scholarly journals Patient-Specific Deep Architectural Model for ECG Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Kan Luo ◽  
Jianqing Li ◽  
Zhigang Wang ◽  
Alfred Cuschieri
Keyword(s):  
Fine Tuning ◽  
Patient Specific ◽  
Small Subset ◽  
Ecg Monitoring ◽  
Heartbeat Classification ◽  
Ecg Analysis ◽  
Time Frequency ◽  
Analysis Task ◽  
Proposed Model ◽  
Novel Model

Heartbeat classification is a crucial step for arrhythmia diagnosis during electrocardiographic (ECG) analysis. The new scenario of wireless body sensor network- (WBSN-) enabled ECG monitoring puts forward a higher-level demand for this traditional ECG analysis task. Previously reported methods mainly addressed this requirement with the applications of a shallow structured classifier and expert-designed features. In this study, modified frequency slice wavelet transform (MFSWT) was firstly employed to produce the time-frequency image for heartbeat signal. Then the deep learning (DL) method was performed for the heartbeat classification. Here, we proposed a novel model incorporating automatic feature abstraction and a deep neural network (DNN) classifier. Features were automatically abstracted by the stacked denoising auto-encoder (SDA) from the transferred time-frequency image. DNN classifier was constructed by an encoder layer of SDA and a softmax layer. In addition, a deterministic patient-specific heartbeat classifier was achieved by fine-tuning on heartbeat samples, which included a small subset of individual samples. The performance of the proposed model was evaluated on the MIT-BIH arrhythmia database. Results showed that an overall accuracy of 97.5% was achieved using the proposed model, confirming that the proposed DNN model is a powerful tool for heartbeat pattern recognition.

scholarly journals Robust Heartbeat Classification for Wearable Single-Lead ECG via Extreme Gradient Boosting

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5290
Author(s):  
Huaiyu Zhu ◽  
Yisheng Zhao ◽  
Yun Pan ◽  
Hanshuang Xie ◽  
Fan Wu ◽  
...  
Keyword(s):  
Gradient Boosting ◽  
Signal Acquisition ◽  
Ecg Monitoring ◽  
Daily Lives ◽  
Heartbeat Classification ◽  
Ecg Analysis ◽  
Ecg Signals ◽  
Extreme Gradient Boosting ◽  
Electrocardiogram Ecg ◽  
Monitoring Devices

Wearable electrocardiogram (ECG) monitoring devices have enabled everyday ECG collection in our daily lives. However, the condition of ECG signal acquisition using wearable devices varies and wearable ECG signals could be interfered with by severe noises, resulting in great challenges of computer-aided automated ECG analysis, especially for single-lead ECG signals without spare channels as references. There remains room for improvement of the beat-level single-lead ECG diagnosis regarding accuracy and efficiency. In this paper, we propose new morphological features of heartbeats for an extreme gradient boosting-based beat-level ECG analysis method to carry out the five-class heartbeat classification according to the Association for the Advancement of Medical Instrumentation standard. The MIT-BIH Arrhythmia Database (MITDB) and a self-collected wearable single-lead ECG dataset are used for performance evaluation in the static and wearable ECG monitoring conditions, respectively. The results show that our method outperforms other state-of-the-art models with an accuracy of 99.14% on the MITDB and maintains robustness with an accuracy of 98.68% in the wearable single-lead ECG analysis.

FINE-TUNING OF MODELLING STRATEGY TO SIMULATE THERMO-MECHANICAL BEHAVIOUR OF DOUBLE FRICTION PENDULUM SEISMIC ISOLATORS UST ESTIMATOR

2019 ◽  
Vol 3 (Special Issue on First SACEE'19) ◽  
pp. 165-172
Author(s):  
Vincenzo Bianco ◽  
Giorgio Monti ◽  
Nicola Pio Belfiore
Keyword(s):  
Multibody Dynamics ◽  
Mechanical Behaviour ◽  
Mechanical Model ◽  
Dynamic Behaviour ◽  
Experimental Testing ◽  
Fine Tuning ◽  
Proposed Model ◽  
Modelling Techniques ◽  
Friction Pendulum

