阅读说明：本技术 心动周期时相检测方法、装置及计算机程序产品 (Cardiac cycle phase detection method, apparatus and computer program product ) 是由 杨靖 葛均波 张英梅 于 2021-09-13 设计创作，主要内容包括：本申请提出了一种心动周期时相检测方法,该方法包括获取待检测图像序列,所述待检测图像序列包括一次采集得到的多个待检测图像,且所述待检测图像按照采集时间顺序排列；将所述待检测图像序列输入心动周期时相检测模型,输出所述待检测图像序列中每一所述待检测图像对应的心动周期时相。该方法通过训练以时间序列深度神经网络为基础的心动周期时相检测模型,实现对没有同步采集心电信号的冠脉造影序列中的每一冠脉造影图像自动进行心动周期时相的预测,只依赖顺序采集的冠脉造影图像,无需额外采集同步的心电信号,使检测结果更加客观、可靠,减少对医生过往经验的依赖性,降低误诊率。(The application provides a cardiac cycle time phase detection method, which comprises the steps of obtaining an image sequence to be detected, wherein the image sequence to be detected comprises a plurality of images to be detected acquired at one time, and the images to be detected are arranged according to an acquisition time sequence; and inputting the image sequence to be detected into a cardiac cycle time phase detection model, and outputting a cardiac cycle time phase corresponding to each image to be detected in the image sequence to be detected. The method realizes the automatic prediction of the cardiac cycle time phase of each coronary angiography image in the coronary angiography sequence without synchronously acquiring the electrocardiosignals by training the cardiac cycle time phase detection model based on the time series deep neural network, only depends on the sequentially acquired coronary angiography images, does not need to additionally acquire the synchronous electrocardiosignals, leads the detection result to be more objective and reliable, reduces the dependency on past experience of doctors, and reduces the misdiagnosis rate.)

1. A method for cardiac cycle phase detection, comprising the steps of:

step S1: acquiring an image sequence to be detected, wherein the image sequence to be detected comprises a plurality of images to be detected acquired at one time, and the images to be detected are arranged according to an acquisition time sequence;

step S2: and inputting the image sequence to be detected into a cardiac cycle time phase detection model, and outputting a cardiac cycle time phase corresponding to each image to be detected in the image sequence to be detected.

2. The cardiac cycle phase detection method as claimed in claim 1, wherein the method of constructing the cardiac cycle time detection model comprises the steps of:

step S21: acquiring a plurality of sample image sequences, wherein the sample image sequences comprise a plurality of sample images acquired at one time, each sample image sequence corresponds to a group of electrocardiosignals, and the sample images are arranged according to the corresponding electrocardiosignal sequence;

step S22: according to the corresponding relation between the sample image and the electrocardiosignal in the sample image sequence, marking the corresponding cardiac cycle time phase of the sample image;

step S23: and inputting the sample image sequence into the cardiac cycle time phase detection model for training to obtain the cardiac cycle time phase detection model, wherein the cardiac cycle time phase detection model is based on a time sequence deep neural network.

3. The cardiac cycle phase detection method according to claim 2, wherein the step S21 further includes: removing sample images in the sample image sequence in which blood vessels are not visible.

4. The cardiac cycle phase detection method according to claim 2, wherein the step S22 further includes: one or more attention cardiac cycle phase is selected, and the corresponding cardiac cycle phase of the sample image is labeled according to the attention cardiac cycle phase.

5. The cardiac cycle phase detection method of claim 4, wherein the cardiac cycle phase of interest is at least one of isovolumetric systolic phase, rapid ejection phase, slow ejection phase, pre-diastole, isovolumetric diastolic phase, rapid filling phase, slow filling phase, and ventricular systolic phase.

6. The cardiac cycle phase detection method of claim 4, wherein the cardiac cycle phase of interest is a ventricular systole and/or diastole phase; marking a sample image corresponding to the QRS wave front of the electrocardiosignal as a ventricular systole; and marking the sample image corresponding to the T wave front of the electrocardiosignal as the prophase diastole.

7. The cardiac cycle phase detection method according to claim 1, wherein the image sequence to be detected is a coronary angiography image sequence with an cardiac gating signal.

