 100 and a memory device 102. The processor 100 receives the ECG signal and performs compression, or receives and restores the compressed ECG signal. The memory device 102 not only stores a processing program of the processor 100, but also provides a memory area necessary for processing a program by the processor 100.

ECG signal compression process

The process flow according to the ECG signal compression method implemented by the ECG signal compression apparatus will be described in detail with reference to FIG. 2.

<QRS detection>

First, the processor 100 of the ECG signal compression apparatus receives the ECG signal and detects the QRS (step 200).

3A illustrates a general ECG signal. The ECG signal is largely composed of P, QRS, and T. The P is a curve due to depolarization of the left and right atrium, and the QRS is a waveform appearing when the left and right ventricles are depolarized due to an electric shock proceeding to Purkinje fibers through the right and left angles in his bundle, and the repolarization state of the ventricles. It represents. Detecting QRS in the ECG signal is well known, and a detailed description thereof will be omitted.

<Pulse unit division and 2D transformation>

When the QRS is detected from the ECG signal, the processor 100 divides the ECG signal based on the QRS. Here, the ECG signal divided based on the QRS is one beat unit.

After dividing the ECG signal, the processor 100 has a length occupied by one beat, and two-dimensionally converts the ECG signal by expressing the height of the ECG signal as luminance (step 202). Here, one of the pulsation units, and the height of the ECG signal expressed as luminance is referred to as one or more segments.

This conversion process will be further described with reference to FIG. 4. 4A illustrates an electrocardiogram signal. The peak of the electrocardiogram signal is an R portion of the QRS, and the electrocardiogram signal is divided based on the R portion.

4 (b) corresponds to one beat, and its length varies according to the beat cycle.

In FIG. 4C, the ECG signal divided by the one beat unit is converted into a single line in which the vertical height is expressed by the luminance to generate a segment, and the segments are vertically stacked to thereby form an ECG signal. It is a dimension conversion.

The segment corresponds to a length of an ECG signal divided into one line by the beat rate, and the luminance of each of the points forming the line corresponds to the vertical height of the waveform of the ECG signal at the corresponding point. That is, each of the points forming the line has a bright color at a point where the vertical height of the ECG signal is high and a dark color at a point where the vertical height of the ECG signal is low.

<Start point determination and shift for ECG signal compression processing>

After the two-dimensional conversion of the ECG signal as described above, the processor 100 determines a starting point for the ECG signal compression process according to the preferred embodiment of the present invention.

The starting point is calculated according to equation (1).

The start point S represents a point for shifting the ECG signal divided and arranged in beat units according to the QRS. The starting point S is a point where Tn becomes minimum.

The Tn is the sum of the differences between the lower and upper segments when the point forward n is set to the boundary of the segment to the left in the QRS position, and represents the similarity between the lower and upper segments.

Vn, i (j) is the jth sample point of the I-th segment when shifted by n, and Vn, i + 1 (j) is the j + 1st sample point of the I-th segment when shifted by n The point where the Tn becomes the minimum means the part with the highest similarity between the upper and lower segments. In other words, the value of S is determined as the value n where Tn is minimum.

When the starting point S is calculated and determined, the processor 100 shifts the two-dimensional converted ECG signal according to the starting point S (step 204).

FIG. 5A illustrates a process of determining a starting point S. The starting point S is a point where the value of Tn obtained while moving by one sample is the minimum. When the start point S is determined, the processor shifts the two-dimensional transformed electrocardiogram signal as shown in FIG. 5B as shown in FIG. 5C. That is, it is FIG.5 (c) which shifted FIG.5 (b) to the right by the said starting point S. FIG. That is, although the segment is based on the QRS position, in the present invention, the start point is defined as a point moved by S from the QRS to the left.

<Sort Complexity>

The processor 100 then implements COMPLEXITY SORTING.

과 다른 세그먼트들과의 복잡도 순서대로 정렬한다. The complexity sort is the least variance segment

And order of complexity with other segments.

The complexity is obtained according to equation (2).

Here, the processor 100 stores the position information on the initial position for each segment, which is used to return each segment to its own position in the future restoration.

<Bit grouping>

Thereafter, the processor 100 performs bit grouping (step 208).

For the bit grouping, the processor 100 performs length normalization on the complexity aligned segments and noise cancellation on the length normalized segments. The processor 100 then detects an edge for the noise canceled segments and analyzes the strength of the detected edge. Grouping is performed based on points at which the strength of the edge is equal to or greater than a predetermined value.

Here, a bit grouping process for an ECG signal having a rapidly varying portion will be described with reference to the example of FIG. 6.

FIG. 6A illustrates length normalization for an ECG signal rapidly changing in the center portion, and FIG. 6B illustrates noise removal for the ECG signal on which the length normalization is performed. (C) detects the edge of the noise canceled ECG signal. The edge occurs where the signal changes abruptly, so the edge indicates the portion of change in the ECG signal. 6C illustrates the strength of the detected edge.

The processor 100 performs bit grouping based on a point at which the strength of the edge is greater than or equal to a predetermined value.

<Sort length>

When the bit grouping is completed, the processor 100 performs period sorting (Period sorting) for each group (step 210). That is, the processor 100 performs lengthwise alignment for each of the plurality of groups generated by the bit grouping.

<Block Break>

If the sorting by length is implemented, the processor 100 divides the block into three blocks for each of the groups (step 212). That is, a first block including a QRS length, a third block starting at the end of the shortest segment in the group and ending at the end of the longest segment in the group, and a first block and the third block It consists of a 2nd block located between them.

