scholarly journals Complex Deep Learning Models for Denoising of Human Heart ECG signals

2020 ◽  
Author(s):  
Corneliu Arsene
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Short Term Memory ◽  
Wearable Sensors ◽  
Real Life ◽  
Ecg Signal ◽  
Great Success ◽  
Processing Unit ◽  
Ecg Signals ◽  
Real Time Applications

Effective and powerful methods for denoising real electrocardiogram (ECG) signals are important for wearable sensors and devices. Deep Learning (DL) models have been used extensively in image processing and other domains with great success but only very recently have been used in processing ECG signals. This paper presents several DL models namely Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Restricted Boltzmann Machine (RBM) together with the more conventional filtering methods (low pass filtering, high pass filtering, Notch filtering) and the standard wavelet-based technique for denoising EEG signals. These methods are trained, tested and evaluated on different synthetic and real ECG datasets taken from the MIT PhysioNet database and for different simulation conditions (i.e. various lengths of the ECG signals, single or multiple records). The results show the CNN model is a performant model that can be used for off-line denoising ECG applications where it is satisfactory to train on a clean part of an ECG signal from an ECG record, and then to test on the same ECG signal, which would have some high level of noise added to it. However, for real-time applications or near-real time applications, this task becomes more cumbersome, as the clean part of an ECG signal is very probable to be very limited in size. Therefore the solution put forth in this work is to train a CNN model on 1 second ECG noisy artificial multiple heartbeat data (i.e. ECG at effort), which was generated in a first instance based on few sequences of real signal heartbeat ECG data (i.e. ECG at rest). Afterwards it would be possible to use the trained CNN model in real life situations to denoise the ECG signal. This corresponds also to reality, where usually the human is put at rest and the ECG is recorded and then the same human is asked to do some physical exercises and the ECG is recorded at effort. The quality of results is assessed visually but also by using the Root Mean Squared (RMS) and the Signal to Noise Ratio (SNR) measures. All CNN models used an NVIDIA TITAN V Graphical Processing Unit (GPU) with 12 GB RAM, which reduces drastically the computational times. Finally, as an element of novelty, the paper presents also a Design of Experiment (DoE) study which intends to determine the optimal structure of a CNN model, which type of study has not been seen in the literature before.

scholarly journals Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier

Algorithms ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 118 ◽  
Author(s):  
Annisa Darmawahyuni ◽  
Siti Nurmaini ◽  
Sukemi ◽  
Wahyu Caesarendra ◽  
Vicko Bhayyu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Short Term Memory ◽  
Imbalanced Data ◽  
Recurrent Network ◽  
Data Partitioning ◽  
Ecg Signal ◽  
Sequential Data ◽  
Ecg Signals ◽  
Sequence Modeling

The interpretation of Myocardial Infarction (MI) via electrocardiogram (ECG) signal is a challenging task. ECG signals’ morphological view show significant variation in different patients under different physical conditions. Several learning algorithms have been studied to interpret MI. However, the drawback of machine learning is the use of heuristic features with shallow feature learning architectures. To overcome this problem, a deep learning approach is used for learning features automatically, without conventional handcrafted features. This paper presents sequence modeling based on deep learning with recurrent network for ECG-rhythm signal classification. The recurrent network architecture such as a Recurrent Neural Network (RNN) is proposed to automatically interpret MI via ECG signal. The performance of the proposed method is compared to the other recurrent network classifiers such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The objective is to obtain the best sequence model for ECG signal processing. This paper also aims to study a proper data partitioning ratio for the training and testing sets of imbalanced data. The large imbalanced data are obtained from MI and healthy control of PhysioNet: The PTB Diagnostic ECG Database 15-lead ECG signals. According to the comparison result, the LSTM architecture shows better performance than standard RNN and GRU architecture with identical hyper-parameters. The LSTM architecture also shows better classification compared to standard recurrent networks and GRU with sensitivity, specificity, precision, F1-score, BACC, and MCC is 98.49%, 97.97%, 95.67%, 96.32%, 97.56%, and 95.32%, respectively. Apparently, deep learning with the LSTM technique is a potential method for classifying sequential data that implements time steps in the ECG signal.

