Recent Advances in Seismocardiography

SCG variability and spectral energy distribution during normal breathing and breath hold at different lung volumes and airway pressures

Seismocardiographic (SCG) signals are chest wall vibrations induced by cardiac activity and are potentially useful for cardiac monitoring and diagnosis. SCG waveform is observed to vary with respiration, but the mechanism of these changes is poorly understood as alterations in autonomic tone, lung volume, heart location and intrathoracic pressure are all varying during the respiratory cycle. Understanding SCG variability and its sources may help reduce variability and increase SCG clinical utility. This study investigated SCG variability during breath holding (BH) at two different lung volumes (i.e., end inspiration and end expiration) and five airway pressures (i.e., 0, ± 2-4, and ± 15-20 cm H2O). Variability during normal breathing was also studied with and without grouping SCG beats into two clusters of similar waveform morphologies (performed using the K-medoid algorithm in an unsupervised machine learning fashion). The study included 15 healthy subjects (11 Females and 4 males, Age: 21 ± 2 y) where SCG, ECG, and spirometry were simultaneously acquired. SCG waveform variability was calculated at each experimental state (i.e., lung volume and airway pressure). Results showed that breath holding was more effective in reducing the intra-state variability of SCG than clustering normal breathing data. For the BH states, the intra-state variability increased as the airway pressure deviated from zero. The subaudible-to-audible energy ratio of the BH states increased as the airway pressure decreased below zero which may be related to the effect of the intrathoracic pressure on cardiac afterload and blood ejection. When combining the BH waveforms at end inspiration and end expiration states (at the same airway pressures) into one group, the intra-state variability increased, which suggests that the lung volume and associated change in heart location were a significant source of variability. The linear trend between airway pressure and waveform changes was found to be statistically significant for BH at end expiration. To confirm these findings, more studies are needed with a larger number of airway pressure levels and larger number of subjects.

An Inertial-Based Wearable System for Monitoring Vital Signs during Sleep

This study explores the feasibility of a wearable system to monitor vital signs during sleep. The system incorporates five inertial measurement units (IMUs) located on the waist, the arms, and the legs. To evaluate the performance of a novel framework, twenty-three participants underwent a sleep study, and vital signs, including respiratory rate (RR) and heart rate (HR), were monitored via polysomnography (PSG). The dataset comprises individuals with varying severity of sleep-disordered breathing (SDB). Using a single IMU sensor positioned at the waist, strong correlations of more than 0.95 with the PSG-derived vital signs were obtained. Low inter-participant mean absolute errors of about 0.66 breaths/min and 1.32 beats/min were achieved, for RR and HR, respectively. The percentage of data available for analysis, representing the time coverage, was 98.3% for RR estimation and 78.3% for HR estimation. Nevertheless, the fusion of data from IMUs positioned at the arms and legs enhanced the inter-participant time coverage of HR estimation by over 15%. These findings imply that the proposed methodology can be used for vital sign monitoring during sleep, paving the way for a comprehensive understanding of sleep quality in individuals with SDB.

Smartphone-Based Recognition of Heart Failure by Means of Microelectromechanical Sensors

BACKGROUND

Heart failure (HF) is the leading cause of hospitalization in individuals over 65 years of age. Identifying noninvasive methods to detect HF may address the epidemic of HF. Seismocardiography which measures cardiac vibrations transmitted to the chest wall has recently emerged as a promising technology to detect HF.

OBJECTIVES

In this multicenter study, the authors examined whether seismocardiography using commercially available smartphones can differentiate control subjects from patients with stage C HF.

METHODS

Both inpatients and outpatients with HF were enrolled from Finland and the United States. Inpatients with HF were assessed within 2 days of admission, and outpatients were assessed in the ambulatory setting. In a prespecified pooled data analysis, algorithms were derived using logistic regression and then validated using a bootstrap aggregation method.

RESULTS

A total of 217 participants with HF (174 inpatients and 172 outpatients) and 786 control subjects from cardiovascular clinics were enrolled. The mean age of participants with acute HF was 64 ± 13 years, 64.9% were male, left ventricular ejection fraction was 39% ± 15%, and median N-terminal pro-B-type natriuretic peptide was 5,778 ng/L (Q1-Q3: 1,933-6,703). The majority (74%) of participants with HF had reduced EF, and 38% had atrial fibrillation. Across both HF cohorts, the algorithms had an area under the receiver operating characteristic curve of 0.95 with a sensitivity of 85%, specificity of 90%, and accuracy of 89% for the detection of HF, with a decision threshold of 0.5. The positive and negative likelihood ratios were 8.50 and 0.17, respectively. The accuracy of the algorithms was not significantly different in subgroups based on age, sex, body mass index, and atrial fibrillation.

CONCLUSIONS

Smartphone-based assessment of cardiac function using seismocardiography is feasible and differentiates patients with HF from control subjects with high diagnostic accuracy. (Recognition of Heart Failure With Micro Electro-mechanical Sensors FI; NCT04444583; Recognition of Heart Failure With Micro Electro-mechanical Sensors [NCT04378179]; Detection of Coronary Artery Disease With Micro Electro-mechanical Sensors; NCT04290091).

Camera-Based Seismocardiogram for Heart Rate Variability Monitoring

Heart rate variability (HRV) is a crucial metric that quantifies the variation between consecutive heartbeats, serving as a significant indicator of autonomic nervous system (ANS) activity. It has found widespread applications in clinical diagnosis, treatment, and prevention of cardiovascular diseases. In this study, we proposed an optical model for defocused speckle imaging, to simultaneously incorporate out-of-plane translation and rotation-induced motion for highly-sensitive non-contact seismocardiogram (SCG) measurement. Using electrocardiogram (ECG) signals as the gold standard, we evaluated the performance of photoplethysmogram (PPG) signals and speckle-based SCG signals in assessing HRV. The results indicated that the HRV parameters measured from SCG signals extracted from laser speckle videos showed higher consistency with the results obtained from the ECG signals compared to PPG signals. Additionally, we confirmed that even when clothing obstructed the measurement site, the efficacy of SCG signals extracted from the motion of laser speckle patterns persisted in assessing the HRV levels. This demonstrates the robustness of camera-based non-contact SCG in monitoring HRV, highlighting its potential as a reliable, non-contact alternative to traditional contact-PPG sensors.

Smartphone-Derived Seismocardiography: Robust Approach for Accurate Cardiac Energy Assessment in Patients with Various Cardiovascular Conditions

Seismocardiography (SCG), a method for measuring heart-induced chest vibrations, is gaining attention as a non-invasive, accessible, and cost-effective approach for cardiac pathologies, diagnosis, and monitoring. This study explores the integration of SCG acquired through smartphone technology by assessing the accuracy of metrics derived from smartphone recordings and their consistency when performed by patients. Therefore, we assessed smartphone-derived SCG's reliability in computing median kinetic energy parameters per record in 220 patients with various cardiovascular conditions. The study involved three key procedures: (1) simultaneous measurements of a validated hardware device and a commercial smartphone; (2) consecutive smartphone recordings performed by both clinicians and patients; (3) patients' self-conducted home recordings over three months. Our findings indicate a moderate-to-high reliability of smartphone-acquired SCG metrics compared to those obtained from a validated device, with intraclass correlation (ICC) > 0.77. The reliability of patient-acquired SCG metrics was high (ICC > 0.83). Within the cohort, 138 patients had smartphones that met the compatibility criteria for the study, with an observed at-home compliance rate of 41.4%. This research validates the potential of smartphone-derived SCG acquisition in providing repeatable SCG metrics in telemedicine, thus laying a foundation for future studies to enhance the precision of at-home cardiac data acquisition.

Maximum a posteriori detection of heartbeats from a chest-worn accelerometer

Objective. Unobtrusive long-term monitoring of cardiac parameters is important in a wide variety of clinical applications, such as the assesment of acute illness severity and unobtrusive sleep monitoring. Here we determined the accuracy and robustness of heartbeat detection by an accelerometer worn on the chest.Approach. We performed overnight recordings in 147 individuals (69 female, 78 male) referred to two sleep centers. Two methods for heartbeat detection in the acceleration signal were compared: one previously described approach, based on local periodicity, and a novel extended method incorporating maximumaposterioriestimation and a Markov decision process to approach an optimal solution.Main results. The maximumaposterioriestimation significantly improved performance, with a mean absolute error for the estimation of inter-beat intervals of only 3.5 ms, and 95% limits of agreement of -1.7 to +1.0 beats per minute for heartrate measurement. Performance held during posture changes and was only weakly affected by the presence of sleep disorders and demographic factors.Significance. The new method may enable the use of a chest-worn accelerometer in a variety of applications such as ambulatory sleep staging and in-patient monitoring.

