Automatic Detection of Aortic Valve Opening Using Seismocardiography in Healthy Individuals

Automatic Detection of Aortic Valve Opening Using Seism cardio graphy in Healthy Individuals

Abstract: Accurate detection of fiducial points in a seism cardiogram (SCG) is a challenging research problem for its clinical application. In this paper, an automated method for detecting aortic valve opening (AO) instants using the dorsoventral component of SCG signal is proposed. This method does not require electrocardiogram (ECG) as a reference signal. After pre-processing the SCG, multiscale wavelet decomposition is carried out to get signal components in different wavelet subbands. The subbands having possible AO peaks are selected by a newly proposed dominant multiscale kurtosis (DMK) and dominant multiscale central frequency (DMCF) based criteria. The signal is reconstructed using selected subbands, and it is emphasized using the weights derived from proposed relative squared dominant multiscale kurtosis (RSDMK). The Shannon energy (SE) followed by autocorrelation coefficients are computed for systole envelope construction. Finally, AO peaks are detected by a Gaussian derivative filtering based scheme. The robustness of the proposed method is tested using clean and noisy SCG signals from CEBS database. Evaluation results show that the method

can achieve an average sensitivity (Se) of 94%, prediction rate (+P) of 90% and detection accuracy (ACC) of 86% approximately over 4585 analyzed beats.

Existing system: This is the cause of generation of IM valley point. In electrical conduction system, this indicates complete depolarization of purkinje fibers and basal depolarization of ventricles (S-wave in ECG). As enough pressure builds up, apex moves inward and the walls are swelled, so overall acceleration increases. This generates AO peak point. By this time, the basal depolarization is completed. After the AO peak, a rapid twisting and inward wall motion decreases the acceleration due to the opening of aortic valve. When the acceleration of inward wall motion stops, it gives the IC valley point. The higher pressure in ventricle drastically increases acceleration of blood in aorta which results RE peak point. In electrical conduction, an is electric line is traced corresponding to this condition. Then, repolarization of ventricle starts, which creates onset of T-wave. As soon as the Aortic valve closes, a slight rebound of heart acceleration gives the AC point. In ECG, the ventricular repolarization stops and produces offset of T-wave.

Furthermore, atrial pressure increases when blood enters into atrium. It causes a rise in the acceleration followed by a sudden fall in the acceleration.

Proposed system: Accurate determination of AO peaks is most important in cardiac monitoring and clinical assessment systems. The SCG may help heartbeat detection and heart rate variability estimation. It is mapped with other cardiovascular signals (ECG, PPG, PCG) . It is also used in cardiac clinical diagnosis in pathological conditions, like coronary artery disease, atrial fibrillation, atrial flutter , identification of respiratory phases (inhalation and exhalation) , estimation of hemodynamic parameters (systolic and diastolic blood pressure, stroke volume, cardiac output) ,and smart garment based wearable healthcare system . An automatic and standalone delineation framework was proposed based on multiple envelopes for fiducial points such as IM, AO and AC by F. Khosrow-Khavar et al. Hoang Nguyen et al. proposed a method to extract heart beat waveform from SCG time series for wearable devices. For HRV estimation from SCG with the help of reference ECG, R-peaks are detected using Pan-Tompkins (PT) algorithm and AO-peaks in the

SCG are detected by ECG-SCG windowing technique. The method is not independently modeled as it needs ECG signal. Also, the selection of window size for AO-peak search is empirical.

Advantages: The systolic profiles are enhanced using RSDMK weights. The envelope of a systolic profile is extracted using the SE operator followed by the autocorrelation feature. Finally, AO peaks are approximated using a FOGD filtering based approach. The method is tested and validated on publically available CEBS database.

Finally, AO peaks are detected using Shannon energy (SE) and autocorrelation features based envelope construction and Gaussian derivative filtering based peak detection logic. The rest of the paper is organized as follows. In Section II, the proposed AO peak detection method is presented. In Section III, we evaluated the performance of the proposed method. Finally, conclusions are drawn in Section IV. Disadvantages: This action generates an is electric line. As soon as mitral valve opens, the atrium volume decreases, causes generation of MO valley point. After a certain interval of time, ventricle starts filling with blood and heart walls move outward, which augments the acceleration and produces RF peak point.

At this moment, the electric impulse travels from SA node to AV node for atrial depolarization. It produces P-wave. In this way, a SCG cycle is generated.

Modules: Seism cardiogram: SEISMOCARDIOGRAM (SCG) is a non invasive technique to measure vibrations on the chest wall induced by heartbeats and cardiac movements in a cardiac cycle.

A cardiac cycle consists of a systole and a diastole, and the SCG has a capability to reflect these cardiac mechanical activities. The change in volume, pressure and shape of the heart during different stages of cardiac cycle produces vibrations on the ribs and tissues near the heart, and due to these variations, pulsations in the chest are generated. This vibrational signal can be non-invasively acquired by

placing a tri-axial accelerometer sensor on the precordial areas of the chest, generally at the lower end of the sternum on the xiphoid process. Though, electrocardiogram (ECG) is considered as a standard tool in cardiac diagnosis, there are few cardiovascular diseases such as structural defects in cardiac valves, which are very difficult to detect using electrocardiography. These abnormalities do not affect electrical conduction of myocardium, but these are manifested in vibration and acoustic signals produced by cardiac mechanics. In acoustic-based diagnosis, heart sounds show valves closing instants only. But, SCG has an ability to identify clinical information like closing.

Physiology and Fiducial Points of SCG: Signal the heart has two different pumps separated by a septum. Each of these pumps comprises of two chambers- atrium and ventricle. These chambers are again separated by atrioventricular and semi-lunar valves. Different phases of a cardiac cycle can be identified with the help of a SCG signal, and these instances/phases are mitral valve opening (MO), mitral valve closure (MC), aortic valve opening (AO), aortic valve Closure (AC), isovolumic contraction time (IVCT), isovolumic relaxation time (IVRT), rapid filling (RF) and rapid ejection (RE) of blood through ventricles. In Fig. 1, a typical SCG signal is shown for two cardiac cycles along with simultaneously recorded ECG. The mitral valve is closed when the blood completely enters from left atrium to left ventricle. As a result, MC notch point is seen as a sudden positive deflection in SCGsignal. At this moment, electric impulse depolarizes purkinje fibers, which results major ventricular depolarization (Rwave in ECG) in the electro bio-potential measurement. Then ventricles start contracting, which causes sharp inward wall motion and decreases acceleration waves.

Dominant multiscale kurtosis: However, the proposed method performs well in the presence of baseline drifts.

Records b001, b003, b006, b018, and b020 contain abrupt changes and unrecognizable distorted beats. Fig. 9 illustrates the detection performance of SCG signal, which consists of distorted beats and unrecognizable heart rhythms. These signals are even very difficult to annotate manually. For such instants, the proposed algorithm gives a large number of misdetections. In addition, the records

b001, b007, and b020 have smaller AO peak morphologies than RE, and the record b011 has similar diastolic structures as systolic profile. For these cases, the proposed method produces more FPs and FNs. The performance of the proposed method for the SCG signal having numerous smaller systolic profiles is presented in Fig. 10. Records b001 and b006 have larger variations in AO amplitudes. Our method gives more FPs for two records 003 and 020 across all the records in the database due to their distorted beats and spurious spikes. Thus, by eliminating these two records (003 and 020), the overall performance are achieved as Se = 94.5%, +P = 93.4% and ACC = 88.65%.