Precordial vibrations provide noninvasive detection of early-stage hemorrhage

Mechanical deconditioning of the heart due to long-term bed rest as observed on seismocardiogram morphology

During head-down tilt bed rest (HDT) the cardiovascular system is subject to headward fluid shifts. The fluid shift phenomenon is analogous to weightlessness experienced during spaceflight microgravity. The purpose of this study was to investigate the effect of prolonged 60-day bed rest on the mechanical performance of the heart using the morphology of seismocardiography (SCG). Three-lead electrocardiogram (ECG), SCG and blood pressure recordings were collected simultaneously from 20 males in a 60-day HDT study (MEDES, Toulouse, France). The study was divided into two campaigns of ten participants. The first commenced in January, and the second in September. Signals were recorded in the supine position during the baseline data collection (BDC) before bed rest, during 6° HDT bed rest and during recovery (R), post-bed rest. Using SCG and blood pressure at the finger, the following were determined: Pulse Transit Time (PTT); and left-ventricular ejection time (LVET). SCG morphology was analyzed using functional data analysis (FDA). The coefficients of the model were estimated over 20 cycles of SCG recordings of BDC12 and HDT52. SCG fiducial morphology AO (aortic valve opening) and AC (aortic valve closing) amplitudes showed significant decrease between BDC12 and HDT52 (p < 0.03). PTT and LVET were also found to decrease through HDT bed rest (p < 0.01). Furthermore, PTT and LVET magnitude of response to bed rest was found to be different between campaigns (p < 0.001) possibly due to seasonal effects on of the cardiovascular system. Correlations between FDA and cardiac timing intervals PTT and LVET using SCG suggests decreases in mechanical strength of the heart and increased arterial stiffness due to fluid shifts associated with the prolonged bed rest.

Wearable Sensors and Machine Learning for Hypovolemia Problems in Occupational, Military and Sports Medicine: Physiological Basis, Hardware and Algorithms

Hypovolemia is a physiological state of reduced blood volume that can exist as either (1) absolute hypovolemia because of a lower circulating blood (plasma) volume for a given vascular space (dehydration, hemorrhage) or (2) relative hypovolemia resulting from an expanded vascular space (vasodilation) for a given circulating blood volume (e.g., heat stress, hypoxia, sepsis). This paper examines the physiology of hypovolemia and its association with health and performance problems common to occupational, military and sports medicine. We discuss the maturation of individual-specific compensatory reserve or decompensation measures for future wearable sensor systems to effectively manage these hypovolemia problems. The paper then presents areas of future work to allow such technologies to translate from lab settings to use as decision aids for managing hypovolemia. We envision a future that incorporates elements of the compensatory reserve measure with advances in sensing technology and multiple modalities of cardiovascular sensing, additional contextual measures, and advanced noise reduction algorithms into a fully wearable system, creating a robust and physiologically sound approach to manage physical work, fatigue, safety and health issues associated with hypovolemia for workers, warfighters and athletes in austere conditions.

A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications

In recent years, cardiovascular diseases are on the rise, and they entail enormous health burdens on global economies. Cardiac vibrations yield a wide and rich spectrum of essential information regarding the functioning of the heart, and thus it is necessary to take advantage of this data to better monitor cardiac health by way of prevention in early stages. Specifically, seismocardiography (SCG) is a noninvasive technique that can record cardiac vibrations by using new cutting-edge devices as accelerometers. Therefore, providing new and reliable data regarding advancements in the field of SCG, i.e., new devices and tools, is necessary to outperform the current understanding of the State-of-the-Art (SoTA). This paper reviews the SoTA on SCG and concentrates on three critical aspects of the SCG approach, i.e., on the acquisition, annotation, and its current applications. Moreover, this comprehensive overview also presents a detailed summary of recent advancements in SCG, such as the adoption of new techniques based on the artificial intelligence field, e.g., machine learning, deep learning, artificial neural networks, and fuzzy logic. Finally, a discussion on the open issues and future investigations regarding the topic is included.

