seismocardiography

This paper presents the development of an accelerometer based system that captures and analyzes Matlab cardiac signals using Seismocardiography (SCG). SCG is an effective non-invasive method of signaling detection in the chest. The project was divided into three phases; the first one was the selection of an accelerometer, data acquisition system (DAQ) and the communication interface. The second one included the digitalization of the biosignal, and finally, the third phase taking place on the SCG digital processing (DSP) for detection of cardiac events. Precordial vibrations were taken using an inertial sensor with a measuring range of ± 1.5g, we continued to digitize the analog signal through the DAQ and the next sent to the Matlab environment for processing with digital filters, bandpass, peak reduction, smoothing and elimination of non-linear trends. In the tests, the SCG system functions as biomedical device that measures the heart's activity and it is presented as a great option for the detection of early abnormalities, as well as helping in resynchronization therapies and chronic heart problems. The test measurements show the capacity to identify nine different cardiac events.

Seismocardiogram (SCG) is a low-cost monitoring method to collect precordial vibrations of sternum due to heartbeats and evaluate cardiac activity. It is mostly used as an auxiliary measurement to the other monitoring methods; however, it carries significant patterns reflecting current cardiovascular health status of subjects. If it is properly collected within a non-clinical environment, it might be able to present preliminary data to physicians before clinic. SCG signals are morphologically noisy. These signals store excessive amount of data. Extracting significant information corresponding to heartbeat complexes is so important. Previously, the method called compressed sensing (CS) had been applied to weed up the redundant information by taking the advantage of sparsity feature in a study. This compressed sensing is based on storing significant signals below the Nyquist rate which suffice for medical diagnosis. It has been feasible to compress SCG signals with 3:1 compression rate at least while maintaining accurate signal reconstruction. Nevertheless, higher compression rates lead to the formation of artifacts on reconstructed signals. This limits a more aggressive compression to reduce the amount of data. The requirement of a different approach which will allow higher compression rates and lower loss of information arises. The purpose of this study is to obtain more competent results by using a method called predefined signature and envelope vector sets (PSEVS) which has been satisfyingly applied to electrocardiogram (ECG) and speech signals. In the study, simultaneously recorded ECG and SCG signals were modeled with the method called PSEVS. The reconstructed signals were compared to the original signals so as to investigate the efficacy of signature-based modeling methods in constructing medically remarkable biosignals for clinical use. After examining the components of reconstructed signals called frame-scaling coefficient, signature and envelope vectors, it has been seen that the error function values of envelope vectors differ from expected values. We concluded that reconstructed SCG signals were not adequate for medical diagnosis.