Gyrocardiography Research Articles

Detection of heart rate using smartphone gyroscope data: a scoping review.

Heart rate (HR) is closely related to heart rhythm patterns, and its irregularity can imply serious health problems. Therefore, HR is used in the diagnosis of many health conditions. Traditionally, HR has been measured through an electrocardiograph (ECG), which is subject to several practical limitations when applied in everyday settings. In recent years, the emergence of smartphones and microelectromechanical systems has allowed innovative solutions for conveniently measuring HR, such as smartphone ECG, smartphone photoplethysmography (PPG), and seismocardiography (SCG). However, these measurements generally rely on external sensor hardware or are highly susceptible to inaccuracies due to the presence of significant levels of motion artifact. Data from gyrocardiography (GCG), however, while largely overlooked for this application, has the potential to overcome the limitations of other forms of measurements. For this scoping review, we performed a literature search on HR measurement using smartphone gyroscope data. In this review, from among the 114 articles that we identified, we include seven relevant articles from the last decade (December 2012 to January 2023) for further analysis of their respective methods for data collection, signal pre-processing, and HR estimation. The seven selected articles' sample sizes varied from 11 to 435 participants. Two articles used a sample size of less than 40, and three articles used a sample size of 300 or more. We provide elaborations about the algorithms used in the studies and discuss the advantages and disadvantages of these methods. Across the articles, we noticed an inconsistency in the algorithms used and a lack of established standardization for performance evaluation for HR estimation using smartphone GCG data. Among the seven articles included, five did not perform any performance evaluation, while the other two used different reference signals (HR and PPG respectively) and metrics for accuracy evaluation. We conclude the review with a discussion of challenges and future directions for the application of GCG technology.

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.

Revolutionizing smartphone gyrocardiography for heart rate monitoring: overcoming clinical validation hurdles.

Accurate heart rate (HR) measurement is crucial for optimal cardiac health, and while conventional methods such as electrocardiography and photoplethysmography are widely used for continuous daily monitoring, they may face practical limitations due to their dependence on external sensors and susceptibility to motion artifacts. In recent years, mechanocardiography (MCG)-based technologies, such as gyrocardiography (GCG) and seismocardiography (SCG), have emerged as promising alternatives to address these limitations. GCG has shown enhanced sensitivity and accuracy for HR detection compared to SCG, although its benefits are often overlooked in the context of the widespread use of accelerometers in HR monitoring applications. In this perspective, we aim to explore the potential and challenges of GCG, while recognizing that other technologies, including photoplethysmography and remote photoplethysmography, also have promising applications for HR monitoring. We propose a roadmap for future research to unlock the transformative capabilities of GCG for everyday heart rate monitoring.

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.

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, 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 s 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.

Multi-modal Framework for Fetal Heart Rate Estimation: Fusion of Low-SNR ECG and Inertial Sensors.

This study presents a novel multi-modal framework for fetal heart rate extraction, which incorporates wearable seismo-cardiography (SCG), gyro-cardiography (GCG), and electrocardiography (ECG) readings from ten pregnant women. Firstly, a signal refinement method based on empirical mode decomposition (EMD) is proposed to extract the desired signal components associated with fetal heart rate (FHR). Afterwards, two techniques are developed to fuse the information from different modalities. The first method, named early fusion, is intended to combine the refined signals of different modalities through intra-modality fusion, intermodality fusion, and FHR estimation. The other fusion approach, i.e., late fusion, includes FHR estimation and intermodality FHR fusion. FHR values are estimated and compared with readings from a simultaneously-recorded cardiotocography (CTG) sensor. It is demonstrated that the best performance belongs to the late-fusion approach with 87.00% of positive percent agreement (PPA), 6.30% of absolute percent error (APE), and 10.55 beats-per-minute (BPM) of root-meansquare-error (RMSE).Clinical Relevance- The proposed framework allows for the continuous monitoring of the health status of the fetus in expectant women. The approach is accurate and cost-effective due to the use of advanced signal processing techniques and lowcost wearable sensors, respectively.

