Seismocardiography: past, present and future

Motion noise cancellation in seismocardiogram of ambulant subjects with dual sensors

This paper presents a dual-sensor method of extracting seismocardiographic (SCG) data from moving adult subjects using chest-worn wireless MEMS accelerometers. A digital signal processing (DSP) system including a normalized least means square (NLMS) adaptive filter is designed and tested in MATLAB. Data results from 10 subjects indicate a detection rate of 98.72% which outperforms our previously-proposed single-sensor scheme. Various sensor positons and possible failure mechanisms are also investigated to further evaluate the system performance. The results reveal that the quality of the SCG signal from moving subjects could be improved by integrating information from multiple sensors at the cost of increasing system complexity.

Detection of atrial fibrillation with seismocardiography

In this paper we study the feasibility of seismocardiography (SCG) for the detection of Atrial Fibrillation (AF). In this preclinical study, data acquired from one patient having paroxysmal AF (no other heart diseases) is used to introduce specific changes in SCG signal due to AF. Observed changes and phenomena create a foundation for the development of SCG-based AF detection algorithms. SCG data was recorded from the sternum of an AF patient in dorso-ventral direction while at rest in a supine position using a three-axis high precision MEMS accelerometer simultaneously with a one-lead ECG. In contrast to ECG, the magnitude of beats registered with SCG varies considerably from beat to beat during AF. We show that the magnitude of the beats is not random but is in relation to beat intervals. It is shown that extra indicators for detecting AF become available when SCG data is combined with electrocardiographical (ECG) data; there is a certain behavior in the electromechanical delay characteristic of the AF. It is discussed how all this information can be taken advantage of in the detection of AF. Today electrocardiography (ECG) is the primary method for diagnosing arrhythmias, but there is a growing need for simpler and more convenient method for detecting asymptomatic AF. Given the very small dimensions of modern MEMS accelerometers (2mm×2mm), a reliable MEMS based measurement may provide totally new venues for arrhythmia detection.

Automatic and Robust Delineation of the Fiducial Points of the Seismocardiogram Signal for Non-invasive Estimation of Cardiac Time Intervals

OBJECTIVE

The purpose of this research was to design a delineation algorithm that could detect specific fiducial points of the seismocardiogram (SCG) signal with or without using the electrocardiogram (ECG) R-wave as the reference point. The detected fiducial points were used to estimate cardiac time intervals. Due to complexity and sensitivity of the SCG signal, the algorithm was designed to robustly discard the low-quality cardiac cycles, which are the ones that contain unrecognizable fiducial points.

METHOD

The algorithm was trained on a dataset containing 48,318 manually annotated cardiac cycles. It was then applied to three test datasets: 65 young healthy individuals (dataset 1), 15 individuals above 44 years old (dataset 2), and 25 patients with previous heart conditions (dataset 3).

RESULTS

The algorithm accomplished high prediction accuracy with the rootmean- square-error of less than 5 ms for all the test datasets. The algorithm overall mean detection rate per individual recordings (DRI) were 74, 68, and 42 percent for the three test datasets when concurrent ECG and SCG were used. For the standalone SCG case, the mean DRI was 32, 14 and 21 percent.

CONCLUSION

When the proposed algorithm applied to concurrent ECG and SCG signals, the desired fiducial points of the SCG signal were successfully estimated with a high detection rate. For the standalone case, however, the algorithm achieved high prediction accuracy and detection rate for only the young individual dataset.

SIGNIFICANCE

The presented algorithm could be used for accurate and non-invasive estimation of cardiac time intervals.

