FIELD OF THE INVENTION

The present invention relates to a method of measuring cardiac vibrations on the body surface, especially to identify feature points associated with the mechanocardiography (MCG).

BACKGROUND OF THE INVENTION

Since cardiovascular diseases have accounted for top three of ten causes of death in Taiwan, public awareness on prevention of cardiovascular diseases is increasing. The people with the cardiovascular diseases include older age, hypertension, diabetes, hyperlipidemia, exposure to tobacco, obesity, and a family history of cardiovascular diseases, etc. The doctor diagnose patients by understanding the detail of patient disease history and checking with the equipment such as electrocardiogram (ECG), stress ECG phonocardiogram, echocardiography, nuclear medical imaging study, cardiac computerized tomography (CT) scan, cardiac magnetic resonance imaging (MRI), etc. There are two major problems of heart valves: valvular stenosis, which is inadequate opening, and valvular regurgitation, which is backward leakage of blood through closed valves. These problems result in elevated pressure or increased volume in heart chambers, leading to deterioration of heart function.

Heart valvular system consists of mitral, tricuspid, aortic and pulmonary valves. Heart valves allow blood to circulate through them when they are open and prevent backward blood flow when they are closed. The mechanisms make unidirectional blood flow possible and preserve energy. Each valve can develop stenotic and regurgitant disorders, and some patients have valvular prolapse of mitral and tricuspide valves, which indicate elongated valves with leaflets prolapsed to the atrial chambers. The circulation of blood in cardiovascular dystem is as following: deoxygenated blood returns to the right atrium from peripheral venous system via the superior and inferior venae cavae (SVC and IVC). The right ventricle relaxes to allow blood to go into the right ventricle via the tricuspid valve and then contracts to pump blood in the pulmonary circulation via the pulmonary valve. After oxygen (O2) and carbon dioxide (CO2) exchange in the lungs, oxygenated blood returns to the left atrium. The left ventricle relaxes to allow blood to go into the left ventricle via the mitral valve and then contracts to pump blood in the systemic circulation via the aortic valve. During ventricular systole and diastole, forward flow is allowed and backward flow is prevented by functioning valvular opening and closing.

The most commonly available exam of cardiac diseases is ECG which provides indirect evidences of valvular diseases. For example, aortic stenosis results in ventricular pressure overload and left ventricular hypertrophy. Thus an increased QRS amplitude, ST segment/T-wave abnormalties can be observed in ECG. However, the same findings can be found in other diseases. The evaluation of cardiac systolic and diastolic function requiers further chest X-ray, echocardiography and nuclear medical tests. Cardiac murmurs generated by valve insufficiency can be heard using a stethoscope. There are some limitations: a phycian being able to check a single auscultation site at a time and the narrow range of human hearing (20 Hz to 20 kHz). Some abnormal heart sounds, such as the third and the fourth heart sounds (S3 and S4) are relatively lower in frequency, which might be beyond the limits of human hearing and are missed in cases. Therefore, phonocardiography is used to record heart sound to check the opening and closing timing of heart valves. There is a time delay between heart valves closing and signal captured on body surface, leading to confusion in clinical evaluation. Chest X-ray reveals calcification of the valves, but it provides limited information of valvular heart diseases and is not a useful tool for continuous monitoring because of radiation. Echocardiography is a useful tool to evaluate cardiac contraction and valvular function. The commonly used echocardiographic modalities include M-mode, 2-D, 3-D, Doppler and contrast echocardiography images. Chamber sizes and valvular motion can be evaluated using M-mode echocardiography, and normal and abnormal blood flow can be detected using Doppler and contrast echocardiography. However, echocardiography machine is bulky, and professional personnel are required, making the exam very inconvenient and not suitable for continuous monitoring.

The conventional ways for checking heart valve defect include ECG, phonocardiogram, echocardiography, and nuclear medical tests. However, these techniques have their limitations while in use. Such as, the ECG can be used to estimate intervals of heart valve operation, but can't be used to check opening and closure of heart valves effectively. The phonocardiograms can be used for checking the opening and closing of heart valves, yet are unable to detect changes in blood flow of the heart. The echocardiography can be used to check the lumen diameter, the heart valve movement, the direction of blood flow, the velocity and turbulence of the blood flow in cardiac vessels, but poses problems in long term monitoring. Thus there is room for improvement and a need to provide a novel method for mechanocardiography that overcomes the shortcomings of conventional ways for checking heart valves. The method not only records heart valve operation and blood flow features for long-term monitoring but also improves convenience and accuracy in measurement.

SUMMARY

Therefore, one of the primary object s of the present invention is to provide a feature point identification method for mechanocardiography that implements mechanocardiography (MCG) and electrocardiography (ECG) simultaneously by measuring vibrations on body surface. The P-wave peak and the R-wave peak of the ECG correspond to the MCG to get two corresponding points. Then a transmitral atrial contraction maximal flow feature point (MFA) between the two corresponding points of the MCG is retrieved. Thus convenience and accuracy of clinical disease assessment are improved.

