Myocardial contractility: a seismocardiography approach

Cardiac Time Intervals Under Motion Using Bimodal Chest E-Tattoos and Multistage Processing

GOAL

We present a wireless, lightweight, stretchable, and chest-conformable sensor, known as the chest e-tattoo, coupled with an advanced signal processing framework to accurately identify various cardiac events, and thereby extract cardiac time intervals (CTIs) even during body motion.

METHODS

We developed a wireless chest e-tattoo featuring synchronous electrocardiography (ECG) and seismocardiography (SCG), with SCG capturing chest vibrations to complement ECG. Motion artifacts often compromise the efficacy of SCG, but the e-tattoo's slim, stretchy design allows strategic placement near the xiphoid process for improved signal quality. Nine participants were monitored during walking and cycling. To accurately extract CTIs, we implemented a multistage signal processing framework, named the FAD framework, combining adaptive Normalized Least Mean Squares (NMLS) filtering, ensemble averaging, and Empirical Mode Decomposition (EMD).

RESULTS

Key CTIs, especially left ventricular ejection time (LVET), were successfully extracted by our hardware-software system and showed strong agreement with those reported by an FDA-cleared bedside monitor even during substantial movements. The pre-ejection period (PEP) measured by the e-tattoo also aligned with previous findings.

CONCLUSION

The bimodal chest e-tattoo combined with the FAD framework enables reliable CTI measurements during various activities.

SIGNIFICANCE

Managing cardiovascular disease at home necessitates continuous monitoring, which has been challenging with wearables due to signal sensitivity to motion. Accurately extracting cardiac events from synchronous SCG and ECG during motion can significantly enhance heart stress response quantification, offering a more comprehensive cardiac health assessment than ECG alone and marking a significant advancement in ambulatory cardiovascular monitoring capabilities.

Cardiac Multi-Frequency Vibration Signal Sensor Module and Feature Extraction Method Based on Vibration Modeling

Cardiovascular diseases pose a long-term risk to human health. This study focuses on the rich-spectrum mechanical vibrations generated during cardiac activity. By combining Fourier series theory, we propose a multi-frequency vibration model for the heart, decomposing cardiac vibration into frequency bands and establishing a systematic interpretation for detecting multi-frequency cardiac vibrations. Based on this, we develop a small multi-frequency vibration sensor module based on flexible polyvinylidene fluoride (PVDF) films, which is capable of synchronously collecting ultra-low-frequency seismocardiography (ULF-SCG), seismocardiography (SCG), and phonocardiography (PCG) signals with high sensitivity. Comparative experiments validate the sensor's performance and we further develop an algorithm framework for feature extraction based on 1D-CNN models, achieving continuous recognition of multiple vibration features. Testing shows that the recognition coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of the 8 features are 0.95, 2.18 ms, and 4.89 ms, respectively, with an average prediction speed of 60.18 us/point, meeting the re-quirements for online monitoring while ensuring accuracy in extracting multiple feature points. Finally, integrating the vibration model, sensor, and feature extraction algorithm, we propose a dynamic monitoring system for multi-frequency cardiac vibration, which can be applied to portable monitoring devices for daily dynamic cardiac monitoring, providing a new approach for the early diagnosis and prevention of cardiovascular diseases.

Postural and longitudinal variability in seismocardiographic signals

Objective. Low frequency cardiovascular vibrations detectable on the chest surface (termed seismocardiography or SCG) may be useful for non-invasive diagnosis and monitoring of various cardiovascular conditions. A potential limitation of using SCG for longitudinal patient monitoring is the existence of intra-subject variability, which can contribute to errors in calculating SCG features. Improved understanding of the contribution of intra-subject variability sources may lead to improved SCG utility. This study aims to quantify postural and longitudinal SCG variability in healthy resting subjects during normal breathing.Approach. SCG and ECG signals were longitudinally acquired in 19 healthy subjects at different postures (supine, 45° head up, and sitting) during five recording sessions over five months. SCG cycles were segmented using the ECG R wave. Unsupervised machine learning was used to reduce SCG variability due to respiration by grouping the SCG signals into two clusters with minimized intra-cluster waveform heterogeneity. Several SCG features were assessed at different postures and longitudinally.Main results. SCG waveform morphological variability was calculated within each cluster (intra-cluster) and between two clusters (inter-cluster) at each posture and data collection session. The variabilities were significantly different between the supine and sitting but not between supine and 45° postures. For the 45° and sitting postures, the intra-cluster variability was not significantly different, while the inter-cluster variability difference was significant. The energy ratio between different frequency bands to total spectral energy in 0.5-50 Hz were calculated and were comparable for all postures. The combined cardiac timing intervals from the two clusters showed significant variation with postural changes. There was significant heart rate difference between the clusters and between postural positions. The SCG features were compared between longitudinal sessions and all features were not significantly different,Significance. Several SCG features significantly varied with posture suggesting that posture needs to be specified when comparing SCG changes over time. Longitudinally comparable SCG feature values suggests that significant longitudinal differences, if observed, may reflect true alternations in the cardiac functioning over time.

