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

BACKGROUND: Heart failure (HF) is the leading cause of hospitalization in individuals over 65 years of age. Identifying noninvasive methods to detect HF may address the epidemic of HF. Seismocardiography which measures cardiac vibrations transmitted to the chest wall has recently emerged as a promising technology to detect HF. OBJECTIVES: In this multicenter study, the authors examined whether seismocardiography using commercially available smartphones can differentiate control subjects from patients with stage C HF. METHODS: Both inpatients and outpatients with HF were enrolled from Finland and the United States. Inpatients with HF were assessed within 2 days of admission, and outpatients were assessed in the ambulatory setting. In a prespecified pooled data analysis, algorithms were derived using logistic regression and then validated using a bootstrap aggregation method. RESULTS: A total of 217 participants with HF (174 inpatients and 172 outpatients) and 786 control subjects from cardiovascular clinics were enrolled. The mean age of participants with acute HF was 64 ± 13 years, 64.9% were male, left ventricular ejection fraction was 39 ± 15%, and median N-terminal pro-B-type natriuretic peptide was 5,778 ng/L (Q1-Q3: 1,933-6,703). The majority (74%) of participants with HF had reduced EF, and 38% had atrial fibrillation. Across both HF cohorts, the algorithms had an area under the receiver operating characteristic curve of 0.95 with a sensitivity of 85%, specificity of 90%, and accuracy of 89% for the detection of HF, with a decision threshold of 0.5. The positive and negative likelihood ratios were 8.50 and 0.17, respectively. The accuracy of the algorithms was not significantly different in subgroups based on age, sex, body mass index, and atrial fibrillation. CONCLUSIONS: Smartphone-based assessment of cardiac function using seismocardiography is feasible and differentiates patients with HF from control subjects with high diagnostic accuracy. (Recognition of Heart Failure With Micro Electro-mechanical Sensors FI [NCT04444583]; Recognition of Heart Failure With Micro Electro-mechanical Sensors [NCT04378179]; Detection of Coronary Artery Disease With Micro Electro-mechanical Sensors [NCT04290091]).

Hemodynamic Bedside Monitoring Instrument with Pressure and Optical Sensors: Validation and Modality Comparison.

Results from two independent clinical validation studies for measuring hemodynamics at the patient's bedside using a compact finger probe are reported. Technology comprises a barometric pressure sensor, and in one implementation, additionally, an optical sensor for photoplethysmography (PPG) is developed, which can be used to measure blood pressure and analyze rhythm, including the continuous detection of atrial fibrillation. The capabilities of the technology are shown in several form factors, including a miniaturized version resembling a common pulse oximeter to which the technology could be integrated in. Several main results are presented: i) the miniature finger probe meets the accuracy requirements of non-invasive blood pressure instrument validation standard, ii) atrial fibrillation can be detected during the blood pressure measurement and in a continuous recording, iii) a unique comparison between optical and pressure sensing mechanisms is provided, which shows that the origin of both modalities can be explained using a pressure-volume model and that recordings are close to identical between the sensors. The benefits and limitations of both modalities in hemodynamic monitoring are further discussed.

Method for measuring jugular venous pulse with a miniature gyroscope sensor patch.

The right internal jugular vein is connected to the right atrium of the heart via the superior vena cava, and consequently its pressure, known as the jugular venous pressure or the jugular venous pulse (JVP), is an important indicator of cardiac function. The JVP can be estimated visually from the neck but it is rather difficult and imprecise. In this article we propose a method to measure the JVP using a motion sensor patch attached to the neck. The JVP signal was extracted from the sensor's 3-axes gyroscope signal and aligned with simultaneously measured ECG and seismocardiogram signals.The method was tested on 20 healthy subjects. The timings of the characteristic JVP waves were compared with the ECG R peaks and seismocardiogram heart sounds S1 and S2. The JVP was reliably measured from 18 subjects with all three waves identified. The timings of the waves were also physiologically plausible when compared to the ECG R peak and the heart sounds. Importantly, the JVP was also found to modulate with respiration, further indicating that the measured signal was indeed the JVP and not the carotid pulse.The results show that the JVP can be measured with a wearable patch-like device registering the delicate motions of the right internal jugular vein. The method has potential to be developed into a clinical tool to measure cardiac health in diseases such as heart failure and chronic obstructive pulmonary disease (COPD).Clinical relevance-The developed method could enable an affordable measurement of clinically important cardiac parameter, jugular venous pulse, as a part of a routine examination.