8. Cardiac cycle phase detection apparatus, comprising the following modules:

the acquisition module is used for acquiring an image sequence to be detected, wherein the image sequence to be detected comprises a plurality of images to be detected acquired at one time, and the images to be detected are arranged according to the acquisition time sequence;

and the detection module is used for inputting the image sequence to be detected into a cardiac cycle time phase detection model and outputting a cardiac cycle time phase corresponding to each image to be detected in the image sequence to be detected.

9. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the cardiac cycle phase detection method of any one of claims 1 to 6.

10. A computer program product, characterized in that it comprises software code portions for performing a cardiac cycle phase detection method according to any one of claims 1 to 6 when the computer program product is run on a computer.

11. A readable storage medium, in which a computer program is stored, the computer program comprising program code for controlling a process to perform the process, the process comprising a cardiac cycle phase detection method according to any one of claims 1 to 6.

Technical Field

The present application relates to the field of smart medical technology, and more particularly, to a method, an apparatus, and a computer program product for detecting a phase of a cardiac cycle.

Background

Accurate diagnosis is a requirement and direction for modern medical development. Coronary angiography plays an important role in the diagnosis and treatment of cardiovascular diseases. Quantitative information of lesions such as the degree of stenosis, lesion length, lumen area, etc. depends on accurate measurement of parameters such as vessel diameter, etc., and subsequent treatment decisions are also based on these quantitative analysis parameters. Under the influence of the periodic pulsation of the heart, the coronary arteries are periodically displaced and also periodically pulsated. Thus, when continuous imaging is performed in coronary angiography, the position and caliber of the blood vessels in the coronary angiography sequence may also be periodically changed. Differences arise if the quantitative analysis of the contrast images is performed at different phases of the cardiac cycle. Therefore, in coronary interpretation, the phase of the cardiac cycle with the least variability (usually end-diastole) needs to be selected for measurement.

In clinical practice, the cardiac cycle phase determination of images in a coronary angiography sequence mainly comprises: (1) and (6) manually judging. Taking clinical coronary angiography quantitative analysis as an example, an analyst browses the whole coronary angiography image sequence, judges an image corresponding to a specific cardiac cycle time phase according to experience, and then performs analysis measurement based on the image; however, even an experienced physician generally can only determine the cardiac cycle phase with distinct characteristics such as ventricular systole or diastole, and the like, and the phase is easy to be misjudged, thereby increasing the variability of the analysis result and the medical cost of the patient. (2) Acquiring electrocardiosignals while performing coronary angiography imaging to obtain an electrocardiosignal group corresponding to a coronary angiography sequence, and enabling images in the coronary angiography sequence to correspond to time phases in the electrocardiosignals one by one, namely the electrocardio-gated coronary angiography sequence. However, the method not only adds extra electrocardiographic measurement steps and time, has high requirements on equipment and operators, but also cannot identify the cardiac cycle time phase corresponding to the coronary angiography image without synchronously acquiring the electrocardiographic signals.

Disclosure of Invention

The embodiment of the application provides a method, a device and a computer program product for detecting a cardiac cycle time phase, which can realize the automatic prediction of the cardiac cycle time phase of each frame of coronary angiography images in a coronary angiography sequence without synchronously acquiring electrocardiosignals by training a time sequence deep neural network-based cardiac cycle time phase detection model.

In a first aspect, an embodiment of the present application provides a cardiac cycle phase detection method, including the following steps: step S1: acquiring an image sequence to be detected, wherein the image sequence to be detected comprises a plurality of images to be detected acquired at one time, and the images to be detected are arranged according to an acquisition time sequence; step S2: and inputting the image sequence to be detected into a cardiac cycle time phase detection model, and outputting a cardiac cycle time phase corresponding to each image to be detected in the image sequence to be detected.

In particular, in some application embodiments, the cardiac cycle phase detection model may further simulate a corresponding electrocardiographic signal according to the image sequence to be detected.

Specifically, in some embodiments, the method for constructing the cardiac cycle time detection model includes the following steps: step S21: obtaining a plurality of sample image sequences, wherein each sample image sequence comprises a plurality of sample images acquired at one time, each sample image sequence corresponds to a group of electrocardiosignals, and the sample images are arranged according to the corresponding electrocardiosignals, namely: the image sequence to be detected is a coronary angiography image sequence with an electrocardio gating signal; step S22: according to the corresponding relation between the sample image and the electrocardiosignal in the sample image sequence, marking the corresponding cardiac cycle time phase of the sample image; step S23: and inputting the sample image sequence into the cardiac cycle time phase detection model for training to obtain the cardiac cycle time phase detection model, wherein the cardiac cycle time phase detection model is based on a time sequence deep neural network.