Quantification of Cardiac Kinetic Energy and Its Changes During Transmural Myocardial Infarction Assessed by Multi-Dimensional Seismocardiography

Introduction: Seismocardiography (SCG) records cardiac and blood-induced motions transmitted to the chest surface as vibratory phenomena. Evidences demonstrate that acute myocardial ischemia (AMI) profoundly affects the SCG signals. Multidimensional SCG records cardiac vibrations in linear and rotational dimensions, and scalar parameters of kinetic energy can be computed. We speculate that AMI and revascularization profoundly modify cardiac kinetic energy as recorded by SCG.

Methods: Under general anesthesia, 21 swine underwent 90 min of myocardial ischemia induced by percutaneous sub-occlusion of the proximal left anterior descending (LAD) coronary artery and subsequent revascularization. Invasive hemodynamic parameters were continuously recorded. SCG was recorded during baseline, immediately and 80 min after LAD sub-occlusion, and immediately and 60 min after LAD reperfusion. iK was automatically computed for each cardiac cycle (iK^CC) in linear (iK_Lin) and rotational (iK_Rot) dimensions. iK was calculated as well during systole and diastole (iK^Sys and iK^Dia, respectively). Echocardiography was performed at baseline and after revascularization, and the left ventricle ejection fraction (LVEF) along with regional left ventricle (LV) wall abnormalities were evaluated.

Results: Upon LAD sub-occlusion, 77% of STEMI and 24% of NSTEMI were observed. Compared to baseline, troponins increased from 13.0 (6.5; 21.3) ng/dl to 170.5 (102.5; 475.0) ng/dl, and LVEF dropped from 65.0 ± 0.0 to 30.6 ± 5.7% at the end of revascularization (both p < 0.0001). Regional LV wall abnormalities were observed as follows: anterior MI, 17.6% (three out of 17); septal MI, 5.8% (one out of 17); antero-septal MI, 47.1% (eight out of 17); and infero-septal MI, 29.4% (five out of 17). In the linear dimension, iKLinCC, iKLinSys, and iKLinDia dropped by 43, 52, and 53%, respectively (p < 0.0001, p < 0.0001, and p = 0.03, respectively) from baseline to the end of reperfusion. In the rotational dimension, iKRotCC and iKRotSys dropped by 30 and 36%, respectively (p = 0.0006 and p < 0.0001, respectively), but iKRotDia did not change (p = 0.41). All the hemodynamic parameters, except the pulmonary artery pulse pressure, were significantly correlated with the parameters of iK, except for the diastolic component.

Conclusions: In this very context of experimental AMI with acute LV regional dysfunction and no concomitant AMI-related heart valve disease, linear and rotational iK parameters, in particular, systolic ones, provide reliable information on LV contractile dysfunction and its effects on the downstream circulation. Multidimensional SCG may provide information on the cardiac contractile status expressed in terms of iK during AMI and reperfusion. This automatic system may empower health care providers and patients to remotely monitor cardiovascular status in the near future.

