Subclinical valve leaflet thrombus assisted identification method, system and signal collection device after transcatheter aortic valve replacement

Abstract

The disclosure provides a subclinical valve leaflet thrombosis auxiliary identification method, system and signal acquisition device after transcatheter aortic valve replacement, belonging to the technical field of heart information acquisition and identification. The subclinical valve leaflet thrombosis auxiliary identification method is used for auxiliary diagnosis of subclinical valve leaflet thrombosis after transcatheter aortic valve replacement, comprising: collecting a heart sound signal, processing the heart sound signal, obtaining preset parameters of the heart sound signal, and the preset parameters at least including time, frequency and energy; determining a systolic period according to the heart sound signal; identifying the highest energy point in the characteristic interval of the systolic period; obtaining the characteristic frequency corresponding to the highest energy point, and judging whether the characteristic frequency is greater than the threshold frequency, if yes, marking the heart sound signal as an abnormal heart sound signal caused by subclinical valve leaflet thrombosis; if not, marking the heart sound signal as a normal heart sound signal. The method solves the problem that the subclinical valve leaflet thrombosis detection mainly uses multi-dimensional computed tomography, which leads to limited scope of application.

Description

Technical Field

[0001] This disclosure relates to the field of cardiac information acquisition and recognition technology, and in particular to a method, system and signal acquisition device for auxiliary identification of subclinical leaflet thrombosis after transcatheter aortic valve replacement. Background Technology

[0002] Subclinical leaflet thrombosis is a common complication after transcatheter aortic valve replacement, with an incidence rate between 15% and 30%. The presence of subclinical leaflet thrombosis reduces leaflet movement, affects the effective orifice area of ​​the aortic valve, increases transvalvular pressure gradient and flow velocity, and ultimately affects valve durability.

[0003] Currently, the primary method for detecting subclinical leaflet thrombosis is multidimensional computed tomography (MDCT). MDCT can identify hallmark features associated with valvular dysfunction and durability, such as low-density leaflet thickening and reduced leaflet movement, thus diagnosing the presence of subclinical leaflet thrombosis. However, MDCT is not suitable for elderly individuals, those with chronic renal insufficiency, or those allergic to contrast agents. Summary of the Invention

[0004] This disclosure provides a method, system, and signal acquisition device for the auxiliary identification of subclinical leaflet thrombosis after transcatheter aortic valve replacement, in order to solve the technical problem that the detection of subclinical leaflet thrombosis in the prior art mainly uses multidimensional computed tomography, which has a limited applicable population.

[0005] To address the aforementioned technical problems, this disclosure provides a method for assisting in the identification of subclinical leaflet thrombosis, used to aid in the diagnosis of subclinical leaflet thrombosis after transcatheter aortic valve replacement, comprising:

[0006] Collect heart sound signals and process the heart sound signals to obtain preset parameters of the heart sound signals, the preset parameters including at least time, frequency and energy;

[0007] The systolic phase is determined based on the heart sound signals;

[0008] Identify the energy peak within the characteristic interval of the contraction phase;

[0009] Obtain the characteristic frequency corresponding to the highest energy point, and determine whether the characteristic frequency is greater than the threshold frequency. If it is, mark the heart sound signal as an abnormal heart sound signal caused by subclinical valve leaflet thrombosis; if not, mark the heart sound signal as a normal heart sound signal.

[0010] To address the aforementioned technical problems, this disclosure also provides a subclinical leaflet thrombosis auxiliary identification system for assisting in the diagnosis of subclinical leaflet thrombosis after transcatheter aortic valve replacement. The subclinical leaflet thrombosis auxiliary identification system is used to implement the aforementioned subclinical leaflet thrombosis auxiliary identification method. The subclinical leaflet thrombosis auxiliary identification system includes:

[0011] Heart sound acquisition unit, used to acquire heart sound signals;

[0012] The heart sound processing unit is used to process the acquired heart sound signals and obtain preset parameters of the heart sound signals, wherein the preset parameters include at least time, frequency and energy.

[0013] The processor unit is configured to determine the systolic phase based on the heart sound signal, identify the energy peak within the characteristic interval of the systolic phase, obtain the characteristic frequency corresponding to the energy peak, and determine whether the characteristic frequency is greater than a threshold frequency. If so, the processor unit outputs the identification result that the heart sound signal is an abnormal heart sound signal caused by subclinical valve leaflet thrombosis; if not, the processor unit outputs the identification result that the heart sound signal is a normal heart sound signal.

[0014] To address the aforementioned technical problems, this disclosure also provides a heart sound and electrocardiogram (ECG) signal acquisition device for implementing the step of simultaneously acquiring human heart sound and ECG signals in the above-mentioned subclinical valve leaflet thrombosis auxiliary identification method. The heart sound and ECG signal acquisition device includes:

[0015] Electrodes, used to acquire the electrocardiogram signals;

[0016] A sound sensor is used to synchronously acquire the heart sound signals;

[0017] The processing unit is used to synchronously digitize the heart sound signal and the electrocardiogram signal.

