Seismocardiography: past, present and future

A Flexible PVDF Sensor for Forcecardiography

Forcecardiography (FCG) uses force sensors to record the mechanical vibrations induced on the chest wall by cardiac and respiratory activities. FCG is usually performed via piezoelectric lead-zirconate titanate (PZT) sensors, which simultaneously record the very slow respiratory movements of the chest, the slow infrasonic vibrations due to emptying and filling of heart chambers, the faster infrasonic vibrations due to movements of heart valves, which are usually recorded via Seismocardiography (SCG), and the audible vibrations corresponding to heart sounds, commonly recorded via Phonocardiography (PCG). However, PZT sensors are not flexible and do not adapt very well to the deformations of soft tissues on the chest. This study presents a flexible FCG sensor based on a piezoelectric polyvinylidene fluoride (PVDF) transducer. The PVDF FCG sensor was compared with a well-assessed PZT FCG sensor, as well as with an electro-resistive respiratory band (ERB), an accelerometric SCG sensor, and an electronic stethoscope for PCG. Simultaneous recordings were acquired with these sensors and an electrocardiography (ECG) monitor from a cohort of 35 healthy subjects (16 males and 19 females). The PVDF sensor signals were compared in terms of morphology with those acquired simultaneously via the PZT sensor, the SCG sensor and the electronic stethoscope. Moreover, the estimation accuracies of PVDF and PZT sensors for inter-beat intervals (IBIs) and inter-breath intervals (IBrIs) were assessed against reference ECG and ERB measurements. The results of statistical analyses confirmed that the PVDF sensor provides FCG signals with very high similarity to those acquired via PZT sensors (median cross-correlation index of 0.96 across all subjects) as well as with SCG and PCG signals (median cross-correlation indices of 0.85 and 0.80, respectively). Moreover, the PVDF sensor provides very accurate estimates of IBIs, with R2 > 0.99 and Bland-Altman limits of agreement (LoA) of [-5.30; 5.00] ms, and of IBrIs, with R2 > 0.96 and LoA of [-0.510; 0.513] s. The flexibility of the PVDF sensor makes it more comfortable and ideal for wearable applications. Unlike PZT, PVDF is lead-free, which increases safety and biocompatibility for prolonged skin contact.

Accuracy of the Instantaneous Breathing and Heart Rates Estimated by Smartphone Inertial Units

Seismocardiography (SCG) and Gyrocardiography (GCG) use lightweight, miniaturized accelerometers and gyroscopes to record, respectively, cardiac-induced linear accelerations and angular velocities of the chest wall. These inertial sensors are also sensitive to thoracic movements with respiration, which cause baseline wanderings in SCG and GCG signals. Nowadays, accelerometers and gyroscopes are widely integrated into smartphones, thus increasing the potential of SCG and GCG as cardiorespiratory monitoring tools. This study investigates the accuracy of smartphone inertial sensors in simultaneously measuring instantaneous heart rates and breathing rates. Smartphone-derived SCG and GCG signals were acquired from 10 healthy subjects at rest. The performances of heartbeats and respiratory acts detection, as well as of inter-beat intervals (IBIs) and inter-breath intervals (IBrIs) estimation, were evaluated for both SCG and GCG via the comparison with simultaneous electrocardiography and respiration belt signals. Heartbeats were detected with a sensitivity and positive predictive value (PPV) of 89.3% and 93.3% in SCG signals and of 97.3% and 97.9% in GCG signals. Moreover, IBIs measurements reported strong linear relationships (R2 > 0.999), non-significant biases, and Bland-Altman limits of agreement (LoA) of ±7.33 ms for SCG and ±5.22 ms for GCG. On the other hand, respiratory acts detection scored a sensitivity and PPV of 95.6% and 94.7% for SCG and of 95.7% and 92.0% for GCG. Furthermore, high R2 values (0.976 and 0.968, respectively), non-significant biases, and an LoA of ±0.558 s for SCG and ±0.749 s for GCG were achieved for IBrIs estimates. The results of this study confirm that smartphone inertial sensors can provide accurate measurements of both instantaneous heart rate and breathing rate without the need for additional devices.

Evaluating Seismocardiography as a Non-Exercise Method for Estimating Maximal Oxygen Uptake

BACKGROUND

The value of maximal oxygen uptake (VO2MAX) is a key health indicator. Usually, VO2MAX is determined with cardiopulmonary exercise testing (CPET), which is cumbersome and time-consuming, making it impractical in many testing scenarios. The aim of this study is to validate a novel seismocardiography sensor (Seismofit®, VentriJect DK, Hellerup, Denmark) for non-exercise estimation of VO2MAX.

METHODS

A cohort of 94 healthy subjects (52% females, 48.2 (8.7) years old) were included in this study. All subjects performed an ergometer CPET. Seismofit® measurements were obtained 10 and 5 min before CPET in resting condition and 5 min after exhaustion.

RESULTS

The CPET VO2MAX was 37.2 (8.6) mL/min/kg, which was not different from the two first Seismofit® estimates at 37.5 (8.1) mL/min/kg (p = 0.28) and 37.3 (7.8) mL/min/kg (p = 0.66). Post-exercise Seismofit® was 33.8 (7.1) mL/min/kg (p < 0.001). The correlation between the CPET and the Seismofit® was r = 0.834 and r = 0.832 for the two first estimates, and the mean average percentage error was 11.4% and 11.2%. Intraclass correlation coefficients between the first and second Seismofit® measurement was 0.993, indicating excellent test-retest reliability.

