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Cardiovascular diseases are the leading cause of death globally. Noninvasive, accurate, and continuous cardiovascular monitoring can enable the preemptive detection of heart diseases and timely intervention to prevent serious cardiac complications. However, unobtrusive, ambulatory, and comprehensive cardiac monitoring is still a challenge as conventional electronics are rigid, heavy, or consume too much power for long‐term measurement. This work presents a thin (200 µm), stretchable (20%), lightweight (2.5 g), wireless, and low‐power (<3 mW) cardiac monitoring device that conforms to the human chest like a temporary tattoo sticker, correspondingly known as an e‐tattoo. This chest e‐tattoo features dual‐mode electro‐mechanical sensing—bio‐electric cardiac signals via electrocardiography and mechanical cardiac rhythm via seismocardiography. A unique peripheral synchronization strategy between the two sensors enables the measurement of systolic time intervals like the pre‐ejection period and the left ventricular ejection time with high accuracy (error = −0.44 ± 8.74 ms) while consuming very low power. The e‐tattoo is validated against clinically approved gold‐standard instruments on five human subjects. The good wearability and low power consumption of this e‐tattoo permit 24‐h continuous ambulatory monitoring.

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Objective To design and validate a novel deep generative model for seismocardiogram (SCG) dataset augmentation. SCG is a noninvasively acquired cardiomechanical signal used in a wide range of cardivascular monitoring tasks; however, these approaches are limited due to the scarcity of SCG data. Methods A deep generative model based on transformer neural networks is proposed to enable SCG dataset augmentation with control over features such as aortic opening (AO), aortic closing (AC), and participant-specific morphology. We compared the generated SCG beats to real human beats using various distribution distance metrics, notably Sliced-Wasserstein Distance (SWD). The benefits of dataset augmentation using the proposed model for other machine learning tasks were also explored. Results Experimental results showed smaller distribution distances for all metrics between the synthetically generated set of SCG and a test set of human SCG, compared to distances from an animal dataset (1.14× SWD), Gaussian noise (2.5× SWD), or other comparison sets of data. The input and output features also showed minimal error (95% limits of agreement for pre-ejection period [PEP] and left ventricular ejection time [LVET] timings are 0.03 ± 3.81 ms and −0.28 ± 6.08 ms, respectively). Experimental results for data augmentation for a PEP estimation task showed 3.3% accuracy improvement on an average for every 10% augmentation (ratio of synthetic data to real data). Conclusion The model is thus able to generate physiologically diverse, realistic SCG signals with precise control over AO and AC features. This will uniquely enable dataset augmentation for SCG processing and machine learning to overcome data scarcity.