A Hidden Markov Model for Seismocardiography

We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate, heart rate variability measures, and cardiac time intervals (the latter requiring a small number of manual annotations). As is shown in the conducted experimental study, the presented algorithm outperforms the state-of-the-art in seismocardiogram-based heart rate and heart rate variability estimation. Moreover, the isovolumic contraction time and the left ventricular ejection time are estimated with mean absolute errors of about 5 [ms] and [Formula: see text], respectively. The proposed algorithm can be applied to any set of inertial sensors; does not require access to any additional sensor modalities; does not make any assumptions on the seismocardiogram morphology; and explicitly models sensor noise and beat-to-beat variations (both in amplitude and temporal scaling) in the seismocardiogram morphology. As such, it is well suited for low-cost implementations using off-the-shelf inertial sensors and targeting, e.g., at-home medical services.

Identification of Location Specific Feature Points in a Cardiac Cycle Using a Novel Seismocardiogram Spectrum System

Seismocardiogram (SCG) or mechanocardiography is a noninvasive cardiac diagnostic method; however, previous studies used only a single sensor to detect cardiac mechanical activities that will not be able to identify location-specific feature points in a cardiac cycle corresponding to the four valvular auscultation locations. In this study, a multichannel SCG spectrum measurement system was proposed and examined for cardiac activity monitoring to overcome problems like, position dependency, time delay, and signal attenuation, occurring in traditional single-channel SCG systems. ECG and multichannel SCG signals were simultaneously recorded in 25 healthy subjects. Cardiac echocardiography was conducted at the same time. SCG traces were analyzed and compared with echocardiographic images for feature point identification. Fifteen feature points were identified in the corresponding SCG traces. Among them, six feature points, including left ventricular lateral wall contraction peak velocity, septal wall contraction peak velocity, transaortic peak flow, transpulmonary peak flow, transmitral ventricular relaxation flow, and transmitral atrial contraction flow were identified. These new feature points were not observed in previous studies because the single-channel SCG could not detect the location-specific signals from other locations due to time delay and signal attenuation. As the results, the multichannel SCG spectrum measurement system can record the corresponding cardiac mechanical activities with location-specific SCG signals and six new feature points were identified with the system. This new modality may help clinical diagnoses of valvular heart diseases and heart failure in the future.

A real-time approach for heart rate monitoring using a Hilbert transform in seismocardiograms

Heart rate monitoring helps in assessing the functionality and condition of the cardiovascular system. We present a new real-time applicable approach for estimating beat-to-beat time intervals and heart rate in seismocardiograms acquired from a tri-axial microelectromechanical accelerometer. Seismocardiography (SCG) is a non-invasive method for heart monitoring which measures the mechanical activity of the heart. Measuring true beat-to-beat time intervals from SCG could be used for monitoring of the heart rhythm, for heart rate variability analysis and for many other clinical applications. In this paper we present the Hilbert adaptive beat identification technique for the detection of heartbeat timings and inter-beat time intervals in SCG from healthy volunteers in three different positions, i.e. supine, left and right recumbent. Our method is electrocardiogram (ECG) independent, as it does not require any ECG fiducial points to estimate the beat-to-beat intervals. The performance of the algorithm was tested against standard ECG measurements. The average true positive rate, positive prediction value and detection error rate for the different positions were, respectively, supine (95.8%, 96.0% and ≃0.6%), left (99.3%, 98.8% and ≃0.001%) and right (99.53%, 99.3% and ≃0.01%). High correlation and agreement was observed between SCG and ECG inter-beat intervals (r  >  0.99) for all positions, which highlights the capability of the algorithm for SCG heart monitoring from different positions. Additionally, we demonstrate the applicability of the proposed method in smartphone based SCG. In conclusion, the proposed algorithm can be used for real-time continuous unobtrusive cardiac monitoring, smartphone cardiography, and in wearable devices aimed at health and well-being applications.