Wherein, in order to screen out useless sample images, step S21 further includes: removing sample images in the sample image sequence in which blood vessels are not visible.

Further, corresponding sample images can be prepared according to the cardiac cycle phase that needs attention. Therefore, in some embodiments, step S22 further includes: one or more attention cardiac cycle phase is selected, and the corresponding cardiac cycle phase of the sample image is labeled according to the attention cardiac cycle phase. Wherein the cardiac cycle phase of interest is at least one of isovolumetric systolic phase, rapid ejection phase, slowed ejection phase, pre-diastole, isovolumetric diastolic phase, rapid filling phase, slowed filling phase, and ventricular systolic phase.

In some embodiments, the cardiac cycle of interest is when the phase is ventricular systole and/or diastole; marking a sample image corresponding to the QRS wave front of the electrocardiosignal as a ventricular systole; and marking the sample image corresponding to the T wave front of the electrocardiosignal as the prophase diastole.

In a second aspect, the present application provides a cardiac cycle phase detection apparatus for implementing the cardiac cycle phase detection method described in the first aspect, the apparatus includes the following modules:

the acquisition module is used for acquiring an image sequence to be detected, wherein the image sequence to be detected comprises a plurality of images to be detected acquired at one time, and the images to be detected are arranged according to the acquisition time sequence;

and the detection module is used for inputting the image sequence to be detected into a cardiac cycle time phase detection model and outputting a cardiac cycle time phase corresponding to each image to be detected in the image sequence to be detected.

In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory and a processor, the memory having a computer program stored therein, the processor being configured to execute the computer program to perform the method for cardiac cycle phase detection as described in any of the embodiments of the present application.

In a fourth aspect, an embodiment of the present application provides a computer program product, where the computer program product includes: a program or instructions which, when run on a computer, causes the computer to perform a cardiac cycle phase detection method as described in any of the embodiments of the above applications.

In a fifth aspect, embodiments of the present application provide a readable storage medium having stored therein a computer program comprising program code for controlling a process to perform a process, the process comprising a cardiac cycle phase detection method according to any of the embodiments of the present application.

The main contributions and innovation points of the embodiment of the application are as follows: the embodiment of the application provides a method, a device and a computer program product for detecting a cardiac cycle time phase, which can realize the automatic prediction of the cardiac cycle time phase of each coronary angiography image in a coronary angiography sequence without synchronously acquiring electrocardiosignals by training a time series deep neural network-based cardiac cycle time phase detection model.

Particularly, the method only depends on the coronary angiography images which are acquired in sequence, and does not need to additionally acquire synchronous electrocardiosignals, so that the detection result is more objective and reliable, the dependence on past experience of doctors is reduced, and the misdiagnosis rate is reduced. Moreover, the model can also simulate a corresponding electrocardiosignal according to a coronary angiography sequence to be used as reference data in diagnosis.

The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

FIG. 1 is a flow chart of a cardiac cycle phase detection method according to an embodiment of the present application;

fig. 2 is a block diagram of a cardiac cycle phase detection apparatus according to an embodiment of the present application;

fig. 3 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with one or more embodiments of the present specification. Rather, they are merely examples of apparatus and methods consistent with certain aspects of one or more embodiments of the specification, as detailed in the claims which follow.

It should be noted that: in other embodiments, the steps of the corresponding methods are not necessarily performed in the order shown and described herein. In some other embodiments, the method may include more or fewer steps than those described herein. Moreover, a single step described in this specification may be broken down into multiple steps for description in other embodiments; multiple steps described in this specification may be combined into a single step in other embodiments.

Example one

In this embodiment, a large number of coronary angiography sequences are obtained as a sample sequence, each coronary angiography image in the sequence is labeled with a corresponding cardiac cycle time phase according to an electrocardiographic signal corresponding to the sample sequence, and the corresponding cardiac cycle time phase is input into a constructed cardiac cycle time phase detection model for training, so that a cardiac cycle time phase detection model is obtained, and the model can input a cardiac cycle time phase corresponding to each coronary angiography image according to the input coronary angiography sequence.