The use of friction pendulum devices has recently attracted the attention of both academic and professional engineers for the protection of structures in seismic areas. Although the effectiveness of these has been shown by the experimental testing carried out worldwide, many aspects still need to be investigated for further improvement and optimisation. A thermo-mechanical model of a double friction pendulum device (based on the most recent modelling techniques adopted in multibody dynamics) is presented in this paper. The proposed model is based on the observation that sliding may not take place as ideally as is indicated in the literature. On the contrary, the fulfilment of geometrical compatibility between the constitutive bodies (during an earthquake) suggests a very peculiar dynamic behaviour composed of a continuous alternation of sticking and slipping phases. The thermo-mechanical model of a double friction pendulum device (based on the most recent modelling techniques adopted in multibody dynamics) is presented. The process of fine-tuning of the selected modelling strategy (available to date) is also described.

Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia

2016 ◽  
Vol 27 (02) ◽  
pp. 1650039 ◽  
Author(s):  
Francesco Carlo Morabito ◽  
Maurizio Campolo ◽  
Nadia Mammone ◽  
Mario Versaci ◽  
Silvana Franceschetti ◽  
...  
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Early Stage ◽  
Processing System ◽  
Fine Tuning ◽  
Permutation Entropy ◽  
Support Vector ◽  
Progressive Dementia ◽  
Time Frequency ◽  
Jakob Disease

A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt–Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features from the time-frequency representation of the EEG signals through continuous wavelet transform (CWT). An average measure of complexity of the EEG signal obtained by permutation entropy (PE) is also included. The dimensionality of the feature space is reduced through a multilayer processing system based on the recently emerged deep learning (DL) concept. The DL processor includes a stacked auto-encoder, trained by unsupervised learning techniques, and a classifier whose parameters are determined in a supervised way by associating the known category labels to the reduced vector of high-level features generated by the previous processing blocks. The supervised learning step is carried out by using either support vector machines (SVM) or multilayer neural networks (MLP-NN). A subset of EEG from patients suffering from Alzheimer’s Disease (AD) and healthy controls (HC) is considered for differentiating CJD patients. When fine-tuning the parameters of the global processing system by a supervised learning procedure, the proposed system is able to achieve an average accuracy of 89%, an average sensitivity of 92%, and an average specificity of 89% in differentiating CJD from RPD. Similar results are obtained for CJD versus AD and CJD versus HC.

On-device Prior Knowledge Incorporated Learning for Personalized Atrial Fibrillation Detection

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-25
Author(s):  
Zhenge Jia ◽  
Yiyu Shi ◽  
Samir Saba ◽  
Jingtong Hu
Keyword(s):  
Atrial Fibrillation ◽  
Prior Knowledge ◽  
Domain Knowledge ◽  
Fine Tuning ◽  
Cardiac Monitoring ◽  
Patient Specific ◽  
Detection Accuracy ◽  
Specific Patient ◽  
Deep Model ◽  
Tuning Strategy

Atrial Fibrillation (AF), one of the most prevalent arrhythmias, is an irregular heart-rate rhythm causing serious health problems such as stroke and heart failure. Deep learning based methods have been exploited to provide an end-to-end AF detection by automatically extracting features from Electrocardiogram (ECG) signal and achieve state-of-the-art results. However, the pre-trained models cannot adapt to each patient’s rhythm due to the high variability of rhythm characteristics among different patients. Furthermore, the deep models are prone to overfitting when fine-tuned on the limited ECG of the specific patient for personalization. In this work, we propose a prior knowledge incorporated learning method to effectively personalize the model for patient-specific AF detection and alleviate the overfitting problems. To be more specific, a prior-incorporated portion importance mechanism is proposed to enforce the network to learn to focus on the targeted portion of the ECG, following the cardiologists’ domain knowledge in recognizing AF. A prior-incorporated regularization mechanism is further devised to alleviate model overfitting during personalization by regularizing the fine-tuning process with feature priors on typical AF rhythms of the general population. The proposed personalization method embeds the well-defined prior knowledge in diagnosing AF rhythm into the personalization procedure, which improves the personalized deep model and eliminates the workload of manually adjusting parameters in conventional AF detection method. The prior knowledge incorporated personalization is feasibly and semi-automatically conducted on the edge, device of the cardiac monitoring system. We report an average AF detection accuracy of 95.3% of three deep models over patients, surpassing the pre-trained model by a large margin of 11.5% and the fine-tuning strategy by 8.6%.