The Delineation of Fiducial Points for Non-Contact Radar Seismocardiogram Signals Without Concurrent ECG

OBJECTIVE

Non-contact sensing of seismocardiogram (SCG) signals through a microwave Doppler radar is promising for biomedical applications. However, the delineation of fiducial points for radar SCG still relies on concurrent ECG which requires a contact sensor and limits the complete non-contact detection of SCG.

METHODS

Instead of ECG, a new reference signal, the radar displacement signal of heartbeat (RDH), was derived through the complex Fourier transform and the band pass filtering of the radar signal. The RDH signal was used to locate each cardiac cycle and mask the systolic profile, which was further used to detect an important fiducial point, aortic valve opening (AO). The beat-to-beat interval was estimated from AO-AO interval and compared with the gold standard, ECG R-to-R interval.

RESULTS

For the 22 subjects in the study, the evaluation of the AOs detected by RDH (AO_RDH) shows the average detection ratio can reach 90%, indicating a high ratio of the AO_RDH that are exactly the same as AO detected using the ECG R-wave (AO_ECG). Additionally, the left ventricular ejection time (LVET) values estimated from the ensemble averaged radar waveform through AO_RDH segmentation are within 2 ms of those through AO_ECG segmentation, for all the detected subjects. Further analysis demonstrates that the beat-to-beat intervals calculated from AO_RDH have an average root-mean-square-deviation (RMSD) of 53.73 ms when compared with ECG R-to-R intervals, and have an average RMSD of 23.47 ms after removing the beats in which AO cannot be identified.

CONCLUSIONS

Radar signal RDH can be used as a reference signal to delineate fiducial points for non-contact radar SCG signals.

SIGNIFICANCE

This study can be applied to develop complete non-contact sensing of SCG and monitoring of vital signs, where contact-based SCG is not feasible.

Wearable Sensors Incorporating Compensatory Reserve Measurement for Advancing Physiological Monitoring in Critically Injured Trauma Patients

Vital signs historically served as the primary method to triage patients and resources for trauma and emergency care, but have failed to provide clinically-meaningful predictive information about patient clinical status. In this review, a framework is presented that focuses on potential wearable sensor technologies that can harness necessary electronic physiological signal integration with a current state-of-the-art predictive machine-learning algorithm that provides early clinical assessment of hypovolemia status to impact patient outcome. The ability to study the physiology of hemorrhage using a human model of progressive central hypovolemia led to the development of a novel machine-learning algorithm known as the compensatory reserve measurement (CRM). Greater sensitivity, specificity, and diagnostic accuracy to detect hemorrhage and onset of decompensated shock has been demonstrated by the CRM when compared to all standard vital signs and hemodynamic variables. The development of CRM revealed that continuous measurements of changes in arterial waveform features represented the most integrated signal of physiological compensation for conditions of reduced systemic oxygen delivery. In this review, detailed analysis of sensor technologies that include photoplethysmography, tonometry, ultrasound-based blood pressure, and cardiogenic vibration are identified as potential candidates for harnessing arterial waveform analog features required for real-time calculation of CRM. The integration of wearable sensors with the CRM algorithm provides a potentially powerful medical monitoring advancement to save civilian and military lives in emergency medical settings.

Effect of Blood Volume Shift Simulated via Head-up Tilt on Photoplethysmography Morphology

