Name
Title | ||
|---|---|---|
ECG-based real-time arrhythmia monitoring using quantized deep neural networks: A feasibility study |
Abstract | ||
|---|---|---|
Continuous ambulatory cardiac monitoring plays a critical role in early detection of abnormality in at-risk patients, thereby increasing the chance of early intervention. In this study, we present an automated ECG classification approach for distinguishing between healthy heartbeats and pathological rhythms. The proposed lightweight solution uses quantized one-dimensional deep convolutional neural networks and is ideal for realtime continuous monitoring of cardiac rhythm, capable of providing one output prediction per second. Raw ECG data is used as the input to the classifier, eliminating the need for complex data preprocessing on lowpowered wearable devices. In contrast to many compute-intensive approaches, the data analysis can be carried out locally on edge devices, providing privacy and portability. The proposed lightweight solution is accurate (sensitivity of 98.5% and specificity of 99.8%), and implemented on a smartphone, it is energy-efficient and fast, requiring 5.85 mJ and 7.65 ms per prediction, respectively. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.compbiomed.2022.105249 | COMPUTERS IN BIOLOGY AND MEDICINE |
Keywords | DocType | Volume |
Arrhythmia, ECG, Deep learning, Continuous monitoring, Heart disease | Journal | 143 |
ISSN | Citations | PageRank |
0010-4825 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Henrique De Melo Ribeiro | 1 | 0 | 0.34 |
| Ahran Arnold | 2 | 0 | 0.34 |
| James P Howard | 3 | 0 | 0.34 |
| Matthew J Shun-Shin | 4 | 0 | 0.34 |
| Ying Zhang | 5 | 163 | 25.25 |
| Darrel P Francis | 6 | 0 | 0.34 |
| Phang B Lim | 7 | 0 | 0.34 |
| Zachary Whinnett | 8 | 0 | 0.34 |
| Massoud Zolgharni | 9 | 0 | 0.68 |