Development and clinical validation of a miniaturized finger probe for bedside hemodynamic monitoring.

Our aim is to develop a blood pressure (BP) measurement technology that could be integrated into a finger-worn pulse oximeter, eliminating the need for a brachial cuff. We present a miniature cuffless tonometric finger probe system that uses the oscillometric method to measure BP. Our approach uses a motorized press that is used to apply pressure to the fingertip to measure BP. We verified the functionality of the device in a clinical trial (n = 43) resulting in systolic and diastolic pressures ((mean ± SD) mmHg) of (-3.5 ± 8.4) mmHg and (-4.0 ± 4.4) mmHg, respectively. Comparison was made with manual auscultation (n = 26) and automated cuff oscillometry (n = 18). In addition to BP, we demonstrated the ability of the device to assess arterial stiffness (n = 18) and detect atrial fibrillation (n = 6). We were able to introduce a sufficiently small device that could be used for convenient ambulatory measurements with minimal discomfort.

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.

An instrument for measuring blood pressure and assessing cardiovascular health from the fingertip.

Despite blood pressure being one the leading modifiable risk factors for cardiovascular disease and death, it is severely under-monitored. For this challenge we propose a finger artery non-invasive tono-oscillometric monitor (FANTOM) which is an automated low-cost instrument for measuring blood pressure and hemodynamic parameters from the fingertip. The sensing technology is highly scalable and could be integrated to a pulse oximeter probe for increased patient comfort. A tonometric cuff-less mechatronic system is used to apply pressure on the fingertip for (i) measuring oscillometric blood pressure, (ii) recording arterial waveform and for (iii) constructing central blood pressure (CBP) waveform. Clinical study on volunteers (n = 33) was performed against a commercially available arm cuff device yielding systolic and diastolic readings ((mean±SD) mmHg) of (-0.9 ± 7.3) mmHg and (-3.3 ± 6.6) mmHg respectively. The results comply with the Association for the Advancement of Medical Instrumentation (AAMI) standard for non-invasive blood pressure monitors. The arterial pulse recording morphology was compared against a volume clamp device (CNSystems CNAP 500) (n = 3) resulting in similar performance. Comparison of CBP against a pulse wave analysis (PWA) device (Atcor Medical Sphygmocor XCEL) (n = 5) revealed central aortic systolic pulse (CASP) and central augmentation index (cAIx) estimates with precision and accuracy of (2.0 ± 3.7) mmHg and (1.4 ± 6.2)% respectively. In conclusion, the results indicate that the proposed technology could be useful in the development of new portable or wearable blood pressure monitors. The sensing technology is highly scalable and could be integrated to a pulse oximeter probe for increased patient comfort.

A Computational Framework for Data Fusion in MEMS-Based Cardiac and Respiratory Gating.

Dual cardiac and respiratory gating is a well-known technique for motion compensation in nuclear medicine imaging. In this study, we present a new data fusion framework for dual cardiac and respiratory gating based on multidimensional microelectromechanical (MEMS) motion sensors. Our approach aims at robust estimation of the chest vibrations, that is, high-frequency precordial vibrations and low-frequency respiratory movements for prospective gating in positron emission tomography (PET), computed tomography (CT), and radiotherapy. Our sensing modality in the context of this paper is a single dual sensor unit, including accelerometer and gyroscope sensors to measure chest movements in three different orientations. Since accelerometer- and gyroscope-derived respiration signals represent the inclination of the chest, they are similar in morphology and have the same units. Therefore, we use principal component analysis (PCA) to combine them into a single signal. In contrast to this, the accelerometer- and gyroscope-derived cardiac signals correspond to the translational and rotational motions of the chest, and have different waveform characteristics and units. To combine these signals, we use independent component analysis (ICA) in order to obtain the underlying cardiac motion. From this cardiac motion signal, we obtain the systolic and diastolic phases of cardiac cycles by using an adaptive multi-scale peak detector and a short-time autocorrelation function. Three groups of subjects, including healthy controls (n = 7), healthy volunteers (n = 12), and patients with a history of coronary artery disease (n = 19) were studied to establish a quantitative framework for assessing the performance of the presented work in prospective imaging applications. The results of this investigation showed a fairly strong positive correlation (average r = 0.73 to 0.87) between the MEMS-derived (including corresponding PCA fusion) respiration curves and the reference optical camera and respiration belt sensors. Additionally, the mean time offset of MEMS-driven triggers from camera-driven triggers was 0.23 to 0.3 ± 0.15 to 0.17 s. For each cardiac cycle, the feature of the MEMS signals indicating a systolic time interval was identified, and its relation to the total cardiac cycle length was also reported. The findings of this study suggest that the combination of chest angular velocity and accelerations using ICA and PCA can help to develop a robust dual cardiac and respiratory gating solution using only MEMS sensors. Therefore, the methods presented in this paper should help improve predictions of the cardiac and respiratory quiescent phases, particularly with the clinical patients. This study lays the groundwork for future research into clinical PET/CT imaging based on dual inertial sensors.

