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