Heart disease is a malignant threat to human health. Electrocardiogram (ECG) tests are used to help diagnose heart disease by recording the heart’s activity. However, automated medical-aided diagnosis with computers usually requires a large volume of labeled clinical data without patients' privacy to train the model, which is an empirical problem that still needs to be solved. To address this problem, we propose a generative adversarial network (GAN), which is composed of a bidirectional long short-term memory(LSTM) and convolutional neural network(CNN), referred as BiLSTM-CNN,to generate synthetic ECG data that agree with existing clinical data so that the features of patients with heart disease can be retained. The model includes a generator and a discriminator, where the generator employs the two layers of the BiLSTM networks and the discriminator is based on convolutional neural networks. The 48 ECG records from individuals of the MIT-BIH database were used to train the model. We compared the performance of our model with two other generative models, the recurrent neural network autoencoder(RNN-AE) and the recurrent neural network variational autoencoder (RNN-VAE). The results showed that the loss function of our model converged to zero the fastest. We also evaluated the loss of the discriminator of GANs with different combinations of generator and discriminator. The results indicated that BiLSTM-CNN GAN could generate ECG data with high morphological similarity to real ECG recordings.
Last. Yoshua Bengio(UdeM: Université de Montréal)H-Index: 192
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A field that has directly benefited from the recent advances in deep learning is automatic speech recognition (ASR). Despite the great achievements of the past decades, however, a natural and robust human–machine speech interaction still appears to be out of reach, especially in challenging environments characterized by significant noise and reverberation. To improve robustness, modern speech recognizers often employ acoustic models based on recurrent neural networks (RNNs) that are naturally ab...
Oct 1, 2017 in ICCV (International Conference on Computer Vision)
#1Jun-Yan Zhu(UC: University of California)H-Index: 42
#2Taesung Park(University of California, Berkeley)H-Index: 10
Last. Alexei A. Efros(University of California, Berkeley)H-Index: 89
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Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples. Our goal is to learn a mapping G : X → Y such that the distribution of images from G(X) is indist...
#2Worku J. Sori(HIT: Harbin Institute of Technology)H-Index: 3
Last. Shaohui Liu(HIT: Harbin Institute of Technology)H-Index: 22
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This paper introduces an OSA detection method based on Recurrent Neural network. At the first step, RR interval (time interval from one R wave to the next R wave) is employed to extract the signals from Apnea- Electrocardiogram (ECG) where all extracted features are then used as an input for the designed deep model. Then an architecture having four recurrent layers and batch normalization layers are designed and trained with the extracted features for OSA detection. Apnea-ECG datasets from physi...
Generative Adversarial Networks (GANs) have shown great promise recently in image generation. Training GANs for language generation has proven to be more difficult, because of the non-differentiable nature of generating text with recurrent neural networks. Consequently, past work has either resorted to pre-training with maximum-likelihood or used convolutional networks for generation. In this work, we show that recurrent neural networks can be trained to generate text with GANs from scratch usin...
Last. Dan Jurafsky(Stanford University)H-Index: 100
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We apply adversarial training to open-domain dialogue generation, training a system to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the task as a reinforcement learning problem where we jointly train two systems: a generative model to produce response sequences, and a discriminator—analagous to the human evaluator in the Turing test— to distinguish between the human-generated dialogues and the machine-generated ones. In this generative adversaria...
Recurrent neural networks (RNNs) stand at the forefront of many recent developments in deep learning. Yet a major difficulty with these models is their tendency to overfit, with dropout shown to fail when applied to recurrent layers. Recent results at the intersection of Bayesian modelling and deep learning offer a Bayesian interpretation of common deep learning techniques such as dropout. This grounding of dropout in approximate Bayesian inference suggests an extension of the theoretical result...
Generative adversarial networks have been proposed as a way of efficiently training deep generative neural networks. We propose a generative adversarial model that works on continuous sequential data, and apply it by training it on a collection of classical music. We conclude that it generates music that sounds better and better as the model is trained, report statistics on generated music, and let the reader judge the quality by downloading the generated songs.
Nov 4, 2016 in ICLR (International Conference on Learning Representations)
#1Soroush Mehri(UdeM: Université de Montréal)H-Index: 5
#2Kundan Kumar(UdeM: Université de Montréal)H-Index: 7
Last. Yoshua Bengio(UdeM: Université de Montréal)H-Index: 192
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In this paper we propose a novel model for unconditional audio generation task that generates one audio sample at a time. We show that our model which profits from combining memory-less modules, namely autoregressive multilayer perceptron, and stateful recurrent neural networks in a hierarchical structure is de facto powerful to capture the underlying sources of variations in temporal domain for very long time on three datasets of different nature. Human evaluation on the generated samples indic...
