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.

Atrial fibrillation (AFib) is the most common sustained heart arrhythmia and is characterized by irregular and excessively frequent ventricular contractions. Early diagnosis of AFib is a key step in the prevention of stroke and heart failure. In this study, we present a comprehensive time-frequency pattern analysis approach for automated detection of AFib from smartphone-derived seismocardiography (SCG) and gyrocardiography (GCG) signals. We sought to assess the diagnostic performance of a smartphone mechanocardiogram (MCG) by considering joint SCG-GCG recordings from 435 subjects including 190 AFib and 245 sinus rhythm (SR) cases. A fully automated AFib detection algorithm consisting of various signal processing and multidisciplinary feature engineering techniques was developed and evaluated through a large set of cross-validation (CV) data including 300 (AFib=150) cardiac patients. The trained model was further tested on an unseen set of recordings including 135 (AFib=40) subjects considered as cross-database (CD). The experimental results showed accuracy, sensitivity, and specificity of approximately 97%, 99%, and 95% for the CV study and up to 95%, 93%, and 97% for the CD test, respectively. The F1 scores were 97% and 96% for the CV and CD, respectively. A positive predictive value of approximately 95% and 92% was obtained respectively for the validation and test sets suggesting high reproducibility and repeatability for mobile AFib detection. Moreover, the kappa coefficient of the method was 0.94 indicating a near-perfect agreement in rhythm classification between the smartphone algorithm and visual interpretation of telemetry recordings. The results support the feasibility of self-monitoring via easy-to-use and accessible MCGs.

Electrical cardioversion (ECV) is the standard treatment for acute atrial fibrillation (AF), but identification of patients with increased risk of ECV failure or early AF recurrence is of importance for rational clinical decision-making. The objective of this study was to derive and validate a clinical risk stratification tool for identifying patients at high risk for unsuccessful outcome after ECV for acute AF. Data on 2,868 patients undergoing 5,713 ECVs of acute AF in 3 Finnish hospitals from 2003 through 2010 (the FinCV study data) were included in the analysis. Patients from western (n = 3,716 cardioversions) and eastern (n = 1,997 cardioversions) hospital regions were used as derivation and validation datasets. The composite of cardioversion failure and recurrence of AF within 30 days after ECV was recorded. A clinical scoring system was created using logistic regression analyses with a repeated-measures model in the derivation data set. A multivariate analysis for prediction of the composite end point resulted in identification of 5 clinical variables for increased risk: Age (odds ratio [OR] 1.31, confidence interval [CI] 1.13 to 1.52), not the First AF (OR 1.55, CI 1.19 to 2.02), Cardiac failure (OR 1.52, CI 1.08 to 2.13), Vascular disease (OR 1.38, CI 1.11 to 1.71), and Short interval from previous AF episode (within 1 month before ECV, OR 2.31, CI 1.83 to 2.91) [hence, the acronym, AF-CVS]. The c-index for the AF-CVS score was 0.67 (95% CI 0.65 to 0.69) with Hosmer-Lemeshow p value 0.84. With high (>5) scores (i.e., 12% to 16% of the patients), the rate of composite end point was ∼40% in both cohorts, and among low-risk patients (score <3), the composite end point rate was ∼10%. In conclusion, the risk of ECV failure and early recurrence of AF can be predicted with simple patient and disease characteristics.

Timely diagnosis of cardiovascular diseases (CVD) is crucial to prevent morbidity and mortality. Atrial fibrillation (AFib) and heart failure (HF) are two prevalent cardiac disorders that are associated with a high risk of morbidity and mortality, especially if they are concurrently present. Current approaches fail to screen many at-risk individuals who would benefit from preventive treatment; while others receive unnecessary interventions. An effective approach to the detection of CVDs is mechanocardiography (MCG) by which translational and rotational precordial chest movements are monitored. In this study, we collected MCG data from a study sample of 300 hospitalized cardiac patients using multidimensional built-in inertial sensors of a smartphone. Our main objective was to detect concurrent AFib and acute decompensated HF (ADHF) using smartphone MCG (or sMCG). To this end, we adopted a supervised machine learning classification using multi-label and hierarchical classification. Logistic regression, random forest, and extreme gradient boosting were used as candidate classifiers. The results of the analysis showed the area under the receiver operating characteristic curve values of 0.98 and 0.85 for AFib and ADHF, respectively. The highest percentages of positive and negative predictive values for AFib were 91.9 and 100; while for ADHF, they were 56.9 and 88.4 for the multi-label classification and 69.9 and 68.8 for the hierarchical classification, respectively. We conclude that using a single sMCG measurement, AFib can be detected accurately whereas ADHF can be detected with moderate certainty.

Objectives: Elevations of cardiac troponin T (cTnT) levels are common after strenuous exercise. We assessed whether the composition of cTnT release after marathon race differs from that of acute myocardial infarction (MI). Methods: Troponin composition was analysed in plasma samples taken from 45 runners after marathon race and from 84 patients with type 1 MI. The concentration of long cTnT (intact and mildly fragmented cTnT) was measured with a novel upconversion luminescence immunoassay, total cTnT with a commercial high-sensitivity cTnT assay, and the ratio of long to total cTnT (troponin ratio) was determined as a measure of troponin fragmentation. Results: Total cTnT exceeded the upper reference limit (>14 ng/L) in 37 (82%) runners. Troponin ratio was lower in runners ((IQR) 0.17 (0.11–0.24) vs 0.62 (0.29–0.96), p<0.001). With increasing troponin release the troponin ratio decreased (r=−0.497, p<0.001) in marathon runners and the concentration of long cTnT remained in all runners below 8.4 ng/L. In contrast to marathon runners, troponin ratio increased (r=0.565, p<0.001) with the increase of cTnT release in patients with MI. The median total and long cTnT concentrations were lower in marathon runners than in patients with MI (25 ng/L vs 835 ng/L and 4.1 vs 385 ng/L, p<0.001 for both). Conclusion: In contrast to type 1 MI, only a small fraction of circulating cTnT exists as intact cTnT or long molecular forms after strenuous exercise and the difference in troponin composition is more pronounced in runners with higher troponin release. Trial registration number: NCT06000930.