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Nocturnal low glucose detection in healthy elderly from one-lead ECG using convolutional denoising autoencoders

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2020年第0期62卷 102054-102054页

作者： Porumb, Mihaela Griffen, Corbin Hattersley, John Pecchia, Leandro Univ Warwick Coventry CV4 7AL W Midlands England Coventry Univ Ctr Sport Exercise & Life Sci Coventry CV4 1DS W Midlands England Univ Coventry & Warwickshire NHS Trust Human Metab Res Unit Warwick England Univ Warwick Warwick England

Hypoglycemia is a condition caused by low blood glucose levels, mainly affecting people with diabetes. Low levels of blood glucose can be dangerous, causing multiple complications over time. Hypoglycemia affects the repolarization characteristics of the heart. This study presents a personalized, deep learningbased approach that enables nocturnal hypoglycemia detection using raw electrocardiogram (ECG) signals, recorded with non-invasive, wearable devices. The study was carried out in a calorimeter room during 24-36 h monitoring on twenty-five, healthy, elderly participants. The results of this study (accuracy of approximate to 90%) provide evidence for the feasibility of a non-invasive, ECG-based hypoglycemia alarming system. Moreover, leveraging an unsupervised method (i.e. convolutional denoising autoencoder) and visualizing the ECG heartbeats in the embeddings space, clear heartbeats clusters grouped by glucose levels could be determined, showing that specific patterns in the input heartbeats are indeed glucose discriminative and the autoencoder could successfully capture those patterns. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Nocturnal hypoglycemia convolutional denoising autoencoder convolutional neural networks

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RunDAE model: Running denoising autoencoder models for denoising ECG signals

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年 166卷 107553-107553页

作者： Samann, Fars Schanze, Thomas TH Mittelhessen THM Inst Biomed Tech IBMT FB Life Sci Engn LSE Giessen Germany Univ Duhok Dept Biomed Engn Duhok Iraq

Background: The denoising autoencoder (DAE) is commonly used to denoise bio-signals such as electrocar-diogram (ECG) signals through dimensional reduction. Typically, the DAE model needs to be trained using correlated input segments such as QRS-aligned segments or long ECG segments. However, using long ECG segments as an input can result in a complex deep DAE model that requires many hidden layers to achieve a low-dimensional representation, which is a major ***: This work proposes a novel DAE model, called running DAE (RunDAE), for denoising short ECG segments without relying on the R-peak detection algorithm for alignment. The proposed RunDAE model employs a sample-by-sample processing approach, considering the correlation between consecutive, overlapped ECG segments. The performance of both the classical DAE and RunDAE models with convolutional and dense layers, respectively, is evaluated using corrupted QRS-aligned and non-aligned ECG segments with physical noise such as motion artifacts, electrode movement, baseline wander, and simulated noise such as Gaussian white ***: The simulation results indicate that 1. QRS-aligned segments are preferable to non-aligned segments, 2. the RunDAE model outperforms the classical DAE model in denoising ECG signals, especially when using dense layers and QRS-aligned segments, 3. training the RunDAE models with normal and arrhythmic ECG signals enhance model's properties/capabilities, and 4. the RunDAE is a multistage, non-causal, nonlinear adaptive ***: A shallow learning model, which consists of a couple of hidden layers, could achieve outstanding denoising performance using only the correlation among neighboring samples.

关键词： denoising autoencoder convolutional denoising autoencoder Running denoising autoencoder Non-aligned ECG segments QRS-aligned ECG segments Overlapping ECG segments Multistage nonlinear adaptive filter

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A Robust Approach for Securing Audio Classification Against Adversarial Attacks

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2020年 15卷 2147-2159页

作者： Esmaeilpour, Mohammad Cardinal, Patrick Koerich, Alessandro Univ Quebec Ecole Technol Super Dept Software & IT Engn Montreal PQ H3C 1K3 Canada

Adversarial audio attacks can be considered as a small perturbation unperceptive to human ears that is intentionally added to an audio signal and causes a machine learning model to make mistakes. This poses a security concern about the safety of machine learning models since the adversarial attacks can fool such models toward the wrong predictions. In this paper we first review some strong adversarial attacks that may affect both audio signals and their 2D representations and evaluate the resiliency of deep learning models and support vector machines (SVM) trained on 2D audio representations such as short time Fourier transform, discrete wavelet transform (DWT) and cross recurrent plot against several state-of-the-art adversarial attacks. Next, we propose a novel approach based on pre-processed DWT representation of audio signals and SVM to secure audio systems against adversarial attacks. The proposed architecture has several preprocessing modules for generating and enhancing spectrograms including dimension reduction and smoothing. We extract features from small patches of the spectrograms using the speeded up robust feature (SURF) algorithm which are further used to transform into cluster distance distribution using the K-Means++ algorithm. Finally, SURF-generated vectors are encoded by this codebook and the resulting codewords are used for training a SVM. All these steps yield to a novel approach for audio classification that provides a good tradeoff between accuracy and resilience. Experimental results on three environmental sound datasets show the competitive performance of the proposed approach compared to the deep neural networks both in terms of accuracy and robustness against strong adversarial attacks.

