3 天前 - … A few sample frames from the NIR datasets and ECG-Fitness databases used in our
… the evaluation of our data augmentation method for training deep learning models in rPPG …

3 天前 - … In this chapter, we propose a method termed “ECG-DinoV2” that treats ECG plots
as … ECG data, we discard the first 2 s data as some records might been padded with 3 s data …

3 天前 - … ECG and Flex sensors, and then these data are processed. Advanced machine
learning techniques are applied, specifically through a time series neural network model, to …

4 天前 - … (ECG), the accelerator can also use low-power techniques … modules to efficiently
minimize data access and memory … of using different optimization techniques across multiple …

6 天前 - … performance of our method compared to traditional machine learning methods on
two publicly … For the purposes of our research, we focused on the EEG, ECG, and GSR data to …

6 天前 - … data, we demonstrate that the proposed method, EchoCLR, significantly outperforms
existing transfer learning and SSL methods … for downstream echocardiogram video-based …

7 天前 - … For this task we use DL techniques with supervised learning, which uses data with
… RNNs is one of deep learning methods designed to process serial data [9]. RNNs works …

7 天前 - … Our methodology involves training a deep neural … of removed data point from the
machine learning models so … In a nutshell, we aim to train a deep neural classifier on ECG Data…

7 天前 - … methods for echocardiography is the large data variability, … The first is the development
of a method for detecting end-… acquisition in the absence of an electrocardiogram (ECG). …

8 天前 - … learning, substantially reducing the labeling burden of ECG in deep learning. …
For data resampling, each ECG lead in the original data contains 10 s × 500 Hz = 5000 sample …