PPG can provide information on cardiovascular responses to fluid shifts from upper to lower part of body under the condition of orthostatic stress. The current study investigated ability of PPG derived LVET and other PPG derived features to identify progressive central hypovolemia induced by head up tilt (HUT) and evaluated potential use of LVET as early noninvasive indicator of blood loss. Continuous finger PPG, blood pressure, and electrocardiography were recorded simultaneously during 5-minutes of baseline and HUT of 20°, 40°, and 60° from 15 participants (age: 26.5 ± 3 years; height: 177 ± 8 cm; weight: 72 ± 10 kg, mean ± SD). Beat-by-beat pulse rate (PR), systolic amplitude (SA), systolic time (ST), diastolic time (DT), and PP Interval (PPI) and Ratio of pulse rate over systolic amplitude (PR/SA) were derived for each stage. LVET was derived from each stage. Friedman test followed by post-hoc analysis using Tukey-HSD was conducted to highlight the significance of changes induced by HUT. Application of 60° HUT (i.e. moderate category simulated hypovolemia) resulted in a significant change in PR (80±3 bpm vs 68±3 bpm, p=0.0008), DT (264±7 ms vs 303±4 ms, p=0.0008), ST (110±6 ms vs 117±7 ms, p=0.02), PP interval (764±39 ms vs 869±25 ms, p=0.0045), PR/SA (112±16 vs 82±21, p=0.012) , SA (0.875± 0.2 vs 1.69±0.6, p=0.012) and LVET(292 vs 351ms,p=0.0008) compared to baseline. LVET has a strong association with the change in central blood volume and may be used as a sensitive early marker of progressive hypovolemia. The findings of the study support the hypothesis of differentiating simulated hypovolemia based on PPG alone. Keywords: Hypovolemia, HUT, LVET.

Harnessing the Manifold Structure of Cardiomechanical Signals for Physiological Monitoring During Hemorrhage

OBJECTIVE

Local oscillation of the chest wall in response to events during the cardiac cycle may be captured using a sensing modality called seismocardiography (SCG), which is commonly used to infer cardiac time intervals (CTIs) such as the pre-ejection period (PEP). An important factor impeding the ubiquitous application of SCG for cardiac monitoring is that morphological variability of the signals makes consistent inference of CTIs a difficult task in the time-domain. The goal of this work is therefore to enable SCG-based physiological monitoring during trauma-induced hemorrhage using signal dynamics rather than morphological features.

METHODS

We introduce and explore the observation that SCG signals follow a consistent low-dimensional manifold structure during periods of changing PEP induced in a porcine model of trauma injury. Furthermore, we show that the distance traveled along this manifold correlates strongly to changes in PEP ( ∆PEP).

RESULTS

∆PEP estimation during hemorrhage was achieved with a median R² of 92.5% using a rapid manifold approximation method, comparable to an ISOMAP reference standard, which achieved an R² of 95.3%.

CONCLUSION

Rapidly approximating the manifold structure of SCG signals allows for physiological inference abstracted from the time-domain, laying the groundwork for robust, morphology-independent processing methods.

SIGNIFICANCE

Ultimately, this work represents an important advancement in SCG processing, enabling future clinical tools for trauma injury management.

Enabling the assessment of trauma-induced hemorrhage via smart wearable systems

As the leading cause of trauma-related mortality, blood loss due to hemorrhage is notoriously difficult to triage and manage. To enable timely and appropriate care for patients with trauma, this work elucidates the externally measurable physiological features of exsanguination, which were used to develop a globalized model for assessing blood volume status (BVS) or the relative severity of blood loss. These features were captured via both a multimodal wearable system and a catheter-based reference and used to accurately infer BVS in a porcine model of hemorrhage (n = 6). Ultimately, high-level features of cardiomechanical function were shown to strongly predict progression toward cardiovascular collapse and used to estimate BVS with a median error of 15.17 and 18.17% for the catheter-based and wearable systems, respectively. Exploring the nexus of biomedical theory and practice, these findings lay the groundwork for digital biomarkers of hemorrhage severity and warrant further study in human subjects.