A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography

Wearable sensing has become a vital approach to cardiac health monitoring, and seismocardiography (SCG) is emerging as a promising technology in this field. However, the applicability of SCG is hindered by motion artifacts, including those encountered in practice of which the strongest source is walking. This holds back the translation of SCG to clinical settings. We therefore investigated techniques to enhance the quality of SCG signals in the presence of motion artifacts. To simulate ambulant recordings, we corrupted a clean SCG dataset with real-walking-vibrational noise. We decomposed the signal using several empirical-mode-decomposition methods and the maximum overlap discrete wavelet transform (MODWT). By combining MODWT, time-frequency masking, and nonnegative matrix factorization, we developed a novel algorithm which leveraged the vertical axis accelerometer to reduce walking vibrations in dorsoventral SCG. The accuracy and applicability of our method was verified using heart rate estimation. We used an interactive selection approach to improve estimation accuracy. The best decomposition method for reduction of motion artifact noise was the MODWT. Our algorithm improved heart rate estimation from 0.1 to 0.8 r-squared at -15 dB signal-to-noise ratio (SNR). Our method reduces motion artifacts in SCG signals up to a SNR of -19 dB without requiring any external assistance from electrocardiography (ECG). Such a standalone solution is directly applicable to the usage of SCG in daily life, as a content-rich replacement for other wearables in clinical settings, and other continuous monitoring scenarios. In applications with higher noise levels, ECG may be incorporated to further enhance SCG and extend its usable range. This work addresses the challenges posed by motion artifacts, enabling SCG to offer reliable cardiovascular insights in more difficult scenarios, and thereby facilitating wearable monitoring in daily life and the clinic.

Quantitative detection of sleep apnea in adults using inertial measurement unit embedded in wristwatch wearable devices

Sleep apnea (SA) is associated with risk of cardiovascular disease, cognitive decline, and accidents due to sleepiness, yet the majority (over 80%) of patients remain undiagnosed. Inertial measurement units (IMUs) are built into modern wearable devices and are capable of long-term continuous measurement with low power consumption. We examined if SA can be detected by an IMU embedded in a wristwatch device. In 122 adults who underwent polysomnography (PSG) examinations, triaxial acceleration and triaxial gyro signals from the IMU were recorded during the PSG. Subjects were divided into a training group and a test groups (both n = 61). In the training group, an algorithm was developed to extract signals in the respiratory frequency band (0.13-0.70 Hz) and detect respiratory events as transient (10-90 s) decreases in amplitude. The respiratory event frequency estimated by the algorithm correlated with the apnea-hypopnea index (AHI) of the PSG with r = 0.84 in the test group. With the cutoff values determined in the training group, moderate-to-severe SA (AHI ≥ 15) was identified with 85% accuracy and severe SA (AHI ≥ 30) with 89% accuracy in the test group. SA can be quantitatively detected by the IMU embedded in wristwatch wearable devices in adults with suspected SA.

Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies

This article explores the importance of wearable and remote technologies in healthcare. The focus highlights its potential in continuous monitoring, examines the specificity of the issue, and offers a view of proactive healthcare. Our research describes a wide range of device types and scientific methodologies, starting from traditional chest belts to their modern alternatives and cutting-edge bioamplifiers that distinguish breathing from chest impedance variations. We also investigated innovative technologies such as the monitoring of thorax micromovements based on the principles of seismocardiography, ballistocardiography, remote camera recordings, deployment of integrated optical fibers, or extraction of respiration from cardiovascular variables. Our review is extended to include acoustic methods and breath and blood gas analysis, providing a comprehensive overview of different approaches to respiratory monitoring. The topic of monitoring respiration with wearable and remote electronics is currently the center of attention of researchers, which is also reflected by the growing number of publications. In our manuscript, we offer an overview of the most interesting ones.

Wearable Systems for Unveiling Collective Intelligence in Clinical Settings

Nowadays, there is an ever-growing interest in assessing the collective intelligence (CI) of a team in a wide range of scenarios, thanks to its potential in enhancing teamwork and group performance. Recently, special attention has been devoted on the clinical setting, where breakdowns in teamwork, leadership, and communication can lead to adverse events, compromising patient safety. So far, researchers have mostly relied on surveys to study human behavior and group dynamics; however, this method is ineffective. In contrast, a promising solution to monitor behavioral and individual features that are reflective of CI is represented by wearable technologies. To date, the field of CI assessment still appears unstructured; therefore, the aim of this narrative review is to provide a detailed overview of the main group and individual parameters that can be monitored to evaluate CI in clinical settings, together with the wearables either already used to assess them or that have the potential to be applied in this scenario. The working principles, advantages, and disadvantages of each device are introduced in order to try to bring order in this field and provide a guide for future CI investigations in medical contexts.

A Deep Learning Approach to Using Wearable Seismocardiography (SCG) for Diagnosing Aortic Valve Stenosis and Predicting Aortic Hemodynamics Obtained by 4D Flow MRI

In this paper, we explored the use of deep learning for the prediction of aortic flow metrics obtained using 4-dimensional (4D) flow magnetic resonance imaging (MRI) using wearable seismocardiography (SCG) devices. 4D flow MRI provides a comprehensive assessment of cardiovascular hemodynamics, but it is costly and time-consuming. We hypothesized that deep learning could be used to identify pathological changes in blood flow, such as elevated peak systolic velocity ([Formula: see text]) in patients with heart valve diseases, from SCG signals. We also investigated the ability of this deep learning technique to differentiate between patients diagnosed with aortic valve stenosis (AS), non-AS patients with a bicuspid aortic valve (BAV), non-AS patients with a mechanical aortic valve (MAV), and healthy subjects with a normal tricuspid aortic valve (TAV). In a study of 77 subjects who underwent same-day 4D flow MRI and SCG, we found that the [Formula: see text] values obtained using deep learning and SCGs were in good agreement with those obtained by 4D flow MRI. Additionally, subjects with non-AS TAV, non-AS BAV, non-AS MAV, and AS could be classified with ROC-AUC (area under the receiver operating characteristic curves) values of 92%, 95%, 81%, and 83%, respectively. This suggests that SCG obtained using low-cost wearable electronics may be used as a supplement to 4D flow MRI exams or as a screening tool for aortic valve disease.

Applications of flexible electronics related to cardiocerebral vascular system

Ensuring accessible and high-quality healthcare worldwide requires field-deployable and affordable clinical diagnostic tools with high performance. In recent years, flexible electronics with wearable and implantable capabilities have garnered significant attention from researchers, which functioned as vital clinical diagnostic-assisted tools by real-time signal transmission from interested targets in vivo. As the most crucial and complex system of human body, cardiocerebral vascular system together with heart-brain network attracts researchers inputting profuse and indefatigable efforts on proper flexible electronics design and materials selection, trying to overcome the impassable gulf between vivid organisms and rigid inorganic units. This article reviews recent breakthroughs in flexible electronics specifically applied to cardiocerebral vascular system and heart-brain network. Relevant sensor types and working principles, electronics materials selection and treatment methods are expounded. Applications of flexible electronics related to these interested organs and systems are specially highlighted. Through precedent great working studies, we conclude their merits and point out some limitations in this emerging field, thus will help to pave the way for revolutionary flexible electronics and diagnosis assisted tools development.

Exploring Microphone Technologies for Digital Auscultation Devices

The aim of this work is to present a preliminary study for the design of a digital auscultation system, i.e., a novel wearable device for patient chest auscultation and a digital stethoscope. The development and testing of the electronic stethoscope prototype is reported with an emphasis on the description and selection of sound transduction systems and analog electronic processing. The focus on various microphone technologies, such as micro-electro-mechanical systems (MEMSs), electret condensers, and piezoelectronic diaphragms, intends to emphasize the most suitable transducer for auscultation. In addition, we report on the design and development of a digital acquisition system for the human body for sound recording by using a modular device approach in order to fit the chosen analog and digital mics. Tests were performed on a designed phantom setup, and a qualitative comparison between the sounds recorded with the newly developed acquisition device and those recorded with two commercial digital stethoscopes is reported.

