Identifying Patients With Coronary Artery Disease Using Rest and Exercise Seismocardiography

Coronary artery disease (CAD) is the most common cause of death globally. Patients with suspected CAD are usually assessed by exercise electrocardiography (ECG). Subsequent tests, such as coronary angiography and coronary computed tomography angiography (CCTA) are performed to localize the stenosis and to estimate the degree of blockage. The present study describes a non-invasive methodology to identify patients with CAD based on the analysis of both rest and exercise seismocardiography (SCG). SCG is a non-invasive technology for capturing the acceleration of the chest induced by myocardial motion and vibrations. SCG signals were recorded from 185 individuals at rest and immediately after exercise. Two models were developed using the characterization of the rest and exercise SCG signals to identify individuals with CAD. The models were validated against related results from angiography. For the rest model, accuracy was 74%, and sensitivity and specificity were estimated as 75 and 72%, respectively. For the exercise model accuracy, sensitivity, and specificity were 81, 82, and 84%, respectively. The rest and exercise models presented a bootstrap-corrected area under the curve of 0.77 and 0.91, respectively. The discrimination slope was estimated 0.32 for rest model and 0.47 for the exercise model. The difference between the discrimination slopes of these two models was 0.15 (95% CI: 0.10 to 0.23, p \u3c 0.0001). Both rest and exercise models are able to detect CAD with comparable accuracy, sensitivity, and specificity. Performance of SCG is better compared to stress-ECG and it is identical to stress-echocardiography and CCTA. SCG examination is fast, inexpensive, and may even be carried out by laypersons

Determining the Respiratory State From a Seismocardiographic Signal - A Machine Learning Approach

Seismocardiography (SCG) is a non-invasive method for measurement of vibrations on the chest wall originating from the heart. Respiration changes the morphology of the SCG-signal and analyzing these changes could improve the diagnostic value of SCG. This study aimed to determine the nasal respiration signal amplitude at mitral closure (MC) and aortic opening (AO) using SCG features. The three proposed methods for this were multiple regression analysis (MRA), support vector regression (SVR), and a neural network (NN). SCG, Electrocardiography and nasal-catheter flow signals were acquired from 18 healthy subjects (age 29± 6). SCG-signal fiducial points were used as features and were found using an automatic algorithm followed by manual verification. Fiducial points amplitudes, timings between these and frequency components formed 12 features. All models were trained on 80% of the data, underwent 10-fold cross-validation and were tested on the remaining 20% of the data. Predictions on test data for MC and AO time points, the Pearson correlations coefficient, and sum of squared errors of prediction were: (rMC, rAO, SSEMC, SSEAO) for the following models: NN (0.908, 0.904, 11.71, 12.05), SVR (0.881, 0.833, 18.95, 19.76) and MRA (0.450, 0.437, 51.21, 51.48). These predictive models show a strong correlation between the SCG-signal and respiration

Low Frequency Mechanical Actuation Accelerates Reperfusion In-Vitro

Background Rapid restoration of vessel patency after acute myocardial infarction is key to reducing myocardial muscle death and increases survival rates. Standard therapies include thrombolysis and direct PTCA. Alternative or adjunctive emergency therapies that could be initiated by minimally trained personnel in the field are of potential clinical benefit. This paper evaluates a method of accelerating reperfusion through application of low frequency mechanical stimulus to the blood carrying vessels. Materials and method We consider a stenosed, heparinized flow system with aortic-like pressure variations subject to direct vessel vibration at the occlusion site or vessel deformation proximal and distal to the occlusion site, versus a reference system lacking any form of mechanical stimulus on the vessels. Results The experimental results show limited effectiveness of the direct mechanical vibration method and a drastic increase in the patency rate when vessel deformation is induced. For vessel deformation at occlusion site 95% of clots perfused within 11 minutes of application of mechanical stimulus, for vessel deformation 60 centimeters from the occlusion site 95% percent of clots perfused within 16 minutes of stimulus application, while only 2.3% of clots perfused within 20 minutes in the reference system. Conclusion The presented in-vitro results suggest that low frequency mechanical actuation applied during the pre-hospitalization phase in patients with acute myocardial infarction have potential of being a simple and efficient adjunct therapy

Accurate and consistent automatic seismocardiogram annotation without concurrent ECG

International audienceSeismocardiography (SCG) is the measurement of vibrations in the sternum caused by the beating of the heart. Precise cardiac mechanical timings that are easily obtained from SCG are critically dependent on accurate identification of fiducial points. So far, SCG annotation has relied on concurrent ECG measurements. An algorithm capable of annotating SCG without the use any other concurrent measurement was designed. We subjected 18 participants to graded lower body negative pressure. We collected ECG and SCG, obtained R peaks from the former, and annotated the latter by hand, using these identified peaks. We also annotated the SCG automatically. We compared the isovolumic moment timings obtained by hand to those obtained using our algorithm. Mean ± confidence interval of the percentage of accurately annotated cardiac cycles were ± 97.2 3.7%, ± 93.0 4.6%, ± 76.9 14.9%, ± 61.6 17.2%, and ± 65.0 14.0% for levels of negative pressure 0, −20, −30, −40, and −50 mmHg. LF/HF ratios, the relative power of low-frequency variations to high-frequency variations in heart beat intervals, obtained from isovolumic moments were also compared to those obtained from R peaks. The mean differences ± confidence interval were ± 0.16 0.18, − ± 0.04 0.46, ± 1.02 0.70, ± 1.22 0.61, and ± 2.31 1.09 for increasing levels of negative pressure. The accuracy and consistency of the algorithm enables the use of SCG as a standaloneheart monitoring tool in healthy individuals at rest, and could serve as a basis for an eventual application in pathological cases