Smartwatch ECGs showed cardiac patients had higher R-wave variability, longer R-wave duration, and lower T-wave amplitude vs healthy individuals, detectable in 77.5% cases.
Do automatically derived parameters from smartwatch single-lead ECGs differ between healthy individuals and patients with cardiac disease?
Smartwatch-derived single-lead ECGs can automatically extract wave parameters that significantly differ between healthy individuals and cardiac patients, suggesting potential utility for remote monitoring.
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Abstract Background Smart-watch derived single-lead electrocardiograms (ECG) are commonly available and increasingly used to detect arrhythmias. However, differences in those ECGs between healthy persons and cardiac patients have not yet been explored. Purpose Investigate the feasibility and accuracy of automatically derived ECG parameters from smartwatch ECGs. Methods We investigated the P-wave, R-wave, and T-wave in healthy people and patients with cardiac disease. A 30 second ECG was recorded with an Apple Watch (Series 9). The raw ECG data was exported, and the specifics of the single waves were automatically extracted: The NeuroKit algorithm calculated the amplitude, the variability, and the duration of each valid wave; then a template-matching quality control algorithm deleted poor cardiac cycles. Results We analysed a total of 160 patients. The algorithm detected at least 10 viable cardiac complexes in 124 patients (77.5%). Of those 124 patients, 53 were healthy and 79 had cardiac disease (coronary artery disease: 37 46.8%; severe valvular disease: 9 11.7%; heart failure: 39 49.4%; arrhythmias: 13 16.5%; cardiomyopathy: 19 24.1%). Cardiac patients were older (65.7±12.6 vs 53.8±15.8 years, p0.001), less frequently female (17.7% vs 68.2%, p0.001), and had a higher body-mass index (29.1±4.9 vs 26.9±5.9 kg/m², p=0.039). We found no difference for the P-wave (Fig 1). Cardiac patients had no difference in R-wave amplitude, however, they exhibited a higher variability in R-wave amplitude and longer R-wave duration than healthy individuals (Fig 1). T-wave amplitude was reduced in cardiac patients with no difference in T-wave amplitude variability or T-wave duration (Fig 1). As age and body-mass index were different between healthy individuals and cardiac patients, we performed linear regression analyses of these variables with ECG parameters (Fig 2). Age was correlated with lower P-wave amplitude (r²=0.051, p=0.027) and had no influence on R-wave (r²=0.029, p=0.100) or T-wave amplitude (r²=0.007, p=0.430) (Fig 2 A-C). Body-mass index was associated with higher P-wave amplitude (r²=0.129, p=0.001), but had no effect on R-wave (r²=0.009, p=0.395) or T-wave amplitude (r²=0.006, p=0.501) (Fig 2 D-E). Conclusion Our results show that smartwatch-derived single-lead ECGs provide measurements that are correlated with cardiac disease, and which could be used to monitor healthy individuals and changes in cardiac patients.
Maassen et al. (Sat,) reported a other. Smartwatch ECGs showed cardiac patients had higher R-wave variability, longer R-wave duration, and lower T-wave amplitude vs healthy individuals, detectable in 77.5% cases.