[0018] The positive and progressive effects of this disclosure are:

[0019] The technical solution disclosed herein utilizes the acoustic characteristics of subclinical leaflet thrombosis, which causes high-energy murmurs in the characteristic range of systole. By determining whether the characteristic frequency corresponding to the highest energy point of the heart sound signal in the characteristic range of systole is greater than a threshold frequency, the abnormality of the heart sound signal can be identified. This achieves the processing and identification of heart sound signals as a form of human information. The identification result can be used as an intermediate result to assist in the diagnosis of whether subclinical leaflet thrombosis has formed after transcatheter aortic valve replacement. This auxiliary diagnosis is applicable to various populations and effectively solves the technical problem that the detection of subclinical leaflet thrombosis in the prior art mainly uses multidimensional computed tomography, which has a limited range of applicable populations. Attached Figure Description

[0020] Figure 1A flowchart of a subclinical valve leaflet thrombosis-assisted identification method provided in an embodiment of this disclosure.

[0021] Figure 2 The illustration shows a heart sound time-frequency energy map generated from a heart sound signal and a synchronously acquired electrocardiogram, according to an embodiment of the present disclosure.

[0022] Figure 3 This is a schematic diagram illustrating the definition of the contraction period in one embodiment of this disclosure.

[0023] Figure 4 This is a schematic diagram of a subclinical leaflet thrombosis-assisted identification system provided in an embodiment of the present disclosure.

[0024] Figure 5 This is a schematic diagram of a heart sound and electrocardiogram signal acquisition device provided in an embodiment of the present disclosure.

[0025] The above and other features, advantages, etc., of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the elements are not necessarily drawn to scale. Detailed Implementation

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used herein, in the specification of the application, is for the purpose of describing particular embodiments only and is not intended to be limiting of this disclosure. The term "comprising" and any corresponding variations thereof in the specification and claims of this disclosure are intended to cover a non-exclusive inclusion.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this disclosure. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] This disclosure is based on a study that assists in the diagnosis of whether subclinical leaflet thrombosis has formed after transcatheter aortic valve replacement. To facilitate the explanation of this disclosure, the study will be introduced below.

[0029] The principles upon which this research is based are as follows.

[0030] During the cardiac cycle, the opening and closing of the valves produces heart sounds as blood flows through them. The presence of subclinical leaflet thrombosis reduces leaflet movement, affecting the effective orifice area of ​​the aortic valve, increasing transvalvular pressure gradient and flow velocity, and ultimately impacting valve durability. In other words, the presence of subclinical leaflet thrombosis alters hemodynamic characteristics, reducing the cross-sectional area of ​​the valve orifice, increasing local resistance, pressure gradient, and flow velocity, and shifting blood flow into a turbulent state. This change in hemodynamic characteristics results in an abnormal murmur that can be detected in the aortic valve auscultation area. Therefore, this study aims to verify how the identification of this murmur can aid in the diagnosis of subclinical leaflet thrombosis.

[0031] The methods used in this study are as follows.

[0032] Patients with severe symptomatic aortic stenosis who had successfully undergone transcatheter aortic valve replacement were recruited. Based on the severity of subclinical leaflet thrombosis, patients were divided into three groups: Group 1 (no subclinical leaflet thrombosis), Group 2 (mild subclinical leaflet thrombosis, characterized by low-density leaflet thickening but not reaching the level of Group 3 below), and Group 3 (moderate to severe subclinical leaflet thrombosis, characterized by at least one leaflet thickening determined to be grade 3 or 4, with leaflet mobility reduced by ≥50%, or two leaflets thickening determined to be grade 2 or with leaflet mobility reduced by ≥50%). For clarity, patients were reclassified into two main categories: those without moderate to severe subclinical leaflet thrombosis (Groups 1 and 2) and those with moderate to severe subclinical leaflet thrombosis (Group 3). Multidimensional computed tomography (CT) scans were performed on patients 30 days and 6 months after discharge to detect low-density leaflet thickening and reduced leaflet mobility, in order to determine subclinical leaflet thrombosis, which is the gold standard for its detection. A four-level grading system was used to describe the degree of leaflet thickening: <25% (limited to the base), >25% and ≤50%, >50% and ≤75%, and >75%. A four-level grading system was also used to describe the degree of reduced leaflet mobility: none (no reduced leaflet mobility), reduced leaflet mobility <50%, reduced leaflet mobility ≥50%, and leaflet immobility. Heart sounds and electrocardiogram (ECG) signals were simultaneously acquired, and acoustic feature analysis was performed on the acquired signals. Statistical analysis of the acoustic data was also conducted.

[0033] The results of this study are as follows.

[0034] A total of 116 patients consented to and participated in the study. At the 1-month follow-up, multidimensional computed tomography (CT) revealed a prevalence of subclinical leaflet thrombosis in 25% of patients, with 11.2% classified as moderate to severe. Analysis of synchrotron heart sound and electrocardiogram (CTECG) results showed that patients in group 3 experienced a high-energy murmur in early systole, while this murmur was absent in patients without subclinical leaflet thrombosis. At 6 months, both CT and CTECG indicated that the subclinical leaflet thrombosis had completely resolved in 9 patients (70%) in group 3 who received anticoagulation therapy. Therefore, based on the deterioration of transvalvular hemodynamics after transcatheter aortic valve replacement, detecting the magnitude of murmurs in specific intervals of heart sound signals can serve as an effective method for the auxiliary diagnosis of subclinical leaflet thrombosis, namely, the subclinical leaflet thrombosis auxiliary identification method disclosed herein.