It is another object of the present invention to provide a feature point identification method for mechanocardiography that retrieves a lateral wall contraction maximal velocity feature point (LCV), a transaortic maximal flow feature point (AF), and a septal wall contraction maximal velocity feature point (SCV) after an R-wave peak corresponding point of the MCG to improve convenience and accuracy of clinical disease assessment.

It is a further object of the present invention to provide a feature point identification method for mechanocardiography that compares the R-wave peak and the T-wave peak of the ECG with the MCG to get two corresponding points. Then a transpulmonary maximal flow feature point (PF) and a lateral wall contraction maximal velocity feature point (LCV) between the two corresponding points are retrieved. Thus convenience and accuracy of clinical disease assessment are both improved.

It is a further object of the present invention to provide a feature point identification method for mechanocardiography that retrieves a transaortic maximal flow feature point (AF), a transpulmonary maximal flow feature point (PF) and a septal wall contraction maximal velocity feature point (SCV) after a lateral wall contraction maximal velocity feature point (LCV) to improve convenience and accuracy of clinical disease assessment.

In order to achieve the above objects, the measurement device of the present invention includes at least one gravity sensor, an electrocardiographic (ECG) sensing module, a processor, a storage unit and a transmission unit. The gravity sensor and the ECG sensing module are electrically coupled to the processor while the processor is electrically coupled to the storage unit and the transmission unit. The storage unit is electrically coupled to the transmission unit.

At least one gravity sensor is placed on at least one of the heart valve auscultation sites correspondingly. The heart valve auscultation sites are on the body surface and correspond to the heart valves. The heart valve auscultation sites include an aortic area, a mitral area, a pulmonary area and a tricuspid area. The ECG sensing module includes three limb leads.

The processor is used to receive at least one MCG obtained by the gravity sensor and the ECG obtained by the ECG sensing module. The processor also retrieves peaks or valleys of P-waves, QRS complexes, and T-waves in the ECG. Then the peaks or valleys of the ECG are compared with at least one MCG to get at least one corresponding point of the MCG Next a plurality of feature points of the MCG before or after the corresponding point is retrieved. The feature points of the MCG include a transmitral atrial contraction maximal flow feature point (MFA), a lateral wall contraction maximal velocity feature point (LCV), a transaortic maximal flow feature point (AF), a transpulmonary maximal flow feature point (PF), and a septal wall contraction maximal velocity feature point (SCV).

The storage unit receives the feature points of the MCG and records the ECG and the MCG from the processor. The storage unit also delivers the ECG and the MCG to a connected receiving device. The receiving device can be a portable device, a computer, or a display.

A feature point identification method for mechanocardiography of the present invention includes the following steps. Arrange a gravity sensor at an aortic area on the body surface which corresponds to the heart valves to get a first MCG (MCG1) via the gravity sensor. Then dispose an electrocardiography (ECG) sensing module on a limb lead attachment region on the body surface to get an ECG Next, retrieve a P-wave peak and an R-wave peak of the ECG and correspond both the P-wave peak and the R-wave peak to the MCG1, respectively, to get a first corresponding point and a second corresponding point. Retrieve a peak with the maximum value between the first corresponding point and the second corresponding point. The peak with the maximum value is a transmitral atrial contraction maximal flow feature point (MFA).

The aortic area is present from the left second intercostal space at the left sternal border, over the sternum rightward, to the right second to third intercostal space at the right sternal border.

The limb lead attachment region includes one right arm (RA), one left arm (LA), and one left leg (LL).

A feature point identification method for mechanocardiography of the present invention includes the following steps. First, place a gravity sensor on an aortic area on the body surface that corresponds to the heart valves to get a first MCG reading (MCG1) by the gravity sensor. Then arrange an electrocardiography (ECG) sensing module at a limb lead attachment region on the body surface to get an ECG. Next, retrieve an R-wave peak of the ECG and correspond the R-wave peak to the MCG1to get a second corresponding point. Retrieve a valley with the minimum value and a peak thereafter in turn within an interval of 0.06 second after the second corresponding point. The peak is a lateral wall contraction maximal velocity feature point (LCV).

A feature point identification method for mechanocardiography of the present invention includes the following steps. In the beginning, arrange a gravity sensor at an aortic area on the body surface that corresponds to the heart valves to get a first MCG reading (MCG1) by the gravity sensor. Then place an electrocardiography (ECG) sensing module on a limb lead attachment region on body surface to get an ECG Next, retrieve an R-wave peak of the ECG and correlate the R-wave peak to the MCG1to get a second corresponding point. Retrieve a peak with the maximum value within an interval of 0.07-0.1 seconds after the second corresponding point O2. The peak with the maximum value is a transaortic maximal flow feature point (AF).

A feature point identification method for mechanocardiography of the present invention includes the following steps. First, place a gravity sensor on an aortic area on the body surface that corresponds to the heart valves to get a first MCG reading (MCG1) by the gravity sensor. Then arrange an electrocardiography (ECG) sensing module at a limb lead attachment region on body surface to get an ECG Next retrieve an R-wave peak of the ECG and correspond the R-wave peak to the MCG1to get a second corresponding point. Retrieve a valley with the minimum value and a peak thereafter in turn within an interval of 0.06 second after the second corresponding point while the peak is a lateral wall contraction maximal velocity feature point (LCV). Then again retrieve a peak after the feature point LCV; this peak is a transaortic maximal flow feature point (AF).