Assessment of left ventricular twist by 3D ballistocardiography and seismocardiography compared with 2D STI echocardiography in a context of enhanced inotropism in healthy subjects

Ballistocardiography (BCG) and Seismocardiography (SCG) assess the vibrations produced by cardiac contraction and blood flow, respectively, by means of micro-accelerometers and micro-gyroscopes. From the BCG and SCG signals, maximal velocities (VMax), integral of kinetic energy (iK), and maximal power (PMax) can be computed as scalar parameters, both in linear and rotational dimensions. Standard echocardiography and 2-dimensional speckle tracking imaging echocardiography were performed on 34 healthy volunteers who were infused with increasing doses of dobutamine (5-10-20 μg/kg/min). Linear VMax of BCG predicts the rates of left ventricular (LV) twisting and untwisting (both p < 0.0001). The linear PMax of both SCG and BCG and the linear iK of BCG are the best predictors of the LV ejection fraction (LVEF) (p < 0.0001). This result is further confirmed by mathematical models combining the metrics from SCG and BCG signals with heart rate, in which both linear PMax and iK strongly correlate with LVEF (R = 0.7, p < 0.0001). In this setting of enhanced inotropism, the linear VMax of BCG, rather than the VMax of SCG, is the metric which best explains the LV twist mechanics, in particular the rates of twisting and untwisting. PMax and iK metrics are strongly associated with the LVEF and account for 50% of the variance of the LVEF.

Cardiovascular adaptation to simulated microgravity and countermeasure efficacy assessed by ballistocardiography and seismocardiography

Head-down bed rest (HDBR) reproduces the cardiovascular effects of microgravity. We tested the hypothesis that regular high-intensity physical exercise (JUMP) could prevent this cardiovascular deconditioning, which could be detected using seismocardiography (SCG) and ballistocardiography (BCG). 23 healthy males were exposed to 60-day HDBR: 12 in a physical exercise group (JUMP), the others in a control group (CTRL). SCG and BCG were measured during supine controlled breathing protocols. From the linear and rotational SCG/BCG signals, the integral of kinetic energy ([Formula: see text]) was computed on each dimension over the cardiac cycle. At the end of HDBR, BCG rotational [Formula: see text] and SCG transversal [Formula: see text] decreased similarly for all participants (- 40% and - 44%, respectively, p < 0.05), and so did orthostatic tolerance (- 58%, p < 0.01). Resting heart rate decreased in JUMP (- 10%, p < 0.01), but not in CTRL. BCG linear [Formula: see text] decreased in CTRL (- 50%, p < 0.05), but not in JUMP. The changes in the systolic component of BCG linear iK were correlated to those in stroke volume and V_O2 max (R = 0.44 and 0.47, respectively, p < 0.05). JUMP was less affected by cardiovascular deconditioning, which could be detected by BCG in agreement with standard markers of the cardiovascular condition. This shows the potential of BCG to easily monitor cardiac deconditioning.

Influence of sympathetic activation on myocardial contractility measured with ballistocardiography and seismocardiography during sustained end-expiratory apnea

Ballistocardiography (BCG) and seismocardiography (SCG) assess vibrations produced by cardiac contraction and blood flow, respectively, through micro-accelerometers and micro-gyroscopes. BCG and SCG kinetic energies (KE) and their temporal integrals (iK) during a single heartbeat are computed in linear and rotational dimensions. Our aim was to test the hypothesis that iK from BCG and SCG are related to sympathetic activation during maximal voluntary end-expiratory apnea. Multiunit muscle sympathetic nerve traffic [burst frequency (BF), total muscular sympathetic nerve activity (tMSNA)] was measured by microneurography during normal breathing and apnea (n = 28, healthy men). iK of BCG and SCG were simultaneously recorded in the linear and rotational dimension, along with oxygen saturation ([Formula: see text]) and systolic blood pressure (SBP). The mean duration of apneas was 25.4 ± 9.4 s. SBP, BF, and tMSNA increased during the apnea compared with baseline (P = 0.01, P = 0.002,and P = 0.001, respectively), whereas [Formula: see text] decreased (P = 0.02). At the end of the apnea compared with normal breathing, changes in iK computed from BCG were related to changes of tMSNA and BF only in the linear dimension (r = 0.85, P < 0.0001; and r = 0.72, P = 0.002, respectively), whereas changes in linear iK of SCG were related only to changes of tMSNA (r = 0.62, P = 0.01). We conclude that maximal end expiratory apnea increases cardiac kinetic energy computed from BCG and SCG, along with sympathetic activity. The novelty of the present investigation is that linear iK of BCG is directly and more strongly related to the rise in sympathetic activity than the SCG, mainly at the end of a sustained apnea, likely because the BCG is more affected by the sympathetic and hemodynamic effects of breathing cessation. BCG and SCG may prove useful to assess sympathetic nerve changes in patients with sleep disturbances.NEW & NOTEWORTHY Ballistocardiography (BCG) and seismocardiography (SCG) assess vibrations produced by cardiac contraction and blood flow, respectively, through micro-accelerometers and micro-gyroscopes. Kinetic energies (KE) and their temporal integrals (iK) during a single heartbeat are computed from the BCG and SCG waveforms in a linear and a rotational dimension. When compared with normal breathing, during an end-expiratory voluntary apnea, iK increased and was positively related to sympathetic nerve traffic rise assessed by microneurography. Further studies are needed to determine whether BCG and SCG can probe sympathetic nerve changes in patients with sleep disturbances.