关键词： Support vector machines Machine learning Robustness Perturbation methods Predictive models Optimization Two dimensional displays Spectrograms environmental sound classification adversarial attack K-means plus plus support vector machines (SVM) convolutional denoising autoencoder

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Deep Spatio-Temporal Representation and Ensemble Classification for Attention Deficit/Hyperactivity Disorder

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2021年 29卷 1-10页

作者： Liu, Shuaiqi Zhao, Ling Wang, Xu Xin, Qi Zhao, Jie Guttery, David S. Zhang, Yu-Dong Hebei Univ Coll Elect & Informat Engn Baoding 071002 Peoples R China Hebei Univ Key Lab Digital Med Engn Hebei Prov Baoding 071002 Peoples R China Hebei Univ Machine Vis Engn Res Ctr Hebei Prov Baoding 071002 Peoples R China Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Leicester Canc Res Inst Leicester LE2 7LX Leics England Univ Leicester Dept Informat Leicester LE1 7RH Leics England

Attention deficit/Hyperactivity disorder (ADHD) is a complex, universal and heterogeneous neurodevelopmental disease. The traditional diagnosis of ADHD relies on the long-term analysis of complex information such as clinical data (electroencephalogram, etc.), patients' behavior and psychological tests by professional doctors. In recent years, functional magnetic resonance imaging (fMRI) has been developing rapidly and is widely employed in the study of brain cognition due to its non-invasive and non-radiation characteristics. We propose an algorithm based on convolutional denoising autoencoder (CDAE) and adaptive boosting decision trees (AdaDT) to improve the results of ADHD classification. Firstly, combining the advantages of convolutional neural networks (CNNs) and the denoising autoencoder (DAE), we developed a convolutional denoising autoencoder to extract the spatial features of fMRI data and obtain spatial features sorted by time. Then, AdaDT was exploited to classify the features extracted by CDAE. Finally, we validate the algorithm on the ADHD-200 test dataset. The experimental results show that our method offers improved classification compared with state-of-the-art methods in terms of the average accuracy of each individual site and all sites, meanwhile, our algorithm can maintain a certain balance between specificity and sensitivity.

关键词： Feature extraction Functional magnetic resonance imaging Convolution Data mining Classification algorithms Noise reduction Machine learning Adaptive boosting decision tree ADHD convolutional denoising autoencoder fMRI classification

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Effective denoising Architecture for Handling Multiple Noises

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Computer Systems Science & Engineering 2023年第3期44卷 2667-2682页

作者： Na Hyoun Kim Namgyu Kim The Graduate School of Business IT of Kookmin University Seoul02707Korea

Object detection,one of the core research topics in computer vision,is extensively used in various industrial *** there have been many studies of daytime images where objects can be easily detected,there is relatively little research on nighttime *** the case of nighttime,various types of noises,such as darkness,haze,and light blur,deteriorate image ***,an appropriate process for removing noise must precede to improve object detection *** there are many studies on removing individual noise,only a few studies handle multiple noises *** this paper,we pro-pose a convolutional denoising autoencoder(CDAE)-based architecture trained on various types of *** also present various composing modules for each noise to improve object detection performance for night *** the exclusively dark(ExDark)Image dataset,experimental results show that the Sequentialfiltering architecture showed superior mean average precision(mAP)compared to other architectures.

关键词： Object detection computer vision nighttime multiple noises convolutional denoising autoencoder

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CCpos: WiFi Fingerprint Indoor Positioning System Based on CDAE-CNN

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SENSORS 2021年第4期21卷 1114-1114页

作者： Qin, Feng Zuo, Tao Wang, Xing Wuhan Univ Sci & Technol Coll Informat Sci & Engn Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Minist Educ Engn Res Ctr Met Automat & Detecting Technol Wuhan 430081 Peoples R China

WiFi is widely used for indoor positioning because of its advantages such as long transmission distance and ease of use indoors. To improve the accuracy and robustness of indoor WiFi fingerprint localization technology, this paper proposes a positioning system CCPos (CADE-CNN Positioning), which is based on a convolutional denoising autoencoder (CDAE) and a convolutional neural network (CNN). In the offline stage, this system applies the K-means algorithm to extract the validation set from the all-training set. In the online stage, the RSSI is first denoised and key features are extracted by the CDAE. Then the location estimation is output by the CNN. In this paper, the Alcala Tutorial 2017 dataset and UJIIndoorLoc are adopted to verify the performance of the CCpos system. The experimental results show that our system has excellent noise immunity and generalization performance. The mean positioning errors on the Alcala Tutorial 2017 dataset and the UJIIndoorLoc are 1.05 m and 12.4 m, respectively.