SeisMote: A Multi-Sensor Wireless Platform for Cardiovascular Monitoring in Laboratory, Daily Life, and Telemedicine

This article presents a new wearable platform, SeisMote, for the monitoring of cardiovascular function in controlled conditions and daily life. It consists of a wireless network of sensorized nodes providing simultaneous multiple measures of electrocardiogram (ECG), acceleration, rotational velocity, and photoplethysmogram (PPG) from different body areas. A custom low-power transmission protocol was developed to allow the concomitant real-time monitoring of 32 signals (16 bit @200 Hz) from up to 12 nodes with a jitter in the among-node time synchronization lower than 0.2 ms. The BluetoothLE protocol may be used when only a single node is needed. Data can also be collected in the off-line mode. Seismocardiogram and pulse transit times can be derived from the collected data to obtain additional information on cardiac mechanics and vascular characteristics. The employment of the system in the field showed recordings without data gaps caused by transmission errors, and the duration of each battery charge exceeded 16 h. The system is currently used to investigate strategies of hemodynamic regulation in different vascular districts (through a multisite assessment of ECG and PPG) and to study the propagation of precordial vibrations along the thorax. The single-node version is presently exploited to monitor cardiac patients during telerehabilitation.

Comparison of Different Methods for Estimating Cardiac Timings: A Comprehensive Multimodal Echocardiography Investigation

Cardiac time intervals are important hemodynamic indices and provide information about left ventricular performance. Phonocardiography (PCG), impedance cardiography (ICG), and recently, seismocardiography (SCG) have been unobtrusive methods of choice for detection of cardiac time intervals and have potentials to be integrated into wearable devices. The main purpose of this study was to investigate the accuracy and precision of beat-to-beat extraction of cardiac timings from the PCG, ICG and SCG recordings in comparison to multimodal echocardiography (Doppler, TDI, and M-mode) as the gold clinical standard. Recordings were obtained from 86 healthy adults and in total 2,120 cardiac cycles were analyzed. For estimation of the pre-ejection period (PEP), 43% of ICG annotations fell in the corresponding echocardiography ranges while this was 86% for SCG. For estimation of the total systolic time (TST), these numbers were 43, 80, and 90% for ICG, PCG, and SCG, respectively. In summary, SCG and PCG signals provided an acceptable accuracy and precision in estimating cardiac timings, as compared to ICG.

Lower Body Negative Pressure: Physiological Effects, Applications, and Implementation

This review presents lower body negative pressure (LBNP) as a unique tool to investigate the physiology of integrated systemic compensatory responses to altered hemodynamic patterns during conditions of central hypovolemia in humans. An early review published in Physiological Reviews over 40 yr ago (Wolthuis et al. Physiol Rev 54: 566-595, 1974) focused on the use of LBNP as a tool to study effects of central hypovolemia, while more than a decade ago a review appeared that focused on LBNP as a model of hemorrhagic shock (Cooke et al. J Appl Physiol (1985) 96: 1249-1261, 2004). Since then there has been a great deal of new research that has applied LBNP to investigate complex physiological responses to a variety of challenges including orthostasis, hemorrhage, and other important stressors seen in humans such as microgravity encountered during spaceflight. The LBNP stimulus has provided novel insights into the physiology underlying areas such as intolerance to reduced central blood volume, sex differences concerning blood pressure regulation, autonomic dysfunctions, adaptations to exercise training, and effects of space flight. Furthermore, approaching cardiovascular assessment using prediction models for orthostatic capacity in healthy populations, derived from LBNP tolerance protocols, has provided important insights into the mechanisms of orthostatic hypotension and central hypovolemia, especially in some patient populations as well as in healthy subjects. This review also presents a concise discussion of mathematical modeling regarding compensatory responses induced by LBNP. Given the diverse applications of LBNP, it is to be expected that new and innovative applications of LBNP will be developed to explore the complex physiological mechanisms that underline health and disease.