Accurate and consistent automatic seismocardiogram annotation without concurrent ECG

Seismocardiography (SCG) is the measurement of vibrations in the sternum caused by the beating of the heart. Precise cardiac mechanical timings that are easily obtained from SCG are critically dependent on accurate identification of fiducial points. So far, SCG annotation has relied on concurrent ECG measurements. An algorithm capable of annotating SCG without the use any other concurrent measurement was designed. We subjected 18 participants to graded lower body negative pressure. We collected ECG and SCG, obtained R peaks from the former, and annotated the latter by hand, using these identified peaks. We also annotated the SCG automatically. We compared the isovolumic moment timings obtained by hand to those obtained using our algorithm. Mean  ±  confidence interval of the percentage of accurately annotated cardiac cycles were [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] for levels of negative pressure 0, -20, -30, -40, and  -50 mmHg. LF/HF ratios, the relative power of low-frequency variations to high-frequency variations in heart beat intervals, obtained from isovolumic moments were also compared to those obtained from R peaks. The mean differences  ±  confidence interval were [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] for increasing levels of negative pressure. The accuracy and consistency of the algorithm enables the use of SCG as a stand-alone heart monitoring tool in healthy individuals at rest, and could serve as a basis for an eventual application in pathological cases.

Moving toward automatic and standalone delineation of seismocardiogram signal

The purpose of this research is to propose an algorithm that could accomplish automatic delineation of the seismocardiogram (SCG) signal without using a reference electrocardiogram R-wave. As a result, the SCG signal could be used, as a stand-alone solution for many cardiovascular medical applications such as hemorrhage detection, cardiac computed tomographic gating, cardiac resynchronization therapy, hemodynamics estimations and diastolic timed vibration. Multiple envelopes were derived from the seismocardiogram signal by using filtering and triple integration. The first envelope is referred as the heart rate envelope, which has the characteristics of having a period of exactly one cardiac cycle and its purpose is to replace the ECG R-wave as a reference point. Our dataset is based on the lower body negative pressure (LBNP) test that was conducted on 18 individuals, containing 21610 cardiac cycles. For 94% of the LBNP dataset, the aforementioned envelope estimated heart rate within 3 beats per minute. Three different peaks of the SCG signal are of our interest: isovolumic contraction (IM), aortic valve opening (AO) and aortic valve closure (AC). For each of these desired peaks of the SCG signal, a different envelope was designed in a manner that its peak is very close to IM, AO and AC, respectively. For the same lower body negative pressure data set, a mean difference of (9, 9, 6) and standard deviation of (8, 9, 9) millisecond between the peak of envelopes and IM, AO and AC is accomplished. This could be used as a good initial estimation of the annotation points.

Preliminary results on quantification of Seismocardiogram morphological changes, using principal component analysis

A methodology, based on principal component analysis, is proposed to quantify beat to beat Seismocardiogram changes. The proposed method was tested over a population of 94 subjects including 35 ischemic heart disease patients. The results showed that there was an insignificant overlap between the diseased and the healthy populations in the number of principal components (NPC) and that further development of this method might yield a classification index for myocardial abnormalities. In addition such an index has potential utility in patient monitoring.

Automatic annotation of seismocardiogram with high-frequency precordial accelerations

Seismocardiogram (SCG) is the low-frequency vibrations signal recorded from the chest using accelerometers. Peaks on dorsoventral and sternal SCG correspond to specific cardiac events. Prior research work has shown the potential of extracting such peaks for various types of monitoring and diagnosis applications. However, annotation of these peaks is not a trivial task and complicated in some subjects. In this paper, an automated method is proposed to annotate these peaks. The high-frequency accelerations obtained from the same accelerometer, used to record SCG with, were used to facilitate the annotation of the SCG. Algorithms were developed for detection of isovolumic moment (IM) and aortic valve closure (AC) points of SCG. Four different envelope calculation methods were used: cardiac sound characteristic waveform (CSCW), Shannon, absolute, and Hilbert. The algorithms were evaluated based on a dataset including 18 subjects undergoing lower body negative pressure and were further tested with another dataset, which included 67 subjects. These datasets had been previously manually annotated. The algorithm based on CSCW envelope calculation produced the highest detection accuracy for both IM and AC. The overall CSCW algorithm detection accuracy for the test dataset was 98.7% and 99.1% for the IM and AC points, respectively.