Grouping Similar Seismocardiographic Signals Using Respiratory Information

Seismocardiography (SCG) offers a potential non-invasive method for cardiac monitoring. Quantification of the effects of different physiological conditions on SCG can lead to enhanced understanding of SCG genesis, and may explain how some cardiac pathologies may affect SCG morphology. In this study, the effect of the respiration on the SCG signal morphology is investigated. SCG, ECG, and respiratory flow rate signals were measured simultaneously in 7 healthy subjects. Results showed that SCG events tended to have two slightly different morphologies. The respiratory flow rate and lung volume information were used to group the SCG events into inspiratory/expiratory groups or low/high lung-volume groups, respectively. Although respiratory flow information could separate similar SCG events into two different groups, the lung volume information provided better grouping of similar SCGs. This suggests that variations in SCG morphology may be due, at least in part, to changes in the intrathoracic pressure or heart location since those parameters correlates more with lung volume than respiratory flow. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features, and better signal characterization, and classification

Cardiac seismocardiography analysis using 2- elements accelerometer sensor array and beamforming technique

Human heart contains a lot of informations that indicate the condition of its operation and health. The informations can be extracted using image, acoustic, electric and vibration signal. The problem with current technology is that it suffers badly with noise and other unwanted interference. To address this noise issue with the latest technology is echocardiography, a diagnostic tool for diagnosing on cardiac contractility and valvular disease. However, this device is quite costly and labour intensive which requires a specialist who is expert and enough experience in using this equipment. Furthermore, most of medical institutes unable to afford the cost of equipment facility. This study aimed to investigate the application of a non-invasive cardiac diagnostic approach using an accelerometer sensor array, coupled with a directional filtering approach to remove the unwanted noise. This work proposed the utilization of directional filtering method to remove noise using body vibration sensor by employing adaptive beamforming method without altering the signal information. Seismocardiography (SCG) was used to capture body vibration signals recorded via vibration sensor that collects information related to the heart pumping activities and later diagnosed the heart disease. The sensor array was used to collect SCG signal for 28 cycle data from normal and abnormal heart conditions of subjects in supine position. It was found that signal of heart disease information in SCG was overlapped with the noise signal. A directional denoising method which comprised of Delay and Sum (DAS) beamforming and Linearly Constrained Minimum Variance (LCMV) beamforming algorithm were applied, and the performance were compared. The result of signal to noise ratio (SNR) for DAS beamforming algorithm on normal subject was 7.11dB and abnormal subject was 4.13dB. For LCMV beamforming algorithm, normal subject was 10.85dB and abnormal subject is 7.04dB. Based on these results, it showed that the LCMV beamforming performed better than DAS as indicated in the SNR improvement by 30%. This SNR improvement represents the better accuracy of heart disease diagnosis

An Investigation of the Relationship Between Respiration and Seismocardiographic Signals Using Signal Processing, Machine Learning and Finite Element Analysis

Cardiovascular disease (CVD) is one of the major causes of death worldwide. Disease management, as well as patient health, can be significantly improved by early detection of patient deterioration and proper intervention. Review of the patient\u27s medical history and physical examination including stethoscope auscultation and electrocardiograms (ECG), echocardiography imaging, numerous blood testing, and computed tomography are common means of evaluating cardiac function. Seismocardiographic (SCG) signals are the vibrations of the chest wall due to the mechanical activity of the heart. These signals can provide useful information about heart function and could be used to diagnose cardiac problems. The variability in SCG waveforms may make it difficult to obtain accurate waveforms, limiting SCG clinical value. Breathing is a well-known source of change in SCG morphology. In this dissertation, SCG variability due to respiration is described, related signal characteristics changes are measured, and the effects of breathing states and maneuvers are discussed. Increased SCG variability understanding can aid in accounting for variability in signal as well as more accurate characterization of significant features in SCG that could correlate with heart health. Direct airflow measurement is frequently used to assess respiration. When direct airflow access is difficult or unavailable, indirect ways to breathing monitoring might be used. The seismocardiographic signal is influenced by respiration. As a result, this signal can be utilized to noninvasively determine the respiratory phases. Hence, SCG may reduce the requirement for direct airflow measurements in situations where SCG signals are easily available. This dissertation extracts respiration derived from SCG in healthy adults using machine learning techniques and compares the results with direct respiration airflow measurements. Finite element method (FEM) was implemented to perform SCG simulation during different breathing states by modeling the myocardial movements propagation to the surface of the chest. SCG waveforms predicted by FEM analysis were comparable with SCG signals measured at the surface of the chest suggesting that myocardial activity is the SCG main source. The effects of increased soft tissue in the chest wall on SCG signal were investigated and were found to decrease SCG amplitude. The research led to an enhanced understanding of the SCG variability sources as well as respiratory phase-detection methods. These discoveries could lead to better non-invasive, low-cost approaches development for managing cardiovascular disorders, which can enhance patient quality of life