A feature point identification method for mechanocardiography of the present invention includes the following steps. First arrange a gravity sensor at an aortic area on the body surface that corresponds to the heart valves to get a first MCG reading (MCG1) by the gravity sensor. Then place an electrocardiography (ECG) sensing module on a limb lead attachment region on the body surface to get an ECG Next retrieve an R-wave peak and a T-wave peak of the ECG1and correspond the R-wave peak and the T-wave peak to the MCG1to get a second corresponding point and a third corresponding point. Again, retrieve a peak with the maximum value within an interval between 0.1 seconds after the second corresponding point and the third corresponding point. The peak with the maximum value is a transpulmonary maximal flow feature point (PF).

A feature point identification method for mechanocardiography of the present invention includes the following steps. At first, set a gravity sensor at an aortic area on the body surface that corresponds to the heart valves to get a first MCG reading (MCG1) by the gravity sensor. Dispose an electrocardiography (ECG) sensing module on a limb lead attachment region on the body surface to get an ECG Then retrieve an R-wave peak and a T-wave peak of the ECG1and correspond the R-wave peak and the T-wave peak to the MCG1to get a second corresponding point and a third corresponding point. Next, retrieve a valley with the minimum value and a peak thereafter in turn within an interval of 0.06 seconds after the second corresponding point. The peak is a lateral wall contraction maximal velocity feature point (LCV). Retrieve a peak with the maximum value within an interval between the feature point LCV and the third corresponding point. The peak with the maximum value is a transpulmonary maximal flow feature point (PF).

A feature point identification method for mechanocardiography of the present invention includes the following steps. First, arrange a gravity sensor at a mitral area on the body surface that corresponds to the heart valves to get a second MCG reading (MCG2) by the gravity sensor. Then place an electrocardiography (ECG) sensing module on a limb lead attachment region on the body surface to get an ECG Next retrieve a R-wave peak and a T-wave peak of the ECG1and correspond the R-wave peak and the T-wave peak to the MCG2to get a fourth corresponding point and a fifth corresponding point. Retrieve a peak with the maximum value in an interval between 0.04 seconds after the fourth corresponding point and the fifth corresponding point while the peak with the maximum value is a lateral wall contraction maximal velocity feature point (LCV).

The mitral area is present from the right fifth intercostal space at the right sternal border to the posterior axillary line.

A feature point identification method for mechanocardiography of the present invention includes the following steps. First, mount a gravity sensor on a pulmonary area on the body surface that corresponds to the heart valves to get a third MCG reading (MCG3) by the gravity sensor. Then dispose an electrocardiography (ECG) sensing module on a limb lead attachment region on the body surface to get an ECG Next, retrieve an R-wave peak of the ECG1and correspond the R-wave peak to the MCG3to get a sixth corresponding point. Retrieve a peak with the maximum value within an interval between 0.07-0.1 seconds after the sixth corresponding point. The peak with the maximum value is a septal wall contraction maximal velocity feature point (SCV).

The pulmonary area is around the second left intercostal space at the left sternal border, up to the first left intercostal space, a lower part of the clavicle, and then down to the third left intercostal space at the left sternal border.

A feature point identification method for mechanocardiography of the present invention includes the following steps. First, arrange a plurality of gravity sensors on an aortic area and a pulmonary area on the body surface that corresponds to the heart valves to get a first MCG reading (MCG1) and a third MCG reading (MCG3), respectively, by the gravity sensors. Then place an electrocardiography (ECG) sensing module on a limb lead attachment region on the body surface to get an ECG Next, retrieve an R-wave peak of the ECG and correspond the R-wave peak to the MCG1to get a second corresponding point. Retrieve a valley with the minimum value and a peak thereafter in turn within an interval of 0.06 seconds after the second corresponding point. The peak is a lateral wall contraction maximal velocity feature point (LCV). Then correspond the feature point LCV to the MCG3to get a seventh corresponding point of the MCG3. At last, retrieve a peak after the seventh corresponding point and this peak is a septal wall contraction maximal velocity feature point (SCV).

A feature point identification method for mechanocardiography of the present invention includes the following steps. First, place a gravity sensor on a tricuspid area on the body surface that corresponds to a heart valve to get a fourth MCG reading (MCG4) by the gravity sensor. Then arrange an electrocardiography (ECG) sensing module on a limb lead attachment region on the body surface to get an ECG Next, retrieve an R-wave peak and a T-wave peak of the ECG1and correspond the R-wave peak and the T-wave peak to the MCG4to get an eighth corresponding point and a ninth corresponding point. Retrieve a peak with the maximum value between the eighth corresponding point and the ninth corresponding point. The peak with the maximum value is a lateral wall contraction maximal velocity feature point (LCV).

The tricuspid area is extended rightward from the left fourth to fifth intercostal space at the right sternal border.