scholarly journals Deep Convolutional Bidirectional LSTM Model for identifying Ventricular Tachyarrhythmia using ECG Signal Variability

2021 ◽  
Author(s):  
MANOJ KUMAR OJHA ◽  
Sulochna Wadhwani ◽  
Arun Kumar Wadhwani ◽  
Anupam Shukla
Keyword(s):  
Deep Learning ◽  
Electrical Activity ◽  
Short Term Memory ◽  
Early Stage ◽  
Ventricular Tachyarrhythmia ◽  
Diagnostic System ◽  
Learning Model ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Deep Learning Model

Abstract An electrocardiogram (ECG) signal is used widely to detect ventricular tachyarrhythmia (VTA) and to diagnose heart disease. Deep learning models and large ECG data have made the diagnosis of VTA an attractive task to demonstrate the power of artificial intelligence in clinical applications. One of the life-threatening complications of VTA is cardiac arrest (CA). The VTA is divided into two categories: ventricular fibrillation (VF) and ventricular tachycardia (VT). Abnormal electrical activity in the ventricle causes VT, which leads to CA, whereas the chaotic electrical activity in the ventricle leads to VF. To improve the clinical diagnostic system and to help cardiologists, it is essential to identify the risk of VTA at an early stage. The goal of this paper is to develop an end-to-end (E2E) deep learning model that uses a convolution neural network (CNN) and a bidirectional long-short term memory network (BiLSTM) to classify VT and VF arrhythmias from multiple ECG databases. The CNN extracts features from ECG signals, and BiLSTM learns information. The ECG signals are acquired from the MIT-BIH malignant ventricular arrhythmia database (VFDB) and the Creighton University VTA database (CUDB). These ECG signals indicate that heart rate variability is a fast and dynamic event. Before the method's implementation, ECG signals are windowed at a fixed size according to annotation information and then normalized within each window. In terms of accuracy and sensitivity, the proposed CNN-BiLSTM deep learning model outperforms existing state-of-the-art methods. These results made it possible to obtain a relatively higher average accuracy (AC) of 99.37%, precision (PE) of 97.12%, a sensitivity (SE) of 98.15%, and F-score (FS) of 98.43%, and an overall accuracy of 99.07%, respectively.

scholarly journals Real-Time Detection of Dictionary DGA Network Traffic Using Deep Learning

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Kate Highnam ◽  
Domenic Puzio ◽  
Song Luo ◽  
Nicholas R. Jennings
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Traffic ◽  
Short Term Memory ◽  
Domain Names ◽  
Control Networks ◽  
Detection Techniques ◽  
Lstm Network ◽  
And Control

AbstractBotnets and malware continue to avoid detection by static rule engines when using domain generation algorithms (DGAs) for callouts to unique, dynamically generated web addresses. Common DGA detection techniques fail to reliably detect DGA variants that combine random dictionary words to create domain names that closely mirror legitimate domains. To combat this, we created a novel hybrid neural network, Bilbo the “bagging” model, that analyses domains and scores the likelihood they are generated by such algorithms and therefore are potentially malicious. Bilbo is the first parallel usage of a convolutional neural network (CNN) and a long short-term memory (LSTM) network for DGA detection. Our unique architecture is found to be the most consistent in performance in terms of AUC, $$F_1$$ F 1 score, and accuracy when generalising across different dictionary DGA classification tasks compared to current state-of-the-art deep learning architectures. We validate using reverse-engineered dictionary DGA domains and detail our real-time implementation strategy for scoring real-world network logs within a large enterprise. In 4 h of actual network traffic, the model discovered at least five potential command-and-control networks that commercial vendor tools did not flag.

scholarly journals Real time monitoring of water Quality using IoT and Deep learning

2021 ◽  
Vol 297 ◽  
pp. 01059
Author(s):  
Saloua Senhaji ◽  
Mohamed Hamlich ◽  
Mohammed Ouazzani Jamil
Keyword(s):  
Water Quality ◽  
Deep Learning ◽  
Real Time ◽  
Environmental Protection Agency ◽  
Short Term Memory ◽  
Chemical Parameters ◽  
Water Parameters ◽  
Term Memory ◽  
Single Board Computer ◽  
Physico Chemical