CONCLUSION

The novel Seismofit® VO2MAX estimate correlates well with CPET VO2MAX, and the accuracy is acceptable for general health assessment. The repeatability of Seismofit® estimates obtained at rest was very high.

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.

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.

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.

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.

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.

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.

Mechanocardiography-Based Measurement System Indicating Changes in Heart Failure Patients during Hospital Admission and Discharge

Heart failure (HF) is a disease related to impaired performance of the heart and is a significant cause of mortality and treatment costs in the world. During its progression, HF causes worsening (decompensation) periods which generally require hospital care. In order to reduce the suffering of the patients and the treatment cost, avoiding unnecessary hospital visits is essential, as hospitalization can be prevented by medication. We have developed a data-collection device that includes a high-quality 3-axis accelerometer and 3-axis gyroscope and a single-lead ECG. This allows gathering ECG synchronized data utilizing seismo- and gyrocardiography (SCG, GCG, jointly mechanocardiography, MCG) and comparing the signals of HF patients in acute decompensation state (hospital admission) and compensated condition (hospital discharge). In the MECHANO-HF study, we gathered data from 20 patients, who each had admission and discharge measurements. In order to avoid overfitting, we used only features developed beforehand and selected features that were not outliers. As a result, we found three important signs indicating the worsening of the disease: an increase in signal RMS (root-mean-square) strength (across SCG and GCG), an increase in the strength of the third heart sound (S3), and a decrease in signal stability around the first heart sound (S1). The best individual feature (S3) alone was able to separate the recordings, giving 85.0% accuracy and 90.9% accuracy regarding all signals and signals with sinus rhythm only, respectively. These observations pave the way to implement solutions for patient self-screening of the HF using serial measurements.

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.

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.

A BLE-Connected Piezoresistive and Inertial Chest Band for Remote Monitoring of the Respiratory Activity by an Android Application: Hardware Design and Software Optimization

Breathing is essential for human life. Issues related to respiration can be an indicator of problems related to the cardiorespiratory system; thus, accurate breathing monitoring is fundamental for establishing the patient’s condition. This paper presents a ready-to-use and discreet chest band for monitoring the respiratory parameters based on the piezoresistive transduction mechanism. In detail, it relies on a strain sensor realized with a pressure-sensitive fabric (EeonTex LTT-SLPA-20K) for monitoring the chest movements induced by respiration. In addition, the band includes an Inertial Measurement Unit (IMU), which is used to remove the motion artefacts from the acquired signal, thereby improving the measurement reliability. Moreover, the band comprises a low-power conditioning and acquisition section that processes the signal from sensors, providing a reliable measurement of the respiration rate (RR), in addition to other breathing parameters, such as inhalation (TI) and exhalation (TE) times, inhalation-to-exhalation ratio (IER), and flow rate (V). The device wirelessly transmits the extracted parameters to a host device, where a custom mobile application displays them. Different test campaigns were carried out to evaluate the performance of the designed chest band in measuring the RR, by comparing the measurements provided by the chest band with those obtained by breath count. In detail, six users, of different genders, ages, and physical constitutions, were involved in the tests. The obtained results demonstrated the effectiveness of the proposed approach in detecting the RR. The achieved performance was in line with that of other RR monitoring systems based on piezoresistive textiles, but which use more powerful acquisition systems or have low wearability. In particular, the inertia-assisted piezoresistive chest band obtained a Pearson correlation coefficient with respect to the measurements based on breath count of 0.96 when the user was seated. Finally, Bland–Altman analysis demonstrated that the developed system obtained 0.68 Breaths Per Minute (BrPM) mean difference (MD), and Limits of Agreement (LoAs) of +3.20 and −1.75 BrPM when the user was seated.

End-to-end sensor fusion and classification of atrial fibrillation using deep neural networks and smartphone mechanocardiography

OBJECTIVE

The purpose of this research is to develop a new deep learning framework for detecting atrial fibrillation (AFib), one of the most common heart arrhythmias, by analyzing the heart's mechanical functioning as reflected in seismocardiography (SCG) and gyrocardiography (GCG) signals. Jointly, SCG and GCG constitute the concept of mechanocardiography (MCG), a method used to measure precordial vibrations with the built-in inertial sensors of smartphones.

APPROACH

We present a modified deep residual neural network model for the classification of sinus rhythm (SR), AFib, and Noise categories from tri-axial SCG and GCG data derived from smartphones. In the model presented, pre-processing including automated early sensor fusion and spatial feature extraction are carried out using attention-based convolutional and residual blocks. Additionally, we use bidirectional long short-term memory layers on top of fully-connected layers to extract both spatial and spatiotemporal features of the multidimensional SCG and GCG signals. The dataset consisted of 728 short measurements recorded from 300 patients. Further, the measurements were divided into disjoint training, validation, and test sets, respectively, of 481 measurements, 140 measurements, and 107 measurements. Prior to ingestion by the model, measurements were split into 10-second segments with 75 percent overlap, pre-processed, and augmented.

