# ECG Pulse Encoding: Three Bio-inspired Encoders for Converting Electrocardiograms to Spiking Neural Network Inputs

> An open-source project that converts traditional ECG signals into spike sequences using three encoding methods—Bens Spiker Algorithm, Delta Modulation, and Level Crossing—to provide efficient neuromorphic inputs for Spiking Neural Networks (SNNs).

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-14T07:43:20.000Z
- 最近活动: 2026-06-14T07:51:11.270Z
- 热度: 150.9
- 关键词: 脉冲神经网络, ECG, 神经形态计算, 信号编码, 医疗AI, MIT-BIH, 心律失常检测, 边缘计算
- 页面链接: https://www.zingnex.cn/en/forum/thread/ecg-3ac5c969
- Canonical: https://www.zingnex.cn/forum/thread/ecg-3ac5c969
- Markdown 来源: floors_fallback

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## Introduction to the ECG Pulse Encoding Open-Source Project: Three Bio-inspired Encoders Facilitate SNN Input Conversion

This article introduces an open-source project (ECG-SPIKE-ENCODING) that converts traditional ECG signals into spike sequences using three encoding methods—Bens Spiker Algorithm, Delta Modulation, and Level Crossing—to provide efficient neuromorphic inputs for Spiking Neural Networks (SNNs). The core value of the project lies in solving the SNN input adaptation problem and promoting the deployment of medical AI on edge devices. The source is GitHub (author: Deshwan25boe10077, release date: June 14, 2026).

## Background and Necessity of Pulse Encoding

Traditional ECG analysis relies on deep learning models such as CNN/RNN, which have limitations like high computational intensity, high power consumption, and large latency, making them unsuitable for edge devices. As the third generation of neural networks, SNNs have advantages of event-driven operation, ultra-low power consumption, biological plausibility, and hardware friendliness (compatible with chips like Intel Loihi). However, they require input in the form of spikes, so encoders are needed to convert continuous ECG signals into spike sequences.

## Detailed Explanation of Three Bio-inspired Encoding Algorithms

The project implements three encoding strategies:
1. Bens Spiker Algorithm (BSA): FIR filtering + threshold comparison, outputs binary spikes, accurately preserves the peak time of R waves, suitable for rate-coded SNNs;
2. Delta Modulation: Tracks the difference between adjacent sampling points, outputs ternary spikes (+1/-1/0), captures waveform shape changes, suitable for temporal SNNs;
3. Level Crossing Encoding: Divides the signal into fixed frequency bands, emits spikes when crossing bands, outputs a sparse event stream, has optimal hardware efficiency, suitable for neuromorphic chips.

## Data Flow and Project Structure

The input data uses record 210 from the MIT-BIH Arrhythmia Database (360Hz sampling rate, approximately 30 minutes, MLII/V1 channels), loaded via the wfdb library. The processing flow is: Raw ECG signal → Pulse Encoder → SNN Input. The project structure includes core encoder scripts (ecg_spike_encoder.py), data conversion scripts (convert_to_csv.py), sample data, and an output directory (storing CSV files of spike sequences from the three encoders).

## Comparison of the Three Encoders' Characteristics

| Feature | BSA | Delta Modulation | Level Crossing |
|---|---|---|---|
| Output Type | Binary (0/1) | Ternary (+1/-1/0) | Sparse events |
| Core Mechanism | FIR filtering + threshold | Temporal difference | Amplitude band crossing |
| Spike Density | Medium | High | Most sparse |
| Hardware Efficiency | Good | Good | Optimal |
| Application Scenario | Rate-coded SNNs | Temporal SNNs | Neuromorphic chips |

## Future Development Directions

The project's planned expansion roadmap includes: 1. Training SNN classifiers using snnTorch; 2. Benchmarking the accuracy of the three encoders in arrhythmia classification tasks; 3. Extending to all 48 records in the MIT-BIH database; 4. Deploying on neuromorphic hardware such as Intel Loihi; 5. Comparing performance with CNN-based ECG classifiers.

## Project Summary and Target Audience

This project combines traditional signal processing with neuromorphic computing to solve the SNN input bottleneck and provides three trade-off solutions. The target audience includes neuromorphic computing researchers, medical AI engineers, edge computing developers, and students in biological signal processing. As neuromorphic hardware matures, such encoding technologies will play an important role in scenarios like wearable medical devices and implantable sensors.