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.

Investigating the estimation of cardiac time intervals using gyrocardiography

Objective: Assessment of cardiac time intervals (CTIs) is essential for monitoring cardiac performance. Recently, gyrocardiography (GCG) has been introduced as a non-invasive technology for cardiac monitoring. GCG measures the chest’s angular precordial vibrations caused by myocardium wall motion using a gyroscope sensor attached to the sternum. In this study, we investigated the accuracy and reproducibility of estimating CTIs from the GCG recordings of 50 adults. Approach: We proposed five fiducial points for the GCG waveforms associated with the opening and closure of aortic and mitral valves. Two annotators annotated the suggested points on each cardiac cycle. The points were compared to the corresponding opening and closing of cardiac valves delineated on Tissue Doppler imaging (TDI) recordings. The fiducial points were annotated on seismocardiography (SCG) and impedance cardiography (ICG) signals recorded simultaneously. Main results: For estimating the timing of mitral valve closure, aortic valve opening, aortic valve closure, and mitral valve opening, 40%, 67%, 75%, and 70% of GCG annotations fell in the corresponding echocardiography ranges, respectively. The results showed moderate-to-excellent (r = 0.4-0.92; p-value < 0.01) correlation between the measured and the reference CTls. A myocardial performance index (Tei index) adapted using joint GCG and SCG resulted in a moderate correlation (r = 0.4; p-value < 0.001). Significance: The findings showed that the CTIs can be easily measured using GCG. Also, we found that using SCG and GCG recordings together could provide an opportunity to estimate CTIs more accurately, and make it possible to calculate the Tei index as an indicator of myocardial performance.

Real-Time Cardiac Beat Detection and Heart Rate Monitoring from Combined Seismocardiography and Gyrocardiography.

Cardiography is an indispensable element of health care. However, the accessibility of at-home cardiac monitoring is limited by device complexity, accuracy, and cost. We have developed a real-time algorithm for heart rate monitoring and beat detection implemented in a custom-built, affordable system. These measurements were processed from seismocardiography (SCG) and gyrocardiography (GCG) signals recorded at the sternum, with concurrent electrocardiography (ECG) used as a reference. Our system demonstrated the feasibility of non-invasive electro-mechanical cardiac monitoring on supine, stationary subjects at a cost of $100, and with the SCG–GCG and ECG algorithms decoupled as standalone measurements. Testing was performed on 25 subjects in the supine position when relaxed, and when recovering from physical exercise, to record 23,984 cardiac cycles at heart rates in the range of 36–140 bpm. The correlation between the two measurements had r2 coefficients of 0.9783 and 0.9982 for normal (averaged) and instantaneous (beat identification) heart rates, respectively. At a sampling frequency of 250 Hz, the average computational time required was 0.088 s per measurement cycle, indicating the maximum refresh rate. A combined SCG and GCG measurement was found to improve accuracy due to fundamentally different noise rejection criteria in the mutually orthogonal signals. The speed, accuracy, and simplicity of our system validated its potential as a real-time, non-invasive, and affordable solution for outpatient cardiac monitoring in situations with negligible motion artifact.

Cardiac monitoring of dogs via smartphone mechanocardiography: a feasibility study

BackgroundIn 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.MethodsA 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.ResultsThe 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.ConclusionWith 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).

Comprehensive Analysis of Cardiogenic Vibrations for Automated Detection of Atrial Fibrillation Using Smartphone Mechanocardiograms