MAIN RESULTS

On the unseen test set, the model delivered average micro- and macro-F1-score of 0.88 (0.87-0.89; 95% CI) and 0.83 (0.83-0.84; 95% CI) for the segment-wise classification as well as 0.95 (0.94-0.96; 95% CI) and 0.95 (0.94-0.96; 95% CI) for the measurement-wise classification, respectively.

SIGNIFICANCE

Our method not only can effectively fuse SCG and GCG signals but also can identify heart rhythms and abnormalities in the MCG signals with remarkable accuracy.

Efficient detection of aortic stenosis using morphological characteristics of cardiomechanical signals and heart rate variability parameters

Recent research has shown promising results for the detection of aortic stenosis (AS) using cardio-mechanical signals. However, they are limited by two main factors: lacking physical explanations for decision-making on the existence of AS, and the need for auxiliary signals. The main goal of this paper is to address these shortcomings through a wearable inertial measurement unit (IMU), where the physical causes of AS are determined from IMU readings. To this end, we develop a framework based on seismo-cardiogram (SCG) and gyro-cardiogram (GCG) morphologies, where highly-optimized algorithms are designed to extract features deemed potentially relevant to AS. Extracted features are then analyzed through machine learning techniques for AS diagnosis. It is demonstrated that AS could be detected with 95.49-100.00% confidence. Based on the ablation study on the feature space, the GCG time-domain feature space holds higher consistency, i.e., 95.19-100.00%, with the presence of AS than HRV parameters with a low contribution of 66.00-80.00%. Furthermore, the robustness of the proposed method is evaluated by conducting analyses on the classification of the AS severity level. These analyses are resulted in a high confidence of 92.29%, demonstrating the reliability of the proposed framework. Additionally, game theory-based approaches are employed to rank the top features, among which GCG time-domain features are found to be highly consistent with both the occurrence and severity level of AS. The proposed framework contributes to reliable, low-cost wearable cardiac monitoring due to accurate performance and usage of solitary inertial sensors.

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.

Effects of acquisition device, sampling rate, and record length on kinocardiography during position-induced haemodynamic changes

BACKGROUND

Kinocardiography (KCG) is a promising new technique used to monitor cardiac mechanical function remotely. KCG is based on ballistocardiography (BCG) and seismocardiography (SCG), and measures 12 degrees-of-freedom (DOF) of body motion produced by myocardial contraction and blood flow through the cardiac chambers and major vessels.

RESULTS

The integral of kinetic energy ([Formula: see text]) obtained from the linear and rotational SCG/BCG signals was computed over each dimension over the cardiac cycle, and used as a marker of cardiac mechanical function. We tested the hypotheses that KCG metrics can be acquired using different sensors, and at 50 Hz. We also tested the effect of record length on the ensemble average on which the metrics were computed. Twelve healthy males were tested in the supine, head-down tilt, and head-up tilt positions to expand the haemodynamic states on which the validation was performed.

CONCLUSIONS

KCG metrics computed on 50 Hz and 1 kHz SCG/BCG signals were very similar. Most of the metrics were highly similar when computed on different sensors, and with less than 5% of error when computed on record length longer than 60 s. These results suggest that KCG may be a robust and non-invasive method to monitor cardiac inotropic activity. Trial registration Clinicaltrials.gov, NCT03107351. Registered 11 April 2017, https://clinicaltrials.gov/ct2/show/NCT03107351?term=NCT03107351&draw=2&rank=1 .

Multichannel seismocardiography: an imaging modality for investigating heart vibrations

OBJECTIVE

Seismocardiography is the measurement of vibration waves caused by the beating heart with accelerometer(s) placed on the chest. Investigating the nature and the behavior of these vibration waves, by comparing measurements from multiple sites, would help to understand the heart's mechanical contraction activity.

APPROACH

Using newly designed multichannel seismocardiogram equipment, it was possible to investigate the vibration waves with 16 three-axis sensors. The equipment performed well with highly precise synchronization rate over 10 min, linear frequency response and high signal quality. The vibration waves were analyzed using the sagittal axis, a single cardiac cycle and focusing on four fiducial points. Two of the fiducial point where the negative and positive peaks associated with aorta valve opening, along with peaks associated with aorta valve closing.

MAIN RESULTS

The respective average centers of mass of the four fiducial points in 13 subjects were at (frontal axis: 35 mm, vertical axis: 5 mm), (31, 6), (26, 24), and (4, -2), relative to the Xiphoid Process. Similar patterns among the subjects were identified for the propagation of the waves across the chest for the four fiducial points.

SIGNIFICANCE

The multichannel seismocardiogram equipment successfully revealed a general pattern present in chest surface vibration maps.

Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications

Gyrocardiography (GCG) is a non-invasive technique of analyzing cardiac vibrations by a MEMS (microelectromechanical system) gyroscope placed on a chest wall. Although its history is short in comparison with seismocardiography (SCG) and electrocardiography (ECG), GCG becomes a technique which may provide additional insight into the mechanical aspects of the cardiac cycle. In this review, we describe the summary of the history, definition, measurements, waveform description and applications of gyrocardiography. The review was conducted on about 55 works analyzed between November 2016 and September 2020. The aim of this literature review was to summarize the current state of knowledge in gyrocardiography, especially the definition, waveform description, the physiological and physical sources of the signal and its applications. Based on the analyzed works, we present the definition of GCG as a technique for registration and analysis of rotational component of local cardiac vibrations, waveform annotation, several applications of the gyrocardiography, including, heart rate estimation, heart rate variability analysis, hemodynamics analysis, and classification of various cardiac diseases.