[0035] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0036] The first aspect of the present disclosure is described below: a method for assisting in the identification of subclinical leaflet thrombosis.

[0037] This disclosed method for the auxiliary identification of subclinical leaflet thrombosis is used to assist in the diagnosis of subclinical leaflet thrombosis after transcatheter aortic valve replacement. Here, we first provide further explanation of this auxiliary diagnostic method. This method identifies abnormal heart sound signals caused by subclinical leaflet thrombosis, especially moderate to severe subclinical leaflet thrombosis. Essentially, it is still the identification of cardiac information, and the identification result is still a cardiac information identification result. However, by using the identification result obtained by this method, it can assist in the diagnosis of the formation of subclinical leaflet thrombosis. This method does not directly diagnose subclinical leaflet thrombosis as a disease, but rather uses the cardiac information identification result obtained by this method to assist in the diagnosis.

[0038] Figure 1 A flowchart illustrating the subclinical leaflet thrombosis-assisted identification method provided in this embodiment of the disclosure. Figure 1 As shown, the method for assisting in the diagnosis of subclinical leaflet thrombosis after transcatheter aortic valve replacement includes the following steps:

[0039] S101, acquire heart sound signals, process the heart sound signals, and obtain preset parameters of the heart sound signals. The preset parameters include at least time, frequency, and energy.

[0040] S102, determine the systolic phase based on heart sound signals;

[0041] S103 identifies the energy peak within the characteristic interval of the contraction phase;

[0042] S104. Obtain the characteristic frequency corresponding to the highest energy point and determine whether the characteristic frequency is greater than the threshold frequency. If yes, then S105 mark the heart sound signal as an abnormal heart sound signal caused by subclinical valve leaflet thrombosis; if no, then S106 mark the heart sound signal as a normal heart sound signal.

[0043] In this embodiment, three preset parameters—time, frequency, and energy—are used to quantitatively analyze the acquired heart sound signals. Specifically, the quantitative analysis method in this embodiment employs wavelet transform, which can obtain better time / frequency characteristics. Of course, in other optional embodiments, short-time Fourier transform and other time-frequency analysis methods can also be used. In this embodiment, the energy is calculated based on the heart sound waveform, such as... Figure 2 As shown, a time-frequency energy graph of heart sounds is displayed. In the graph, the horizontal axis represents time, and the vertical axis represents frequency. The intensity of the color in the graph indicates the energy level; the darker the color, the greater the energy. Generally, energy is related to amplitude and frequency; the greater the amplitude, the higher the energy value; the greater the frequency, the higher the energy level. Since amplitude varies with body weight, acquisition position, and posture, while frequency, as an inherent characteristic of sound waves, is not affected by acquisition conditions or environment, frequency is selected as the judgment indicator in this disclosure.

[0044] Since energy can reflect the loudness of heart sounds, a higher abnormal energy level during systole indicates the presence of a murmur during systole, for example... Figure 2 The diagram illustrates a typical abnormal murmur associated with subclinical thrombosis. Identifying this abnormal murmur can aid in the diagnosis of subclinical valve leaflet thrombosis. Specifically, when a murmur occurs during systole, the energy peak within the murmur's range is identified. The corresponding characteristic frequency is then obtained. This characteristic frequency is compared to the threshold frequency corresponding to subclinical valve leaflet thrombosis. If the characteristic frequency is greater than the threshold frequency, it indicates a murmur caused by subclinical valve leaflet thrombosis, thus aiding in the diagnosis that the subject has a high probability of subclinical valve leaflet thrombosis. It should be noted that the threshold frequency corresponding to subclinical valve leaflet thrombosis is a value obtained from summarizing clinical data; the relevant statistical methods and data will be explained in more detail below.

[0045] The method described in this embodiment identifies whether a heart sound signal is abnormal by determining whether the characteristic frequency corresponding to the highest energy point of the heart sound signal within the characteristic interval of systole is greater than the threshold frequency corresponding to subclinical leaflet thrombosis. This achieves the processing and recognition of this physiological information of the human body, and the recognition result obtained by this subclinical leaflet thrombosis auxiliary identification method can be used as an intermediate result information to assist in the diagnosis of whether subclinical leaflet thrombosis has formed after transcatheter aortic valve replacement. This auxiliary diagnosis is applicable to various populations and effectively solves the technical problem that the detection of subclinical leaflet thrombosis in the prior art mainly uses multidimensional computed tomography, which has a limited range of applicable populations.

[0046] Furthermore, in embodiments of this disclosure, determining the systolic phase based on heart sound signals includes:

[0047] Acquire electrocardiogram signals synchronized with heart sound signals;

[0048] The first and second heart sounds are determined based on the electrocardiogram (ECG) signal.

[0049] The systolic period is determined based on the first and second heart sounds.

