Deep learning-based ECG classification using a TensorFlow Lite model
| dc.contributor.author | Sharma, Kushagra | |
| dc.contributor.examiningcommittee | Sherif, Sherif (Electrical and Computer Engineering) | en_US |
| dc.contributor.examiningcommittee | Akcora, Cuneyt (Computer Science) | en_US |
| dc.contributor.supervisor | Eskicioglu, Rasit | |
| dc.date.accessioned | 2023-01-20T22:30:59Z | |
| dc.date.available | 2023-01-20T22:30:59Z | |
| dc.date.copyright | 2022-12-20 | |
| dc.date.issued | 2022-12-20 | |
| dc.date.submitted | 2022-12-21T02:25:05Z | en_US |
| dc.degree.discipline | Biomedical Engineering | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | The number of IoT devices in healthcare is expected to rise sharply due to significantly 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. In this work, we have developed machine learning models to be deployed on Raspberry Pi. We present an evaluation of our TensorFlow Model with various classification classes. We also present the evaluation of the corresponding TensorFlow Lite FlatBuffers to demonstrate their minimal run-time requirements while maintaining acceptable accuracy. Additionally, to address the problem of sensor and data integration when using multiple devices, we propose a unified server on our Edge Node. | en_US |
| dc.description.note | February 2023 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/37155 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | ECG | en_US |
| dc.subject | Deep Learning | en_US |
| dc.subject | Artificial Intelligence | en_US |
| dc.subject | Classification | en_US |
| dc.title | Deep learning-based ECG classification using a TensorFlow Lite model | en_US |
| dc.type | master thesis | en_US |
| local.subject.manitoba | no | en_US |