Atrial fibrillation (AFib) is the most common sustained heart arrhythmia and is characterized by irregular and excessively frequent ventricular contractions. Early diagnosis of AFib is a key step in the prevention of stroke and heart failure. In this paper, we present a comprehensive time–frequency pattern analysis approach for automated detection of AFib from smartphone-derived seismocardiography (SCG) and gyrocardiography (GCG) signals. We sought to assess the diagnostic performance of a smartphone mechanocardiogram (MCG) by considering joint SCG–GCG recordings from 435 subjects including 190 AFib and 245 sinus rhythm cases. A fully automated AFib detection algorithm consisting of various signal processing and multidisciplinary feature engineering techniques was developed and evaluated through a large set of cross-validation (CV) data including 300 (AFib = 150) cardiac patients. The trained model was further tested on an unseen set of recordings including 135 (AFib = 40) subjects considered as cross-database (CD). The experimental results showed accuracy, sensitivity, and specificity of approximately 97%, 99%, and 95% for the CV study and up to 95%, 93%, and 97% for the CD test, respectively. The F1 scores were 97% and 96% for the CV and CD, respectively. A positive predictive value of approximately 95% and 92% was obtained, respectively, for the validation and test sets suggesting high reproducibility and repeatability for mobile AFib detection. Moreover, the kappa coefficient of the method was 0.94 indicating a near-perfect agreement in rhythm classification between the smartphone algorithm and visual interpretation of telemetry recordings. The results support the feasibility of self-monitoring via easy-to-use and accessible MCGs.

A Binary Classification of Cardiovascular Abnormality Using Time-Frequency Features of Cardio-mechanical Signals.

This paper introduces a novel method of binary classification of cardiovascular abnormality using the time-frequency features of cardio-mechanical signals, namely seismocardiography (SCG) and gyrocardiography (GCG) signals. A digital signal processing framework is proposed which utilizes decision tree and support vector machine methods with features generated by continuous wavelet transform. Experimental measurements were collected from twelve patients with cardiovascular diseases as well as twelve healthy subjects to evaluate the proposed method. Results reveal an overall accuracy of more than 94% with the best performance achieved from SVM classifiers with GCG training features. This suggests that the proposed solution could be a promising method for classifying cardiovascular abnormalities.

Multiclass Classifier based Cardiovascular Condition Detection Using Smartphone Mechanocardiography

Cardiac translational and rotational vibrations induced by left ventricular motions are measurable using joint seismocardiography (SCG) and gyrocardiography (GCG) techniques. Multi-dimensional non-invasive monitoring of the heart reveals relative information of cardiac wall motion. A single inertial measurement unit (IMU) allows capturing cardiac vibrations in sufficient details and enables us to perform patient screening for various heart conditions. We envision smartphone mechanocardiography (MCG) for the use of e-health or telemonitoring, which uses a multi-class classifier to detect various types of cardiovascular diseases (CVD) using only smartphone’s built-in internal sensors data. Such smartphone App/solution could be used by either a healthcare professional and/or the patient him/herself to take recordings from their heart. We suggest that smartphone could be used to separate heart conditions such as normal sinus rhythm (SR), atrial fibrillation (AFib), coronary artery disease (CAD), and possibly ST-segment elevated myocardial infarction (STEMI) in multiclass settings. An application could run the disease screening and immediately inform the user about the results. Widespread availability of IMUs within smartphones could enable the screening of patients globally in the future, however, we also discuss the possible challenges raised by the utilization of such self-monitoring systems.

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.

Gyrocardiography: a new non-invasive approach in the study of mechanical motions of the heart. Concept, method and initial observations.

The pumping action of the heart is performed by contraction of the myocardium fibers. We present a non-invasive technique named gyrocardiography (GCG) that comprises a sensor of angular motion, gyroscope, configured to obtain three-dimensional angular velocity signals. A tri-axial micro electromechanical (MEMS) gyroscope sensor was attached to the surface of the chest to obtain gyrocardiogram. Color-coded Doppler tissue imaging (DTI) was recorded simultaneously and synchronized with the GCG in an off-line analysis. By placing a region of interest longitudinally around the myocardium in DTI allowed us to investigate whether GCG can provide information indicative of the tissue velocity and relative strain rate of the myocardium. Experimental observations by simultaneously recorded GCG and color DTI suggests that a gyroscope sensor attached to the chest is indeed capable to monitor the myocardial deformation during the cardiac cycle and therefore can provide a gateway to clinically relevant information.