Quantification of Cardiac Kinetic Energy and Its Changes During Transmural Myocardial Infarction Assessed by Multi-Dimensional Seismocardiography

Continuous real-time assessment of acute cognitive stress from cardiac mechanical signals captured by a skin-like patch

The inability to objectively quantify cognitive stress in real-time with wearable devices is a crucial unsolved problem with serious negative consequences for dementia and mental disability patients and those seeking to improve their quality of life. Here, we introduce a skin-like, wireless sternal patch that captures changes in cardiac mechanics due to stress manifesting in the seismocardiogram (SCG) signals. Judicious optimization of the device's micro-structured interconnections and elastomer integration yields a device that sufficiently matches the skin's mechanics, robustly yet gently adheres to the skin without aggressive tapes, and captures planar and longitudinal SCG waves well. The device transmits SCG beats in real-time to a user's device, where derived features relate to the heartbeat's mechanical morphology. The signals are assessed by a series of features in a support vector machine regressor. Controlled studies, compared to gold standard cortisol and following the validated imaging test, show an R-squared correlation of 0.79 between the stress prediction and cortisol change, significantly improving over prior works. Likewise, the system demonstrates excellent robustness to external temperature and physical recovery status while showing excellent accuracy and wearability in full-day use.

Mechanocardiography detects improvement of systolic function caused by resynchronization pacing

Objective.Cardiac resynchronization therapy (CRT) is commonly used to manage heart failure with dyssynchronous ventricular contraction. CRT pacing resynchronizes the ventricular contraction, while AAI (single-chamber atrial) pacing does not affect the dyssynchronous function. This study compared waveform characteristics during CRT and AAI pacing at similar pacing rates using seismocardiogram (SCG) and gyrocardiogram (GCG), collectively known as mechanocardiogram (MCG).Approach.We included 10 patients with heart failure with reduced ejection fraction and previously implanted CRT pacemakers. ECG and MCG recordings were taken during AAI and CRT pacing at a heart rate of 80 bpm. Waveform characteristics, including energy, vertical range (amplitude) during systole and early diastole, electromechanical systole (QS2) and left ventricular ejection time (LVET), were derived by considering 6 MCG axes and 3 MCG vectors across frequency ranges of >1 Hz, 20-90 Hz, 6-90 Hz and 1-20 Hz.Main results.Significant differences were observed between CRT and AAI pacing. CRT pacing consistently exhibited higher energy and vertical range during systole compared to AAI pacing (p< 0.05). However, QS2, LVET and waveform characteristics around aortic valve closure did not differ between the pacing modes. Optimal differences were observed in SCG-Y, GCG-X, and GCG-Y axes within the frequency range of 6-90 Hz.Significance.The results demonstrate significant differences in MCG waveforms, reflecting improved mechanical cardiac function during CRT. This information has potential implications for predicting the clinical response to CRT. Further research is needed to explore the differences in signal characteristics between responders and non-responders to CRT.

Accuracy of a Clinical Applicable Method for Prediction of VO2max Using Seismocardiography

Cardiorespiratory fitness measured as ˙VO2max is considered an important variable in the risk prediction of cardiovascular disease and all-cause mortality. Non-exercise ˙VO2max prediction models are applicable, but lack accuracy. Here a model for the prediction of ˙VO2max using seismocardiography (SCG) was investigated. 97 healthy participants (18-65 yrs., 51 females) underwent measurement of SCG at rest in the supine position combined with demographic data to predict ˙VO2max before performing a graded exercise test (GET) on a cycle ergometer for determination of ˙VO2max using pulmonary gas exchange measurements for comparison. Accuracy assessment revealed no significant difference between SCG and GET ˙VO2max (mean±95% CI; 38.3±1.6 and 39.3±1.6 ml·min-1·kg-1, respectively. P=0.075). Further, a Pearson correlation of r=0.73, a standard error of estimate (SEE) of 5.9 ml·min-1·kg-1, and a coefficient of variation (CV) of 8±1% were found. The SCG ˙VO2max showed higher accuracy, than the non-exercise model based on the FRIENDS study, when this was applied to the present population (bias=-3.7±1.3 ml·min-1·kg-1, p<0.0001. r=0.70. SEE=7.4 ml·min-1·kg-1, and CV=12±2%). The SCG ˙VO2max prediction model is an accurate method for the determination of ˙VO2max in a healthy adult population. However, further investigation on the validity and reliability of the SCG ˙VO2max prediction model in different populations is needed for consideration of clinical applicability.