Ballistocardiography and seismocardiography detection of hemodynamic changes during simulated obstructive apnea

OBJECTIVE

To investigate if modern seismocardiography (SCG) and ballistocardiography (BCG) are useful in the detection of hemodynamic changes occurring during simulated obstructive apneic events.

METHODS

Forty-seven healthy volunteers performed a voluntary maximum Mueller maneuver (MM) for 10 s, and SCG and BCG signals were simultaneously taken. The kinetic energy of a set of cardiac cycles before and during the apneic episode was automatically computed from the rotational and linear channels of the SCG and BCG waveforms and its temporal integral (i K) was derived (unit of measure: microjoules per second (µJ·s)). The estimated transmural pressure (eP _TM ) was assessed as the difference between systemic blood pressure and maximal inspiratory pressure (MIP). The Wilcoxon sign-rank test was used to evaluate differences in energy measurements between normal respiration and the loaded inspiration maneuver. Cardiac kinetic energies and the MIP produced during the MM were compared by linear regression analysis following log transformation in order to assess the correlation between variables.

MAIN RESULTS

The [Formula: see text] during normal breathing increased from 1.1(0.8; 1.4) to 1.9(1.4; 4.3) µJ·s during MM (p < 0.001). Meanwhile, [Formula: see text] increased from 54 (31; 92) to 84 (44; 153) µJ·s, (p < 0.001). The [Formula: see text] and [Formula: see text] of a set of cardiac cycles during the MM were negatively associated with the MIP (r: -0.59, p < 0.001 and r: -0.53, p = 0.001 for [Formula: see text] and [Formula: see text], respectively). When eP _TM was considered, this association became positive (r: +0.58, p < 0.001 and r:+0.60, p < 0.001, for [Formula: see text] and [Formula: see text], respectively). When the i K _LIN was considered as the comparative factor, correlations with the MIP and eP _TM were weak and insignificant. Men had higher values of i K than women.

SIGNIFICANCE

Simulated obstructive apnea elicits large rotational i K swings, which are related to the intensity of the inspiratory effort and, as such, to the intensity of the left ventricular afterload. Computation of cardiac kinetic energy through BCG and SCG may shed further light on the impact of obstructive respiratory events on the cardiovascular system.

Accurate Detection of Dobutamine-induced Haemodynamic Changes by Kino-Cardiography: A Randomised Double-Blind Placebo-Controlled Validation Study

Non-invasive remote detection of cardiac and blood displacements is an important topic in cardiac telemedicine. Here we propose kino-cardiography (KCG), a non-invasive technique based on measurement of body vibrations produced by myocardial contraction and blood flow through the cardiac chambers and major vessels. KCG is based on ballistocardiography and measures 12 degrees-of-freedom (DOF) of body motion. We tested the hypothesis that KCG reliably assesses dobutamine-induced haemodynamic changes in healthy subjects. Using a randomized double-blinded placebo-controlled crossover study design, dobutamine and placebo were infused to 34 volunteers (25 ± 2 years, BMI 22 ± 2 kg/m², 18 females). Baseline recordings were followed by 3 sessions of increasing doses of dobutamine (5, 10, 20 μg/kg.min) or saline solution. During each session, stroke volume (SV) and cardiac output (CO) were determined by echocardiography and followed by a 90 s KCG recording. Measured linear accelerations and angular velocities were used to compute total Kinetic energy (iK) and power (Pmax). KCG sorted dobutamine infusion vs. placebo with 96.9% accuracy. Increases in SV and CO were correlated to iK (r = +0.71 and r = +0.8, respectively, p < 0.0001). Kino-cardiography, with 12-DOF, allows detecting dobutamine-induced haemodynamic changes with a high accuracy and present a major improvement over single axis ballistocardiography or seismocardiography.