Specifically, with reference to FIG. 1, the method includes steps S1-S2:

step S1: acquiring an image sequence to be detected, wherein the image sequence to be detected comprises a plurality of images to be detected; and the images to be detected are sorted according to the corresponding electrocardiosignals.

In the step, continuous coronary angiography images of the same person within a period of time are obtained by a coronary angiography imaging method, and the images are sequentially arranged according to the time for acquiring the coronary angiography images to obtain an image sequence as an image sequence to be detected.

Step S2: and inputting the image sequence to be detected into a cardiac cycle time phase detection model, and outputting a cardiac cycle time phase corresponding to each image to be detected in the image sequence to be detected.

In the step, the image sequence to be detected is input into a cardiac cycle time phase detection model, the model identifies each image to be detected in the image sequence to be detected, and the cardiac cycle time phase corresponding to each image to be detected is predicted.

Specifically, the model is obtained by training a time series model based on a deep neural network, and the model can be classified and predicted according to an image sequence. That is to say a sequence of images recorded in chronological order, which data sequence corresponds in the present embodiment to a sequence of images to be detected consisting of images to be detected arranged in chronological order of acquisition. The method has the advantages that the time sequence model reflects the change trend and the change rule of the coronary angiography images acquired according to the acquisition time sequence according to the existing historical training data, analyzes the image sequence to be detected, provides data support for classifying each image to be detected in the image sequence to be detected, and accurately predicts the cardiac cycle time phase corresponding to each image to be detected in the image sequence to be detected, which is recorded according to the time sequence.

The method of training the cardiac cycle phase detection model includes steps S21-S23:

step S21: obtaining a plurality of sample image sequences, wherein each sample image sequence comprises a plurality of sample images, each sample image sequence corresponds to a group of electrocardiosignals, and the sample images are arranged according to the corresponding electrocardiosignal sequence;

step S22: according to the corresponding relation between the sample image and the electrocardiosignal in the sample image sequence, marking the corresponding cardiac cycle time phase of the sample image;

step S23: and inputting the sample image sequence into the cardiac cycle time phase detection model for training to obtain the cardiac cycle time phase detection model, wherein the cardiac cycle time phase detection model is based on a time sequence deep neural network.

In step S21, the sample image sequence is also a continuous coronary image acquired by the same person over a period of time, different sample image sequences may be acquired from the same person or different persons, and different sample images are usually acquired from different persons in order to improve the network' S bloom and robustness. And acquiring an electrocardiosignal corresponding to the coronary angiography image at the same time by using an electrocardio measuring device while acquiring the coronary angiography image, so as to obtain an electrocardiosignal group corresponding to the sample image sequence. That is, all sample images in the sample image sequence can correspond to the electrocardiographic signals in the corresponding electrocardiographic signal group one by one, and the sample images in the sample image sequence are arranged according to the acquisition time.

Further, the sample image sequence may be further divided into a training image sequence for training and a test image sequence for testing.

In particular, since a conventional coronary angiography method is performed by using a thin catheter such as a hair to pass through an artificial entrance at the wrist of a radial artery or the thigh of a femoral artery to the coronary artery opening of the heart, and then a contrast medium is injected into the coronary artery by imaging under X-ray, the internal morphology of the coronary artery can be displayed, and the blood vessel condition of the coronary artery can be observed. Moreover, the imaging frame frequency of coronary angiography is generally less than 30Hz, the whole imaging time from the beginning of the perfusion of the contrast agent to the complete extinction varies from one person to another, but generally takes 2 to 3 cardiac cycles, and the acquisition frequency of the electrocardiosignals can reach 300-400Hz, which is much higher than the imaging frame frequency of coronary angiography. Thus, portions of the sample image in some of the sequence of sample images are not showing blood vessels, so in some embodiments, sample images in the sequence of sample images where blood vessels are not visible are removed.

In step S22, each sample image in the sample image sequence is labeled with a corresponding cardiac cycle phase according to the correspondence between the sample image and the electrocardiographic signal described in step S21.