Time Domain And Frequency Domain Heart Rate Variability Analysis on Gyrocardiograms

Heart rate variability (HRV) is a valuable noninvasive tool of assessing the state of cardiovascular autonomic function. The interest in heart rate monitoring without electrodes led to the rise of alternative heart beat monitoring methods, such as gyrocardiography (GCG). The purpose of this study was to compare HRV indices calculated on GCG and ECG signals. The study on time domain and and frequency domain heart rate variability analysis was conducted on electrocardiograms and gyrocardiograms registered on 29 healthy male volunteers. ECG signals were used as a reference and the HRV analysis was performed using PhysioNet Cardiovascular Signal Toolbox. The results of HRV analysis show great similarity and strong linear correlation of HRV indices calculated from ECG and GCG indicate the feasibility and reliability of HRV analysis based on gyrocardiograms.

Relationship of the Respiration Waveform to a Chest Worn Inertial Sensor

Demand of portable health monitoring has been growing due to increasing cardiovascular and respiratory diseases. While both cardiovascular monitoring and respiratory monitoring have been developed independently, there lacks a simple integrated solution to monitor both simultaneously. Seismocardiography (SCG), a method of recording cardiac vibrations with an accelerometer can also be used to extract respiratory information via low frequency chest oscillations. This study used an inertial measurement unit which pairs a 3-axis accelerometer and a 3-axis gyroscope to monitor respiration while maintaining optimum placement protocol for recording SCG. Additionally, the connection between inertial measurement and both respiratory rate and volume were explored based on their correlation with a Spirometer. Respiratory volume was shown to have moderate correlation with chest motion with an average best-case correlation coefficient of 0.679 across acceleration and gyration. The techniques described will assist the design of future SCG algorithms by understanding the sources behind their modulation from respiration. This paper shows that a simplified processing technique can be added to SCG algorithms for respiration monitoring.

Analyzing Heart Rate Estimation from Vibrational Cardiography with Different Orientations

Remote health monitoring is a widely discussed topic due to its potential to improve quality and delivery of medical treatment and the global increase in cardiovascular diseases.

OBJECTIVE

Seismocardiography and Gyrocardiography have been shown to provide reliable heart rate information. A simple and efficient setup was developed for the monitoring of mechanical signals at the sternum. An algorithm based in autocorrelation was run on subjects with different orientations in order to detect heart rate.

METHODS

Subjects performed several tests where both SCG and GCG were recorded using an inertial measurement unit, a Raspberry Pi and a BIOPAC acquisition system. A total of 2335 cardiac cycles were obtained from 5 subjects. Heart rate was determined on a per second basis and compared with an electrocardiography (ECG) reference by correlation coefficients. Ensemble averages were used to visualize differences in VCG morphology.

RESULTS

Heart rate estimation obtained from VCG signals across all 5 subjects was referenced with ECG and achieved an r-squared correlation coefficient of 0.956 when supine and 0.975 when standing, compared to 0.965 across the entire dataset.

CONCLUSION

Autocorrelated Differential Algorithm was able to successfully detect heart rate, regardless of orientation and posture.

SIGNIFICANCE

Changes in orientation of the body during measurement introduce inaccuracies. This work shows that the algorithm is resistant to orientation and more adaptable to everyday life.

Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms on Healthy Volunteers

Physiological variation of the interval between consecutive heartbeats is known as the heart rate variability (HRV). HRV analysis is traditionally performed on electrocardiograms (ECG signals) and has become a useful tool in the diagnosis of different clinical and functional conditions. The progress in the sensor technique encouraged the development of alternative methods of analyzing cardiac activity: Seismocardiography and gyrocardiography. In our study we performed HRV analysis on ECG, seismocardiograms (SCG signals) and gyrocardiograms (GCG signals) using the PhysioNet Cardiovascular Toolbox. The heartbeats in ECG were detected using the Pan–Tompkins algorithm and the heartbeats in SCG and GCG signals were detected as peaks within 100 ms from the occurrence of the ECG R waves. The results of time domain, frequency domain and nonlinear HRV analysis on ECG, SCG and GCG signals are similar and this phenomenon is confirmed by very strong linear correlation of HRV indices. The differences between HRV indices obtained on ECG and SCG and on ECG and GCG were statistically insignificant and encourage using SCG or GCG for HRV estimation. Our results of HRV analysis confirm stronger correlation of HRV indices computed on ECG and GCG signals than on ECG and SCG signals because of greater tolerance to inter-subject variability and disturbances.