Cardiac function and posttraumatic stress disorder: a review of the literature and case report

INTRODUCTION

Posttraumatic stress disorder (PTSD) can induce an elevation in sympathetic tone; however, research pertaining to the cardiac cycle in patients with PTSD is limited.

METHODS

A literature review was conducted with PubMed, MEDLINE and Web of Science. Articles discussing changes and associations in echocardiography and PTSD or related symptoms were synthesized for the current review. We have also included data from a case report of a male participant aged 33 years experiencing potentially psychologically traumatic events, who wore a noninvasive cardiac sensor to assess the timing intervals and contractility parameters of the cardiac cycle using seismocardiography. The intervals included systolic time, isovolumic contraction time (IVCT) and isovolumic relaxation time (IVRT). Calculations of systolic (IVCT/systole), diastolic (IVRT/systole) and myocardial [(IVCT+IVRT)/systole] performance indices were completed.

RESULTS

The review identified 55 articles, 14 of which assessed cardiac function using echocardiography in patients with PTSD symptoms. Cardiac dysfunction varied across studies, with diastolic and systolic impairments found in patients with PTSD. Our case study showed that occupational stress elevated cardiac performance indices, suggesting increased ventricular stress and supporting results in the existing literature.

CONCLUSIONS

The literature review results suggest that a controlled approach to assessing cardiac function in patients with PTSD is required. The case study results further suggest that acute bouts of stress can alter cardiac function, with potential for sustained occupational stress to induce changes in cardiac function. Cardiac monitoring can be used prospectively to identify changes induced by potentially psychologically traumatic event exposures that can lead to the development of PTSD symptoms.

Waveform Similarity Analysis Using Graph Mining for the Optimization of Sensor Positioning in Wearable Seismocardiography

OBJECTIVE

A major concern with wearable devices aiming to measure the seismocardiogram (SCG) signal is the variability of SCG waveform with the sensor position and a lack of a standard measurement procedure. We propose a method to optimize sensor positioning based on the similarity among waveforms collected through repeated measurements.

METHOD

we design a graph-theoretical model to evaluate the similarity of SCG signals and apply the proposed methodology to signals collected by sensors placed in different positions on the chest. A similarity score returns the optimal measurement position based on the repeatability of SCG waveforms. We tested the methodology on signals collected by using two wearable patches based on optical technology placed in two positions: mitral and aortic valve auscultation site (inter-position analysis). 11 healthy subjects were enrolled in this study. Moreover, we evaluated the influence of the subject's posture on waveform similarity with a view on ambulatory use (inter-posture analysis).

RESULTS

the highest similarity among SCG waveforms is obtained with the sensor on the mitral valve and the subject lying down.

CONCLUSIONS

our approach aims to be a step forward in the optimization of sensor positioning in the field of wearable seismocardiography. We demonstrate that the proposed algorithm is an effective method to estimate similarity among waveforms and outperforms the state-of-the-art in comparing SCG measurement sites.

SIGNIFICANCE

results obtained from this study can be exploited to design more efficient protocols for SCG recording in both research studies and future clinical examinations.

Validity and reliability of seismocardiography for the estimation of cardiorespiratory fitness

Background

Low cardiorespiratory fitness (ie, peak oxygen consumption [V . O2peak]) is associated with cardiovascular disease and all-cause mortality and is recognized as an important clinical tool in the assessment of patients. Cardiopulmonary exercise test (CPET) is the gold standard procedure for determination of V . O2peak but has methodological challenges as it is time-consuming and requires specialized equipment and trained professionals. Seismofit is a chest-mounted medical device for estimating V . O2peak at rest using seismocardiography.

Objective

The purpose of this study was to investigate the validity and reliability of Seismofit V . O2peak estimation in a healthy population.

Methods

On 3 separate days, 20 participants (10 women) underwent estimations of V . O2peak with Seismofit (×2) and Polar Fitness Test (PFT) in randomized order and performed a graded CPET on a cycle ergometer with continuous pulmonary gas exchange measurements.

Results

Seismofit V . O2peak showed a significant bias of -3.1 ± 2.4 mL·min-1·kg-1 (mean ± 95% confidence interval) and 95% limits of agreement (LoA) of ±10.8 mL·min-1·kg-1 compared to CPET. The mean absolute percentage error (MAPE) was 12.0%. Seismofit V . O2peak had a coefficient of variation of 4.5% ± 1.3% and an intraclass correlation coefficient of 0.95 between test days and a bias of 0.0 ± 0.4 mL·min-1·kg-1 with 95% LoA of ±1.6 mL·min-1·kg-1 in test-retest. In addition, Seismofit showed a 2.4 mL·min-1·kg-1 smaller difference in 95% LoA than PFT compared to CPET.

Conclusion

The Seismofit is highly reliable in its estimation of V . O2peak. However, based on the measurement error and MAPE >10%, the Seismofit V . O2peak estimation model needs further improvement to be considered for use in clinical settings.

ECG-Free Heartbeat Detection in Seismocardiography and Gyrocardiography Signals Provides Acceptable Heart Rate Variability Indices in Healthy and Pathological Subjects

Cardio-mechanical monitoring techniques, such as Seismocardiography (SCG) and Gyrocardiography (GCG), have received an ever-growing interest in recent years as potential alternatives to Electrocardiography (ECG) for heart rate monitoring. Wearable SCG and GCG devices based on lightweight accelerometers and gyroscopes are particularly appealing for continuous, long-term monitoring of heart rate and its variability (HRV). Heartbeat detection in cardio-mechanical signals is usually performed with the support of a concurrent ECG lead, which, however, limits their applicability in standalone cardio-mechanical monitoring applications. The complex and variable morphology of SCG and GCG signals makes the ECG-free heartbeat detection task quite challenging; therefore, only a few methods have been proposed. Very recently, a template matching method based on normalized cross-correlation (NCC) has been demonstrated to provide very accurate detection of heartbeats and estimation of inter-beat intervals in SCG and GCG signals of pathological subjects. In this study, the accuracy of HRV indices obtained with this template matching method is evaluated by comparison with ECG. Tests were performed on two public datasets of SCG and GCG signals from healthy and pathological subjects. Linear regression, correlation, and Bland-Altman analyses were carried out to evaluate the agreement of 24 HRV indices obtained from SCG and GCG signals with those obtained from ECG signals, simultaneously acquired from the same subjects. The results of this study show that the NCC-based template matching method allowed estimating HRV indices from SCG and GCG signals of healthy subjects with acceptable accuracy. On healthy subjects, the relative errors on time-domain indices ranged from 0.25% to 15%, on frequency-domain indices ranged from 10% to 20%, and on non-linear indices were within 8%. The estimates obtained on signals from pathological subjects were affected by larger errors. Overall, GCG provided slightly better performances as compared to SCG, both on healthy and pathological subjects. These findings provide, for the first time, clear evidence that monitoring HRV via SCG and GCG sensors without concurrent ECG is feasible with the NCC-based template matching method for heartbeat detection.

Real-Time Seismocardiogram Feature Extraction Using Adaptive Gaussian Mixture Models

Wearable systems can provide accurate cardiovascular evaluations by estimating hemodynamic indices in real-time. Key hemodynamic parameters can be non-invasively estimated using the seismocardiogram (SCG), a cardiomechanical signal whose features link to cardiac events like aortic valve opening (AO) and closing (AC). However, tracking a single SCG feature is unreliable due to physiological changes, motion artifacts, and external vibrations. This work proposes an adaptable Gaussian Mixture Model (GMM) to track multiple AO/AC correlated features in quasi-real-time from the SCG. The GMM calculates the likelihood of an extremum being an AO/AC feature for each SCG beat. The Dijkstra algorithm selects heartbeat-related extrema, and a Kalman filter updates the GMM parameters while filtering features. Tracking accuracy is tested on a porcine hypovolemia dataset with varying noise levels. Blood volume loss estimation accuracy is also evaluated using the tracked features on a previously developed model. Experimental results show a 4.5 ms tracking latency and average root mean square errors (RMSE) of 1.47 ms for AO and 7.67 ms for AC at 10 dB noise, and 6.18 ms for AO and 15.3 ms for AC at -10 dB noise. When considering all AO/AC correlated features, the combined RMSE remains in similar ranges, specifically 2.70 ms for AO and 11.91 ms for AC at 10 dB noise, and 7.50 ms for AO and 16.35 ms for AC at -10 dB noise. The proposed algorithm offers low latency and RMSE for all tracked features, making it suitable for real-time processing. These systems enable accurate, timely extraction of hemodynamic indices for many cardiovascular monitoring applications, including trauma care in field settings.