An Independent Component Analysis Approach to Motion Noise Cancelation of Cardio-Mechanical Signals

This paper proposes a new framework for measuring sternal cardio-mechanical signals from moving subjects using multiple sensors. An array of inertial measurement units are attached to the chest wall of subjects to measure the seismocardiogram (SCG) from accelerometers and the gyrocardiogram (GCG) from gyroscopes. A digital signal processing method based on constrained independent component analysis is applied to extract the desired cardio-mechanical signals from the mixture of vibration observations. Electrocardiogram and photoplethysmography modalities are evaluated as reference sources for the constrained independent component analysis algorithm. Experimental studies with 14 young, healthy adult subjects demonstrate the feasibility of extracting seismo- and gyrocardiogram signals from walking and jogging subjects, with speeds of 3.0 mi/h and 4.6 mi/h, respectively. Beat-to-beat and ensemble-averaged features are extracted from the outputs of the algorithm. The beat-to-beat cardiac interval results demonstrate average detection rates of 91.44% during walking and 86.06% during jogging from SCG, and 87.32% during walking and 76.30% during jogging from GCG. The ensemble-averaged pre-ejection period (PEP) calculation results attained overall squared correlation coefficients of 0.9048 from SCG and 0.8350 from GCG with reference PEP from impedance cardiogram. Our results indicate that the proposed framework can improve the motion tolerance of cardio-mechanical signals in moving subjects. The effective number of recordings during day time could be potentially increased by the proposed framework, which will push forward the implementation of cardio-mechanical monitoring devices in mobile healthcare.

Development and evaluation of a novel system for inducing orthostatic challenge by tilt tests and lower body negative pressure

Lower body negative pressure (LBNP) is a method derived from space medicine, which in recent years has been increasingly used by clinicians to assess the efficiency of the cardiovascular regulatory mechanisms. LBNP with combined tilt testing is considered as an effective form of training to prevent orthostatic intolerance. We have developed a prototype system comprising a tilt table and LBNP chamber, and tested it in the context of the feasibility of the device for assessing the pilots' efficiency. The table allows for controlled tilting in the range from -45 to +80° at the maximum change rate of 45°/s. The LBNP value can smoothly be adjusted down to -100 mmHg at up to 20 mmHg/s. 17 subjects took part in the pilot study. A 24-minute scenario included -100 mmHg supine LBNP, head up tilt (HUT) and -60 mmHg LBNP associated with HUT, separated by resting phases. The most noticeable changes were observed in stroke volume (SV). During supine LBNP, HUT and the combined stimulus, a decrease of the SV value by 20%, 40% and below 50%, respectively, were detected. The proposed system can map any pre-programed tilt and LBNP profiles, and the pilot study confirmed the efficiency of performing experimental procedures.

Motorized adaptive compression system for enhancing venous return: A feasibility study on healthy individuals

Notwithstanding the extensive use of conventional compression devices in managing venous disorders, these modalities have shortages that diminish their treatment efficacy and lessen patient adherence to therapy. The purpose of this study was to develop an improved compression system that eliminates the flaws of the existing devices. A motorized bandage was designed that takes advantage of continuous feedback from force-sensing resistors to apply reproducible, controlled pressure on the lower extremities. The performance of the device in enhancing venous return was explored in a pilot test on 11 healthy participants, wherein graded lower body negative pressure was employed as a surrogate of passive standing. Each subject underwent two experiments; with and without pressure application over the calves. A two-way repeated-measures analysis of variance revealed a significant difference in the mean hemodynamic responses when the compression bandage was in action (p < .05). Specifically, a meaningful increase was observed in mean arterial pressure by 5%, diastolic blood pressure by 8% and left ventricular ejection time by 4%; and a significant decrease of 5% and 6% was noticed in heart rate and pulse pressure, respectively. These results demonstrate the capability of the designed system in attenuating the imposed orthostatic stress on cardiovascular system.

