Intracranial microembolus detection method based on ultrasonic chirp signal

Abstract

The invention discloses an intracranial microembolus detection method based on an ultrasonic chirp signal, and the method comprises the steps: employing a chirp waveform as a transmitting signal, and transmitting a chirp waveform signal to the intracranial at the same position for many times; receiving echo signals reflected back in the cranium, and recording echo data after each time of emission; and carrying out slow time analysis on the echo signal, judging whether a chirp signal exists in the echo signal or not, and if so, judging that microemboli exists in the encephalic. According to the invention, the accuracy of microembolus detection can be improved, and confusion of microembolus signals and artifact signals is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of intracranial microemboli detection, and more specifically, to an intracranial microemboli detection method based on ultrasonic chirp signals. Background Art

[0002] The "Guidelines for the Diagnosis and Treatment of Acute Ischemic Stroke in China 2023" points out that acute ischemic stroke is the most common type of stroke, accounting for 69.6% to 72.8% of new strokes in my country. The presence of microemboli in the blood is considered to be one of the many independent risk factors for ischemic stroke. The guidelines recommend TCD monitoring of microembolic signals as one of the methods for examining vascular lesions. Therefore, the detection of intracranial microemboli is of great significance for the prevention and treatment of cerebrovascular diseases. Microembolic signals are characterized by short duration (<100ms), increased relative intensity (in the range of 3~60dB), unidirectional appearance on spectral Doppler, accompanied by a sharp whistle, and a time difference between the signal at two different depths.

[0003] There are two main methods for distinguishing microembolic signals from artifact signals: direction identification and dual-depth time difference identification. Direction identification: Since most artifact signals have no directionality and can appear above or below the baseline, they can usually be distinguished from microembolic signals. However, in a few cases, artifacts may also show a unidirectional or completely unidirectional performance. When this occurs, it is easy to confuse with microembolic signals and difficult to distinguish, which increases the difficulty of microembolic detection and the accuracy of diagnosis. Identification by dual-depth time difference: Microembolic signals generated by embolic sources far away from the monitored blood vessels have a time difference between the dual depths, while artifact signals have no time difference. Detecting whether there is a time difference has become an effective method for distinguishing the two. However, this method is not suitable for the characteristics of microemboli detected at the origin of microemboli. In addition to short time course, increased signal intensity, and low crackling, the diagnostic criteria for microembolic signals at the stenosis of the middle cerebral artery at the origin of microemboli also have the characteristics of multiple frequencies, which are manifested as unidirectional high-frequency parts and sometimes bidirectional low-frequency parts. Moreover, since there is no obvious time difference between the dual depths at the origin of the embolus, it cannot be calculated.

[0004] The use of direction identification and dual-depth time difference to identify microembolic signals has certain limitations in accuracy and reliability, and it is difficult to meet clinical needs. In the detection of intracranial microemboli, the mutation of the spectral Doppler signal is often confused with the microembolic signal, especially when encountering interference signals. This signal mutation is very similar to the microembolic signal, which can easily lead to misjudgment and increase the difficulty of detection. Therefore, it is of great significance to avoid the confusion between microembolic signals and pseudo-difference signals through specific signal processing methods, thereby improving the accuracy of detection. Summary of the invention

[0005] The present invention provides an intracranial microembolus detection method based on ultrasonic chirp signals, which solves the problem that the existing microembolus signal detection is inaccurate and easily leads to misjudgment, can improve the accuracy of microembolus detection, and avoid confusion between microembolus signals and pseudo-difference signals.

[0006] To achieve the above objectives, the present invention provides the following technical solutions:

[0007] A method for detecting intracranial microemboli based on ultrasonic chirp signals, comprising:

[0008] Using chirp waveform as the transmitting signal, the chirp waveform signal is transmitted into the skull at the same location multiple times;

[0009] Receive the echo signal reflected from the skull and record the echo data after each transmission;

[0010] The echo signal is subjected to slow time analysis to determine whether a chirp signal exists in the echo signal. If so, it is determined that microemboli exist in the skull.

[0011] Preferably, it also includes:

[0012] The phase of the echo signal is calculated by Hilbert transform, and the continuity and nonlinear characteristics of the phase are analyzed. If the phase shows nonlinear characteristics, it is determined that the corresponding signal source is a microembolus.