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

Background

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

Methods

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

Results

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

Conclusions

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

Precordial Vibrations: A Review of Wearable Systems, Signal Processing Techniques, and Main Applications

Recently, the ever-growing interest in the continuous monitoring of heart function in out-of-laboratory settings for an early diagnosis of cardiovascular diseases has led to the investigation of innovative methods for cardiac monitoring. Among others, wearables recording seismic waves induced on the chest surface by the mechanical activity of the heart are becoming popular. For what concerns wearable-based methods, cardiac vibrations can be recorded from the thorax in the form of acceleration, angular velocity, and/or displacement by means of accelerometers, gyroscopes, and fiber optic sensors, respectively. The present paper reviews the currently available wearables for measuring precordial vibrations. The focus is on sensor technology and signal processing techniques for the extraction of the parameters of interest. Lastly, the explored application scenarios and experimental protocols with the relative influencing factors are discussed for each technique. The goal is to delve into these three fundamental aspects (i.e., wearable system, signal processing, and application scenario), which are mutually interrelated, to give a holistic view of the whole process, beyond the sensor aspect alone. The reader can gain a more complete picture of this context without disregarding any of these 3 aspects.

Mechanocardiography in the Detection of Acute ST Elevation Myocardial Infarction: The MECHANO-STEMI Study

Novel means to minimize treatment delays in patients with ST elevation myocardial infarction (STEMI) are needed. Using an accelerometer and gyroscope on the chest yield mechanocardiographic (MCG) data. We investigated whether STEMI causes changes in MCG signals which could help to detect STEMI. The study group consisted of 41 STEMI patients and 49 control patients referred for elective coronary angiography and having normal left ventricular function and no valvular heart disease or arrhythmia. MCG signals were recorded on the upper sternum in supine position upon arrival to the catheterization laboratory. In this study, we used a dedicated wearable sensor equipped with 3-axis accelerometer, 3-axis gyroscope and 1-lead ECG in order to facilitate the detection of STEMI in a clinically meaningful way. A supervised machine learning approach was used. Stability of beat morphology, signal strength, maximum amplitude and its timing were calculated in six axes from each window with varying band-pass filters in 2-90 Hz range. In total, 613 features were investigated. Using logistic regression classifier and leave-one-person-out cross validation we obtained a sensitivity of 73.9%, specificity of 85.7% and AUC of 0.857 (SD = 0.005) using 150 best features. As a result, mechanical signals recorded on the upper chest wall with the accelerometers and gyroscopes differ significantly between STEMI patients and stable patients with normal left ventricular function. Future research will show whether MCG can be used for the early screening of STEMI.

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

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

A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications

In recent years, cardiovascular diseases are on the rise, and they entail enormous health burdens on global economies. Cardiac vibrations yield a wide and rich spectrum of essential information regarding the functioning of the heart, and thus it is necessary to take advantage of this data to better monitor cardiac health by way of prevention in early stages. Specifically, seismocardiography (SCG) is a noninvasive technique that can record cardiac vibrations by using new cutting-edge devices as accelerometers. Therefore, providing new and reliable data regarding advancements in the field of SCG, i.e., new devices and tools, is necessary to outperform the current understanding of the State-of-the-Art (SoTA). This paper reviews the SoTA on SCG and concentrates on three critical aspects of the SCG approach, i.e., on the acquisition, annotation, and its current applications. Moreover, this comprehensive overview also presents a detailed summary of recent advancements in SCG, such as the adoption of new techniques based on the artificial intelligence field, e.g., machine learning, deep learning, artificial neural networks, and fuzzy logic. Finally, a discussion on the open issues and future investigations regarding the topic is included.