Forcecardiography: A Novel Technique to Measure Heart Mechanical Vibrations onto the Chest Wall

This paper presents forcecardiography (FCG), a novel technique to measure local, cardiac-induced vibrations onto the chest wall. Since the 19th century, several techniques have been proposed to detect the mechanical vibrations caused by cardiovascular activity, the great part of which was abandoned due to the cumbersome instrumentation involved. The recent availability of unobtrusive sensors rejuvenated the research field with the most currently established technique being seismocardiography (SCG). SCG is performed by placing accelerometers onto the subject's chest and provides information on major events of the cardiac cycle. The proposed FCG measures the cardiac-induced vibrations via force sensors placed onto the subject's chest and provides signals with a richer informational content as compared to SCG. The two techniques were compared by analysing simultaneous recordings acquired by means of a force sensor, an accelerometer and an electrocardiograph (ECG). The force sensor and the accelerometer were rigidly fixed to each other and fastened onto the xiphoid process with a belt. The high-frequency (HF) components of FCG and SCG were highly comparable (r > 0.95) although lagged. The lag was estimated by cross-correlation and resulted in about tens of milliseconds. An additional, large, low-frequency (LF) component, associated with ventricular volume variations, was observed in FCG, while not being visible in SCG. The encouraging results of this feasibility study suggest that FCG is not only able to acquire similar information as SCG, but it also provides additional information on ventricular contraction. Further analyses are foreseen to confirm the advantages of FCG as a technique to improve the scope and significance of pervasive cardiac monitoring.

Modeling Consistent Dynamics of Cardiogenic Vibrations in Low-Dimensional Subspace

The seismocardiogram (SCG) measures the movement of the chest wall in response to underlying cardiovascular events. Though this signal contains clinically-relevant information, its morphology is both patient-specific and highly transient. In light of recent work suggesting the existence of population-level patterns in SCG signals, the objective of this study is to develop a method which harnesses these patterns to enable robust signal processing despite morphological variability. Specifically, we introduce seismocardiogram generative factor encoding (SGFE), which models the SCG waveform as a stochastic sample from a low-dimensional subspace defined by a unified set of generative factors. We then demonstrate that during dynamic processes such as exercise-recovery, learned factors correlate strongly with known generative factors including aortic opening (AO) and closing (AC), following consistent trajectories in subspace despite morphological differences. Furthermore, we found that changes in sensor location affect the perceived underlying dynamic process in predictable ways, thereby enabling algorithmic compensation for sensor misplacement during generative factor inference. Mapping these trajectories to AO and AC yielded R2 values from 0.81-0.90 for AO and 0.72-0.83 for AC respectively across five sensor positions. Identification of consistent behavior of SCG signals in low dimensions corroborates the existence of population-level patterns in these signals; SGFE may also serve as a harbinger for processing methods that are abstracted from the time domain, which may ultimately improve the feasibility of SCG utilization in ambulatory and outpatient settings.

Is Continuous Heart Rate Monitoring of Livestock a Dream or Is It Realistic? A Review

For all homoeothermic living organisms, heart rate (HR) is a core variable to control the metabolic energy production in the body, which is crucial to realize essential bodily functions. Consequently, HR monitoring is becoming increasingly important in research of farm animals, not only for production efficiency, but also for animal welfare. Real-time HR monitoring for humans has become feasible though there are still shortcomings for continuously accurate measuring. This paper is an effort to estimate whether it is realistic to get a continuous HR sensor for livestock that can be used for long term monitoring. The review provides the reported techniques to monitor HR of living organisms by emphasizing their principles, advantages, and drawbacks. Various properties and capabilities of these techniques are compared to check the potential to transfer the mostly adequate sensor technology of humans to livestock in term of application. Based upon this review, we conclude that the photoplethysmographic (PPG) technique seems feasible for implementation in livestock. Therefore, we present the contributions to overcome challenges to evolve to better solutions. Our study indicates that it is realistic today to develop a PPG sensor able to be integrated into an ear tag for mid-sized and larger farm animals for continuously and accurately monitoring their HRs.

Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVETand Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram

Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HDTM and Osypka ICON-CoreTM. In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation.

A Unified Framework for Quality Indexing and Classification of Seismocardiogram Signals

The seismocardiogram (SCG) is a noninvasively-obtained cardiovascular bio-signal that has gained traction in recent years, however is limited by its susceptibility to noise and motion artifacts. Because of this, signal quality must be assured before data are used to inform clinical care. Common methods of signal quality assurance include signal classification or assignment of a numerical quality index. Such tasks are difficult with SCG because there is no accepted standard for signal morphology. In this paper, we propose a unified method of quality indexing and classification that uses multi-subject-based methods to overcome this challenge. Dynamic-time feature matching is introduced as a novel method of obtaining the distance between a signal and reference template, with this metric, the signal quality index (SQI) is defined as a function of the inverse distance between the SCG and a large set of template signals. We demonstrate that this method is able to stratify SCG signals on held-out subjects based on their level of motion-artifact corruption. This method is extended, using the SQI as a feature for classification by ensembled quadratic discriminant analysis. Classification is validated by demonstrating, for the first time, both detection and localization of SCG sensor misplacement, achieving an F1 score of 0.83 on held-out subjects. This paper may provide a necessary step toward automating the analysis of SCG signals, addressing many of the key limitations and concerns precluding the method from being widely used in clinical and physiological sensing applications.

A Novel Adaptive Recursive Least Squares Filter to Remove the Motion Artifact in Seismocardiography

This paper presents a novel adaptive recursive least squares filter (ARLSF) for motion artifact removal in the field of seismocardiography (SCG). This algorithm was tested with a consumer-grade accelerometer. This accelerometer was placed on the chest wall of 16 subjects whose ages ranged from 24 to 35 years. We recorded the SCG signal and the standard electrocardiogram (ECG) lead I signal by placing one electrode on the right arm (RA) and another on the left arm (LA) of the subjects. These subjects were asked to perform standing and walking movements on a treadmill. ARLSF was developed in MATLAB to process the collected SCG and ECG signals simultaneously. The SCG peaks and heart rate signals were extracted from the output of ARLSF. The results indicate a heartbeat detection accuracy of up to 98%. The heart rates estimated from SCG and ECG are similar under both standing and walking conditions. This observation shows that the proposed ARLSF could be an effective method to remove motion artifact from recorded SCG signals.