[0050] Figure 3 The figure shows cardiac hemodynamic curves and corresponding phonocardiograms and electrocardiograms. The three curves at the top of the figure represent the time-pressure curves of the aorta, left atrium, and left ventricle, respectively. The horizontal axis of each curve represents time (not shown in the figure), and the vertical axis represents pressure (not shown in the figure). The hemodynamic significance of the intersection points of the curves are as follows: intersection point a represents mitral valve closure, intersection point b represents aortic valve opening, intersection point c represents aortic valve closure, and intersection point d represents mitral valve opening. Figure 2 The two curves at the bottom center represent the electrocardiogram (ECG) and the phonocardiogram (PCH). The ECG shows the P wave, Q wave, R wave, S wave, and T wave, while the PCH shows the first heart sound S1, the second heart sound S2, the third heart sound S3, and the fourth heart sound S4. In this embodiment, the corresponding PCH and ECG are plotted using synchronously acquired heart sound and ECG signals. The first and second heart sounds in the PCH can be identified from the R, S, and T waves in the ECG, and then the systolic period is determined based on the relationship between the first and second heart sounds and the systolic period.

[0051] Further, in embodiments of this disclosure, determining the first and second heart sounds based on the electrocardiogram signal includes:

[0052] The same time axis is used to display electrocardiogram signals and heart sound signals;

[0053] Identify the R peak in electrocardiogram signals;

[0054] The first and second heart sounds were identified based on the R peak.

[0055] In this embodiment, in the phonocardiogram and electrocardiogram corresponding to the synchronously acquired heart sound signal and electrocardiogram signal, since the peak of the R wave (i.e., the R peak) in the electrocardiogram indicates the beginning of the first heart sound, the beginning of the first heart sound in the phonocardiogram can be determined accordingly. This method is simpler and more convenient.

[0056] Furthermore, in the embodiments of this disclosure, the systolic period is the period from the opening of the aortic valve to the closing of the aortic valve, and the characteristic interval is the period from the beginning of the systolic period to the midline of the systolic period.

[0057] like Figure 3 As shown, the systolic period is the interval between the systolic initiation line (point b, representing aortic valve opening) and the systolic end line (point c, representing aortic valve closure). The characteristic interval is the interval between the systolic initiation line and the systolic midline. The selection of the characteristic interval is based on clinical data analysis from the aforementioned study on the auxiliary diagnosis of subclinical leaflet thrombosis after transcatheter aortic valve replacement. Specifically, the study found that patients with subclinical leaflet thrombosis after transcatheter aortic valve replacement uniquely exhibited high-energy abnormal murmurs in their heart sound signals. Statistical analysis revealed that this murmur was concentrated in a specific interval, defined as the characteristic interval. This characteristic interval is located in the early systolic phase. For easier quantitative description, this disclosure, based on clinical statistics, defines the systolic period as the time from aortic valve opening to aortic valve closure. Furthermore, based on the range of abnormal murmur occurrence, the period from the beginning of systole to the systolic midline was selected as the characteristic interval. Therefore, a quantitative and specific judgment standard is provided for the auxiliary diagnosis of whether a patient has subclinical leaflet thrombosis.

[0058] Furthermore, in the embodiments of this disclosure, the threshold frequency is 25 Hz. In other alternative embodiments, the threshold frequency may be 20 Hz or other frequencies greater than 20 Hz.

[0059] In this embodiment, the selection of a threshold frequency of 25 Hz was also based on clinical data analysis from the aforementioned study on the auxiliary diagnosis of subclinical leaflet thrombosis after transcatheter aortic valve replacement. The study found that on day 30, the median sound frequencies of the highest energy points in the target spectra of groups 1, 2, and 3 were 20 Hz (interquartiles: 17–23 Hz), 22 Hz (interquartiles: 19–24 Hz), and 53 Hz (interquartiles: 45–67 Hz), respectively. In patients with moderate to severe subclinical leaflet thrombosis, a non-baseline murmur was observed from the onset of systole to the midline of systole, characterized by an energy frequency lower than the first heart sound. It should be noted that the characteristic frequency refers to the frequency corresponding to the highest energy point within the murmur range, not the frequency corresponding to the energy of the first heart sound itself. Receiver operating characteristic (ROC) curves showed that the phono-cardiogram (VCG) synchronization method with a threshold frequency of 25 Hz had the highest diagnostic efficiency for moderate to severe subclinical leaflet thrombosis, with a sensitivity of 84.62%, a specificity of 91.26%, and an area under the curve (AUC) of 0.920 (95% CI: 0.855–0.962, p<0.001). The positive likelihood ratio (LR+) was 9.68, the negative likelihood ratio (LR-) was 0.17, and the Kappa value was 0.61 (p<0.001), indicating strong concordance in the diagnosis of subclinical leaflet thrombosis. Case studies were selected to further evaluate VCG synchronization and multidimensional computed tomography (CT) scans in patients with and without subclinical leaflet thrombosis. In 13 patients with moderate to severe subclinical leaflet thrombosis who received anticoagulation therapy 30 days to 6 months after transcatheter aortic valve replacement, the prevalence of low-density leaflet thickening significantly decreased to 70% (9 / 13), and valvular function was fully restored. Correspondingly, abnormal murmurs associated with subclinical leaflet thrombosis disappeared, indicating that the auxiliary diagnosis of subclinical leaflet thrombosis was consistent with the diagnostic results of multidimensional computed tomography. This study demonstrates the effectiveness of the identification method disclosed herein for the auxiliary diagnosis of subclinical leaflet thrombosis, and the effectiveness of selecting 25 Hz as the threshold for determining whether a murmur is caused by subclinical leaflet thrombosis.

