Automatic Identification of Systolic Time Intervals in Seismocardiogram

Abstract

Continuous and non-invasive monitoring of hemodynamic parameters through unobtrusive wearable sensors can potentially aid in early detection of cardiac abnormalities, and provides a viable solution for long-term follow-up of patients with chronic cardiovascular diseases without disrupting the daily life activities. Electrocardiogram (ECG) and siesmocardiogram (SCG) signals can be readily acquired from light-weight electrodes and accelerometers respectively, which can be employed to derive systolic time intervals (STI). For this purpose, automated and accurate annotation of the relevant peaks in these signals is required, which is challenging due to the inter-subject morphological variability and noise prone nature of SCG signal. In this paper, an approach is proposed to automatically annotate the desired peaks in SCG signal that are related to STI by utilizing the information of peak detected in the sliding template to narrow-down the search for the desired peak in actual SCG signal. Experimental validation of this approach performed in conventional/controlled supine and realistic/challenging seated conditions, containing over 5600 heart beat cycles shows good performance and robustness of the proposed approach in noisy conditions. Automated measurement of STI in wearable configuration can provide a quantified cardiac health index for long-term monitoring of patients, elderly people at risk and health-enthusiasts.

Gyrocardiography: A New Non-invasive Monitoring Method for the Assessment of Cardiac Mechanics and the Estimation of Hemodynamic Variables

“…In a recent study, sophisticated in vivo experimental examinations and a complementary mathematical model revealed that BCG waves are formed due to blood pressure gradients in the ascending and descending aorta 17 . SCG and BCG, which are typically based on using accelerometers and force sensors, can be used for unobtrusive long term monitoring of LV to estimate hemodynamic variables, cardiac abnormalities, and breathing disorders via low-cost wearable or portable devices 18–26 . Recent studies have also briefly described the feasibility of heart monitoring using built in accelerometer and gyroscope sensors in Google glasses, wrist worn devices, smart phones, and chest worn patches 27–30 .…”

Detection and Analysis of Heartbeats in Seismocardiogram Signals

“…For example, by means of small MEMS (Micro Electro-Mechanical Systems) accelerometers attached to the chest wall, it is possible to record vibrations induced by cardiac activity: the study of such waveforms is the object of SCG. Indeed, analyses on SCG waveforms highlighted precise relations to specific events in the heart cycle [10]; SCG records traces of heart mechanical activity, which of course correlates to electrical activity inferred by standard ECG techniques: Figure 1 shows the temporal relation between ECG and SCG traces. SCG, in particular, allows for noticing many relevant events such as Mitral valve Opening (MO), and Closure (MC), Isovolumetric Moment (IM) contraction, Aortic valve Opening (AO) and Closure (AC).…”

Recent Advances in Seismocardiography

“…Most research groups applied conventional band-pass filters to remove baseline wandering, body movements, and breathing artefacts from SCG signals [26,36,38,41,45,46,55,[58][59][60][61][62][63]67,71,75,76,[78][79][80]82,93]. A few studies utilized or proposed more advanced noise removal techniques [64,76,88,[94][95][96].…”

“…More studies are needed that compare different filtering methods in clinical and ambulatory settings. [26,36,38,41,45,46,55,[58][59][60][61][62][63]67,71,75,76,[78][79][80]82,93] Adaptive filtering Motion artefact removal [88,95] Averaging theory Motion artefact removal [101] Comb filtering Removing respiration noise from radar signal [50] Empirical mode decomposition Baseline wandering, breathing and body movement artefact removal [76,94,95] Independent component analysis Motion artefact removal [102] Median filtering [96] Morphological filtering [95] Polynomial smoothing Motion artefact removal [103] Savitzky-Golay filtering Motion artefact removal [83,103] Wavelet denoising Segmentation of HSs and SCG [64,95,96] Wiener filtering [94] 2.…”