Access to safe drinking water is one of the most pressing issues facing many developing countries. Water must meet Environmental Protection Agency (E.P.A.) requirements. The normal method of measuring physico-chemical parameters is to take samples manually and send them to the laboratory to check the water quality. In this paper, we proposed a new intelligent design of a real-time water quality monitoring system using Deep Learning technology. This system is composed of several sensors that allow us to measure water parameters (physico-chemical parameters), bacteriological parameters and organoleptic parameters) and to detect the presence of certain substances (undesirable substances, toxic substances) and of a single-board/mobile computer module, Internet and other accessories. Water parameters are automatically detected by the single-board computer. Raspberry Pi3 model B. The single board computer receives the data from the sensors and this data is sent to the web server using the Internet module. It is able to detect the water quality situation worldwide. The data will be analysed in real time. The application of deep learning to these areas has been an important research topic. The Long-Short Term Memory (LSTM) network has been shown to be well suited for processing and predicting large events with long intervals and delays in the time series. LSTM networks have the ability to retain long-term memory.

scholarly journals Meta-neural-network for real-time and passive deep-learning-based object recognition

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jingkai Weng ◽  
Yujiang Ding ◽  
Chengbo Hu ◽  
Xue-Feng Zhu ◽  
Bin Liang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Acoustic Scattering ◽  
Scattered Wave ◽  
Disease Diagnosis ◽  
Great Success ◽  
Destructive Testing ◽  
Unit Cells ◽  
Handwritten Digit

AbstractAnalyzing scattered wave to recognize object is of fundamental significance in wave physics. Recently-emerged deep learning technique achieved great success in interpreting wave field such as in ultrasound non-destructive testing and disease diagnosis, but conventionally need time-consuming computer postprocessing or bulky-sized diffractive elements. Here we theoretically propose and experimentally demonstrate a purely-passive and small-footprint meta-neural-network for real-time recognizing complicated objects by analyzing acoustic scattering. We prove meta-neural-network mimics a standard neural network despite its compactness, thanks to unique capability of its metamaterial unit-cells (dubbed meta-neurons) to produce deep-subwavelength phase shift as training parameters. The resulting device exhibits the “intelligence” to perform desired tasks with potential to overcome the current limitations, showcased by two distinctive examples of handwritten digit recognition and discerning misaligned orbital-angular-momentum vortices. Our mechanism opens the route to new metamaterial-based deep-learning paradigms and enable conceptual devices automatically analyzing signals, with far-reaching implications for acoustics and related fields.

scholarly journals End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2085 ◽  
Author(s):  
Rami M. Jomaa ◽  
Hassan Mathkour ◽  
Yakoub Bazi ◽  
Md Saiful Islam
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Attack Detection ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Biometric Systems ◽  
Fingerprint Biometrics ◽  
End To End ◽  
The Impact

Although fingerprint-based systems are the commonly used biometric systems, they suffer from a critical vulnerability to a presentation attack (PA). Therefore, several approaches based on a fingerprint biometrics have been developed to increase the robustness against a PA. We propose an alternative approach based on the combination of fingerprint and electrocardiogram (ECG) signals. An ECG signal has advantageous characteristics that prevent the replication. Combining a fingerprint with an ECG signal is a potentially interesting solution to reduce the impact of PAs in biometric systems. We also propose a novel end-to-end deep learning-based fusion neural architecture between a fingerprint and an ECG signal to improve PA detection in fingerprint biometrics. Our model uses state-of-the-art EfficientNets for generating a fingerprint feature representation. For the ECG, we investigate three different architectures based on fully-connected layers (FC), a 1D-convolutional neural network (1D-CNN), and a 2D-convolutional neural network (2D-CNN). The 2D-CNN converts the ECG signals into an image and uses inverted Mobilenet-v2 layers for feature generation. We evaluated the method on a multimodal dataset, that is, a customized fusion of the LivDet 2015 fingerprint dataset and ECG data from real subjects. Experimental results reveal that this architecture yields a better average classification accuracy compared to a single fingerprint modality.