[0060] Furthermore, in this embodiment, the subclinical leaflet thrombosis-assisted identification method further includes: establishing a user database model and performing machine learning and comparative analysis on user data to provide early warnings for users who may have abnormal heart sound signals caused by subclinical leaflet thrombosis; wherein, the user data includes at least: heart sound signals, electrocardiogram signals, and identification results. This also includes data filtering and validity analysis of the collected information. For valid data, a signal processing method based on continuous wavelet transform is used for preliminary feature extraction, and a deep learning-based artificial intelligence algorithm is used to further extract heart sound signals and electrocardiogram signals, thereby obtaining accurate electrocardiograms, heart sound maps, and corresponding energy distribution maps. After processing and analyzing the currently collected user data using the analysis algorithm, user data comparison is performed, that is, comparing the analyzed user data with the user's historical data and with known disease data to determine whether the heart sound signals and electrocardiogram signals are developing towards the formation of abnormal murmurs caused by subclinical leaflet thrombosis, or have already formed abnormal murmurs caused by subclinical leaflet thrombosis, thereby determining the user's health status. The analysis results of user data can be directly displayed and trigger alerts. Simultaneously, the analysis results can be submitted via network to medical support systems such as nurse stations, data centers, and the patient's attending physician. If anomalies are detected in user information, such as significant changes compared to historical data, or data patterns similar to known disease patterns, an alert is triggered to alert the user to potential risks. Furthermore, when data analysis determines that a user's current frequency threshold is significantly higher than a characteristic threshold, it indicates an emergency situation. In this case, the database system can send out distress signals, such as to emergency centers or pre-set emergency contacts, to improve the timeliness of rescue efforts.

[0061] The second aspect of this disclosure is described below: a subclinical leaflet thrombosis-assisted identification system.

[0062] The subclinical leaflet thrombosis auxiliary identification system provided in this embodiment is used to assist in the diagnosis of subclinical leaflet thrombosis after transcatheter aortic valve replacement, realizing the above-mentioned subclinical leaflet thrombosis auxiliary identification method. Figure 4 As shown, the subclinical valve leaflet thrombosis auxiliary identification system 400 includes: a heart sound acquisition unit 401 for acquiring heart sound signals; a heart sound processing unit 402 for processing the acquired heart sound signals and obtaining preset parameters of the heart sound signals, the preset parameters including at least time, frequency and energy; and a processor unit 403 for determining the systolic phase based on the heart sound signals, identifying the energy peak point within the characteristic interval of the systolic phase, obtaining the characteristic frequency corresponding to the energy peak point, and determining whether the characteristic frequency is greater than a threshold frequency. If so, the system outputs the identification result of the heart sound signal as an abnormal heart sound signal caused by subclinical valve leaflet thrombosis; otherwise, the system outputs the identification result of the heart sound signal as a normal heart sound signal.

[0063] The subclinical leaflet thrombosis auxiliary identification system 400 provided in this embodiment identifies whether the heart sound signal is abnormal by judging whether the characteristic frequency corresponding to the highest energy point in the characteristic interval of the heart sound signal during systole is greater than the threshold frequency. It realizes the processing and identification of heart sound signal as human information, and then uses the obtained identification result as an intermediate result information to assist in the diagnosis of whether subclinical leaflet thrombosis has formed after transcatheter aortic valve replacement. This auxiliary diagnosis is applicable to various populations and effectively solves the technical problem that the detection of subclinical leaflet thrombosis in the existing technology mainly adopts multidimensional computed tomography scanning, which has a limited range of applicable populations.

[0064] Furthermore, in the embodiments of this disclosure, the subclinical valve leaflet thrombosis auxiliary identification system 400 further includes: an electrocardiogram (ECG) acquisition unit 404, used to simultaneously acquire ECG signals when the heart sound acquisition unit 401 acquires heart sound signals; and an ECG processing unit 405, used to process the ECG signals acquired by the ECG acquisition unit 404; wherein, the processor unit 403 is capable of determining the first and second heart sounds of the heart sound signal based on the ECG signals, and determining the systolic phase based on the first and second heart sounds.

[0065] Furthermore, in the embodiments of this disclosure, the subclinical valve leaflet thrombosis auxiliary identification system 400 further includes: a database unit 406, used to establish a user database model and perform machine learning and comparative analysis on user data to provide early warning for users who may have abnormal heart sound signals caused by subclinical valve leaflet thrombosis; wherein, the user data includes at least: heart sound signals, electrocardiogram signals and identification results.