Clinical assessment of a non-invasive wearable MEMS pressure sensor array for monitoring of arterial pulse waveform, heart rate and detection of atrial fibrillation.

There is an unmet clinical need for a low cost and easy to use wearable devices for continuous cardiovascular health monitoring. A flexible and wearable wristband, based on microelectromechanical sensor (MEMS) elements array was developed to support this need. The performance of the device in cardiovascular monitoring was investigated by (i) comparing the arterial pressure waveform recordings to the gold standard, invasive catheter recording (n = 18), (ii) analyzing the ability to detect irregularities of the rhythm (n = 7), and (iii) measuring the heartrate monitoring accuracy (n = 31). Arterial waveforms carry important physiological information and the comparison study revealed that the recordings made with the wearable device and with the gold standard device resulted in almost identical (r = 0.9-0.99) pulse waveforms. The device can measure the heart rhythm and possible irregularities in it. A clustering analysis demonstrates a perfect classification accuracy between atrial fibrillation (AF) and sinus rhythm. The heartrate monitoring study showed near perfect beat-to-beat accuracy (sensitivity = 99.1%, precision = 100%) on healthy subjects. In contrast, beat-to-beat detection from coronary artery disease patients was challenging, but the averaged heartrate was extracted successfully (95% CI: -1.2 to 1.1 bpm). In conclusion, the results indicate that the device could be useful in remote monitoring of cardiovascular diseases and personalized medicine.

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).

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.

Atrial Fibrillation Detection via Accelerometer and Gyroscope of a Smartphone.

We present a smartphone-only solution for the detection of atrial fibrillation (AFib), which utilizes the built-in accelerometer and gyroscope sensors [inertial measurement unit, (IMU)] in the detection. Depending on the patient's situation, it is possible to use the developed smartphone application either regularly or occasionally for making a measurement of the subject. The smartphone is placed on the chest of the patient who is adviced to lay down and perform a noninvasive recording, while no external sensors are needed. After that, the application determines whether the patient suffers from AFib or not. The presented method has high potential to detect paroxysmal ("silent") AFib from large masses. In this paper, we present the preprocessing, feature extraction, feature analysis, and classification results of the envisioned AFib detection system based on clinical data acquired with a standard mobile phone equipped with Google Android OS. Test data was gathered from 16 AFib patients (validated against ECG), as well as a control group of 23 healthy individuals with no diagnosed heart diseases. We obtained an accuracy of 97.4% in AFib versus healthy classification (a sensitivity of 93.8% and a specificity of 100%). Due to the wide availability of smart devices/sensors with embedded IMU, the proposed methods could potentially also scale to other domains such as embedded body-sensor networks.

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.

Automated Detection of Atrial Fibrillation Based on Time-Frequency Analysis of Seismocardiograms.

In this paper, a novel method to detect atrial fibrillation (AFib) from a seismocardiogram (SCG) is presented. The proposed method is based on linear classification of the spectral entropy and a heart rate variability index computed from the SCG. The performance of the developed algorithm is demonstrated on data gathered from 13 patients in clinical setting. After motion artifact removal, in total 119 min of AFib data and 126 min of sinus rhythm data were considered for automated AFib detection. No other arrhythmias were considered in this study. The proposed algorithm requires no direct heartbeat peak detection from the SCG data, which makes it tolerant against interpersonal variations in the SCG morphology, and noise. Furthermore, the proposed method relies solely on the SCG and needs no complementary electrocardiography to be functional. For the considered data, the detection method performs well even on relatively low quality SCG signals. Using a majority voting scheme that takes five randomly selected segments from a signal and classifies these segments using the proposed algorithm, we obtained an average true positive rate of [Formula: see text] and an average true negative rate of [Formula: see text] for detecting AFib in leave-one-out cross-validation. This paper facilitates adoption of microelectromechanical sensor based heart monitoring devices for arrhythmia detection.

