Mojtaba Jafari Tadi

Accelerometer-Based method for extracting respiratory and cardiac gating information for dual gating during nuclear medicine imaging

Both respiratory and cardiac motions reduce the quality and consistency of medical imaging specifically in nuclear medicine imaging. Motion artifacts can be eliminated by gating the image acquisition based on the respiratory phase and cardiac contractions throughout the medical imaging procedure. Electrocardiography (ECG), 3-axis accelerometer, and respiration belt data were processed and analyzed from ten healthy volunteers. Seismocardiography (SCG) is a noninvasive accelerometer-based method that measures accelerations caused by respiration and myocardial movements. This study was conducted to investigate the feasibility of the accelerometer-based method in dual gating technique. The SCG provides accelerometer-derived respiratory (ADR) data and accurate information about quiescent phases within the cardiac cycle. The correct information about the status of ventricles and atria helps us to create an improved estimate for quiescent phases within a cardiac cycle. The correlation of ADR signals with the reference respiration belt was investigated using Pearson correlation. High linear correlation was observed between accelerometer-based measurement and reference measurement methods (ECG and Respiration belt). Above all, due to the simplicity of the proposed method, the technique has high potential to be applied in dual gating in clinical cardiac positron emission tomography (PET) to obtain motion-free images in the future.

A real-time approach for heart rate monitoring using a Hilbert transform in seismocardiograms.

Heart rate monitoring helps in assessing the functionality and condition of the cardiovascular system. We present a new real-time applicable approach for estimating beat-to-beat time intervals and heart rate in seismocardiograms acquired from a tri-axial microelectromechanical accelerometer. Seismocardiography (SCG) is a non-invasive method for heart monitoring which measures the mechanical activity of the heart. Measuring true beat-to-beat time intervals from SCG could be used for monitoring of the heart rhythm, for heart rate variability analysis and for many other clinical applications. In this paper we present the Hilbert adaptive beat identification technique for the detection of heartbeat timings and inter-beat time intervals in SCG from healthy volunteers in three different positions, i.e. supine, left and right recumbent. Our method is electrocardiogram (ECG) independent, as it does not require any ECG fiducial points to estimate the beat-to-beat intervals. The performance of the algorithm was tested against standard ECG measurements. The average true positive rate, positive prediction value and detection error rate for the different positions were, respectively, supine (95.8%, 96.0% and ≃0.6%), left (99.3%, 98.8% and ≃0.001%) and right (99.53%, 99.3% and ≃0.01%). High correlation and agreement was observed between SCG and ECG inter-beat intervals (r  >  0.99) for all positions, which highlights the capability of the algorithm for SCG heart monitoring from different positions. Additionally, we demonstrate the applicability of the proposed method in smartphone based SCG. In conclusion, the proposed algorithm can be used for real-time continuous unobtrusive cardiac monitoring, smartphone cardiography, and in wearable devices aimed at health and well-being applications.

Seismocardiography: Toward heart rate variability (HRV) estimation

Heart rate variability (HRV), the variation in the beat-to-beat heart rate, is a key indicator of the cardiovascular condition of an individual. The purpose of this study was to cross-validate the beat-by-beat time variations in seismocardiography (SCG) with electrocardiography (ECG) for determining ultra-short term HRV indices. Twenty healthy young volunteers were examined in this study by performing an ultra-short term data acquisition protocol. Kubios HRV software was utilized to assess the HRV parameters. The HRV indices were analyzed in both time-domain and frequency-domain processes. High linear relationship (r>0.98) and agreement was observed between the HRV indexes calculated from SCG and ECG data. In conclusion, SCG and ECG HRV indices were found to be statistically close enough to warrant the use of SCG for estimating HRV.

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.

