We propose a novel deep learning based denoising filter selection algorithm for noisy Electrocardiograph (ECG) signal preprocessing. ECG signals measured under clinical conditions, such as those acquired using skin contact devices in hospitals, often contain baseline signal disturbances and unwanted artefacts; indeed for signals obtained outside of a clinical environment, such as heart rate signatures recorded using non-contact radar systems, the measurements contain greater levels of noise than those acquired under clinical conditions. In this paper, we focus on heart rate signals acquired using non-contact radar systems for use in assisted living environments. Such signals contain more noise than those measured under clinical conditions and thus require a novel signal noise removal method capable of adaptive determining filters. Currently, the most common method of removing noise from such a waveform is through the use of filters; the most popular filtering method amongst which is the wavelet filter. There are, however, circumstances in which using a different filtering method may result in higher signal-to-noise-ratios (SNR) for a waveform; in this paper, we investigate the wavelet and elliptical filtering methods for the task of reducing noise in ECG signals acquired using assistive technologies. Our proposed convolutional neural network architecture classifies (with 92.8% accuracy) the optimum filtering method for noisy signal based on its expected SNR value.
Monitoring of electrocardiogram (ECG) provides vital information as well as any cardiovascular anomalies. Recent advances in the technology of wearable electronics have enabled compact devices to acquire personal physiological signals in the home setting; however, signals are usually contaminated with high level noise. Thus, an efficient ECG filtering scheme is a dire need. In this paper, a novel method using Ensemble Kalman Filter (EnKF) is developed for denoising ECG signals. We also intensively explore various filtering algorithms, including Savitzky-Golay (SG) filter, Ensemble Empirical mo…
Electrocardiogram (ECG) signal is an important physiological signal which contains cardiac information and is the basis to diagnosis cardiac related diseases. In this paper, several innovative and efficient methods based on adaptive filter and empirical mode decomposition (EMD) to denoise ECG signal contaminated by various kinds of noise, including baseline wander (BW), power line interference (PLI), electrode motion artifact (EM) and muscle artifact (MA), are proposed. We first present a novel method based on EMD and adaptive filter for the removal of BW and PLI in ECG signal. We then extend …
The transformer based model (e.g., FusingTF) has been employed recently for Electrocardiogram (ECG) signal classification. However, the high-dimensional embedding obtained via 1-D convolution and positional encoding can lead to the loss of the signal's own temporal information and a large amount of training parameters. In this paper, we propose a new method for ECG classification, called low-dimensional denoising embedding transformer (LDTF), which contains two components, i.e., low-dimensional denoising embedding (LDE) and transformer learning. In the LDE component, a low-dimensional represen…
Generative Adversarial Networks (GAN) are known to produce synthetic data that are difficult to discern from real ones by humans. In this paper we present an approach to use GAN to produce realistically looking ECG signals. We utilize them to train and evaluate a denoising autoencoder that achieves state-of-the-art filtering quality for ECG signals. It is demonstrated that generated data improves the model performance compared to the model trained on real data only. We also investigate an effect of transfer learning by reusing trained discriminator network for denoising model.