[0066] Furthermore, the subclinical valve leaflet thrombosis auxiliary identification system 400 of this embodiment also includes a communication unit (not shown in the figure) for data transmission between the heart sound acquisition unit 401, heart sound processing unit 402, electrocardiogram acquisition unit 404, electrocardiogram processing unit 405, processor unit 403, and database unit 406. The communication unit can employ various types of communication methods, such as wired, wireless communication links, or fiber optic cables. It should be noted that the aforementioned wireless connection methods can include, but are not limited to, 3G / 4G / 5G / 6G connections, Wi-Fi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB connections, local area networks (LANs), wide area networks (WANs), the Internet (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as other currently known or future-developed network connection methods. The communication unit can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol), and can interconnect with any form or medium of digital data communication (e.g., communication networks).

[0067] In addition, in some optional embodiments, the subclinical valve leaflet thrombosis-assisted identification system 400 also includes a user terminal device and a server terminal device, with the database unit 406 potentially located within the server terminal device. Users can interact with the server terminal device via a communication unit using the user terminal device to receive or send data. Various user terminal applications can be installed on the user terminal device, such as video streaming and playback applications, web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc. The user terminal device can be various electronic devices with touchscreen displays and / or web browsing capabilities, including but not limited to smartphones, tablets, e-book readers, MP3 (Multimedia Audio Layer 3) players, MP4 (Multimedia Audio Layer 4) players, head-mounted displays, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PMPs (Portable Multimedia Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and mobile terminals such as digital TVs and desktop computers.

[0068] In addition, in some optional implementations, database unit 406 can store information such as user name, gender, age, height, weight, medical records, and cardiac monitoring data. Database unit 406 can receive human vital sign data transmitted from user devices, automatically store, archive, analyze, extract features, and identify the data, and then transmit the analysis results (such as analysis reports, health advice, or disease warnings) to the user devices via a communication unit. Alternatively, database unit 406 may not send its analysis results, but the user can view them by logging into the cloud data center through their user device. Furthermore, with user permission, the collected human vital sign data and analysis results can be accessed by medical professionals or institutions as a reference for further medical examinations or diagnoses. Also with user permission, third-party medical research, data analysis, data statistics, and data mining experts can conduct more in-depth analysis and research on the data. Their research results can be uploaded to database unit 406 for user viewing. Users can also select and customize different analysis and feature extraction algorithms on database unit 406, and pay usage fees to the algorithm developers through database unit 406.

[0069] Furthermore, in some optional implementations, database unit 406 can process relevant data based on artificial intelligence (AI) technology. AI is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results. Database unit 406 can learn from datasets of human characteristic monitoring data from one or more users. Machine learning algorithms can discover patterns and relationships among several independent and interdependent variables derived from the data. Continuous consideration of these variables or other learned variables from continuous updates of the algorithm allows the machine learning algorithm to probabilistically determine, for example, classification, diagnosis, and / or prediction of a patient's condition. As an example, the machine learning algorithm can be configured to employ any one or more of Bayesian, random forest, decision tree, linear regression, deep learning, neural networks, and / or dimensionality reduction techniques.

[0070] The third aspect of this disclosure is described below: a device for acquiring heart sounds and electrocardiogram signals.

[0071] like Figure 5 As shown, this embodiment also provides a heart sound and electrocardiogram (ECG) signal acquisition device 500 for synchronously acquiring human heart sound signals and ECG signals. The heart sound and ECG signal acquisition device 500 includes: an electrode 501 for acquiring ECG signals; a sound sensor 502 for synchronously acquiring heart sound signals; and a processing unit 503 for synchronously digitizing the heart sound signals and ECG signals. The electrode 501 serves as an ECG acquisition unit, the sound sensor 502 serves as a heart sound acquisition unit, and the processing unit 503 serves as an integrated unit of the ECG processing unit and the heart sound processing unit.

[0072] In addition, in some alternative embodiments, a circuit module is provided within the processing unit 503. The sound sensor 502 is integrated with the electrode 501 and connected to the circuit module, transmitting the collected heart sound signals and electrocardiogram signals to the circuit module for processing to obtain stable heart sound signals and electrocardiogram signals.

[0073] In addition, in some optional embodiments, the heart sound and electrocardiogram (ECG) signal acquisition device 500 also includes an adhesive patch. The adhesive patch, made of medical-grade silicone, is used to secure the heart sound and ECG signal acquisition device 500 to the skin. The adhesive patch ensures normal operation of the heart sound and ECG signal acquisition device 500 with minimal impact on the user's daily life and work, facilitating long-term wear and testing. In the prior art, the detection of subclinical valve leaflet thrombosis mainly uses multidimensional computed tomography (CT) scanning, but its applicability is limited. For example, it requires specialized hospital facilities and is not suitable for elderly patients or patients with renal insufficiency. The aforementioned wearable heart sound and ECG signal acquisition device 500 provides an effective solution, overcoming these limitations through remote, non-invasive detection. This device allows for continuous use by the patient outside of a hospital environment. Furthermore, the wearable heart sound and ECG signal acquisition device 500 facilitates remote medical guidance and comprehensive management throughout the monitoring process. This feature is particularly beneficial for elderly and rural patients, as it allows for continuous and easy-to-use monitoring outside of clinical settings. In addition, the device can be integrated with a mobile application to further ensure that patients can manage their own health independently while maintaining close communication with medical institutions.