A new algorithm for segmentation of cardiac quiescent phases and cardiac time intervals using seismocardiography

Systolic time intervals (STI) have significant diagnostic values for a clinical assessment of the left ventricle in adults. This study was conducted to explore the feasibility of using seismocardiography (SCG) to measure the systolic timings of the cardiac cycle accurately. An algorithm was developed for the automatic localization of the cardiac events (e.g. the opening and closing moments of the aortic and mitral valves). Synchronously acquired SCG and electrocardiography (ECG) enabled an accurate beat to beat estimation of the electromechanical systole (QS2), pre-ejection period (PEP) index and left ventricular ejection time (LVET) index. The performance of the algorithm was evaluated on a healthy test group with no evidence of cardiovascular disease (CVD). STI values were corrected based on Weissler’s regression method in order to assess the correlation between the heart rate and STIs. One can see from the results that STIs correlate poorly with the heart rate (HR) on this test group. An algorithm was developed to visualize the quiescent phases of the cardiac cycle. A color map displaying the magnitude of SCG accelerations for multiple heartbeats visualizes the average cardiac motions and thereby helps to identify quiescent phases. High correlation between the heart rate and the duration of the cardiac quiescent phases was observed.

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

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

A smartphone-only solution for detecting indications of acute myocardial infarction

In this paper we consider the detection of indications of acute myocardial infarction (AMI) through a smartphone only solution. AMI is a serious heart condition where a blood vessel of the heart is fully or partially blocked e.g. by a rupture of an atherosclerotic plaque, the arrival of oxygen to the heart muscle is disturbed, and part of the heart muscle tissue dies (irreversible injury) due to insufficient oxygen supply. When a person feels obscure acute chest pain (angina pectoris), it may be caused, for instance, by heartburn or it may be a symptom of AMI. The goal of this paper is to develop a solution, which could either be integrated into an emergency App for the use of telemedicine by trained medical personnel or as a standalone solution to smartphone users in order to help recognizing this life-threatening condition earlier. The developed solution extracts the heart signal of a patient who lies in supine position by utilizing the built-in accelerometer and gyroscope within a smart device (e.g. a smartphone), which is placed on the chest of the patient. The solution does not require any external sensors for the smartphone to operate, but in the future it could be supplemented with ECG, for instance, to improve its performance. We have collected data with smartphone running Google Android from 17 AMI patients before and after percutaneous coronary intervention (PCI), and in addition, control recordings were performed in 23 healthy individuals (CG) and in 12 patients with stable coronary artery disease (CAD) before elective PCI.

Automatic identification of signal quality for heart beat detection in cardiac MEMS signals

Rapidly growing number of heart monitoring systems based on microelectromechanical sensor (MEMS) devices demands highly accurate processing algorithms for indicating heartbeat location. In clinical applications based on the analysis of cardiovascular mechanical motion, the signal may vary due to a variety of reasons including motion artifacts, location of the sensor and posture of the person being tested. Also reasons related to the internal device characteristics affect to the signal formation. The purpose of this paper is to address the problem of selecting the best axis when using 3-axis MEMS accelerometer and gyroscope sensors. The formation of noise and artifacts in the observed data depends, for example, on the posture of the monitored person. In this paper, the application for the axis selection is heartbeat detection, and we show that by selecting the optimal axis benefits can be obtained in terms of detection accuracy. With 10-fold cross-validation and with KSVM classifier we obtained the sensitivity and specificity of 83.9% and 86.1%, respectively, in using seismocardiogram waveform for the sepatation between good quality and low quality data. With using gyrocardiogram waveforms we obtained the sensitivity and specificity of 95.9% and 80.0%, respectively.

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.

Heartbeat Detection Using Multidimensional Cardiac Motion Signals and Dynamic Balancing

Ballistocardiography (BCG) is seeing a new renaissance mainly due to access of new miniaturized and sensitive MEMS accelometers and gyroscopes that provides us a new tool for unobstrusive measurement of cardiac signals. These signal, however, suffer from high signal morphology variability and commonly signals are at least partly of low quality. A characteristic of a BCG signal is commonly a brief oscillation associated with each heartbeat which caused by the hearts mechanical movement. We developed an algorithm to detect these wavelets using an envelope enhancement filtering and subsequent dynamic balancing to alleviate the problem of high peak amplitude variability. The beat detection resulted in 0.87 % missed beats and 0.31 % false beats using the gyroY axis of the mobile phone’s integrated motion sensors. Also it is shown, that if the used axis could be chosen optimally for each measurement accuracy of 0.22 % missed beats and 0.21 % false beats could be reached within the used measurements. A photoplethysmography (PPG) signal was used as a verification reference. The data set consisted 2 min recordings from 66 healthy subjects and in total 8870 beats.