scholarly journals Different Forecasting Horizons Based Performance Analysis of Electricity Load Forecasting Using Multilayer Perceptron Neural Network

Forecasting ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 804-838
Author(s):  
Manogaran Madhiarasan ◽  
Mohamed Louzazni
Keyword(s):  
Neural Network ◽  
Power System ◽  
Multilayer Perceptron ◽  
Load Forecasting ◽  
Consumer Information ◽  
Fine Tuning ◽  
Atmospheric Parameters ◽  
Proposed Model ◽  
Electricity Load ◽  
Electricity Load Forecasting

With an uninterrupted power supply to the consumer, it is obligatory to balance the electricity generated by the electricity load. The effective planning of economic dispatch, reserve requirements, and quality power provision for accurate consumer information concerning the electricity load is needed. The burden on the power system engineers eased electricity load forecasting is essential to ensure the enhanced power system operation and planning for reliable power provision. Fickle nature, atmospheric parameters influence makes electricity load forecasting a very complex and challenging task. This paper proposed a multilayer perceptron neural network (MLPNN) with an association of recursive fine-tuning strategy-based different forecasting horizons model for electricity load forecasting. We consider the atmospheric parameters as the inputs to the proposed model, overcoming the atmospheric effect on electricity load forecasting. Hidden layers and hidden neurons based on performance investigation performed. Analyzed performance of the proposed model with other existing models; the comparative performance investigation reveals that the proposed forecasting model performs rigorous with a minimal evaluation index (mean square error (MSE) of 1.1506 × 10-05 for Dataset 1 and MSE of 4.0142 × 10-07 for Dataset 2 concern to the single hidden layer and MSE of 2.9962 × 10-07 for Dataset 1, and MSE of 1.0425 × 10-08 for Dataset 2 concern to two hidden layers based proposed model) and compared to the considered existing models. The proposed neural network possesses a good forecasting ability because we develop based on various atmospheric parameters as the input variables, which overcomes the variance. It has a generic performance capability for electricity load forecasting. The proposed model is robust and more reliable.

scholarly journals A dynamic model for series and parallel resistance of photovoltaic cell using material properties extraction and energy tunnel

2017 ◽  
Vol 16 (1) ◽  
pp. 9-21
Author(s):  
Kotchapong Sumanonta ◽  
Pasist Suwanapingkarl ◽  
Pisit Liutanakul
Keyword(s):  
Series Resistance ◽  
Photovoltaic Cell ◽  
Photovoltaic Module ◽  
Practical Application ◽  
Pv Module ◽  
Proposed Model ◽  
Outdoor Test ◽  
The Relationship ◽  
Parallel Resistance ◽  
Novel Model

This article presents a novel model for the equivalent circuit of a photovoltaic module. This circuit consists of the following important parameters: a single diode, series resistance (Rs) and parallel resistance (Rp) that can be directly adjusted according to ambient temperature and the irradiance. The single diode in the circuit is directly related to the ideality factor (m), which represents the relationship between the materials and significant structures of PV module such as mono crystalline, multi crystalline and thin film technology.  Especially, the proposed model in this article is to present the simplified model that can calculate the results of I-V curves faster and more accurate than other methods of the previous models. This can show that the proposed models are more suitable for the practical application. In addition, the results of the proposed model are validated by the datasheet, the practical data in the laboratory (indoor test) and the onsite data (outdoor test). This ensures that the less than 0.1% absolute errors of the model can be accepted.

scholarly journals A recommendations model with multiaspect awareness and hierarchical user-product attention mechanisms

