ArticleFrontiers in digital health2025
MYSTETH-home-based heart monitoring.
Article in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Wearable Flexible Sensors for Cardiovascular Disease Monitoring.Advanced materials (Deerfield Beach, Fla.) · 2026Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The MySteth is an intelligent medical tool designed for cardiac disease screening, utilizing either a stethoscope or smartphone to record heart sounds. Normal heart sounds in healthy individuals consist of "lub" and "dub" noises, while murmurs-additional sounds during heartbeats-can indicate cardiac anomalies such as valve dysfunctions and rapid blood flow, categorized as systolic or diastolic. Method: MySteth was developed and tested using heart sounds recorded via smartphone and digital stethoscope. For ensuring the clinical validity of the data, all heart sound samples were meticulously annotated by human experts-super-specialized cardiologists with extensive experience in cardiac diagnostics. To achieve high classification accuracy, MySteth employs a hybrid CNN-LSTM model combined with Linear Predictive Coding (LPC) for preprocessing. The study involves classifying recorded heart sounds into normal heartbeats and murmurs, with murmurs further divided into systolic and diastolic categories. Results: The tool demonstrated an accuracy of 92% in distinguishing normal heartbeats from murmurs, 91% in classifying murmurs into systolic and diastolic types, and 90% in further categorizing systolic murmurs into Ejection Systolic Murmurs (ESM) and Pansystolic Murmurs (PSM). MySteth is accessible and affordable, requiring minimal equipment, as most individuals already own a smartphone, and digital stethoscopes are commonly available. This ease of use facilitates both professional and home-based heart monitoring, especially beneficial in remote areas with limited healthcare access. Discussion: MySteth is an at-home heart diagnostic tool that leverages deep learning to classify heart sounds into normal, ESM, PSM, and diastolic murmurs. Its user-friendly design and minimal hardware requirements ensure broad adoption across various healthcare settings, facilitating timely and accurate preliminary heart investigations. This capability is crucial in combating the global burden of cardiovascular diseases. MySteth's scalability and ease of deployment underscore its potential in early cardiovascular disease diagnosis, particularly in underserved regions, thereby promoting preventive healthcare.
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Registered trials
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