在低成本单片机上实现心律失常分类，精度超96%。
Dense Neural Network Based Arrhythmia Classification on Low-cost and Low-compute Micro-controller
- 用两层密集网络在Arduino Nano上部署心律分类模型。
- 模型仅1.267KB，F1分数78.3%，准确率96.38%。
- 适合资源受限的便携式心电监测设备开发者。
心电图（ECG）监测设备价格昂贵，通常在2000至10000美元之间。已有研究在微控制器单元（MCU）上实现ECG系统，可将工业开发成本降低达20倍。但为匹配专业级系统并有效显示心搏，亟需高效的心律失常检测算法。本研究在Arduino Nano（搭载ATMega328，16MHz主频，2KB SRAM，32KB程序内存）上开发了一种密集神经网络，使用AD8232 SparkFun单导联心率监测器作为传感器。模型包含两层（不含输入层），分别有10个和4个神经元，采用Sigmoid激活函数。探索了四种激活函数选择方案。模型大小为1.267KB，对四种心律失常类型分类的宏平均F1得分为78.3%，准确率为96.38%，浮点运算量仅0.001314 MOps。
原文摘要 · Abstract (English)
The electrocardiogram (ECG) monitoring device is an expensive albeit essential device for the treatment and diagnosis of cardiovascular diseases (CVD). The cost of this device typically ranges from $2000 to $10000. Several studies have implemented ECG monitoring systems in micro-controller units (MCU) to reduce industrial development costs by up to 20 times. However, to match industry-grade systems and display heartbeats effectively, it is essential to develop an efficient algorithm for detecting arrhythmia (irregular heartbeat). Hence in this study, a dense neural network is developed to detect arrhythmia on the Arduino Nano. The Nano consists of the ATMega328 microcontroller with a 16MHz clock, 2KB of SRAM, and 32KB of program memory. Additionally, the AD8232 SparkFun Single-Lead Heart Rate Monitor is used as the ECG sensor. The implemented neural network model consists of two layers (excluding the input) with 10 and four neurons respectively with sigmoid activation function. However, four approaches are explored to choose the appropriate activation functions. The model has a size of 1.267 KB, achieves an F1 score (macro-average) of 78.3\% for classifying four types of arrhythmia, an accuracy rate of 96.38%, and requires 0.001314 MOps of floating-point operations (FLOPs).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。