[0074] The features of the embodiments according to this disclosure are briefly summarized and listed below.

[0075] According to one or more embodiments of this disclosure, a method for assisting in the identification of subclinical leaflet thrombosis is provided for the auxiliary diagnosis of subclinical leaflet thrombosis after transcatheter aortic valve replacement, comprising:

[0076] Collect heart sound signals and process the heart sound signals to obtain preset parameters of the heart sound signals, the preset parameters including at least time, frequency and energy;

[0077] The systolic phase is determined based on the heart sound signals;

[0078] Identify the energy peak within the characteristic interval of the contraction phase;

[0079] Obtain the characteristic frequency corresponding to the highest energy point, and determine whether the characteristic frequency is greater than the threshold frequency. If it is, mark the heart sound signal as an abnormal heart sound signal caused by subclinical valve leaflet thrombosis; if not, mark the heart sound signal as a normal heart sound signal.

[0080] According to one or more embodiments of this disclosure, a method for assisting in the identification of subclinical valve leaflet thrombosis is provided, wherein determining the systolic phase based on the heart sound signal includes:

[0081] Acquire electrocardiogram signals synchronized with the heart sound signals;

[0082] The first and second heart sounds of the heart sound signal are determined based on the electrocardiogram signal;

[0083] The systolic phase is determined based on the first heart sound and the second heart sound.

[0084] According to one or more embodiments of this disclosure, a method for assisting in the identification of subclinical valve leaflet thrombosis is provided, wherein determining the first and second heart sounds of the heart sound signal based on the electrocardiogram signal includes:

[0085] The electrocardiogram signal and the heart sound signal are displayed using the same time axis;

[0086] Identify the R peak in the electrocardiogram signal;

[0087] The first heart sound and the second heart sound were identified based on the R peak.

[0088] According to one or more embodiments of this disclosure, a method for assisting in the identification of subclinical valve leaflet thrombosis is provided, wherein the systolic period is the period from the opening of the aortic valve to the closing of the aortic valve, and the characteristic interval is the period from the beginning of the systolic period to the midline of the systolic period.

[0089] According to one or more embodiments of this disclosure, a subclinical leaflet thrombosis-assisted identification method is provided, wherein the threshold frequency is at least 20 Hz.

[0090] According to one or more embodiments of this disclosure, a method for assisting in the identification of subclinical valve leaflet thrombosis is provided, the method further comprising:

[0091] A user database model is established, and machine learning and comparative analysis are performed on user data to provide early warning for users who may have abnormal heart sound signals caused by subclinical valve leaflet thrombosis.

[0092] The user data includes at least the heart sound signal, the electrocardiogram signal, and the recognition result.

[0093] According to one or more embodiments of this disclosure, a subclinical leaflet thrombosis auxiliary identification system is provided for assisting in the diagnosis of subclinical leaflet thrombosis after transcatheter aortic valve replacement. The subclinical leaflet thrombosis auxiliary identification system is used to implement the above-described subclinical leaflet thrombosis auxiliary identification method. The subclinical leaflet thrombosis auxiliary identification system includes:

[0094] Heart sound acquisition unit, used to acquire heart sound signals;

[0095] The heart sound processing unit is used to process the acquired heart sound signals and obtain preset parameters of the heart sound signals, wherein the preset parameters include at least time, frequency and energy.

[0096] The processor unit is configured to determine the systolic phase based on the heart sound signal, identify the energy peak within the characteristic interval of the systolic phase, obtain the characteristic frequency corresponding to the energy peak, and determine whether the characteristic frequency is greater than a threshold frequency. If so, the processor unit outputs the identification result that the heart sound signal is an abnormal heart sound signal caused by subclinical valve leaflet thrombosis; if not, the processor unit outputs the identification result that the heart sound signal is a normal heart sound signal.

[0097] According to one or more embodiments of this disclosure, a subclinical valve leaflet thrombosis-assisted identification system is provided, the subclinical valve leaflet thrombosis-assisted identification system further comprising:

[0098] An electrocardiogram (ECG) acquisition unit is used to simultaneously acquire ECG signals while the heart sound acquisition unit acquires the heart sound signals;

[0099] An electrocardiogram (ECG) processing unit is used to process the acquired ECG signals;

[0100] The processor unit is capable of determining the first and second heart sounds of the heart sound signal based on the electrocardiogram signal, and determining the systolic phase based on the first and second heart sounds.

[0101] According to one or more embodiments of this disclosure, a subclinical valve leaflet thrombosis-assisted identification system is provided, the subclinical valve leaflet thrombosis-assisted identification system further comprising:

[0102] The database unit is used to build a user database model and perform machine learning and comparative analysis on user data to provide early warning for users who may have abnormal heart sound signals caused by subclinical valve leaflet thrombosis.

[0103] The user data includes at least the heart sound signal, the electrocardiogram signal, and the recognition result.