SSEMG-Net: A Spectrogram-Based Mamba Network for Surface Electromyography Denoising
National Taiwan University（国立台湾大学）；Academia Sinica（中央研究院）；Stevens Institute of Technology（史蒂文斯理工学院）；Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心）
School of Electrical and Electronics Engineering（电气与电子工程学院）；University of Manchester（曼彻斯特大学）
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 te…
Electrocardiographic signal is a subject to multiple noises, caused by various factors. It is therefore a standard practice to denoise such signal before further analysis. With advances of new branch of machine learning, called deep learning, new methods are available that promises state-of-the-art performance for this task. We present a novel approach to denoise electrocardiographic signals with deep recurrent denoising neural networks. We utilize a transfer learning technique by pretraining the network using synthetic data, generated by a dynamic ECG model, and fine-tuning it with a real dat…
Cardiovascular diseases are the leading cause of death worldwide, accounting for 17.3 million deaths per year. The electrocardiogram (ECG) is a non-invasive technique widely used for the detection of cardiac diseases. To increase diagnostic sensitivity, ECG is acquired during exercise stress tests or in an ambulatory way. Under these acquisition conditions, the ECG is strongly affected by some types of noise, mainly by baseline wander (BLW). In this work were implemented nine methods widely used for the elimination of BLW, which are: interpolation using cubic splines, FIR filter, IIR filter, l…
Healthcare Technology Innovation Centre Indian Institute of Technology Madras（印度理工学院马德拉斯分校医疗技术创新中心）；Department of Electrical Engineering Indian Institute of Technology Madras（印度理工学院马德拉斯分校电气工程系）
Continuous monitoring of cardiac health under free living condition is crucial to provide effective care for patients undergoing post operative recovery and individuals with high cardiac risk like the elderly. Capacitive Electrocardiogram (cECG) is one such technology which allows comfortable and long term monitoring through its ability to measure biopotential in conditions without having skin contact. cECG monitoring can be done using many household objects like chairs, beds and even car seats allowing for seamless monitoring of individuals. This method is unfortunately highly susceptible to …
MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal
National Taiwan University（台湾大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；National Institute of Information and Communications Technology（信息通信研究机构）
There have been several attempts to quantify the diagnostic distortion caused by algorithms that perform low-dimensional electrocardiogram (ECG) representation. However, there is no universally accepted quantitative measure that allows the diagnostic distortion arising from denoising, compression, and ECG beat representation algorithms to be determined. Hence, the main objective of this work was to develop a framework to enable biomedical engineers to efficiently and reliably assess diagnostic distortion resulting from ECG processing algorithms. We propose a semiautomatic framework for quantif…
Currently, the telehealth monitoring field has gained huge attention due to its noteworthy use in day-to-day life. This advancement has led to an increase in the data collection of electrophysiological signals. Due to this advancement, electrocardiogram (ECG) signal monitoring has become a leading task in the medical field. ECG plays an important role in the medical field by analysing cardiac physiology and abnormalities. However, these signals are affected due to numerous varieties of noises, such as electrode motion, baseline wander and white noise etc., which affects the diagnosis accuracy.…
National Taiwan University（国立台湾大学）；Academia Sinica（中央研究院）；National Taiwan University of Science and Technology（国立台湾科技大学）
Electrocardiogram (ECG) artifact contamination often occurs in surface electromyography (sEMG) applications when the measured muscles are in proximity to the heart. Previous studies have developed and proposed various methods, such as high-pass filtering, template subtraction and so forth. However, these methods remain limited by the requirement of reference signals and distortion of original sEMG. This study proposed a novel denoising method to eliminate ECG artifacts from the single-channel sEMG signals using fully convolutional networks (FCN). The proposed method adopts a denoise autoencode…
According to the World Health Organization, around 36% of the annual deaths are associated with cardiovascular diseases and 90% of heart attacks are preventable. Electrocardiogram signal analysis in ambulatory electrocardiography, during an exercise stress test, and in resting conditions allows cardiovascular disease diagnosis. However, during the acquisition, there is a variety of noises that may damage the signal quality thereby compromising their diagnostic potential. The baseline wander is one of the most undesirable noises. In this work, we propose a novel algorithm for BLW noise filterin…
We describe techniques and specifications of MATLAB software to process ambulatory electrocardiogram (ECG) data. Through template-based beat identification and simple pattern recognition models on the intervals between regular heart beats, we filter noisy sections of waveform and ectopic beats. Our end-to-end process can be used towards analysis of ECG and calculation of heart rate variability metrics after beat adjustments, removals and interpolation. Classification and noise detection is assessed on the human-annotated MIT-BIH Arrythmia and Noise Stress Test Databases.
Various emotions can produce variations in electrocardiograph (ECG) signals, distinct emotions can be distinguished by different changes in ECG signals. This study is about emotion recognition using ECG signals. Data for four emotions, namely happy, exciting, calm, and tense, is gathered. The raw data is then de-noised with a finite impulse filter. We use the Discrete Cosine Transform (DCT) to extract characteristics from the obtained data to increase the accuracy of emotion recognition. The classifiers Support Vector Machine (SVM), Random Forest, and K-NN are explored. To find the optimal par…
MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network
National Yang Ming Chiao Tung University（国立阳明交通大学）；National Taiwan University（国立台湾大学）；University of Palermo（巴勒莫大学）；Academic Sinica（中央研究院）；Research Center for Information Technology Innovation（资讯科技创新研究中心）
Due to the multiple imperfections during the signal acquisition, Electrocardiogram (ECG) datasets are typically contaminated with numerous types of noise, like salt and pepper and baseline drift. These datasets may contain different recordings with various types of noise [1] and thus, denoising may not be the easiest task. Furthermore, usually, the number of labeled bio-signals is very limited for a proper classification task.
SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal Denoising
National Yang Ming Chiao Tung University（国立阳明交通大学）；National Taiwan University（国立台湾大学）；National Taipei University of Technology（国立台北科技大学）；National Taiwan University of Science and Technology（国立台湾科技大学）；Academia Sinica（中央研究院）
Université de Sousse（苏塞大学）；Ecole Nationale d’Ingénieurs de Sousse（苏塞国立工程学院）；Higher Institute of Applied Mathematics and Computer Science（应用数学与计算机科学高等学院）；Faculty of Sciences（理学院）；University of Tabuk（塔布克大学）
Biosignals are nowadays important subjects for scientific researches from both theory and applications especially with the appearance of new pandemics threatening humanity such as the new Coronavirus. One aim in the present work is to prove that Wavelets may be successful machinery to understand such phenomena by applying a step forward extension of wavelets to multiwavelets. We proposed in a first step to improve the multiwavelet notion by constructing more general families using independent components for multi-scaling and multiwavelet mother functions. A special multiwavelet is then introdu…
Least-Squares Adaptive Filter-Based Cohen's Class Time-Frequency Distribution for Signal Denoising
Nanjing University of Information Science and Technology（南京信息工程大学）；Hubei University（湖北大学）；Shanghai Jiao Tong University（上海交通大学）；Nanjing Institute of Technology（南京工程学院）
When approaching graph signal processing tasks, graphs are usually assumed to be perfectly known. However, in many practical applications, the observed (inferred) network is prone to perturbations which, if ignored, will hinder performance. Tailored to those setups, this paper presents a robust formulation for the problem of graph-filter identification from input-output observations. Different from existing works, our approach consists in addressing the robust identification by formulating a joint graph denoising and graph-filter identification problem. Such a problem is formulated as a non-co…
We address the problem of signal denoising and pattern recognition in processing batch-mode time-series data by combining linear time-invariant filters, orthogonal multiresolution representations, and sparsity-based methods. We propose a novel approach to designing higher-order zero-phase low-pass, high-pass, and band-pass infinite impulse response filters as matrices, using spectral transformation of the state-space representation of digital filters. We also propose a proximal gradient-based technique to factorize a special class of zero-phase high-pass and band-pass digital filters so that t…
The classification accuracy of electrocardiogram signal is often affected by diverse factors in which mislabeled training samples issue is one of the most influential problems. In order to mitigate this negative effect, the method of cross validation is introduced to identify the mislabeled samples. The method utilizes the cooperative advantages of different classifiers to act as a filter for the training samples. The filter removes the mislabeled training samples and retains the correctly labeled ones with the help of 10-fold cross validation. Consequently, a new training set is provided to t…
Continuous long-term monitoring of electrocardiography (ECG) signals is crucial for the early detection of cardiac abnormalities such as arrhythmia. Non-clinical ECG recordings acquired by Holter and wearable ECG sensors often suffer from severe artifacts such as baseline wander, signal cuts, motion artifacts, variations on QRS amplitude, noise, and other interferences. Usually, a set of such artifacts occur on the same ECG signal with varying severity and duration, and this makes an accurate diagnosis by machines or medical doctors extremely difficult. Despite numerous studies that have attem…
University of Arizona（亚利桑那大学）；Rowan University（罗文大学）；Bio5 Institute（Bio5研究所）
Objective: Electrocardiogram (ECG) signals commonly suffer noise interference, such as baseline wander. High-quality and high-fidelity reconstruction of the ECG signals is of great significance to diagnosing cardiovascular diseases. Therefore, this paper proposes a novel ECG baseline wander and noise removal technology. Methods: We extended the diffusion model in a conditional manner that was specific to the ECG signals, namely the Deep Score-Based Diffusion model for Electrocardiogram baseline wander and noise removal (DeScoD-ECG). Moreover, we deployed a multi-shots averaging strategy that i…
Automated QRS detection methods depend on the ECG data which is sampled at a certain frequency, irrespective of filter-based traditional methods or convolutional network (CNN) based deep learning methods. These methods require a selection of the sampling frequency at which they operate in the very first place. While working with data from two different datasets, which are sampled at different frequencies, often, data from both the datasets may need to resample at a common target frequency, which may be the frequency of either of the datasets or could be a different one. However, choosing data …
