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