Performance Analysis of Gyroscope and Accelerometer Sensors for Seismocardiography-Based Wearable Pre-Ejection Period Estimation

OBJECTIVE

Systolic time intervals, such as the pre-ejection period (PEP), are important parameters for assessing cardiac contractility that can be measured non-invasively using seismocardiography (SCG). Recent studies have shown that specific points on accelerometer- and gyroscope-based SCG signals can be used for PEP estimation. However, the complex morphology and inter-subject variation of the SCG signal can make this assumption very challenging and increase the root mean squared error (RMSE) when these techniques are used to develop a global model.

METHODS

In this study, we compared gyroscope- and accelerometer-based SCG signals, individually and in combination, for estimating PEP to show the efficacy of these sensors in capturing valuable information regarding cardiovascular health. We extracted general time-domain features from all the axes of these sensors and developed global models using various regression techniques.

RESULTS

In single-axis comparison of gyroscope and accelerometer, angular velocity signal around head to foot axis from the gyroscope provided the lowest RMSE of 12.63 ± 0.49 ms across all subjects. The best estimate of PEP, with a RMSE of 11.46 ± 0.32 ms across all subjects, was achieved by combining features from the gyroscope and accelerometer. Our global model showed 30% lower RMSE when compared to algorithms used in recent literature.

CONCLUSION

Gyroscopes can provide better PEP estimation compared to accelerometers located on the mid-sternum. Global PEP estimation models can be improved by combining general time domain features from both sensors.

SIGNIFICANCE

This work can be used to develop a low-cost wearable heart-monitoring device and to generate a universal estimation model for systolic time intervals using a single- or multiple-sensor fusion.

Universal Pre-Ejection Period Estimation Using Seismocardiography: Quantifying the Effects of Sensor Placement and Regression Algorithms

Seismocardiography (SCG), the measurement of local chest vibrations due to the heart and blood movement, is a non-invasive technique to assess cardiac contractility via systolic time intervals such as the pre-ejection period (PEP). Recent studies show that SCG signals measured before and after exercise can effectively classify compensated and decompensated heart failure (HF) patients through PEP estimation. However, the morphology of the SCG signal varies from person to person and sensor placement making it difficult to automatically estimate PEP from SCG and electrocardiogram signals using a global model. In this proof-of-concept study, we address this problem by extracting a set of timing features from SCG signals measured from multiple positions on the upper body. We then test global regression models that combine all the detected features to identify the most accurate model for PEP estimation obtained from the best performing regressor and the best sensor location or combination of locations. Our results show that ensemble regression using XGBoost with a combination of sensors placed on the sternum and below the left clavicle provide the best RMSE = 11.6 ± 0.4 ms across all subjects. We also show that placing the sensor below the left or right clavicle rather than the conventional placement on the sternum results in more accurate PEP estimates.

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

Gyrocardiography (GCG) is a new non-invasive technique for assessing heart motions by using a sensor of angular motion – gyroscope – attached to the skin of the chest. In this study, we conducted simultaneous recordings of electrocardiography (ECG), GCG, and echocardiography in a group of subjects consisting of nine healthy volunteer men. Annotation of underlying fiducial points in GCG is presented and compared to opening and closing points of heart valves measured by a pulse wave Doppler. Comparison between GCG and synchronized tissue Doppler imaging (TDI) data shows that the GCG signal is also capable of providing temporal information on the systolic and early diastolic peak velocities of the myocardium. Furthermore, time intervals from the ECG Q-wave to the maximum of the integrated GCG (angular displacement) signal and maximal myocardial strain curves obtained by 3D speckle tracking are correlated. We see GCG as a promising mechanical cardiac monitoring tool that enables quantification of beat-by-beat dynamics of systolic time intervals (STI) related to hemodynamic variables and myocardial contractility.