Non-linear Heart Rate and Blood Pressure Interaction in Response to Lower-Body Negative Pressure

Early detection of hemorrhage remains an open problem. In this regard, blood pressure has been an ineffective measure of blood loss due to numerous compensatory mechanisms sustaining arterial blood pressure homeostasis. Here, we investigate the feasibility of causality detection in the heart rate and blood pressure interaction, a closed-loop control system, for early detection of hemorrhage. The hemorrhage was simulated via graded lower-body negative pressure (LBNP) from 0 to -40 mmHg. The research hypothesis was that a significant elevation of causal control in the direction of blood pressure to heart rate (i.e., baroreflex response) is an early indicator of central hypovolemia. Five minutes of continuous blood pressure and electrocardiogram (ECG) signals were acquired simultaneously from young, healthy participants (27 ± 1 years, N = 27) during each LBNP stage, from which heart rate (represented by RR interval), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) were derived. The heart rate and blood pressure causal interaction (RR↔SBP and RR↔MAP) was studied during the last 3 min of each LBNP stage. At supine rest, the non-baroreflex arm (RR→SBP and RR→MAP) showed a significantly (p < 0.001) higher causal drive toward blood pressure regulation compared to the baroreflex arm (SBP→RR and MAP→RR). In response to moderate category hemorrhage (-30 mmHg LBNP), no change was observed in the traditional marker of blood loss i.e., pulse pressure (p = 0.10) along with the RR→SBP (p = 0.76), RR→MAP (p = 0.60), and SBP→RR (p = 0.07) causality compared to the resting stage. Contrarily, a significant elevation in the MAP→RR (p = 0.004) causality was observed. In accordance with our hypothesis, the outcomes of the research underscored the potential of compensatory baroreflex arm (MAP→RR) of the heart rate and blood pressure interaction toward differentiating a simulated moderate category hemorrhage from the resting stage. Therefore, monitoring baroreflex causality can have a clinical utility in making triage decisions to impede hemorrhage progression.

Combined Seismo- and Gyro-Cardiography: A More Comprehensive Evaluation of Heart-Induced Chest Vibrations

This paper reports on the combined analysis of seismocardiogram (SCG) and gyrocardiogram (GCG) recordings. An inertial measurement unit (IMU) consisting of a three-axis micro-electromechanical (MEMS) accelerometer and a three-axis MEMS gyroscope is used to record heart-induced mechanical vibrations from the chest wall of the subjects. An electrocardiogram and an impedance cardiogram (ICG) sensor are also used as references for segmenting the cardiac cycles and recording the aortic valve opening and closure (AO and AC) events, respectively. A simplified model is proposed to explain the mechanical coupling of the chest wall to the IMU. Correlations and time differences are analyzed for the annotation of GCG and its first derivative with respect to ICG and SCG as references. Experimental results indicate a precise identification of systolic points such as the AO and AC events. The left ventricular ejection time and pre-ejection period metrics calculated from gyroscope recordings are also shown to accurately track their corresponding trends acquired from ICG signals. Waveform similarity analyses indicate that the first derivative of GCG has a better similarity with SCG than the GCG signal itself. Experimental results also suggest that interdevice differences in GCG recordings would need to be addressed before this technology can gain widespread application.

Multimodal chest surface motion data for respiratory and cardiovascular monitoring applications

Chest surface motion is of significant importance as it contains information of respiratory and cardiac systems together with the complex coupling between these two systems. Chest surface motion is not only critical in radiotherapy, but also useful in personalized systems for continuous cardiorespiratory monitoring. In this dataset, a multimodal setup is employed to simultaneously acquire cardiorespiratory signals. These signals include high-density trunk surface motion (from 16 distinct locations) with VICON motion capture system, nasal breathing from a thermal sensor, respiratory effort from a strain belt and electrocardiogram in lead-II configuration. This dataset contains 72 trials recorded from 11 participants with a cumulative duration of approximately 215 min under various conditions such as normal breathing, breath-hold, irregular breathing and post-exercise recovery. The presented dataset is not only useful for evaluating prediction algorithms for radiotherapy applications, but can also be employed for the development of techniques to evaluate the cardio-mechanics and hemodynamic parameters of chest surface motion.