scholarly journals Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 982 ◽  
Author(s):  
Hyo Lee ◽  
Ihsan Ullah ◽  
Weiguo Wan ◽  
Yongbin Gao ◽  
Zhijun Fang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Large Scale ◽  
Recognition Rate ◽  
Experimental Results ◽  
Learning Approach ◽  
Deep Model ◽  
Proposed Model ◽  
Real Time Applications ◽  
Model Recognition

Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extracted and fed into a deep network for training and testing. The SqueezeNet architecture with bypass connections between the Fire modules, a variant of the vanilla SqueezeNet, is employed for this study, which makes our MMR system more efficient. The experimental results on our collected large-scale vehicle datasets indicate that the proposed model achieves 96.3% recognition rate at the rank-1 level with an economical time slice of 108.8 ms. For inference tasks, the deployed deep model requires less than 5 MB of space and thus has a great viability in real-time applications.

scholarly journals An LSTM-Based Deep Learning Approach for Classifying Malicious Traffic at the Packet Level

2019 ◽  
Vol 9 (16) ◽  
pp. 3414 ◽  
Author(s):  
Ren-Hung Hwang ◽  
Min-Chun Peng ◽  
Van-Linh Nguyen ◽  
Yu-Lun Chang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Short Term Memory ◽  
Learning Technologies ◽  
Word Embedding ◽  
Detection Systems ◽  
Real Time Analysis ◽  
Detection Delay ◽  
Novel Approach ◽  
Prior Literature

Recently, deep learning has been successfully applied to network security assessments and intrusion detection systems (IDSs) with various breakthroughs such as using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to classify malicious traffic. However, these state-of-the-art systems also face tremendous challenges to satisfy real-time analysis requirements due to the major delay of the flow-based data preprocessing, i.e., requiring time for accumulating the packets into particular flows and then extracting features. If detecting malicious traffic can be done at the packet level, detecting time will be significantly reduced, which makes the online real-time malicious traffic detection based on deep learning technologies become very promising. With the goal of accelerating the whole detection process by considering a packet level classification, which has not been studied in the literature, in this research, we propose a novel approach in building the malicious classification system with the primary support of word embedding and the LSTM model. Specifically, we propose a novel word embedding mechanism to extract packet semantic meanings and adopt LSTM to learn the temporal relation among fields in the packet header and for further classifying whether an incoming packet is normal or a part of malicious traffic. The evaluation results on ISCX2012, USTC-TFC2016, IoT dataset from Robert Gordon University and IoT dataset collected on our Mirai Botnet show that our approach is competitive to the prior literature which detects malicious traffic at the flow level. While the network traffic is booming year by year, our first attempt can inspire the research community to exploit the advantages of deep learning to build effective IDSs without suffering significant detection delay.

scholarly journals ECG signal classification based on Deep Learning by using Convolutional Neural Network (CNN)

2020 ◽  
Vol 3 (3) ◽  
pp. 12-23
Author(s):  
Aqeel M. Hamad alhussainy ◽  
Ammar D. Jasim
Keyword(s):  
Deep Learning ◽  
Classification System ◽  
World Health ◽  
Geometric Transformation ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Result Show ◽  
Automatic Methods ◽  
Health Organization

Cardiovascular diseases (CVDs) are consider  the main cause  of death today According to World Health Organization (WHO),and because that ECG signal is very important tool in monitoring and diagnosis of these disease , different automatic methods were proposed based on this signal. [1]. The manual analysis of ECG signals is suffered different challenges such as differeculty of detecting and classify waveform of this signal, So, many machine learning methods  are  explored to describe  the anomalies ECG signal accurately . Deep learning (DL) can be used in ECG classification, it can improve the quality of the automatic classification system. In this paper , we have proposed a deep learning classification system by using  different layers of convolution, rectifier and pooling operations  that can be used to increase feature extraction of ECG signal.        We have proposed two models, one is used for input signal of 1-D, in which we designed model for classification csv type of data for ECG signal, while in the second proposed system, we used model for 2-D signal after convert it from its csv type .  2-D signal (ECG image) is used in order to augment the two dimensional signal with different methods to increase the accuracy of the model by training it with geometric transformation of the original input images such as rotation, shearing etc.The results are compared with AlexNet and other  models  based on the metrics, which are    used to measure the performance of the proposed work, the result show that, the proposed models improve the efficiency of the classification  in the two systems.

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