Referring to FIG. 7 illustrating an example of dividing blocks as described above, each of the first to third groups (Group1 to Group3) generated through the bit grouping process is divided into three blocks again, and the three blocks include a QRS length. A first block, a third block starting at the end of the shortest segment in the group and ending at the end of the longest segment in the group, and a second block located between the first block and the third block. It's a block.

<Length normalization>

The processor 100 then performs length normalization (step 214). The length normalization is to fill each line horizontally by using B-SPLINE interpolation in the empty space where no segment exists in the third block of each group. Here, the processor 100 stores information for de-interpolation at the time of the interpolation. That is, the processor 100 configures length information on each line as information for deinterpolation and stores the length information in the restoration information.

<Block normalization>

The processor 100 then normalizes the average of each block (step 216). The average normalization adjusts the average value of each line to be equal to the average value of the block, and stores difference value information with the average value of the block for each line as block normalization information.

By the length normalization and the average normalization, the ECG signal as shown in FIG. 8 is filled with a space portion of each block to have a rectangular shape.

<Encode, save, transfer>

The processor 100 encodes each of the groups according to the JPEG 2000 scheme, and stores the position information, the block normalization information, and the information for deinterpolation generated during the conversion as restoration information.

As described above, the present invention can significantly reduce the size of an image by performing encoding by dividing it into blocks, and solve the step edge problem at the group boundary due to the difference between groups. In addition, there is an effect that can reduce the high frequency components through the average normalization process.

Compressed ECG signal recovery process

The process of reconstructing the compressed ECG signal according to the present invention described above will be described with reference to FIG.

When the encoded ECG signal and the reconstruction information are input, the processor 100 decodes the ECG signal by JPEG 2000 (300).

Thereafter, the processor 100 restores an average value of each line to an original value based on block normalization information included in the restoration information (step 302). That is, according to the block normalization information consisting of difference values of average values of blocks for each line, the difference values are restored in a manner of adding back the difference values.

The processor 100 performs deinterpolation on the third block in which the interpolation has been performed among the normalized and restored blocks (step 302). In this case, the length information of the lines used in the interpolation of the block is registered in the restoration information, and the processor 100 restores the length of each line by performing deinterpolation based on the restoration information.

When the deinterpolation of the third block is completed, the processor 100 moves the positions of the restored segments according to the position information included in the restoration information (step 306).

Thereafter, the processor 100 performs one-dimensional transformation on the respective segments (step 308). That is, each segment is connected by one line, and each point forming the line is converted to have a vertical height corresponding to its brightness.

As a result, the restoration of the compressed ECG signal according to the present invention is completed.

1 is a block diagram of an electrocardiogram signal processing apparatus according to a preferred embodiment of the present invention.

2 is a flow chart of an ECG signal compression method according to a preferred embodiment of the present invention.

3 is a waveform diagram of a typical ECG signal.

4 is a diagram illustrating a two-dimensional conversion process of an electrocardiogram signal according to a preferred embodiment of the present invention.

5 is a diagram illustrating a compression process start point detection process and a shift process of an ECG signal according to a preferred embodiment of the present invention.

6 illustrates a grouping process according to a preferred embodiment of the present invention.

7 is a diagram illustrating a blocking process according to a preferred embodiment of the present invention.

8 illustrates intrablock interpolation results according to a preferred embodiment of the present invention.

9 is a flow chart of a method for recovering a compressed ECG signal in accordance with a preferred embodiment of the present invention.

Claims (6)

In the ECG signal compression method, By dividing the ECG signal for multiple beats into beats, It has a line corresponding to the pulsating period, converts the points forming the line to have a luminance corresponding to the vertical height of the ECG signal at each position to generate a segment, Stacking the segments vertically to two-dimensionally transform the ECG signal; Horizontally moving the segments with respect to a starting point having a minimum vertical change amount (Tn) between the vertically stacked segments; Calculating a complexity for the horizontally shifted segments and sorting the segments according to the calculated complexity; Detecting an edge for the aligned segments and dividing the edge into the plurality of groups based on a point at which the detected edge is greater than or equal to a predetermined value; For each of the plurality of groups, performing segments-by-length alignment; For each of the plurality of groups, a first block and a remainder are set as a first block from the end position of the shortest segment of the length-aligned segments to the end position of the longest segment, and within the first block. Implementing interpolation in the; When the interpolation is completed, performing encoding on each of a plurality of blocks constituting the plurality of groups. ECG signal compression method comprising a. The method of claim 1, The second block is divided into a block including a QRS portion and a block composed of the remaining portions in the corresponding segment. And encoding for each of the divided groups at the time of encoding. The method of claim 1, Configuring and storing location information for each segment and information for deinterpolation as restoration information; Electrocardiogram signal compression method characterized in that it further comprises. The method according to claim 1 or 2, For each of the lines included in each of the divided blocks, After detecting a difference value between the average value of the line and the average value of the block to which the line belongs, replacing the average value of the line with the average value of the block, and storing the difference value as reconstruction information; Electrocardiogram signal compression method characterized in that it further comprises. In the ECG signal recovery method, Decoding a plurality of segments and reconstruction information, which are divided into a plurality of blocks and are encoded, for each of the plurality of blocks; Restoring lengths of the plurality of segments by deinterpolating the interpolated blocks among the decoded blocks using information for deinterpolation included in the reconstruction information; Moving the position of the segment whose length has been restored according to the position information included in the restoration information; Aligning the moved segments in a line and performing one-dimensional conversion such that each of the points forming the line has a vertical height corresponding to its brightness; ECG signal recovery method comprising the. The method of claim 5, Restoring an average value of each line according to difference value information between a block average value included in the reconstruction information and an average value of the line, the average value of each line in the block normalized to the average value of the corresponding block for each of the blocks; Electrocardiogram signal recovery method further comprising.

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