Non-contact heart vibration measurement using computer vision-based seismocardiography

Seismocardiography (SCG) is the noninvasive measurement of local vibrations of the chest wall produced by the mechanical activity of the heart and has shown promise in providing clinical information for certain cardiovascular diseases including heart failure and ischemia. Conventionally, SCG signals are recorded by placing an accelerometer on the chest. In this paper, we propose a novel contactless SCG measurement method to extract them from chest videos recorded by a smartphone. Our pipeline consists of computer vision methods including the Lucas-Kanade template tracking to track an artificial target attached to the chest, and then estimate the SCG signals from the tracked displacements. We evaluated our pipeline on 14 healthy subjects by comparing the vision-based SCG[Formula: see text] estimations with the gold-standard SCG[Formula: see text] measured simultaneously using accelerometers attached to the chest. The similarity between SCG[Formula: see text] and SCG[Formula: see text] was measured in the time and frequency domains using the Pearson correlation coefficient, a similarity index based on dynamic time warping (DTW), and wavelet coherence. The average DTW-based similarity index between the signals was 0.94 and 0.95 in the right-to-left and head-to-foot directions, respectively. Furthermore, SCG[Formula: see text] signals were utilized to estimate the heart rate, and these results were compared to the gold-standard heart rate obtained from ECG signals. The findings indicated a good agreement between the estimated heart rate values and the gold-standard measurements (bias = 0.649 beats/min). In conclusion, this work shows promise in developing a low-cost and widely available method for remote monitoring of cardiovascular activity using smartphone videos.

Heartbeat Detection in Gyrocardiography Signals without Concurrent ECG Tracings

A heartbeat generates tiny mechanical vibrations, mainly due to the opening and closing of heart valves. These vibrations can be recorded by accelerometers and gyroscopes applied on a subject's chest. In particular, the local 3D linear accelerations and 3D angular velocities of the chest wall are referred to as seismocardiograms (SCG) and gyrocardiograms (GCG), respectively. These signals usually exhibit a low signal-to-noise ratio, as well as non-negligible amplitude and morphological changes due to changes in posture and the sensors' location, respiratory activity, as well as other sources of intra-subject and inter-subject variability. These factors make heartbeat detection a complex task; therefore, a reference electrocardiogram (ECG) lead is usually acquired in SCG and GCG studies to ensure correct localization of heartbeats. Recently, a template matching technique based on cross correlation has proven to be particularly effective in recognizing individual heartbeats in SCG signals. This study aims to verify the performance of this technique when applied on GCG signals. Tests were conducted on a public database consisting of SCG, GCG, and ECG signals recorded synchronously on 100 patients with valvular heart diseases. The results show that the template matching technique identified heartbeats in GCG signals with a sensitivity and positive predictive value (PPV) of 87% and 92%, respectively. Regression, correlation, and Bland-Altman analyses carried out on inter-beat intervals obtained from GCG and ECG (assumed as reference) reported a slope of 0.995, an intercept of 4.06 ms (R² > 0.99), a Pearson's correlation coefficient of 0.9993, and limits of agreement of about ±13 ms with a negligible bias. A comparison with the results of a previous study obtained on SCG signals from the same database revealed that GCG enabled effective cardiac monitoring in significantly more patients than SCG (95 vs. 77). This result suggests that GCG could ensure more robust and reliable cardiac monitoring in patients with heart diseases with respect to SCG.

Symmetric Projection Attractor Reconstruction Analysis as a Method to Assess Seismocardiogram Quality in a Healthy Population

Signal quality significantly affects the processing, analysis, and interpretation of biomedical signals. There are many procedures for assessing signal quality that use averaged numerical values, thresholding, analysis in the time or frequency domain, or nonlinear approaches. An interesting approach to the assessment of signal quality is using symmetric projection attractor reconstruction (SPAR) analysis, which transforms an entire signal into a two-dimensional plot that reflects the waveform morphology. In this study, we present an application of SPAR to evaluate the quality of seismocardiograms (SCG signals) from the CEBS database, a publicly available seismocardiogram signal database. Visual inspection of symmetric projection attractors suggests that high-quality (clean) seismocardiogram projections resemble six-pointed asterisks (*), and any deviation from this shape suggests the influence of noise and artifacts.Clinical relevance- SPAR analysis enables quick identification of noise and artifacts that can affect the reliability of the diagnosis of cardiovascular diseases based on SCG signals.

Systolic Time Interval Extraction in Hypertensive and Hypotensive Pig Models Using Wearable Near-Field Radio-Frequency Sensors

Screening and monitoring for cardiovascular diseases (CVDs) can be enabled by analyzing systolic time intervals (STIs). As CVDs have a strong causal correlation with hypertension, it is important to validate STI sensor accuracy in hypertensive hearts to ensure consistent performance in this prevalent cardiac disease state. This work presents STI extraction using a non-invasive near-field radio-frequency (RF) sensor during normotension, hypertension, and hypotension in a pig model. Waveform features of semilunar and atrioventricular valve dynamics during systole were extracted to derive isovolumic contraction time (ICT) and left ventricular ejection time (LVET), benchmarked by a phonocardiogram and aortic catheterization. Study-wide mean relative ICT and LVET errors were -4.4ms and -3.6ms, respectively, demonstrating high accuracy during both normal and abnormal systemic pressures.Clinical relevance- This work demonstrates accurate STI extraction with relative error less than 5 ms from a non-invasive near-field RF sensor during normotensive, hypotensive, and hypertensive systemic pressures, validating the sensor's accuracy as a screening tool during this disease state.

DR.BEAT: First Insights into a Study to Collect Baseline BCG Data with a Sensor-Based Wearable Prototype in Heart-Healthy Adults

The DR.BEAT project aims at the further development of a measurement system for recording ballistocardiographic signals into a body-worn sensor system combined with extensive signal processing, data evaluation and visualization. With a first breadboard prototype, an explorative feasibility study for acquiring initial signals of healthy cardiac activity in adults was performed. This paper briefly presents the DR.BEAT project, the breadboard prototype, the study conducted, and initial insights into the study results. The signals obtained in the study exhibit the seismocardiographic characteristics as reported in the literature and form the basis for further development of the hardware as well as the pre-processing and automated analysis algorithms in the DR.BEAT project.Clinical Relevance- The characteristics of ballisto- and seismocardiographic signals allow to infer about the mechanical work of the heart. The development of a body-worn sensor system to record ballisto- and seismocardiographic signals, compact enough for everyday wear, enables the acquisition of heart-specific parameters in terrestrial as well as extraterrestrial application scenarios. Combined with extensive signal analysis and visualization, it holds the potential to monitor heart health in a variety of contexts and support its maintenance and improvement.

Publicly available signal databases containing seismocardiographic signals - the state in early 2023

The development of information and communication technologies (ICT) changed many aspects of our lives, including cardiovascular research. This area of research is affected by the availability of open databases that can help conduct basic and applied research. In this study, we summarize the current state of knowledge in publicly available signal databases with seismocardiographic (SCG) signals in January 2023. Based on Google search results for the expression "seismocardiography dataset", we have found and described five databases with seismocardiograms, including three databases that contain SCG signals from healthy subjects, one database with data from porcine subjects, and one signal database with data obtained from human patients with valvular heart disease (VHD). All contain additional signals for reference points in the cardiac cycle. The most significant limitations of the described data sets are gender bias toward male subjects, the imbalance between healthy subjects, and subjects with two cardiovascular diseases (VHD and hemorrhage).

ECG-Free Heartbeat Detection in Seismocardiography Signals via Template Matching

Cardiac monitoring can be performed by means of an accelerometer attached to a subject's chest, which produces the Seismocardiography (SCG) signal. Detection of SCG heartbeats is commonly carried out by taking advantage of a simultaneous electrocardiogram (ECG). SCG-based long-term monitoring would certainly be less obtrusive and easier to implement without an ECG. Few studies have addressed this issue using a variety of complex approaches. This study proposes a novel approach to ECG-free heartbeat detection in SCG signals via template matching, based on normalized cross-correlation as heartbeats similarity measure. The algorithm was tested on the SCG signals acquired from 77 patients with valvular heart diseases, available from a public database. The performance of the proposed approach was assessed in terms of sensitivity and positive predictive value (PPV) of the heartbeat detection and accuracy of inter-beat intervals measurement. Sensitivity and PPV of 96% and 97%, respectively, were obtained by considering templates that included both systolic and diastolic complexes. Regression, correlation, and Bland-Altman analyses carried out on inter-beat intervals reported slope and intercept of 0.997 and 2.8 ms (R² > 0.999), as well as non-significant bias and limits of agreement of ±7.8 ms. The results are comparable or superior to those achieved by far more complex algorithms, also based on artificial intelligence. The low computational burden of the proposed approach makes it suitable for direct implementation in wearable devices.