关键词： WiFi fingerprint positioning convolutional denoising autoencoder convolutional neural network K-means

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CDAE: Towards Empowering denoising in Side-Channel Analysis 21st

CDAE: Towards Empowering Denoising in Side-Channel Analysis

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21st International Conference on Information and Communications Security (ICICS)

作者： Yang, Guang Li, Huizhong Ming, Jingdian Zhou, Yongbin Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9783030415792;9783030415785

Side-Channel Analysis (SCA) plays a crucial role in hardware security evaluation. However, side-channel acquisitions (a.k.a. traces) usually contain noises that often impose negative effects on key-recovery efficiency. In this paper, we propose convolutional denoising autoencoder (CDAE) for noise reduction in SCA. CDAE is composed of multiple layers of convolution operators, learning an end-to-end mapping from noisy traces to clean traces by minimizing the l(2) loss of noisy-clean trace pairs. The convolutional layers capture the abstraction of the traces while eliminating noises. We argue that CDAE is very suitable for profiled SCA especially when the attacker has a large amount of traces in the offline profiling phase. Once the network training is done, our denoising network can be applied to individual new noisy traces for the attacker to launch online attacks. To validate the effectiveness of our method, we train CDAE to denoise traces and then perform Template Attacks (TA) in three high noise jamming scenarios, including unprotected (GPU and FPGA based) and protected (MCU based) AES implementations. Our method can significantly outperform the state-of-the-art Singular Spectrum Analysis (SSA) denoising method on both information theoretic metrics and security metrics. Results show that CDAE achieves at least similar to 4x Signal-to-Noise Ratio (SNR) gain, thus TA with denoising preprocessing requires at most 50% of the traces in the attack phase.

关键词： Side-channel analysis convolutional denoising autoencoder Preprocessing tool Security evaluation Deep learning

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Optimal Compensation of MEMS Gyroscope Noise Kalman Filter Based on Conv-DAE and MultiTCN-Attention Model in Static Base Environment

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SENSORS 2022年第19期22卷 7249-7249页

作者： Huo, Zimin Wang, Fuchao Shen, Honghai Sun, Xin Zhang, Jingzhong Li, Yaobin Chu, Hairong Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Key Lab Airborne Opt Imaging & Measurement Changchun 130033 Peoples R China Forest Protect Res Inst Heilongjiang Prov Harbin 150040 Peoples R China

Errors in microelectromechanical systems (MEMS) inertial measurement units (IMUs) are large, complex, nonlinear, and time varying. The traditional noise reduction and compensation methods based on traditional models are not applicable. This paper proposes a noise reduction method based on multi-layer combined deep learning for the MEMS gyroscope in the static base state. In this method, the combined model of MEMS gyroscope is constructed by convolutional denoising Auto-Encoder (Conv-DAE) and Multi-layer Temporal convolutional Neural with the Attention Mechanism (MultiTCN-Attention) model. Based on the robust data processing capability of deep learning, the noise features are obtained from the past gyroscope data, and the parameter optimization of the Kalman filter (KF) by the Particle Swarm Optimization algorithm (PSO) significantly improves the filtering and noise reduction accuracy. The experimental results show that, compared with the original data, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 77.81% and 76.44% on the x and y axes, respectively;compared with the existing MEMS gyroscope noise compensation method based on the Autoregressive Moving Average with Kalman filter (ARMA-KF) model, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 44.00% and 46.66% on the x and y axes, respectively, reducing the noise impact by nearly three times.

关键词： MEMS gyroscope convolutional denoising autoencoder temporal convolutional network attention mechanism Particle Swarm Optimization algorithm Kalman filter

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Comparative analysis of the denoising effect of unstructured vs. convolutional autoencoders

Comparative analysis of the denoising effect of unstructured...

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Emerging Trends in Smart Technologies (ICETST), 2020 International Conference on

作者： Sajid Majeed Yusra Mansoor Sana Qabil Farooq Majeed Behraj Khan National University of Computer and Emerging Sciences Karachi Pakistan

ISBN: (数字)9781728171135

ISBN: (纸本)9781728171142

Adding noise to data affects the prediction of a discriminator. Some like a deep neural network extract features directly from inputs, where the quality of the features may be affected by the amount of the noise. A deep neural model specifically meant for feature extraction is the autoencoder, while it has also been extended to perform denoising. In this paper, we investigate the denoising effect of an encoder on different nature as well as different amounts of additive noise. The experiments are evaluated on a linearized autoencoder as well as a convolutional autoencoder, which is especially meant for image data. denoising autoencoders (DAE) and convolutional denoising autoencoders (CDAE) are evaluated by introducing with Gaussian, Salt and Pepper, and Poisson types of noise with a factor of 0.5. The results shows 0.12, 0.09, 0.47 Mean Squared Error (MSE) for DAE and 0.13, 0.10 and 0.9 MSE in case of CDAE with the same amount of noise factor added, alluding to the insight that a lack of focus on structure in the model may help it focus more on the denoising task.

关键词： Image denoising CNN denoising autoencoder convolutional denoising autoencoder Noise reduction image denoising Mean square error Noise figure Noise ENCODER

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