Influence of Empirical Mode Decomposition on Heart Rate Variability Indices Obtained from Smartphone Seismocardiograms

Heart rate variability (HRV) is a physiological variation of time interval between consecutive heart beats caused by the activity of autonomic nervous system. Seismocardiography (SCG) is a non-invasive method of analyzing cardiac vibrations and can be used to obtain inter-beat intervals required to perform HRV analysis. Heart beats on SCG signals are detected as the occurrences of aortic valve opening (AO) waves. Morphological variations between subjects complicate developing annotation algorithms. To overcome this obstacle we propose the empirical mode decomposition (EMD) to improve the signal quality. We used two algorithms to determine the influence of EMD on HRV indices: the first algorithm uses a band-pass filter and the second algorithm uses EMD as the first step. Higher beat detection performance was achieved for algorithm with EMD (Se=0.926, PPV=0.926 for all analyzed beats) than the algorithm with a band-pass filter (Se=0.859, PPV=0.855). The influence of analyzed algorithms on HRV indices is low despite the differences of heart beat detection performance between analyzed algorithms.

Estimation of Beat-to-Beat Interval and Systolic Time Intervals Using Phono- and Seismocardiograms

Systolic time intervals Pre-Ejection Period (PEP) and Left Ventricular Ejection Time (LVET) are widely used indicators of cardiac functions. While accurate assessment of them requires costly equipment such as echocardiography devices, a satisfactory estimation can be done by analyzing signals from simple accelerometer and microphone attached to human chest. This paper reports a study where heart rate and the systolic intervals were derived from phonocardiogram (PCG) and seismocardiogram (SCG) simultaneously. Both sensors, the microphone for PCG and the accelerometer for SCG were attached on the chest wall, close to sternum (PCG) and apex of the heart (SCG). The signals were acquired from 10 participants in a 33-minute laboratory protocol with synchronized ECG measurements. Both signals went through an identical processing path: band pass filtering, envelope extraction with Hilbert transformation and peak detection from the envelope signal. In heart rate estimation, PCG and SCG reached 84% and 93% accuracy, respectively. The systolic interval accuracy estimation was based on deviation analysis as the absolute reference values for PEP and LVET were not available. In PEP estimation, the average standard deviations during the rest periods of the protocol were 4 ms for PCG and 8 ms for SCG. In LVET estimation, the deviations were nearly 10 fold compared to PEP. However, the results show that both methods can be used for accurate heart rate estimation and with careful mechanical attachment also PEP can be accurately derived from both. Due to sharper envelope signal waveform, PEP estimation was more accurate with PCG than with SCG.

Comparison of multiple cardiac signal acquisition technologies for heart rate variability analysis

Heart rate variability analysis is a recognized non-invasive tool that is used to assess autonomic nervous system regulation in various clinical settings and medical conditions. A wide variety of HRV analysis methods have been proposed, but they all require a certain number of cardiac beats intervals. There are many ways to record cardiac activity: electrocardiography, phonocardiography, plethysmocardiography, seismocardiography. However, the feasibility of performing HRV analysis with these technologies and particularly their ability to detect autonomic nervous system changes still has to be studied. In this study, we developed a technology allowing the simultaneous monitoring of electrocardiography, phonocardiography, seismocardiography, photoplethysmocardiography and piezoplethysmocardiography and investigated whether these sensors could be used for HRV analysis. We therefore tested the evolution of several HRV parameters computed from several sensors before, during and after a postural change. The main findings of our study is that even if most sensors were suitable for mean HR computation, some of them demonstrated limited agreement for several HRV analyses methods. We also demonstrated that piezoplethysmocardiography showed better agreement with ECG than other sensors for most HRV indexes.

Assessment of Ultra-Short Heart Variability Indices Derived by Smartphone Accelerometers for Stress Detection

Body acceleration due to heartbeat-induced reaction forces can be measured as mobile phone accelerometer (m-ACC) signals. Our aim was to test the feasibility of using m-ACC to detect changes induced by stress by ultra-short heart rate variability (USV) indices (standard deviation of normal-to-normal interval—SDNN and root mean square of successive differences—RMSSD). Sixteen healthy volunteers were recruited; m-ACC was recorded while in supine position, during spontaneous breathing at rest conditions (REST) and during one minute of mental stress (MS) induced by arithmetic serial subtraction task, simultaneous with conventional electrocardiogram (ECG). Beat occurrences were extracted from both ECG and m-ACC and used to compute USV indices using 60, 30 and 10 s durations, both for REST and MS. A feasibility of 93.8% in the beat-to-beat m-ACC heart rate series extraction was reached. In both ECG and m-ACC series, compared to REST, in MS the mean beat duration was reduced by 15% and RMSSD decreased by 38%. These results show that short term recordings (up to 10 s) of cardiac activity using smartphone’s accelerometers are able to capture the decrease in parasympathetic tone, in agreement with the induced stimulus.

