Deep Learning-based ECG Classification on Raspberry PI using a Tensorflow Lite Model based on PTB-XL Dataset

TL;DR

This paper demonstrates the deployment of a deep learning ECG classification model on Raspberry Pi using TensorFlow Lite, leveraging the PTB-XL dataset to address resource constraints in IoT healthcare devices.

Contribution

It introduces a TensorFlow Lite model for ECG classification on embedded systems, optimizing for minimal runtime while maintaining accuracy, based on the PTB-XL dataset.

Findings

TensorFlow Lite model runs efficiently on Raspberry Pi.

Model achieves acceptable accuracy with minimal runtime.

Effective deployment of ECG classification on resource-constrained devices.

Abstract

The number of IoT devices in healthcare is expected to rise sharply due to increased demand since the COVID-19 pandemic. Deep learning and IoT devices are being employed to monitor body vitals and automate anomaly detection in clinical and non-clinical settings. Most of the current technology requires the transmission of raw data to a remote server, which is not efficient for resource-constrained IoT devices and embedded systems. Additionally, it is challenging to develop a machine learning model for ECG classification due to the lack of an extensive open public database. To an extent, to overcome this challenge PTB-XL dataset has been used. In this work, we have developed machine learning models to be deployed on Raspberry Pi. We present an evaluation of our TensorFlow Model with two classification classes. We also present the evaluation of the corresponding TensorFlow Lite FlatBuffers to demonstrate their minimal run-time requirements while maintaining acceptable accuracy.