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

Automatic and Non-Invasive Delineation of the Seismocardiogram Signal for the Estimation of Cardiac Time Intervals with Applications in Diastolic Timed Vibration and Early Stage Hemorrhage Detection

Seismocardiography is the non-invasive measurement of the heart vibration by placing an accelerometer on the human chest. Due to its non-invasive nature, the seismocardiogram signal could be embedded inside portable devices for the purpose of health monitoring and remote diagnosis. With the combination of the electrocardiogram (ECG) signal, cardiac time intervals (CTI) could be extracted. CTIs are timing intervals that are associated with specific events of the cardiac cycle. The research community has explored the potential of CTI in the diagnosis of chronic myocardial disease, ischemic and coronary artery disease, arterial hypertension, cardiac resynchronization therapy, and implantable cardioverter de-fibrillator. For the extracted CTIs to be useful in a medical device, the seismocardiogram signal (SCG) has to be automatically delineated. Upon the automatic delineation of CTIs, the timing parameters could be either combined with other physiological signals to create new indices that have unique physiological interpretations or to be used as a complimentary technology. Hence, The present dissertation has three main objectives: (1) automatic SCG delineation algorithm, (2) application of cardiac time intervals (extracted from SCG) for generating aunique index for early stage hemorrhage detection, and (3) complementary technology for optimization of the diastolic timed vibration therapy. For the first objective, the proposed delineation algorithm had the capability to correctly estimate the CTIs while discarding low-quality cardiac cycles, which are the ones thatdon’t have identifiable fiducial points. For the second objective, the combination of the electrocardiogram, seismocardiogram, and photoplethysmogram signals was used to design a hemorrhage progression index, which ultimately was applied for early stage detection. For the last objective, the extracted CTIs were applied to the “diastolic timed vibration”, which is a potential therapy for patients with acute ischemia during the pre-hospitalization phase. A calibration methodology was proposed for diastole detection in real-time