Non-invasive cardiac kinetic energy distribution: a new marker of heart failure with impaired ejection fraction (KINO-HF)

Background

Heart failure (HF) remains a major cause of mortality, morbidity, and poor quality of life. 44% of HF patients present impaired left ventricular ejection fraction (LVEF). Kinocardiography (KCG) technology combines ballistocardiography (BCG) and seismocardiography (SCG). It estimates myocardial contraction and blood flow through the cardiac chambers and major vessels through a wearable device. Kino-HF sought to evaluate the potential of KCG to distinguish HF patients with impaired LVEF from a control group.

Methods

Successive patients with HF and impaired LVEF (iLVEF group) were matched and compared to patients with normal LVEF ≥ 50% (control). A 60 s KCG acquisition followed cardiac ultrasound. The kinetic energy from KCG signals was computed in different phases of the cardiac cycle (i K s y s t o l i c ; Δ i K d i a s t o l i c ) as markers of cardiac mechanical function.

Results

Thirty HF patients (67 [59; 71] years, 87% male) were matched with 30 controls (64.5 [49; 73] years, 87% male). SCG Δ i K d i a s t o l i c , BCG i K s y s t o l i c , BCG Δ i K d i a s t o l i c were lower in HF than controls (p < 0.05), while SCG i K s y s t o l i c was similar. Furthermore, a lower SCG i K s y s t o l i c was associated with an increased mortality risk during follow-up.

Conclusions

KINO-HF demonstrates that KCG can distinguish HF patients with impaired systolic function from a control group. These favorable results warrant further research on the diagnostic and prognostic capabilities of KCG in HF with impaired LVEF.Clinical Trial Registration: NCT03157115.

Detecting heart failure using novel bio-signals and a knowledge enhanced neural network

BACKGROUND

Clinical decisions about Heart Failure (HF) are frequently based on measurements of left ventricular ejection fraction (LVEF), relying mainly on echocardiography measurements for evaluating structural and functional abnormalities of heart disease. As echocardiography is not available in primary care, this means that HF cannot be detected on initial patient presentation. Instead, physicians in primary care must rely on a clinical diagnosis that can take weeks, even months of costly testing and clinical visits. As a result, the opportunity for early detection of HF is lost.

METHODS AND RESULTS

The standard 12-Lead ECG provides only limited diagnostic evidence for many common heart problems. ECG findings typically show low sensitivity for structural heart abnormalities and low specificity for function abnormalities, e.g., systolic dysfunction. As a result, structural and functional heart abnormalities are typically diagnosed by echocardiography in secondary care, effectively creating a diagnostic gap between primary and secondary care. This diagnostic gap was successfully reduced by an AI solution, the Cardio-HART™ (CHART), which uses Knowledge-enhanced Neural Networks to process novel bio-signals. Cardio-HART reached higher performance in prediction of HF when compared to the best ECG-based criteria: sensitivity increased from 53.5% to 82.8%, specificity from 85.1% to 86.9%, positive predictive value from 57.1% to 70.0%, the F-score from 56.4% to 72.2%, and area under curve from 0.79 to 0.91. The sensitivity of the HF-indicated findings is doubled by the AI compared to the best rule-based ECG-findings with a similar specificity level: from 38.6% to 71%.

CONCLUSION

Using an AI solution to process ECG and novel bio-signals, the CHART algorithms are able to predict structural, functional, and valve abnormalities, effectively reducing this diagnostic gap, thereby allowing for the early detection of most common heart diseases and HF in primary care.

Non-contact wearable synchronous measurement method of electrocardiogram and seismocardiogram signals

Cardiovascular disease is one of the leading threats to human lives and its fatality rate still rises gradually year by year. Driven by the development of advanced information technologies, such as big data, cloud computing, and artificial intelligence, remote/distributed cardiac healthcare is presenting a promising future. The traditional dynamic cardiac health monitoring method based on electrocardiogram (ECG) signals only has obvious deficiencies in comfortableness, informativeness, and accuracy under motion state. Therefore, a non-contact, compact, wearable, synchronous ECG and seismocardiogram (SCG) measuring system, based on a pair of capacitance coupling electrodes with ultra-high input impedance, and a high-resolution accelerometer were developed in this work, which can collect the ECG and SCG signals at the same point simultaneously through the multi-layer cloth. Meanwhile, the driven right leg electrode for ECG measurement is replaced by the AgCl fabric sewn to the outside of the cloth for realizing the total gel-free ECG measurement. Besides, synchronous ECG and SCG signals at multiple points on the chest surface were measured, and the recommended measuring points were given by their amplitude characteristics and the timing sequence correspondence analysis. Finally, the empirical mode decomposition algorithm was used to adaptively filter the motion artifacts within the ECG and SCG signals for measuring performance enhancement under motion states. The results demonstrate that the proposed non-contact, wearable cardiac health monitoring system can effectively collect ECG and SCG synchronously under various measuring situations.

Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms of Healthy Volunteers and Patients with Valvular Heart Diseases

Heart rate variability (HRV) is the physiological variation in the intervals between consecutive heartbeats that reflects the activity of the autonomic nervous system. This parameter is traditionally evaluated based on electrocardiograms (ECG signals). Seismocardiography (SCG) and/or gyrocardiography (GCG) are used to monitor cardiac mechanical activity; therefore, they may be used in HRV analysis and the evaluation of valvular heart diseases (VHDs) simultaneously. The purpose of this study was to compare the time domain, frequency domain and nonlinear HRV indices obtained from electrocardiograms, seismocardiograms (SCG signals) and gyrocardiograms (GCG signals) in healthy volunteers and patients with valvular heart diseases. An analysis of the time domain, frequency domain and nonlinear heart rate variability was conducted on electrocardiograms and gyrocardiograms registered from 29 healthy male volunteers and 30 patients with valvular heart diseases admitted to the Columbia University Medical Center (New York City, NY, USA). The results of the HRV analysis show a strong linear correlation with the HRV indices calculated from the ECG, SCG and GCG signals and prove the feasibility and reliability of HRV analysis despite the influence of VHDs on the SCG and GCG waveforms.

Linear and Non-Linear Heart Rate Variability Indexes from Heart-Induced Mechanical Signals Recorded with a Skin-Interfaced IMU

Heart rate variability (HRV) indexes are becoming useful in various applications, from better diagnosis and prevention of diseases to predicting stress levels. Typically, HRV indexes are retrieved from the heart's electrical activity collected with an electrocardiographic signal (ECG). Heart-induced mechanical signals recorded from the body's surface can be utilized to record the mechanical activity of the heart and, in turn, extract HRV indexes from interbeat intervals (IBIs). Among others, accelerometers and gyroscopes can be used to register IBIs from precordial accelerations and chest wall angular velocities. However, unlike electrical signals, the morphology of mechanical ones is strongly affected by body posture. In this paper, we investigated the feasibility of estimating the most common linear and non-linear HRV indexes from accelerometer and gyroscope data collected with a wearable skin-interfaced Inertial Measurement Unit (IMU) positioned at the xiphoid level. Data were collected from 21 healthy volunteers assuming two common postures (i.e., seated and lying). Results show that using the gyroscope signal in the lying posture allows accurate results in estimating IBIs, thus allowing extracting of linear and non-linear HRV parameters that are not statistically significantly different from those extracted from reference ECG.

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.

Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review

Ballistocardiography (BCG) and seismocardiography (SCG) are non-invasive techniques used to record the micromovements induced by cardiovascular activity at the body's center of mass and on the chest, respectively. Since their inception, their potential for evaluating cardiovascular health has been studied. However, both BCG and SCG are impacted by respiration, leading to a periodic modulation of these signals. As a result, data processing algorithms have been developed to exclude the respiratory signals, or recording protocols have been designed to limit the respiratory bias. Reviewing the present status of the literature reveals an increasing interest in applying these techniques to extract respiratory information, as well as cardiac information. The possibility of simultaneous monitoring of respiratory and cardiovascular signals via BCG or SCG enables the monitoring of vital signs during activities that require considerable mental concentration, in extreme environments, or during sleep, where data acquisition must occur without introducing recording bias due to irritating monitoring equipment. This work aims to provide a theoretical and practical overview of cardiopulmonary interaction based on BCG and SCG signals. It covers the recent improvements in extracting respiratory signals, computing markers of the cardiorespiratory interaction with practical applications, and investigating sleep breathing disorders, as well as a comparison of different sensors used for these applications. According to the results of this review, recent studies have mainly concentrated on a few domains, especially sleep studies and heart rate variability computation. Even in those instances, the study population is not always large or diversified. Furthermore, BCG and SCG are prone to movement artifacts and are relatively subject dependent. However, the growing tendency toward artificial intelligence may help achieve a more accurate and efficient diagnosis. These encouraging results bring hope that, in the near future, such compact, lightweight BCG and SCG devices will offer a good proxy for the gold standard methods for assessing cardiorespiratory function, with the added benefit of being able to perform measurements in real-world situations, outside of the clinic, and thus decrease costs and time.

Changes in Forcecardiography Heartbeat Morphology Induced by Cardio-Respiratory Interactions

The cardiac function is influenced by respiration. In particular, various parameters such as cardiac time intervals and the stroke volume are modulated by respiratory activity. It has long been recognized that cardio-respiratory interactions modify the morphology of cardio-mechanical signals, e.g., phonocardiogram, seismocardiogram (SCG), and ballistocardiogram. Forcecardiography (FCG) records the weak forces induced on the chest wall by the mechanical activity of the heart and lungs and relies on specific force sensors that are capable of monitoring respiration, infrasonic cardiac vibrations, and heart sounds, all simultaneously from a single site on the chest. This study addressed the changes in FCG heartbeat morphology caused by respiration. Two respiratory-modulated parameters were considered, namely the left ventricular ejection time (LVET) and a morphological similarity index (MSi) between heartbeats. The time trends of these parameters were extracted from FCG signals and further analyzed to evaluate their consistency within the respiratory cycle in order to assess their relationship with the breathing activity. The respiratory acts were localized in the time trends of the LVET and MSi and compared with a reference respiratory signal by computing the sensitivity and positive predictive value (PPV). In addition, the agreement between the inter-breath intervals estimated from the LVET and MSi and those estimated from the reference respiratory signal was assessed via linear regression and Bland-Altman analyses. The results of this study clearly showed a tight relationship between the respiratory activity and the considered respiratory-modulated parameters. Both the LVET and MSi exhibited cyclic time trends that remarkably matched the reference respiratory signal. In addition, they achieved a very high sensitivity and PPV (LVET: 94.7% and 95.7%, respectively; MSi: 99.3% and 95.3%, respectively). The linear regression analysis reported almost unit slopes for both the LVET (R² = 0.86) and MSi (R² = 0.97); the Bland-Altman analysis reported a non-significant bias for both the LVET and MSi as well as limits of agreement of ±1.68 s and ±0.771 s, respectively. In summary, the results obtained were substantially in line with previous findings on SCG signals, adding to the evidence that FCG and SCG signals share a similar information content.

Comparison between Chest-Worn Accelerometer and Gyroscope Performance for Heart Rate and Respiratory Rate Monitoring

The demand for wearable devices to simultaneously monitor heart rate (HR) and respiratory rate (RR) values has grown due to the incidence increase in cardiovascular and respiratory diseases. The use of inertial measurement unit (IMU) sensors, embedding both accelerometers and gyroscopes, may ensure a non-intrusive and low-cost monitoring. While both accelerometers and gyroscopes have been assessed independently for both HR and RR monitoring, there lacks a comprehensive comparison between them when used simultaneously. In this study, we used both accelerometers and gyroscopes embedded in a single IMU sensor for the simultaneous monitoring of HR and RR. The following main findings emerged: (i) the accelerometer outperformed the gyroscope in terms of accuracy in both HR and RR estimation; (ii) the window length used to estimate HR and RR values influences the accuracy; and (iii) increasing the length over 25 s does not provide a relevant improvement, but accuracy improves when the subject is seated or lying down, and deteriorates in the standing posture. Our study provides a comprehensive comparison between two promising systems, highlighting their potentiality for real-time cardiorespiratory monitoring. Furthermore, we give new insights into the influence of window length and posture on the systems' performance, which can be useful to spread this approach in clinical settings.

Wearables in Cardiovascular Disease

Wearable devices stand to revolutionize the way healthcare is delivered. From consumer devices that provide general health information and screen for medical conditions to medical-grade devices that allow collection of larger datasets that include multiple modalities, wearables have a myriad of potential uses, especially in cardiovascular disorders. In this review, we summarize the underlying technologies employed in these devices and discuss the regulatory and economic aspects of such devices as well as the future implications of their use.

Respiratory-Induced Amplitude Modulation of Forcecardiography Signals

Forcecardiography (FCG) is a novel technique that records the weak forces induced on the chest wall by cardio-respiratory activity, by using specific force sensors. FCG sensors feature a wide frequency band, which allows us to capture respiration, heart wall motion, heart valves opening and closing (similar to the Seismocardiogram, SCG) and heart sounds, all simultaneously from a single contact point on the chest. As a result, the raw FCG sensors signals exhibit a large component related to the respiratory activity, referred to as a Forcerespirogram (FRG), with a much smaller, superimposed component related to the cardiac activity (the actual FCG) that contains both infrasonic vibrations, referred to as LF-FCG and HF-FCG, and heart sounds. Although respiration can be readily monitored by extracting the very low-frequency component of the raw FCG signal (FRG), it has been observed that the respiratory activity also influences other FCG components, particularly causing amplitude modulations (AM). This preliminary study aimed to assess the consistency of the amplitude modulations of the LF-FCG and HF-FCG signals within the respiratory cycle. A retrospective analysis was performed on the FCG signals acquired in a previous study on six healthy subjects at rest, during quiet breathing. To this aim, the AM of LF-FCG and HF-FCG were first extracted via a linear envelope (LE) operation, consisting of rectification followed by low-pass filtering; then, the inspiratory peaks were located both in the LE of LF-FCG and HF-FCG, and in the reference respiratory signal (FRG). Finally, the inter-breath intervals were extracted from the obtained inspiratory peaks, and further analyzed via statistical analyses. The AM of HF-FCG exhibited higher consistency within the respiratory cycle, as compared to the LF-FCG. Indeed, the inspiratory peaks were recognized with a sensitivity and positive predictive value (PPV) in excess of 99% in the LE of HF-FCG, and with a sensitivity and PPV of 96.7% and 92.6%, respectively, in the LE of LF-FCG. In addition, the inter-breath intervals estimated from the HF-FCG scored a higher R2 value (0.95 vs. 0.86) and lower limits of agreement (± 0.710 s vs. ±1.34 s) as compared to LF-FCG, by considering those extracted from the FRG as the reference. The obtained results are consistent with those observed in previous studies on SCG. A possible explanation of these results was discussed. However, the preliminary results obtained in this study must be confirmed on a larger cohort of subjects and in different experimental conditions.

Contactless Heartbeat Measurement Using Speckle Vibrometry

Monitoring of heart rate in patients in the general ward is necessary to assess the clinical situation of the patient. Currently, this is done via spot-checks on pulse rate manually or on heart rate using Electrocardiogram (ECG) by nurses. More frequent measurements would allow early detection of adverse cardiac events. In this work, we investigate a contactless measurement setup combined with a signal processing pipeline, which is based on speckle vibrometry (SV), to perform contactless heart rate monitoring of human subjects in a supine position, mimicking a resting scenario in the general ward. Our results demonstrate the feasibility of extracting heart rate with SV through varying textile thicknesses (i.e., 8 mm, 32 mm and 64 mm), with an error smaller than 3 beats per minute on average compared to the ground-truth heart rate derived from ECG.