Automatic annotation of peaks in seismocardiogram for systolic time intervals

Siemocardiography is a non-invasive technique for cardiomechanical assessment by analyzing the local vibrations on chest surface which can be readily acquired from cost-effective accelerometers. The peaks in siesmocardiogram (SCG) signal correspond to underlying mechanical events in heart cycle and have numerious potential clinical and health-awareness applications. However, utilization of SCG signal requires annotation of these peaks that is challenging due to variations in inter-subject morphology and noise prone characteristics of SCG signal. In this paper, we propose an approach to automatically annotate the desired peaks in SCG signal that are required for systolic time intervals (STI). The approach is based on formulating sliding template for the oncoming beat which is less noisier and hence desired peak detection is easier. The information of peak detected in the sliding template is then used to narrow-down the search of desired peak in actual signal.

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.

Automatic Identification of Systolic Time Intervals in Seismocardiogram

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.

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.

A Wearable Patch to Enable Long-Term Monitoring of Environmental, Activity and Hemodynamics Variables

We present a low power multi-modal patch designed for measuring activity, altitude (based on high-resolution barometric pressure), a single-lead electrocardiogram, and a tri-axial seismocardiogram (SCG). Enabled by a novel embedded systems design methodology, this patch offers a powerful means of monitoring the physiology for both patients with chronic cardiovascular diseases, and the general population interested in personal health and fitness measures. Specifically, to the best of our knowledge, this patch represents the first demonstration of combined activity, environmental context, and hemodynamics monitoring, all on the same hardware, capable of operating for longer than 48 hours at a time with continuous recording. The three-channels of SCG and one-lead ECG are all sampled at 500 Hz with high signal-to-noise ratio, the pressure sensor is sampled at 10 Hz, and all signals are stored to a microSD card with an average current consumption of less than 2 mA from a 3.7 V coin cell (LIR2450) battery. In addition to electronic characterization, proof-of-concept exercise recovery studies were performed with this patch, suggesting the ability to discriminate between hemodynamic and electrophysiology response to light, moderate, and heavy exercise.

A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation

Advanced hemodynamic monitoring is a critical component of treatment in clinical situations where aggressive yet guided hemodynamic interventions are required in order to stabilize the patient and optimize outcomes. While there are many tools at a physician’s disposal to monitor patients in a hospital setting, the reality is that none of these tools allow hi-fidelity assessment or continuous monitoring towards early detection of hemodynamic instability. We present an advanced automated analytical system which would act as a continuous monitoring and early warning mechanism that can indicate pending decompensation before traditional metrics can identify any clinical abnormality. This system computes novel features or bio-markers from both heart rate variability (HRV) as well as the morphology of the electrocardiogram (ECG). To compare their effectiveness, these features are compared with the standard HRV based bio-markers which are commonly used for hemodynamic assessment. This study utilized a unique database containing ECG waveforms from healthy volunteer subjects who underwent simulated hypovolemia under controlled experimental settings. A support vector machine was utilized to develop a model which predicts the stability or instability of the subjects. Results showed that the proposed novel set of features outperforms the traditional HRV features in predicting hemodynamic instability.

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.

Surface chest motion decomposition for cardiovascular monitoring

Surface chest motion can be easily monitored with a wide variety of sensors such as pressure belts, fiber Bragg gratings and inertial sensors, etc. The current applications of these sensors are mainly restricted to respiratory motion monitoring/analysis due to the technical challenges involved in separation of the cardiac motion from the dominant respiratory motion. The contribution of heart to the surface chest motion is relatively very small as compared to the respiratory motion. Further, the heart motion spectrally overlaps with the respiratory harmonics and their separation becomes even more challenging. In this paper, we approach this source separation problem with independent component analysis (ICA) framework. ICA with reference (ICA-R) yields only desired component with improved separation, but the method is highly sensitive to the reference generation. Several reference generation approaches are developed to solve the problem. Experimental validation of these proposed approaches is performed with chest displacement data and ECG obtained from healthy subjects under normal breathing and post-exercise conditions. The extracted component morphologically matches well with the collected ECG. Results show that the proposed methods perform better than conventional band pass filtering.