Comparison of HRV indices obtained from ECG and SCG signals from CEBS database

BACKGROUND

Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the recent years, there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing vibrations generated by the heart using an accelerometer. In this study, we compare HRV indices obtained from SCG and ECG on signals from combined measurement of ECG, breathing and seismocardiogram (CEBS) database and determine the influence of heart beat detector on SCG signals.

METHODS

We considered two heart beat detectors on SCG signals: reference detector using R waves from ECG signal to detect heart beats in SCG and a heart beat detector using only SCG signal. We performed HRV analysis and calculated time and frequency features.

RESULTS

Beat detection performance of tested algorithm on all SCG signals is quite good on 85,954 beats ([Formula: see text], [Formula: see text]) despite lower performance on noisy signals. Correlation between HRV indices was calculated as coefficient of determination ([Formula: see text]) to determine goodness of fit to linear model. The highest [Formula: see text] values were obtained for mean interbeat interval ([Formula: see text] for reference algorithm, [Formula: see text] in the worst case), [Formula: see text] and [Formula: see text] ([Formula: see text] for the best case, [Formula: see text] for the worst case) and the lowest were obtained for [Formula: see text] ([Formula: see text] in the worst case). Using robust model improved achieved correlation between HRV indices obtained from ECG and SCG signals except the [Formula: see text] values of pNN50 values in signals p001-p020 and for all analyzed signals.

CONCLUSIONS

Calculated HRV indices derived from ECG and SCG are similar using two analyzed beat detectors, except SDNN, RMSSD, NN50, pNN50, and [Formula: see text]. Relationship of HRV indices derived from ECG and SCG was influenced by used beat detection method on SCG signal.

Cardiac monitoring of dogs via smartphone mechanocardiography: a feasibility study

Background

In the context of monitoring dogs, usually, accelerometers have been used to measure the dog’s movement activity. Here, we study another application of the accelerometers (and gyroscopes)—seismocardiography (SCG) and gyrocardiography (GCG)—to monitor the dog’s heart. Together, 3-axis SCG and 3-axis GCG constitute of 6-axis mechanocardiography (MCG), which is inbuilt to most modern smartphones. Thus, the objective of this study is to assess the feasibility of using a smartphone-only solution to studying dog’s heart.

Methods

A clinical trial (CT) was conducted at the University Small Animal Hospital, University of Helsinki, Finland. 14 dogs (3 breeds) including 18 measurements (about one half of all) where the dog’s status was such that it was still and not panting were further selected for the heart rate (HR) analysis (each signal with a duration of 1 min). The measurement device in the CT was a custom Holter monitor including synchronized 6-axis MCG and ECG. In addition, 16 dogs (9 breeds, one mixed-breed) were measured at home settings by the dog owners themselves using Sony Xperia Android smartphone sensor to further validate the applicability of the method.

Results

The developed algorithm was able to select 10 good-quality signals from the 18 CT measurements, and for 7 of these, the automated algorithm was able to detect HR with deviation below or equal to 5 bpm (compared to ECG). Further visual analysis verified that, for approximately half of the dogs, the signal quality at home environment was sufficient for HR extraction at least in some signal locations, while the motion artifacts due to dog’s movements are the main challenges of the method.

Conclusion

With improved data analysis techniques for managing noisy measurements, the proposed approach could be useful in home use. The advantage of the method is that it can operate as a stand-alone application without requiring any extra equipment (such as smart collar or ECG patch).

Recent Advances in Seismocardiography

Cardiovascular disease is a major cause of death worldwide. New diagnostic tools are needed to provide early detection and intervention to reduce mortality and increase both the duration and quality of life for patients with heart disease. Seismocardiography (SCG) is a technique for noninvasive evaluation of cardiac activity. However, the complexity of SCG signals introduced challenges in SCG studies. Renewed interest in investigating the utility of SCG accelerated in recent years and benefited from new advances in low-cost lightweight sensors, and signal processing and machine learning methods. Recent studies demonstrated the potential clinical utility of SCG signals for the detection and monitoring of certain cardiovascular conditions. While some studies focused on investigating the genesis of SCG signals and their clinical applications, others focused on developing proper signal processing algorithms for noise reduction, and SCG signal feature extraction and classification. This paper reviews the recent advances in the field of SCG.

Heart Rate Variability Analysis on CEBS Database Signals

Heart rate variability (HRV) is a valuable noninvasive tool of assessing the state of cardiovascular autonomic function. Over the recent years there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing cardiovascular vibrations. The purpose of this study is to compare HRV indices calculated on SCG and ECG signals from Combined measurement of ECG, breathing and seismocardiogram (CEBS) database. The authors use 20 signals lasting 200 s acquired from patients in supine position and compare heart rate variability parameters from the seismocardiogram and ECG reference signal. They assessed the performance of heart beat detector on SCG channel. The results of modified version of SCG heart beat detection prove its good performance on signals with higher sampling frequency. Strong linear correlation of HRV indices calculated from ECG and SCG prove the reliability of SCG in HRV analysis performed on signals from CEBS Database.