A Cardiac Early Warning System with Multi Channel SCG and ECG Monitoring for Mobile Health

Use of information and communication technology such as smart phone, smart watch, smart glass and portable health monitoring devices for healthcare services has made Mobile Health (mHealth) an emerging research area. Coronary Heart Disease (CHD) is considered as a leading cause of death world wide and an increasing number of people die prematurely due to CHD. Under such circumstances, there is a growing demand for a reliable cardiac monitoring system to catch the intermittent abnormalities and detect critical cardiac behaviors which lead to sudden death. Use of mobile devices to collect Electrocardiography (ECG), Seismocardiography (SCG) data and efficient analysis of those data can monitor a patient’s cardiac activities for early warning. This paper presents a novel cardiac data acquisition method and combined analysis of Electrocardiography (ECG) and multi channel Seismocardiography (SCG) data. An early warning system is implemented to monitor the cardiac activities of a person and accuracy assessment of the early warning system is conducted for the ECG data only. The assessment shows 88% accuracy and effectiveness of our proposed analysis, which implies the viability and applicability of the proposed early warning system.

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.

Quantifying and Reducing Motion Artifacts in Wearable Seismocardiogram Measurements During Walking to Assess Left Ventricular Health

GOAL

Our objective is to provide a framework for extracting signals of interest from the wearable seismocardiogram (SCG) measured during walking at normal (subject's preferred pace) and moderately fast (1.34-1.45 m/s) speeds.

METHODS

We demonstrate, using empirical mode decomposition (EMD) and feature tracking algorithms, that the pre-ejection period (PEP) can be accurately estimated from a wearable patch that simultaneously measures electrocardiogram and sternal acceleration signals. We also provide a method to determine the minimum number of heartbeats required for an accurate estimate to be obtained for the PEP from the accelerometer signals during walking.

RESULTS

The EMD-based denoising approach provides a statistically significant increase in the signal-to-noise ratio of wearable SCG signals and also improves estimation of PEP during walking.

CONCLUSION

The algorithms described in this paper can be used to provide hemodynamic assessment from wearable SCG during walking.

SIGNIFICANCE

A major limitation in the use of the SCG, a measure of local chest vibrations caused by cardiac ejection of blood in the vasculature, is that a user must remain completely still for high-quality measurements. The motion can create artifacts and practically render the signal unreadable. Addressing this limitation could allow, for the first time, SCG measurements to be obtained reliably during movement-aside from increasing the coverage throughout the day of cardiovascular monitoring, analyzing SCG signals during movement would quantify the cardiovascular system's response to stress (exercise), and thus provide a more holistic assessment of overall health.

Seismocardiography: past, present and future

This paper presents an overview of seismocardiography (SCG) as a noninvasive cardiology method. The paper represents a brief historical background to the SCG, an assessment of the technology at present, and an evaluation of the challenges we must address. These challenges include the development and clarification of definitions, standards, and annotations.

Heart monitoring systems--a review

To diagnose health status of the heart, heart monitoring systems use heart signals produced during each cardiac cycle. Many types of signals are acquired to analyze heart functionality and hence several heart monitoring systems such as phonocardiography, electrocardiography, photoplethysmography and seismocardiography are used in practice. Recently, focus on the at-home monitoring of the heart is increasing for long term monitoring, which minimizes risks associated with the patients diagnosed with cardiovascular diseases. It leads to increasing research interest in portable systems having features such as signal transmission capability, unobtrusiveness, and low power consumption. In this paper we intend to provide a detailed review of recent advancements of such heart monitoring systems. We introduce the heart monitoring system in five modules: (1) body sensors, (2) signal conditioning, (3) analog to digital converter (ADC) and compression, (4) wireless transmission, and (5) analysis and classification. In each module, we provide a brief introduction about the function of the module, recent developments, and their limitation and challenges.

Simplified detection of myocardial ischemia by seismocardiography. Differentiation between causes of altered myocardial function

Seismocardiography (SCG) is a noninvasive technique for recording cardiac vibrations. Changes in these waves have been correlated with chronic and acute alterations in myocardial function. This analysis is complex and clinical integration limited. The current study aimed to simplify the utilization of SCG by fast Fourier transformation for a reliable discrimination between different intra- and postoperative causes of hypotension (i.e., myocardial ischemia or hypovolemia). We operated on nine pigs and recorded SCG at baseline, at hypovolemia (occlusion of the inferior vena cava), and at ischemia (occlusion of the right coronary artery). In conclusion, SCG enables detection and differentiation of ischemia and hypovolemia as important causes of altered myocardial function during and after surgery. Thus, this simple and noninvasive diagnostic tool may be used intra- and postoperatively to identify patients at risk.