Quantifying Respiration Effects on Cardiac Vibrations using Teager Energy Operator and Gradient Boosted Trees

This work proposes a novel beat scoring system for quantifying the effects of exhalation and inhalation on the seismocardiogram (SCG) signals in rest and physiologically modulated conditions. Data from 19 subjects during rest, listening to classical music and recovery states were used. First, the SCG and electrocardiogram (ECG) signals were segmented into exhalation and inhalation phases using the respiration signal; and a representative SCG beat for each exhale and inhale phase was constructed using the ECG R-peak locations. Second, the significant differences across the exhalation- and inhalation-induced SCG beats were detected and extracted using the Teager- Kaiser energy operator. Finally, a gradient-based beat scoring system was developed using extreme gradient boosted trees and monotonic mapping. For the rest, classical music and recovery sessions, the area under the receiver operating characteristic curve was found to be 0.978, 0.874, 0.985, respectively. On the other hand, the kernel density estimation distributions of the inhalation and exhalation scores had an overlap of 14.2%, 41.2%, 10.6%, respectively. Overall, our results show that different physiological modulations directly change the effect of respiration on the SCG morphology, thus standardization across the beats should be studied for achieving more reliable and accurate investigation of cardiovascular parameters. Clinical relevance - Such a system can potentially allow for more informed and clinically useful SCG analysis by providing valuable insights regarding the intra-recording variability caused by the respiratory system.

Heartbeat Detection in Seismocardiograms with Semantic Segmentation

Heartbeat detection is an essential part of cardiac signal analysis because it is recognized as a representative measure of cardiac function. The gold standard for heartbeat detection is to locate QRS complexes in electrocardiograms. Due to the development of sensors and information and communication technologies (ICT), seismocardiography (SCG) is becoming a viable alternative to electrocardiography to monitor heart rate. In this work, we propose a system for detecting the heartbeat based on seismocardiograms using deep learning methods. The study was carried out with a publicly available data set (CEBS) that contains simultaneous measurements of ECG, breathing signal, and seismocardiograms. Our approach to heartbeat detection in seismocardiograms uses a model based on a ResNet-based convolutional neural network and contains a squeeze and excitation unit. Our model scored state-of-the-art results (Jaccard and F1 score above 97%) on the test dataset, demonstrating its high reliability.

Automatic Detection of Aortic Valve Events Using Deep Neural Networks on Cardiac Signals From Epicardially Placed Accelerometer

BACKGROUND

Miniaturized accelerometers incorporated in pacing leads attached to the myocardium, are used to monitor cardiac function. For this purpose functional indices must be extracted from the acceleration signal. A method that automatically detects time of aortic valve opening (AVO) and aortic valve closure (AVC) will be helpful for such extraction. We tested if deep learning can be used to detect these valve events from epicardially attached accelerometers, using high fidelity pressure measurements to establish ground truth for these valve events.

METHOD

A deep neural network consisting of a CNN, an RNN, and a multi-head attention module was trained and tested on 130 recordings from 19 canines and 159 recordings from 27 porcines covering different interventions. Due to limited data, nested cross-validation was used to assess the accuracy of the method.

RESULT

The correct detection rates were 98.9% and 97.1% for AVO and AVC in canines and 98.2% and 96.7% in porcines when defining a correct detection as a prediction closer than 40 ms to the ground truth. The incorrect detection rates were 0.7% and 2.3% for AVO and AVC in canines and 1.1% and 2.3% in porcines. The mean absolute error between correct detections and their ground truth was 8.4 ms and 7.2 ms for AVO and AVC in canines, and 8.9 ms and 10.1 ms in porcines.

CONCLUSION

Deep neural networks can be used on signals from epicardially attached accelerometers for robust and accurate detection of the opening and closing of the aortic valve.

Deep Learning for Computational Hemodynamics: A Brief Review of Recent Advances

Computational fluid dynamics (CFD) modeling of blood flow plays an important role in better understanding various medical conditions, designing more effective drug delivery systems, and developing novel diagnostic methods and treatments. However, despite significant advances in computational technology and resources, the expensive computational cost of these simulations still hinders their transformation from a research interest to a clinical tool. This bottleneck is even more severe for image-based, patient-specific CFD simulations with realistic boundary conditions and complex computational domains, which make such simulations excessively expensive. To address this issue, deep learning approaches have been recently explored to accelerate computational hemodynamics simulations. In this study, we review recent efforts to integrate deep learning with CFD and discuss the applications of this approach in solving hemodynamics problems, such as blood flow behavior in aorta and cerebral arteries. We also discuss potential future directions in the field. In this review, we suggest that incorporating physiologic understandings and underlying fluid mechanics laws in deep learning models will soon lead to a paradigm shift in the development novel non-invasive computational medical decisions.

Computer-Aided Detection of Fiducial Points in Seismocardiography through Dynamic Time Warping

Accelerometer-based devices have been employed in seismocardiography fiducial point detection with the aid of quasi-synchronous alignment between echocardiography images and seismocardiogram signals. However, signal misalignments have been observed, due to the heartbeat cycle length variation. This paper not only analyzes the misalignments and detection errors but also proposes to mitigate the issues by introducing reference signals and adynamic time warping (DTW) algorithm. Two diagnostic parameters, the ratio of pre-ejection period to left ventricular ejection time (PEP/LVET) and the Tei index, were examined with two statistical verification approaches: (1) the coefficient of determination (R2) of the parameters versus the left ventricular ejection fraction (LVEF) assessments, and (2) the receiver operating characteristic (ROC) classification to distinguish the heart failure patients with reduced ejection fraction (HFrEF). Favorable R2 values were obtained, R2 = 0.768 for PEP/LVET versus LVEF and R2 = 0.86 for Tei index versus LVEF. The areas under the ROC curve indicate the parameters that are good predictors to identify HFrEF patients, with an accuracy of more than 92%. The proof-of-concept experiments exhibited the effectiveness of the DTW-based quasi-synchronous alignment in seismocardiography fiducial point detection. The proposed approach may enable the standardization of the fiducial point detection and the signal template generation. Meanwhile, the program-generated annotation data may serve as the labeled training set for the supervised machine learning.

An Overview of the Sensors for Heart Rate Monitoring Used in Extramural Applications

This work presents an overview of the main strategies that have been proposed for non-invasive monitoring of heart rate (HR) in extramural and home settings. We discuss three categories of sensing according to what physiological effect is used to measure the pulsatile activity of the heart, and we focus on an illustrative sensing modality for each of them. Therefore, electrocardiography, photoplethysmography, and mechanocardiography are presented as illustrative modalities to sense electrical activity, mechanical activity, and the peripheral effect of heart activity. In this paper, we describe the physical principles underlying the three categories and the characteristics of the different types of sensors that belong to each class, and we touch upon the most used software strategies that are currently adopted to effectively and reliably extract HR. In addition, we investigate the strengths and weaknesses of each category linked to the different applications in order to provide the reader with guidelines for selecting the most suitable solution according to the requirements and constraints of the application.

A Comparison of Heart Pulsations Provided by Forcecardiography and Double Integration of Seismocardiogram

Seismocardiography (SCG) is largely regarded as the state-of-the-art technique for continuous, long-term monitoring of cardiac mechanical activity in wearable applications. SCG signals are acquired via small, lightweight accelerometers fixed on the chest. They provide timings of important cardiac events, such as heart valves openings and closures, thus allowing the estimation of cardiac time intervals of clinical relevance. Forcecardiography (FCG) is a novel technique that records the cardiac-induced vibrations of the chest wall by means of specific force sensors, which proved capable of monitoring respiration, heart sounds and infrasonic cardiac vibrations, simultaneously from a single contact point on the chest. A specific infrasonic component captures the heart walls displacements and looks very similar to the Apexcardiogram. This low-frequency component is not visible in SCG recordings, nor it can be extracted by simple filtering. In this study, a feasible way to extract this information from SCG signals is presented. The proposed approach is based on double integration of SCG. Numerical double integration is usually very prone to large errors, therefore a specific numerical procedure was devised. This procedure yields a new displacement signal (DSCG) that features a low-frequency component (LF-DSCG) very similar to that of the FCG (LF-FCG). Experimental tests were carried out using an FCG sensor and an off-the-shelf accelerometer firmly attached to each other and placed onto the precordial region. Simultaneous recordings were acquired from both sensors, together with an electrocardiogram lead (used as a reference). Quantitative morphological comparison confirmed the high similarity between LF-FCG and LF-DSCG (normalized cross-correlation index >0.9). Statistical analyses suggested that LF-DSCG, although achieving a fair sensitivity in heartbeat detection (about 90%), has not a very high consistency within the cardiac cycle, leading to inaccuracies in inter-beat intervals estimation. Future experiments with high-performance accelerometers and improved processing methods are envisioned to investigate the potential enhancement of the accuracy and reliability of the proposed method.