[0104] According to one or more embodiments of this disclosure, a heart sound and electrocardiogram (ECG) signal acquisition device is provided for synchronously acquiring human heart sound signals and ECG signals, the heart sound and ECG signal acquisition device comprising:

[0105] Electrodes, used to acquire the electrocardiogram signals;

[0106] A sound sensor is used to synchronously acquire the heart sound signals;

[0107] The processing unit is used to synchronously digitize the heart sound signal and the electrocardiogram signal.

[0108] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0109] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in sequential order. Multitasking and parallel processing may be advantageous in certain environments. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0110] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for assisting in the identification of subclinical valve leaflet thrombosis, characterized in that, Used to aid in the diagnosis of subclinical leaflet thrombosis after transcatheter aortic valve replacement, including: Collect heart sound signals and process the heart sound signals to obtain preset parameters of the heart sound signals, the preset parameters including at least time, frequency and energy; The systolic phase is determined based on the heart sound signals; Identify the energy peak within the noise range of the characteristic interval of the contraction period; The frequency corresponding to the highest energy point is obtained as the characteristic frequency, and it is determined whether the characteristic frequency is greater than the threshold frequency. If it is, the heart sound signal is marked as an abnormal heart sound signal caused by subclinical valve leaflet thrombosis; if not, the heart sound signal is marked as a normal heart sound signal. Wherein, the systolic period is the period from the opening of the aortic valve to the closing of the aortic valve, the characteristic interval is the period from the beginning of the systolic period to the midline of the systolic period, and the threshold frequency is at least 20 Hz; Wherein, when the threshold frequency is ≥25 Hz, the subclinical leaflet thrombosis auxiliary identification method is used to assist in the identification of moderate to severe subclinical leaflet thrombosis. The moderate to severe subclinical leaflet thrombosis is defined as at least one leaflet thickening >50% and reduced mobility ≥50%, or two leaflets thickening >25% and ≤50% or reduced mobility ≥50%.

2. The method according to claim 1, characterized in that, Determining the systolic phase based on the heart sound signal includes: Acquire electrocardiogram signals synchronized with the heart sound signals; The first and second heart sounds of the heart sound signal are determined based on the electrocardiogram signal; The systolic phase is determined based on the first heart sound and the second heart sound.

3. The method according to claim 2, characterized in that, The step of determining the first and second heart sounds of the heart sound signal based on the electrocardiogram signal includes: The electrocardiogram signal and the heart sound signal are displayed using the same time axis; Identify the R peak in the electrocardiogram signal; The first heart sound and the second heart sound were identified based on the R peak.

4. The method according to claim 2, characterized in that, The subclinical leaflet thrombosis-assisted identification method also includes: A user database model is established, and machine learning and comparative analysis are performed on user data to provide early warning for users who may have abnormal heart sound signals caused by subclinical valve leaflet thrombosis. The user data includes at least the heart sound signal, the electrocardiogram signal, and the recognition result.

5. A subclinical valve leaflet thrombosis auxiliary identification system, characterized in that, For assisting in the diagnosis of subclinical leaflet thrombosis after transcatheter aortic valve replacement, the subclinical leaflet thrombosis auxiliary identification system is used to implement the method as described in any one of claims 1-4, and the subclinical leaflet thrombosis auxiliary identification system comprises: Heart sound acquisition unit, used to acquire heart sound signals; The heart sound processing unit is used to process the acquired heart sound signals and obtain preset parameters of the heart sound signals, wherein the preset parameters include at least time, frequency and energy. The processor unit is configured to determine the systolic phase based on the heart sound signal, identify the energy peak within the murmur range of the characteristic interval of the systolic phase, obtain the frequency corresponding to the energy peak as the characteristic frequency, and determine whether the characteristic frequency is greater than a threshold frequency. If so, the processor unit outputs the identification result that the heart sound signal is an abnormal heart sound signal caused by subclinical valve leaflet thrombosis; if not, the processor unit outputs the identification result that the heart sound signal is a normal heart sound signal.

6. The system according to claim 5, characterized in that, The subclinical leaflet thrombosis auxiliary identification system also includes: An electrocardiogram (ECG) acquisition unit is used to simultaneously acquire ECG signals when the heart sound acquisition unit acquires the heart sound signals; An electrocardiogram (ECG) processing unit is used to process the acquired ECG signals; The processor unit is capable of determining the first and second heart sounds of the heart sound signal based on the electrocardiogram signal, and determining the systolic phase based on the first and second heart sounds.

7. The system according to claim 6, characterized in that, The subclinical leaflet thrombosis auxiliary identification system also includes: The database unit is used to build a user database model and perform machine learning and comparative analysis on user data to provide early warning for users who may have abnormal heart sound signals caused by subclinical valve leaflet thrombosis. The user data includes at least the heart sound signal, the electrocardiogram signal, and the recognition result.

8. A heart sound and electrocardiogram (ECG) signal acquisition device, used to implement the step of synchronously acquiring human heart sound signals and ECG signals as described in any one of claims 2-4, wherein the heart sound and ECG signal acquisition device comprises: Electrodes, used to acquire the electrocardiogram signals; A sound sensor is used to synchronously acquire the heart sound signals; The processing unit is used to synchronously digitize the heart sound signal and the electrocardiogram signal.

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