Realization and Technology Acceptance Test of a Wearable Cardiac Health Monitoring and Early Warning System with Multi-Channel MCGs and ECG

In this work, a wearable smart clothing system for cardiac health monitoring with a multi-channel mechanocardiogram (MCG) has been developed to predict the myo-cardiac left ventricular ejection fraction (LVEF) function and to provide early risk warnings to the subjects. In this paper, the realization of the core of this system, i.e., the Cardiac Health Assessment and Monitoring Platform (CHAMP), with respect to its hardware, firmware, and wireless design features, is presented. The feature values from the CHAMP system have been correlated with myo-cardiac functions obtained from actual heart failure (HF) patients. The usability of this MCG-based cardiac health monitoring smart clothing system has also been evaluated with technology acceptance model (TAM) analysis and the results indicate that the subject shows a positive attitude toward using this wearable MCG-based cardiac health monitoring and early warning system.

Definition of Fiducial Points in the Normal Seismocardiogram

The purpose of this work is to define fiducial points in the seismocardiogram (SCG) and to correlate them with physiological events identified in ultrasound images. For 45 healthy subjects the SCG and the electrocardiogram (ECG) were recorded simultaneously at rest. Immediately following the SCG and ECG recordings ultrasound images of the heart were also obtained at rest. For all subjects a mean SCG signal was calculated and all fiducial points (peaks and valleys) were identified and labeled in the same way across all signals. Eight physiologic events, including the valve openings and closings, were annotated from ultrasound as well and the fiducial points were correlated with those physiologic events. A total of 42 SCG signals were used in the data analysis. The smallest mean differences (±SD) between the eight events found in the ultrasound images and the fiducial points, together with their correlation coefficients (r) were: atrial systolic onset: -2 (±16) ms, r = 0.75 (p < 0.001); peak atrial inflow: 13 (±19) ms, r = 0.63 (p < 0.001); mitral valve closure: 4 (±11) ms, r = 0.71 (p < 0.01); aortic valve opening: -3 (±11) ms, r = 0.60 (p < 0.001); peak systolic inflow: 13 (±23) ms, r = 0.42 (p < 0.01); aortic valve closure: -5 (±12) ms, r = 0.94 (p < 0.001); mitral valve opening: -7 (±19) ms, r = 0.87 (p < 0.001) and peak early ventricular filling: -18 (±28 ms), r = 0.79 (p < 0.001). In conclusion eight physiologic events characterizeing the cardiac cycle, are associated with reproducible, well-defined fiducial points in the SCG.

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.

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.

A novel dual gating approach using joint inertial sensors: implications for cardiac PET imaging

Positron emission tomography (PET) is a non-invasive imaging technique which may be considered as the state of art for the examination of cardiac inflammation due to atherosclerosis. A fundamental limitation of PET is that cardiac and respiratory motions reduce the quality of the achieved images. Current approaches for motion compensation involve gating the PET data based on the timing of quiescent periods of cardiac and respiratory cycles. In this study, we present a novel gating method called microelectromechanical (MEMS) dual gating which relies on joint non-electrical sensors, i.e. tri-axial accelerometer and gyroscope. This approach can be used for optimized selection of quiescent phases of cardiac and respiratory cycles. Cardiomechanical activity according to echocardiography observations was investigated to confirm whether this dual sensor solution can provide accurate trigger timings for cardiac gating. Additionally, longitudinal chest motions originating from breathing were measured by accelerometric- and gyroscopic-derived respiratory (ADR and GDR) tracking. The ADR and GDR signals were evaluated against Varian real-time position management (RPM) signals in terms of amplitude and phase. Accordingly, high linear correlation and agreement were achieved between the reference electrocardiography, RPM, and measured MEMS signals. We also performed a Ge-68 phantom study to evaluate possible metal artifacts caused by the integrated read-out electronics including mechanical sensors and semiconductors. The reconstructed phantom images did not reveal any image artifacts. Thus, it was concluded that MEMS-driven dual gating can be used in PET studies without an effect on the quantitative or visual accuracy of the PET images. Finally, the applicability of MEMS dual gating for cardiac PET imaging was investigated with two atherosclerosis patients. Dual gated PET images were successfully reconstructed using only MEMS signals and both qualitative and quantitative assessments revealed encouraging results that warrant further investigation of this method.

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

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

Time-Frequency Distribution of Seismocardiographic Signals: A Comparative Study

Accurate estimation of seismocardiographic (SCG) signal features can help successful signal characterization and classification in health and disease. This may lead to new methods for diagnosing and monitoring heart function. Time-frequency distributions (TFD) were often used to estimate the spectrotemporal signal features. In this study, the performance of different TFDs (e.g., short-time Fourier transform (STFT), polynomial chirplet transform (PCT), and continuous wavelet transform (CWT) with different mother functions) was assessed using simulated signals, and then utilized to analyze actual SCGs. The instantaneous frequency (IF) was determined from TFD and the error in estimating IF was calculated for simulated signals. Results suggested that the lowest IF error depended on the TFD and the test signal. STFT had lower error than CWT methods for most test signals. For a simulated SCG, Morlet CWT more accurately estimated IF than other CWTs, but Morlet did not provide noticeable advantages over STFT or PCT. PCT had the most consistently accurate IF estimations and appeared more suited for estimating IF of actual SCG signals. PCT analysis showed that actual SCGs from eight healthy subjects had multiple spectral peaks at 9.20 ± 0.48, 25.84 ± 0.77, 50.71 ± 1.83 Hz (mean ± SEM). These may prove useful features for SCG characterization and classification.