Recurrent neural network (RNN) has been broadly applied to natural language process (NLP) problems. This kind of neural network is designed for modeling sequential data and has been testified to be quite efficient in sequential tagging tasks. In this paper, we propose to use bi-directional RNN with long short-term memory (LSTM) units for Chinese word segmentation, which is a crucial task for modeling Chinese sentences and articles. Classical methods focus on designing and combining hand-craft fe...
Last. Raf Dewil(Katholieke Universiteit Leuven)H-Index: 37
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Abstract null null Biochar has recently proven to be effective in various applications including soil amendment for crop production and removal of pollutants from the contaminated water and soil environments. However, major studies on the alterations in bulk density, porosity, water holding capacity, acidity and mineral content of the soils due to the addition of biochar have been carried out in lab-scales. Innumerable studies have investigated the properties of the biochar prepared from various...
The detection of breast cancer using medical images popularly known as histopathology images often leads to a confusion and disagreement among pathologist. The most invasive cancer in females: breast cancer is affecting 12% for population worldwide. Breast cancer has no physical symptoms unless there is sign of painless lump. Breast cancer pathology images are classified into: normal, benign and malignant. Breast cancer cell has sub-classes interrelated to cells’ variability, organization and de...
Abstract null null In arid areas, where water resources are extremely scarce, the consumption of agricultural water resources is generally higher than 80% of the total water resources. Reasonable irrigation practices and effective water conservation methods are critical to the efficient utilization of water resources. In order to analyze the rationality of existing irrigation mode and evaluate the effect of plastic mulch on irrigation water migration, this research monitored the hydrogen and oxy...
Last. Zongmin Wang(Zhengzhou University)H-Index: 8
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Abstract null null An electrocardiogram (ECG) consists of complex segments, such as P-QRS-T waves. Manual ECG annotation is challenging and time-consuming, even for specialist physicians. The shortage of labelled ECG data is one of the essential factors that affect ECG intelligent analysis's long-term development. This study proposes an intelligent ECG-assisted annotation system, that not only supplements labelled data, but also significantly reduces the workload compared with manual annotation....
Benign prostatic hyperplasia (BPH) is a common disease which causes various health problems for elderly men such as urinary retention, recurring urinary tract infection and bladder stones. The aim of this study is to evaluate the therapeutic effects of Juniperus communis L. seed extract (JCS) on BPH in male Wistar rats. To this end, 30 rats were divided into 5 groups (N = 6): group 1 (vehicle), group 2 (disease control), group 3 (standard medicine; 10 mg/kg finasteride), and groups 4 and 5 were ...
Last. Vikas Kumar(UIdaho: University of Idaho)H-Index: 2
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Abstract null null The additive effects of insect meal supplementation to diets containing a high soy protein inclusion were evaluated for growth performance, antioxidant potential and fillet nutritional quality of juvenile rainbow trout (Oncorhynchus mykiss). A basal diet (Soy) with a high-level soy protein (21% soybean meal, SBM plus 4–5.5% soy protein concentrate, SPC) was compared to similarly formulated soy-based diets supplemented with low (8%) and high levels (16%) of insect meal for diet...
Abstract null null This paper proposes a two-dimensional (2D) bidirectional long short-term memory generative adversarial network (GAN) to produce synthetic standard 12-lead ECGs corresponding to four types of signals—left ventricular hypertrophy (LVH), left branch bundle block (LBBB), acute myocardial infarction (ACUTMI), and Normal. It uses a fully automatic end-to-end process to generate and verify the synthetic ECGs that does not require any visual inspection. The proposed model is able to p...
Last. Wei Sun(The Wallace H. Coulter Department of Biomedical Engineering)H-Index: 88
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Abstract null null To noninvasively assess the risk of aneurysm rupture and dissection, an accurate material failure metric of the aortic wall is crucial. Previous studies used deterministic or isotropic failure metrics for the aortic wall. However, experimental studies have shown that aortic wall tensile strengths in circumferential and axial directions are significantly different (i.e., anisotropic) and vary greatly among patients. In this study, we developed a new probabilistic and anisotropi...