[0013] Preferably, it also includes:

[0014] The echo signals corresponding to the multiple transmissions of the chirp waveform are arranged in the slow time direction, and are sampled within a set period of time. If the selected echo data conforms to the chirp function characteristics, the corresponding signal source is determined to be a microembolus.

[0015] Preferably, the performing slow time analysis on the echo signal comprises:

[0016] Taking the slow time as the horizontal coordinate and the moving speed as the vertical coordinate, a frequency domain analysis diagram of the echo signal is established;

[0017] It is determined whether the frequency change range of the echo signal is proportional to the preset moving speed of the microemboli. If so, it is determined that the echo signal has a chirp signal.

[0018] Preferably, the slow time analysis of the echo signal further comprises:

[0019] Performing Fourier transform on the echo signal on the frequency analysis graph to obtain a corresponding bandwidth;

[0020] The bandwidth of the echo signal is compared with a preset microembolus bandwidth, and if they are consistent, it is determined that a chirp signal exists in the echo signal.

[0021] Preferably, it also includes:

[0022] Direction identification is performed on the echo signal with the chirp signal. If the echo signal has directionality, it is determined that the corresponding signal source is a microembolus.

[0023] Preferably, it also includes:

[0024] The echo signal with the chirp signal is subjected to dual-depth time difference identification. If there is a time difference between the dual depths of the echo signal, it is determined that the corresponding signal source is a microembolus.

[0025] The present invention provides a method for detecting intracranial microemboli based on ultrasonic chirp signals, which accurately identifies the presence of intracranial microemboli by emitting CHIRP ultrasonic signals at the same position multiple times and combining slow-time signal analysis and phase processing. The method solves the problem that the existing detection of microembolic signals is inaccurate and easily leads to misjudgment, improves the accuracy of microembolic detection, and avoids confusion between microembolic signals and pseudo-difference signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the specific embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below.

[0027] Figure 1 It is a schematic diagram of a method for detecting intracranial microemboli based on ultrasonic chirp signals provided by the present invention.

[0028] Figure 2 It is a frequency domain analysis diagram of the echo signal provided by an embodiment of the present invention.

[0029] Figure 3 It is a schematic diagram of the bandwidth of the echo signal after Fourier transformation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable persons skilled in the art to better understand the solutions of the embodiments of the present invention, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and implementation modes.

[0031] In view of the problem of inaccurate detection of intracranial microemboli, the present invention provides a method for detecting intracranial microemboli based on ultrasonic chirp signals, which solves the problem that the existing detection of microembolic signals is inaccurate and easily leads to misjudgment, can improve the accuracy of microembolic detection, and avoid confusion between microembolic signals and pseudo-error signals.

[0032] like Figure 1As shown, a method for detecting intracranial microemboli based on ultrasonic chirp signals comprises:

[0033] S1: Use chirp waveform as the transmission signal and transmit chirp waveform signal to the brain at the same location multiple times.

[0034] S2: Receive the echo signal reflected from the skull and record the echo data after each transmission.

[0035] S3: Performing slow time analysis on the echo signal to determine whether there is a chirp signal in the echo signal. If yes, it is determined that there are microemboli in the skull.

[0036] Specifically, CHIRP signal, also known as chirp signal, is a special frequency modulation signal, which is characterized by a linear increase or decrease in frequency over time. At the same position, chirp signals are emitted at specific intervals, and echo signals are sampled at specific positions to obtain slow-time signals. Slow-time analysis is performed to determine the change characteristics of echo signals to determine whether chirp signals exist in echo signals, and whether microemboli exist in the brain based on chirp signals and phase characteristics. This method can improve the accuracy of microemboli detection and avoid confusion between microemboli signals and artifact signals.

[0037] The method further comprises: calculating the phase of the echo signal by Hilbert transform, analyzing the continuity and nonlinear characteristics of the phase, and if the phase shows nonlinear characteristics, determining that the corresponding signal source is a microembolus.

[0038] The method also includes: arranging the echo signals corresponding to the multiple transmitted chirp waveforms in the slow time direction, sampling within a set time period, and if the selected echo data meets the chirp function characteristics, determining that the corresponding signal source is a microembolus.