Manual interpretation and classification of ECG signals lack both accuracy and reliability. These continuous time-series signals are more effective when represented as an image for CNN-based classification. A continuous Wavelet transform filter is used here to get corresponding images. In achieving the best result generic CNN architectures lack sufficient accuracy and also have a higher run-time. To address this issue, we propose an ensemble method of transfer learning-based models to classify ECG signals. In our work, two modified VGG-16 models and one InceptionResNetV2 model with added featu…
Over the years, progressive improvements in denoising performance have been achieved by several image denoising algorithms that have been proposed. Despite this, many of these state-of-the-art algorithms tend to smooth out the denoised image resulting in the loss of some image details after denoising. Many also distort images of lower resolution resulting in a partial or complete structural loss. In this paper, we address these shortcomings by proposing a collaborative filtering-based (CoFiB) denoising algorithm. Our proposed algorithm performs weighted sparse domain collaborative denoising by…
School of Computer Science and Technology, Harbin Institute of Technology (HIT)（哈尔滨工业大学计算机科学与技术学院）；School of Physics and Astronomy, The University of Manchester（曼彻斯特大学物理与天文学院）；SPACEnter Space Science and Technology Institute（SPACEnter空间科学与技术研究院）；Northeastern University（东北大学）；Beijing Electric Power Hospital（北京电力医院）
Heart disease is one of the most common diseases causing morbidity and mortality. Electrocardiogram (ECG) has been widely used for diagnosing heart diseases for its simplicity and non-invasive property. Automatic ECG analyzing technologies are expected to reduce human working load and increase diagnostic efficacy. However, there are still some challenges to be addressed for achieving this goal. In this study, we develop an algorithm to identify multiple abnormalities from 12-lead ECG recordings. In the algorithm pipeline, several preprocessing methods are firstly applied on the ECG data for de…
Optimal Wiener-Filter Solutions for Denoising of Graph Signals on Directed Graphs
Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院神经-X研究所）；Department of Radiology and Medical Informatics, University of Geneva（日内瓦大学放射学与医学信息学系）
Leading denoising methods such as 3D block matching (BM3D) are patch-based. However, they can suffer from frequency domain artefacts and require to specify explicit noise models. We present a patch-based method that avoids these drawbacks. It combines a simple and fast patch reordering with a non-linear smoothing. The smoothing rewards both patch and pixel similarities in a multiplicative way. We perform experiments on real world images with additive white Gaussian noise (AWGN), and on electron microscopy data with a more general additive noise model. Our filter outperforms BM3D in 77% of the …
Investigation on the electrocardiogram (ECG) signals is an essential way to diagnose heart disease since the ECG process is noninvasive and easy to use. This work presents a supraventricular arrhythmia prediction model consisting of a few stages, including filtering of noise, a unique collection of ECG characteristics, and automated learning classifying model to classify distinct types, depending on their severity. We de-trend and de-noise a signal to reduce noise to better determine functionality before extractions are performed. After that, we present one R-peak detection method and Q-S dete…
The human heart is a complex system exhibiting stochastic nature, as reflected in electrocardiogram (ECG) signals. ECG signal is a weak, non-stationary, and nonlinear signal, which indicates the health of a heart in terms of temporal variations of electromagnetic pulses from the heart. Abnormal fluctuations in ECG signal invokes the possibility of various cardiovascular disorders, which is diagnosed through intuitive analysis of the ECG reports by the medical practitioners. This could be made fast, accurate, and simple by imposing advanced nonlinear tools on the recorded ECG signals. In this p…
CPR: Causal Physiological Representation Learning for Robust ECG Analysis under Distribution Shifts
Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China（创新工程学院，澳门科技大学，澳门，中国）；Shenzhen University of Advanced Technology, Shenzhen, China（深圳先进技术大学，深圳，中国）
Swiss Center for Electronics and Microtechnology (CSEM)（瑞士电子与微技术中心）
We developed a convolutional recurrent neural network to classify 12-lead ECG signals for the challenge of PhysioNet/ Computing in Cardiology 2020 as team Pink Irish Hat. The model combines convolutional and recurrent layers, takes sliding windows of ECG signals as input and yields the probability of each class as output. The convolutional part extracts features from each sliding window. The bi-directional gated recurrent unit (GRU) layer and an attention layer aggregate these features from all windows into a single feature vector. Finally, a dense layer outputs class probabilities. The final …