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.

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.

An adaptive approach for heartbeat detection based on S-transform in seismocardiograms.

This study presents a new technique which allows identification of individual heartbeats from seismocardiograms (SCG) with high accuracy. Our method is electrocardiogram (ECG) independent and designed based upon S-transform and Shannon energy. The S-transform which is a time-frequency (TF) representation first provides frequency-dependent resolution while preserving a direct relationship with Fourier spectrum. Subsequently, individual heartbeats are detected in the time domain by calculating the Shannon energy (SSE) of each obtained local spectrum and employing other techniques such as successive mean quantization transform (SMQT) and adaptive thresholding. A total of 30 recordings were analysed in this study by measuring SCG and simultaneous electrocardiogram (ECG) in supine position. The performance of the algorithm was tested using the standard ECGs obtained from each test subject. The obtained results were as follows (sensitivity, precision, and detection error rate): (98.0%, 98.4% and 0.2%). In conclusion, the results confirmed that combination of S-transform, Shannon energy, and other techniques considerably enhanced the efficiency for the heartbeat detection in seismocardiograms.

Detection of atrial fibrillation with seismocardiography.

In this paper we study the feasibility of seismocardiography (SCG) for the detection of Atrial Fibrillation (AF). In this preclinical study, data acquired from one patient having paroxysmal AF (no other heart diseases) is used to introduce specific changes in SCG signal due to AF. Observed changes and phenomena create a foundation for the development of SCG-based AF detection algorithms. SCG data was recorded from the sternum of an AF patient in dorso-ventral direction while at rest in a supine position using a three-axis high precision MEMS accelerometer simultaneously with a one-lead ECG. In contrast to ECG, the magnitude of beats registered with SCG varies considerably from beat to beat during AF. We show that the magnitude of the beats is not random but is in relation to beat intervals. It is shown that extra indicators for detecting AF become available when SCG data is combined with electrocardiographical (ECG) data; there is a certain behavior in the electromechanical delay characteristic of the AF. It is discussed how all this information can be taken advantage of in the detection of AF. Today electrocardiography (ECG) is the primary method for diagnosing arrhythmias, but there is a growing need for simpler and more convenient method for detecting asymptomatic AF. Given the very small dimensions of modern MEMS accelerometers (2mm×2mm), a reliable MEMS based measurement may provide totally new venues for arrhythmia detection.

Beat-by-Beat Quantification of Cardiac Cycle Events Detected From Three-Dimensional Precordial Acceleration Signals.

The vibrations produced by the cardiovascular system that are coupled to the precordium can be noninvasively detected using accelerometers. This technique is called seismocardiography. Although clinical applications have been proposed for seismocardiography, the physiology underlying the signal is still not clear. The relationship of seismocardiograms of on the back-to-front axis and cardiac events is fairly well known. However, the 3-D seismocardiograms detectable with modern accelerometers have not been quantified in terms of cardiac cycle events. A major reason for this might be the degree of intersubject variability observed in 3-D seismocardiograms. We present a method to quantify 3-D seismocardiography in terms of cardiac cycle events. First, cardiac cycle events are identified from the seismocardiograms, and then, assigned a number based on the location in which the corresponding event was found. 396 cardiac cycle events from 9 healthy subjects and 120 cardiac cycle events from patients suffering from atrial flutter were analyzed. Despite the weak intersubject correlation of the waveforms (0.05, 0.27, and 0.15 for the x-, y-, and z-axes, respectively), the present method managed to find latent similarities in the seismocardiograms of healthy subjects. We observed that in healthy subjects the distribution of cardiac cycle event coordinates was centered on specific locations. These locations were different in patients with atrial flutter. The results suggest that spatial distribution of seismocardiographic cardiac cycle events might be used to discriminate healthy individuals and those with a failing heart.