[0039] In practical applications, such as Figure 2 As shown in Figure 1, the characteristic of the chirp function after slow time sampling is that the frequency changes evenly, and the range of frequency change is proportional to the moving speed of the microemboli.

[0040] Further, the performing slow time analysis on the echo signal comprises:

[0041] A frequency domain analysis diagram of the echo signal is established with the slow time as the horizontal axis and the moving speed as the vertical axis.

[0042] It is determined whether the frequency change range of the echo signal is proportional to the preset moving speed of the microemboli. If so, it is determined that the echo signal has a chirp signal.

[0043] Further, the slow time analysis of the echo signal further includes:

[0044] Perform Fourier transform on the echo signal on the frequency analysis graph to obtain a corresponding bandwidth.

[0045] The bandwidth of the echo signal is compared with a preset microembolus bandwidth, and if they are consistent, it is determined that a chirp signal exists in the echo signal.

[0046] In practical applications, the echo signal is subjected to Fourier transform, such as Figure 3 As shown, the faster the signal, the wider its bandwidth.

[0047] The method further comprises: performing direction identification on the echo signal with the chirp signal, and if the echo signal has directionality, determining that the corresponding signal source is a microembolus.

[0048] The method further comprises: performing dual-depth time difference identification on the echo signal with the chirp signal, and if there is a time difference between the dual depths of the echo signal, determining that the corresponding signal source is a microembolus.

[0049] It can be seen that the present invention provides a method for detecting intracranial microemboli based on ultrasonic chirp signals, which accurately identifies the presence of intracranial microemboli by emitting chirp ultrasonic signals at the same position multiple times and combining slow-time signal analysis and phase processing. The problem of inaccurate detection of microembolic signals and easy misjudgment is solved, and the accuracy of microembolic detection can be improved to avoid confusion between microembolic signals and pseudo-difference signals.

[0050] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made in accordance with the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A method for detecting intracranial microemboli based on ultrasonic chirp signals, characterized in that: include: Using chirp waveform as the transmitting signal, the chirp waveform signal is transmitted into the skull at the same location multiple times; Receive the echo signal reflected from the skull and record the echo data after each transmission; The echo signal is subjected to slow time analysis to determine whether a chirp signal exists in the echo signal. If so, it is determined that microemboli exist in the skull.

2. The method for detecting intracranial microemboli based on ultrasonic chirp signals according to claim 1, characterized in that: Also includes: The phase of the echo signal is calculated by Hilbert transform, and the continuity and nonlinear characteristics of the phase are analyzed. If the phase shows nonlinear characteristics, it is determined that the corresponding signal source is a microembolus.

3. The method for detecting intracranial microemboli based on ultrasonic chirp signals according to claim 2, characterized in that: Also includes: The echo signals corresponding to the multiple transmissions of the chirp waveform are arranged in the slow time direction, and are sampled within a set period of time. If the selected echo data conforms to the chirp function characteristics, the corresponding signal source is determined to be a microembolus.

4. The method for detecting intracranial microemboli based on ultrasonic chirp signals according to claim 3, characterized in that: The performing slow time analysis on the echo signal comprises: Using slow time as the horizontal coordinate and moving speed as the vertical coordinate, a frequency analysis diagram of the echo signal is established; It is determined whether the frequency change range of the echo signal is proportional to the preset moving speed of the microemboli. If so, it is determined that the echo signal has a chirp signal.

5. The method for detecting intracranial microemboli based on ultrasonic chirp signals according to claim 4, characterized in that: The performing slow time analysis on the echo signal further comprises: Performing Fourier transform on the echo signal on the frequency analysis graph to obtain a corresponding bandwidth; The bandwidth of the echo signal is compared with a preset microembolus bandwidth, and if they are consistent, it is determined that a chirp signal exists in the echo signal.

6. The method for detecting intracranial microemboli based on ultrasonic chirp signals according to claim 5, characterized in that: Also includes: Direction identification is performed on the echo signal with the chirp signal. If the echo signal has directionality, it is determined that the corresponding signal source is a microembolus.

7. The method for detecting intracranial microemboli based on ultrasonic chirp signals according to claim 6, characterized in that: Also includes: The echo signal with the chirp signal is subjected to dual-depth time difference identification. If there is a time difference between the dual depths of the echo signal, it is determined that the corresponding signal source is a microembolus.

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