Name
Abstract | ||
|---|---|---|
In this paper, we propose a lightweight neural network for real-time electrocardiogram (ECG) anomaly detection and system level power reduction of wearable Internet of Things (IoT) Edge sensors. The proposed network utilizes a novel hybrid architecture consisting of Long Short Term Memory (LSTM) cells and Multi-Layer Perceptrons (MLP). The LSTM block takes a sequence of coefficients representing t... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TBCAS.2021.3137646 | IEEE Transactions on Biomedical Circuits and Systems |
Keywords | DocType | Volume |
Electrocardiography,Feature extraction,Wireless sensor networks,Training,Databases,Wireless communication,Power demand | Journal | 16 |
Issue | ISSN | Citations |
1 | 1932-4545 | 2 |
PageRank | References | Authors |
0.40 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gawsalyan Sivapalan | 1 | 2 | 0.40 |
| Koushik Nundy | 2 | 2 | 0.40 |
| Soumyabrata Dev | 3 | 62 | 13.94 |
| Barry Cardiff | 4 | 9 | 5.28 |
| Chacko John Deepu | 5 | 44 | 5.81 |