The cardiac cycle refers to the process from the beginning of one heart beat to the beginning of the next heart beat, which the cardiovascular system goes through, that is, each contraction and relaxation of the heart constitutes one cardiac cycle, generally, each cardiac cycle is 0.8 seconds, wherein the contraction period is 0.11 seconds, and the rest are the relaxation periods. If the change of the ventricular pressure, the ventricular volume, the blood flow and the valve activity in each phase of the cardiac cycle is centered on the diastole and systole of the ventricles, the whole cardiac cycle can also act according to 8 main phases, and the 8 phases of the cardiac cycle are respectively: isovolumetric systolic phase, rapid ejection phase, slowed ejection phase, pre-diastole phase, isovolumetric diastolic phase, rapid filling phase, slowed filling phase, and ventricular systolic phase, wherein pre-diastole phase is also referred to as end-systole phase and ventricular systolic phase is also referred to as end-diastole phase.

That is, a specific sample image labeling strategy can be formulated according to the detection requirement.

In some embodiments, the sample images in the sample image sequence may be labeled according to the 8 cardiac cycle phases, and the finally trained cardiac cycle detection model may be used to predict one of the 8 cardiac cycle phases corresponding to each image to be detected in the image sequence to be detected.

In other embodiments, only a certain cardiac cycle phase or a certain number of cardiac cycle phases need to be focused as the focused cardiac cycle phase, the sample images corresponding to the focused cardiac cycle phase are labeled accordingly, and the remaining images do not explicitly label the corresponding cardiac cycle phase, and can be labeled "other" collectively. This has the advantage that the number of sample image sequences required for training can be reduced, reducing a lot of labeling work, in particular the prediction accuracy is higher than that required for predicting all phases of the cardiac cycle.

For example, if the phases of the cardiac cycle of interest are pre-diastole and ventricular systole, then the sample images corresponding to the pre-diastole and the ventricular systole are explicitly labeled in the same sample image sequence, and the other sample images are labeled "other". Similarly, if only a certain cardiac cycle time phase is concerned, only the corresponding sample image in the sample image sequence needs to be explicitly labeled. Specifically, in the same sample image sequence, the sample image corresponding to the QRS wavefront of the electrocardiographic signal may be labeled as the ventricular systole, and the sample image corresponding to the T wavefront of the electrocardiographic signal may be labeled as the pre-diastole.

In step S23, the cardiac cycle phase detection model is based on the time-series deep neural network, and the labeled sample image sequence is input into the model for training, so as to finally obtain the cardiac cycle phase detection model for detecting the cardiac cycle phase corresponding to each coronary angiography image in the coronary angiography sequence, that is, the cardiac cycle phase detection model in this embodiment.

And inputting the image sequence to be detected into the trained cardiac cycle time phase detection model, and correspondingly predicting the image to be detected in the image sequence to be detected as one or more cardiac cycle time phases marked during training. For example, when the phases of the cardiac cycle are considered as the pre-diastole phase and the ventricular systole phase, the sequence of the images to be detected is input into the phase detection model of the cardiac cycle, and finally the phase of the cardiac cycle corresponding to each image in the sequence of the images to be detected is output, wherein the detection results are 3: images predicted to be in ventricular diastole, images predicted to be in ventricular systole, and other images.

In addition, the embodiment can be used as a single software module to perform post-processing image analysis of the coronary angiography image, and can also be combined with a coronary angiography imaging hardware system to give the phase information of the coronary angiography image corresponding to the cardiac cycle in real time in the imaging process.

Example two

Based on the same concept, the present embodiment further provides a cardiac cycle phase detection device for implementing the cardiac cycle phase detection method described in the first embodiment, and specifically referring to fig. 2, fig. 2 is a structural block diagram of the cardiac cycle phase detection device according to the embodiment of the present application, and as shown in fig. 2, the device includes:

the acquisition module is used for acquiring an image sequence to be detected, wherein the image sequence to be detected comprises a plurality of images to be detected acquired at one time, and the images to be detected are arranged according to the acquisition time sequence;

and the detection module is used for inputting the image sequence to be detected into a cardiac cycle time phase detection model and outputting a cardiac cycle time phase corresponding to each image to be detected in the image sequence to be detected.

EXAMPLE III

The present embodiment further provides an electronic device, referring to fig. 3, comprising a memory 404 and a processor 402, wherein the memory 404 stores a computer program, and the processor 402 is configured to execute the computer program to perform the steps of any one of the cardiac cycle phase detection methods according to the first embodiment.

Specifically, the processor 402 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.