Body Acoustics for the Non-Invasive Diagnosis of Medical Conditions

In the past few decades, many non-invasive monitoring methods have been developed based on body acoustics to investigate a wide range of medical conditions, including cardiovascular diseases, respiratory problems, nervous system disorders, and gastrointestinal tract diseases. Recent advances in sensing technologies and computational resources have given a further boost to the interest in the development of acoustic-based diagnostic solutions. In these methods, the acoustic signals are usually recorded by acoustic sensors, such as microphones and accelerometers, and are analyzed using various signal processing, machine learning, and computational methods. This paper reviews the advances in these areas to shed light on the state-of-the-art, evaluate the major challenges, and discuss future directions. This review suggests that rigorous data analysis and physiological understandings can eventually convert these acoustic-based research investigations into novel health monitoring and point-of-care solutions.

Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals

Managing hypertension (HPT) remains a significant challenge for humanity. Despite advancements in blood pressure (BP)-measuring systems and the accessibility of effective and safe anti-hypertensive medicines, HPT is a major public health concern. Headaches, dizziness and fainting are common symptoms of HPT. In HPT patients, normalcy may be observed at one instant and abnormality may prevail during a long duration of 24 h ambulatory BP. This may cause difficulty in identifying patients with HPT, and hence there is a possibility that individuals may be untreated or administered insufficiently. Most importantly, uncontrolled HPT can lead to severe complications (stroke, heart attack, kidney disease, and heart failure), mainly ignoring the signs in nascent stages. HPT in the beginning stages may not present distinct symptoms and may be difficult to diagnose from standard physiological signals. Hence, ballistocardiography (BCG) signal was used in this study to detect HPT automatically. The processed signals from BCG were converted into scalogram images using a continuous wavelet transform (CWT) and were then fed into a 2-D convolutional neural network model (2D-CNN). The model was trained to learn and recognize BCG patterns of healthy controls (HC) and HPT classes. Our proposed model obtained a high classification accuracy of 86.14% with a ten-fold cross-validation (CV) strategy. Hence, this is the first use of a 2D-CNN model (deep-learning algorithm) to detect HPT employing BCG signals.

Can Seismocardiogram Fiducial Points Be Used for the Routine Estimation of Cardiac Time Intervals in Cardiac Patients?

The indexes of cardiac mechanics can be derived from the cardiac time intervals, CTIs, i.e., the timings among the opening and closure of the aortic and mitral valves and the Q wave in the ECG. Traditionally, CTIs are estimated by ultrasound (US) techniques, but they may also be more easily assessed by the identification of specific fiducial points (FPs) inside the waveform of the seismocardiogram (SCG), i.e., the measure of the thorax micro-accelerations produced by the heart motion. While the correspondence of the FPs with the valve movements has been verified in healthy subjects, less information is available on whether this methodology may be routinely employed in the clinical practice for the monitoring of cardiac patients, in which an SCG waveform distortion is expected because of the heart dysfunction. In this study we checked the SCG shape in 90 patients with myocardial infarction (MI), heart failure (HF), or transplanted heart (TX), referred to our hospital for rehabilitation after an acute event or after surgery. The SCG shapes were classified as traditional (T) or non-traditional (NT) on whether the FPs were visible or not on the basis of nomenclature previously proposed in literature. The T shape was present in 62% of the patients, with a higher ∓ prevalence in MI (79%). No relationship was found between T prevalence and ejection fraction (EF). In 20 patients with T shape, we checked the FPs correspondence with the real valve movements by concomitant SCG and US measures. When compared with reference values in healthy subjects available in the literature, we observed that the Echo vs. FP differences are significantly more dispersed in the patients than in the healthy population with higher differences for the estimation of the mitral valve closure (−17 vs. 4 ms on average). Our results indicate that not every cardiac patient has an SCG waveform suitable for the CTI estimation, thus before starting an SCG-based CTI monitoring a preliminary check by a simultaneous SCG-US measure is advisable to verify the applicability of the methodology.

Latent characteristics and neural manifold of brain functional network under acupuncture

Acupuncture can regulate the cognition of brain system, and different manipulations are the keys of realizing the curative effect of acupuncture on human body. Therefore, it is crucial to distinguish and monitor the different acupuncture manipulations automatically. In this brief, in order to enhance the robustness of electroencephalogram (EEG) detection against noise and interference, we propose an acupuncture manipulation detecting framework based on supervised ISOMAP and recurrent neural network (RNN). Primarily, the low-dimensional embedding neural manifold of brain dynamical functional network is extracted via the reconstructed geodetic distance. It is found that there exhibits stronger acupuncture-specific reconfiguration of brain network. Besides, we show that the distance travel along this manifold correlates strongly with changes of acupuncture manipulations. The low-dimensional brain topological structure of all subjects shows crescent-like feature when acupuncturing at Zusanli acupoints, and fixed-points are varying under diverse manipulation methods. Moreover, Takagi-Sugeno-Kang (TSK) classifier is adopted to identify acupuncture manipulations according to the nonlinear characteristics of neural manifolds. Compared with different classifier, TSK can further improve the accuracy of manipulation identification at 96.71%. The results demonstrate the effectiveness of our model in detecting the acupuncture manipulations, which may provide neural biomarkers for acupuncture physicians.

Detection of Aortic Valve Opening and Estimation of Pre-Ejection Period in Forcecardiography Recordings

Forcecardiography (FCG) is a novel technique that measures the local forces induced on the chest wall by the mechanical activity of the heart. Specific piezoresistive or piezoelectric force sensors are placed on subjects’ thorax to measure these very small forces. The FCG signal can be divided into three components: low-frequency FCG, high-frequency FCG (HF-FCG) and heart sound FCG. HF-FCG has been shown to share a high similarity with the Seismocardiogram (SCG), which is commonly acquired via small accelerometers and is mainly used to locate specific fiducial markers corresponding to essential events of the cardiac cycle (e.g., heart valves opening and closure, peaks of blood flow). However, HF-FCG has not yet been demonstrated to provide the timings of these markers with reasonable accuracy. This study addresses the detection of the aortic valve opening (AO) marker in FCG signals. To this aim, simultaneous recordings from FCG and SCG sensors were acquired, together with Electrocardiogram (ECG) recordings, from a few healthy subjects at rest, both during quiet breathing and apnea. The AO markers were located in both SCG and FCG signals to obtain pre-ejection periods (PEP) estimates, which were compared via statistical analyses. The PEPs estimated from FCG and SCG showed a strong linear relationship (r > 0.95) with a practically unit slope, and 95% of their differences were found to be distributed within ± 4.6 ms around small biases of approximately 1 ms, corresponding to percentage differences lower than 5% of the mean measured PEP. These preliminary results suggest that FCG can provide accurate AO timings and PEP estimates.

Estimation of Changes in Intracardiac Hemodynamics Using Wearable Seismocardiography and Machine Learning in Patients with Heart Failure: A Feasibility Study

OBJECTIVE

Tracking changes in hemodynamic congestion and the consequent proactive readjustment of treatment has shown efficacy in reducing hospitalizations for patients with heart failure (HF). However, the cost-prohibitive nature of these invasive sensing systems precludes their usage in the large patient population affected by HF. The objective of this research is to estimate the changes in pulmonary artery mean pressure (PAM) and pulmonary capillary wedge pressure (PCWP) following vasodilator infusion during right heart catheterization (RHC), using changes in simultaneously recorded wearable seismocardiogram (SCG) signals captured with a small wearable patch.

METHODS

A total of 20 patients with HF (20% women, median age 55 (interquartile range (IQR), 44-64) years, ejection fraction 24 (IQR, 16-43)) were fitted with a wearable sensing patch and underwent RHC with vasodilator challenge. We divided the dataset randomly into a trainingtesting set (n=15) and a separate validation set (n=5). We developed globalized (population) regression models to estimate changes in PAM and PCWP from the changes in simultaneously recorded SCG.

RESULTS

The regression model estimated both pressures with good accuracies: root-mean-square-error (RMSE) of 2.5 mmHg and R2 of 0.83 for estimating changes in PAM, and RMSE of 1.9 mmHg and R2 of 0.93 for estimating changes in PCWP for the training-testing set, and RMSE of 2.7 mmHg and R2 of 0.81 for estimating changes in PAM, and RMSE of 2.9 mmHg and R2 of 0.95 for estimating changes in PCWP for the validation set respectively.

CONCLUSION

Changes in wearable SCG signals may be used to track acute changes in intracardiac hemodynamics in patients with HF.

SIGNIFICANCE

This method holds promise in tracking longitudinal changes in hemodynamic congestion in hemodynamically-guided remote home monitoring and treatment for patients with HF.