2020 ◽  
Vol 17 (3) ◽  
pp. 849-865
Author(s):  
Zhongqin Bi ◽  
Shuming Dou ◽  
Zhe Liu ◽  
Yongbin Li
Keyword(s):  
State Of The Art ◽  
Weight Vector ◽  
User Preferences ◽  
The Other ◽  
Attention Networks ◽  
Proposed Model ◽  
Network Methods ◽  
Public Datasets ◽  
Novel Model ◽  
Attention Weight

Neural network methods have been trained to satisfactorily learn user/product representations from textual reviews. A representation can be considered as a multiaspect attention weight vector. However, in several existing methods, it is assumed that the user representation remains unchanged even when the user interacts with products having diverse characteristics, which leads to inaccurate recommendations. To overcome this limitation, this paper proposes a novel model to capture the varying attention of a user for different products by using a multilayer attention framework. First, two individual hierarchical attention networks are used to encode the users and products to learn the user preferences and product characteristics from review texts. Then, we design an attention network to reflect the adaptive change in the user preferences for each aspect of the targeted product in terms of the rating and review. The results of experiments performed on three public datasets demonstrate that the proposed model notably outperforms the other state-of-the-art baselines, thereby validating the effectiveness of the proposed approach.

scholarly journals Gated Recurrent Unit Networks and Discrete Wavelet Transforms Applied to Forecasting and Trading in the Stock Market

2020 ◽  
Author(s):  
Victor Biazon ◽  
Reinaldo Bianchi
Keyword(s):  
Stock Market ◽  
Wavelet Transforms ◽  
Stock Price ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Time Step ◽  
Proposed Model ◽  
Gated Recurrent Unit ◽  
Novel Model ◽  
Unit Network

Trading in the stock market always comes with the challenge of deciding the best action to take on each time step. The problem is intensified by the theory that it is not possible to predict stock market time series as all information related to the stock price is already contained in it. In this work we propose a novel model called Discrete Wavelet Transform Gated Recurrent Unit Network (DWT-GRU). The model learns from the data to choose between buying, holding and selling, and when to execute them. The proposed model was compared to other recurrent neural networks, with and without wavelets preprocessing, and the buy and hold strategy. The results shown that the DWT-GRU outperformed all the set baselines in the analysed stocks of the Brazilian stock market.

Wavelet ECG Analysis in Time-Frequency Domain of the QRS-Complex in Individuals with Left Bundle Branch Block

2017 ◽  
pp. 179-183
Author(s):  
Kalliopi Papathoma ◽  
Stavros Chatzimiltiadis ◽  
Nikolaos Maglaveras ◽  
Ioanna Chouvarda ◽  
Efstratios Theofilogiannakos ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Left Bundle Branch Block ◽  
Qrs Complex ◽  
Bundle Branch Block ◽  
Ecg Analysis ◽  
Time Frequency

scholarly journals Multi-source fault identification based on combined deep learning

2020 ◽  
Vol 309 ◽  
pp. 03037
Author(s):  
Dongqiu Xing ◽  
Rui Chen ◽  
Lihua Qi ◽  
Jing Zhao ◽  
Yi Wang
Keyword(s):  
Deep Learning ◽  
Learning Strategy ◽  
Rolling Bearing ◽  
Fine Tuning ◽  
Fault Identification ◽  
Support Vector ◽  
Industrial Systems ◽  
Classifier Design ◽  
Time Frequency ◽  
Deep Model

This study establishes a multi-source fault identification method based on a combined deep learning strategy to identify a multi-source fault effectively in the fault diagnosis of complex industrial systems. This framework is composed of feature extraction and classifier design. In the first state, the signal is transformed to the time-frequency domain and the time-frequency feature is learned using stacked denoising autoencoders. A learning method that consists of unsupervised pre-learning and supervised fine-tuning is used to train this deep model. In the second state, a model for an ensemble multiple support vector machine classifier is created to recognize fault information. Ten types of rolling bearing signals were adopted in a simulation experiment to validate the effectiveness of the proposed framework. The results demonstrate that the joint model helps to obtain higher recognition accuracy.

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