{"slug": "real-time-arrhythmia-detection-at-the-edge-deploying-tinyml-on-esp32-for-raw-ecg", "title": "Real-Time Arrhythmia Detection at the Edge: Deploying TinyML on ESP32 for Raw ECG Analysis", "summary": "A developer built a real-time arrhythmia detector using TinyML on an ESP32 microcontroller, processing raw ECG data locally with TensorFlow Lite for Microcontrollers. The system uses a quantized 1D-CNN model trained on the MIT-BIH Arrhythmia Database to classify heart rhythms, enabling low-latency, privacy-preserving health monitoring on wearable devices.", "body_md": "In the world of wearable health technology, the holy grail has always been moving intelligence from the cloud to the edge. Waiting for a cloud server to analyze your heart rhythm is not just a latency issue—it's a privacy and battery life concern. Today, we are diving deep into **TinyML**, **Edge AI**, and **ECG signal processing** to build a real-time abnormality detector.\n\nBy leveraging **TensorFlow Lite for Microcontrollers** and the versatile **ESP32**, we can process raw electrocardiogram (ECG) data locally. This approach ensures low-latency detection of arrhythmias while keeping sensitive medical data on-device. If you've been looking to bridge the gap between high-level deep learning and low-level embedded systems, you're in the right place!\n\nThe pipeline involves capturing a high-frequency analog signal, cleaning it, and feeding it into a quantized Convolutional Neural Network (CNN). Here is how the data flows through our ESP32:\n\n``` php\ngraph TD\n    A[Raw ECG Signal/Sensor] -->|ADC Sampling| B(Preprocessing: Bandpass Filter)\n    B --> C{Buffer Management}\n    C -->|Windowed Segment| D[TFLite Micro Inference Engine]\n    D --> E{CNN Model Classification}\n    E -->|Normal| F[Log: Sinus Rhythm]\n    E -->|Abnormal| G[Trigger Alert: Arrhythmia]\n    G -->|Bluetooth/Wi-Fi| H[Mobile Dashboard]\n```\n\nTo follow this advanced guide, you'll need:\n\nBefore we touch the C++ code, we need a model. Typically, we use the MIT-BIH Arrhythmia Database to train a 1D-CNN. The crucial step is **Post-Training Quantization**.\n\nSince the ESP32 doesn't have a dedicated NPU, we convert our 32-bit float model into an **8-bit integer (INT8)** model. This reduces the size by 4x and speeds up inference significantly without a massive drop in accuracy.\n\nWe need to load the model as a byte array and set up the TFLite interpreter. Here is a simplified implementation of the inference loop.\n\n```\n#include <TensorFlowLite.h>\n#include \"model_data.h\" // Your exported INT8 model\n#include \"tensorflow/lite/micro/all_ops_resolver.h\"\n#include \"tensorflow/lite/micro/micro_interpreter.h\"\n\n// Constants for the ECG window\nconst int kTensorArenaSize = 30 * 1024; // 30KB Arena\nuint8_t tensor_arena[kTensorArenaSize];\n\n// TFLite globals\ntflite::MicroInterpreter* interpreter = nullptr;\nTfLiteTensor* input = nullptr;\n\nvoid setup() {\n    Serial.begin(115200);\n\n    // 1. Load the model\n    static tflite::MicroMutableOpResolver<5> resolver;\n    resolver.AddConv2D();\n    resolver.AddMaxPool2D();\n    resolver.AddFullyConnected();\n    resolver.AddSoftmax();\n    resolver.AddReshape();\n\n    static tflite::MicroInterpreter static_interpreter(\n        tflite::GetModel(g_model_data), resolver, tensor_arena, kTensorArenaSize);\n    interpreter = &static_interpreter;\n    interpreter->AllocateTensors();\n\n    input = interpreter->input(0);\n}\n\nvoid loop() {\n    // 2. Simulate/Read ECG Data (Sampled at 125Hz or 250Hz)\n    float sample = analogRead(34); \n    // ... [Preprocessing Logic: Normalization & Filtering] ...\n\n    // 3. Fill the Input Tensor\n    for (int i = 0; i < input->dims->data[1]; i++) {\n        input->data.f[i] = processed_samples[i]; \n    }\n\n    // 4. Run Inference\n    TfLiteStatus invoke_status = interpreter->Invoke();\n    if (invoke_status != kTfLiteOk) {\n        Serial.println(\"Inference failed!\");\n        return;\n    }\n\n    // 5. Analyze Results\n    float normal_score = interpreter->output(0)->data.f[0];\n    float abnormal_score = interpreter->output(0)->data.f[1];\n\n    if (abnormal_score > 0.8) {\n        Serial.println(\"⚠️ Warning: Abnormal Heart Rhythm Detected!\");\n    }\n}\n```\n\nWhen moving from a prototype to a production-grade wearable, you'll encounter challenges like signal noise from muscle movement (EMG) and power consumption. For those looking to implement robust noise-cancellation algorithms or more efficient memory management in TinyML deployments, I highly recommend checking out more production-ready examples.\n\nPro-Tip: For a deep dive into advanced signal processing patterns and optimizing TensorFlow Lite for mission-critical edge applications, visit the[WellAlly Tech Blog]. They provide excellent resources on scaling these localized AI models to enterprise-grade health solutions.\n\nRaw ECG data is messy. You'll need a digital Bandpass Filter (usually 0.5Hz to 40Hz) to remove baseline wander and high-frequency noise. In C++, this can be implemented as a simple IIR filter to keep the computational overhead low on the ESP32.\n\n```\n// Example: Simple Low-pass filter component\nfloat lowPass(float input, float prevOutput, float alpha) {\n    return prevOutput + alpha * (input - prevOutput);\n}\n```\n\nDeploying a CNN on an ESP32 for real-time ECG analysis isn't just a \"cool project\"—it's a glimpse into the future of decentralized healthcare. By processing data locally, we respect user privacy and reduce the load on our infrastructure.\n\n**What's next for your TinyML journey?**\n\nHappy hacking! 🚀💻\n\n*If you enjoyed this tutorial, don't forget to ❤️ and bookmark it! For more advanced Edge AI content, keep an eye on my profile or visit the official WellAlly Blog.*", "url": "https://wpnews.pro/news/real-time-arrhythmia-detection-at-the-edge-deploying-tinyml-on-esp32-for-raw-ecg", "canonical_source": "https://dev.to/beck_moulton/real-time-arrhythmia-detection-at-the-edge-deploying-tinyml-on-esp32-for-raw-ecg-analysis-6ae", "published_at": "2026-06-29 00:24:00+00:00", "updated_at": "2026-06-29 00:57:22.251546+00:00", "lang": "en", "topics": ["machine-learning"], "entities": ["ESP32", "TensorFlow Lite for Microcontrollers", "MIT-BIH Arrhythmia Database", "Convolutional Neural Network"], "alternates": {"html": "https://wpnews.pro/news/real-time-arrhythmia-detection-at-the-edge-deploying-tinyml-on-esp32-for-raw-ecg", "markdown": "https://wpnews.pro/news/real-time-arrhythmia-detection-at-the-edge-deploying-tinyml-on-esp32-for-raw-ecg.md", "text": "https://wpnews.pro/news/real-time-arrhythmia-detection-at-the-edge-deploying-tinyml-on-esp32-for-raw-ecg.txt", "jsonld": "https://wpnews.pro/news/real-time-arrhythmia-detection-at-the-edge-deploying-tinyml-on-esp32-for-raw-ecg.jsonld"}}