Memory 404 may include, among other things, mass storage 404 for data or instructions. By way of example, and not limitation, memory 404 may include a Hard Disk Drive (Hard Disk Drive, abbreviated to HDD), a floppy Disk Drive, a Solid State Drive (SSD), flash memory, an optical Disk, a magneto-optical Disk, tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 404 may include removable or non-removable (or fixed) media, where appropriate. Memory 404 may include removable or non-removable (or fixed) media, where appropriate. The memory 404 may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory 404 is a Non-Volatile (Non-Volatile) memory. In particular embodiments, Memory 404 includes Read-Only Memory (ROM) and Random Access Memory (RAM). The ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), Electrically rewritable ROM (EAROM), or FLASH Memory (FLASH), or a combination of two or more of these, where appropriate. The RAM may be a Static Random-Access Memory (SRAM) or a Dynamic Random-Access Memory (DRAM), where the DRAM may be a Fast Page Mode Dynamic Random-Access Memory 404 (FPMDRAM), an Extended data output Dynamic Random-Access Memory (eddram), a Synchronous Dynamic Random-Access Memory (SDRAM), and the like.

Memory 404 may be used to store or cache various data files for processing and/or communication use, as well as possibly computer program instructions for execution by processor 402.

The processor 402 may implement any of the data warehousing methods described in the above embodiments by reading and executing computer program instructions stored in the memory 404.

Optionally, the electronic apparatus may further include a transmission device 406 and an input/output device 408, where the transmission device 406 is connected to the processor 402, and the input/output device 408 is connected to the processor 402.

The transmitting device 406 may be used to receive or transmit data via a network. Specific examples of the network described above may include wired or wireless networks provided by communication providers of the electronic devices. In one example, the transmission device includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmitting device 406 may be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.

The input and output devices 408 are used to input or output information. In this embodiment, the input information may be a current data table such as an epidemic situation trend document, feature data, a template table, and the like, and the output information may be a feature fingerprint, a fingerprint template, text classification recommendation information, a file template configuration mapping table, a file template configuration information table, and the like.

Optionally, in this embodiment, the processor 402 may be configured to execute the following steps by a computer program:

step S1: acquiring an image sequence to be detected, wherein the image sequence to be detected comprises a plurality of images to be detected acquired at one time, and the images to be detected are arranged according to an acquisition time sequence;

step S2: and inputting the image sequence to be detected into a cardiac cycle time phase detection model, and outputting a cardiac cycle time phase corresponding to each image to be detected in the image sequence to be detected.

It should be noted that, for specific examples in this embodiment, reference may be made to examples described in the foregoing embodiments and optional implementations, and details of this embodiment are not described herein again.

In addition, in combination with any one of the cardiac cycle phase detection methods in the first embodiment, the embodiments of the present application may be implemented as a computer program product. The computer program product comprises: a program or instructions which, when run on a computer, causes the computer to perform the steps of implementing the cardiac cycle phase detection method of any one of the above embodiments.

In addition, in combination with any one of the cardiac cycle phase detection methods in the first embodiment, the present application embodiment can be implemented by providing a readable storage medium. The readable storage medium having stored thereon a computer program; the computer program, when executed by a processor, implements the steps of any of the cardiac cycle phase detection methods of the first embodiment described above.

In general, the various embodiments may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. Some aspects of the invention may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device, although the invention is not limited thereto. While various aspects of the invention may be illustrated and described as block diagrams, flow charts, or using some other pictorial representation, it is well understood that these blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.

Embodiments of the invention may be implemented by computer software executable by a data processor of the mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products) including software routines, applets and/or macros can be stored in any device-readable data storage medium and they include program instructions for performing particular tasks. The computer program product may comprise one or more computer-executable components configured to perform embodiments when the program is run. The one or more computer-executable components may be at least one software code or a portion thereof. Further in this regard it should be noted that any block of the logic flow as in the figures may represent a program step, or an interconnected logic circuit, block and function, or a combination of a program step and a logic circuit, block and function. The software may be stored on physical media such as memory chips or memory blocks implemented within the processor, magnetic media such as hard or floppy disks, and optical media such as, for example, DVDs and data variants thereof, CDs. The physical medium is a non-transitory medium.

It should be understood by those skilled in the art that various features of the above embodiments can be combined arbitrarily, and for the sake of brevity, all possible combinations of the features in the above embodiments are not described, but should be considered as within the scope of the present disclosure as long as there is no contradiction between the combinations of the features.

The above examples are merely illustrative of several embodiments of the present application, and the description is more specific and detailed, but not to be construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.