Danphe Bio-Signal API

AI-powered ECG, PPG & EDA Analysis API for Developers

12 followers

AI-powered ECG, PPG & EDA Analysis API for Developers

12 followers

Danphe Bio-Signal API is an AI-powered REST API for analyzing ECG, PPG, and EDA signals in real time. Build digital health applications faster with developer-friendly APIs, interactive Playground, comprehensive documentation, and a free research plan. Generate an API key in minutes and start integrating today.

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Launch tags:Health & Fitness•API•Health

Launch Team

Framer AI AgentsDesign and publish professional sites with AI

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Maker

📌

Hi Product Hunt! 👋 I'm Nischal, founder of Danphe Intelligence Lab. Today we're excited to launch the Danphe Bio-Signal API. Our goal is simple: make advanced biosignal analysis accessible through an easy-to-use REST API. Whether you're building digital health apps, wearable platforms, AI healthcare solutions, or conducting research, you can start analyzing ECG, PPG, and EDA signals in minutes. 🚀 Free Research Plan available 🔑 Instant API Keys 🧪 Interactive Playground 📚 Complete Documentation We're actively looking for feedback from the Product Hunt community. Please let us know what you'd improve, what features you'd like to see, or where we can make the developer experience better. Thanks for checking us out! ❤️

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2mo ago

How does the API handle noisy or low-quality signals in real time, especially for PPG data from consumer wearables? Curious whether the model degrades gracefully or requires specific preprocessing on our end.

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2mo ago

Maker

@evval588200 Thanks so much for the great question, Şevval! The API performs basic validation and quality checks on uploaded signals before analysis. For PPG from consumer wearables, we recommend standard preprocessing (such as motion artifact reduction and filtering) to achieve the best results. If signal quality is low, the API is designed to return informative feedback rather than silently producing unreliable outputs. Improving robustness to noisy real-world wearable data is an active area of development, and your feedback is very valuable.

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2mo ago

How accurate is the signal analysis compared to clinically validated tools, especially for noisy real-world wearable data?

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2mo ago

Maker

@azizkayoluhx6g thank you so much aziz for your question. on clinical ECG data (PTB-XL) we hit 0.92 AUROC with a calibrated confidence score, so it's not just a raw guess — it comes with real statistical backing. But honestly, the more interesting answer is for noisy wearable data specifically — that's not something we try to "fix" with a better filter, it's built into the design. The model scores signal quality + how out-of-distribution the input looks, and if either is bad, it just says "not confident enough" instead of confidently guessing wrong. We tested this by throwing motion-corrupted and out-of-distribution signals at it — abstention rate shoots up on the bad data, while accuracy on what it does answer stays solid. So the pitch isn't "100% accurate always" — it's "trustworthy about when it doesn't know," which honestly matters more for wearables than another accuracy number 🙂 Happy to share the numbers if you're curious!

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1mo ago

Took it for a quick spin in the Playground and the ECG endpoint returned heart rate variability metrics in under a second. Docs are clear enough that I had it running locally without much back-and-forth.

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2mo ago

Maker

@erifehalil29098 Thank you, Şerife! I'm really happy to hear you were able to get started quickly and that the documentation helped you integrate without much friction. Developer experience has been one of our biggest priorities from day one. We'll continue improving the API and docs based on community feedback. Thanks again for taking the time to try it!

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2mo ago

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