Electrocardiogram Signal Quality Research Articles

Stretchable Ag/AgCl Nanowire Dry Electrodes for High-Quality Multimodal Bioelectronic Sensing.

Bioelectrical signal measurements play a crucial role in clinical diagnosis and continuous health monitoring. Conventional wet electrodes, however, present limitations as they are conductive gel for skin irritation and/or have inflexibility. Here, we developed a cost-effective and user-friendly stretchable dry electrode constructed with a flexible network of Ag/AgCl nanowires embedded in polydimethylsiloxane (PDMS). We compared the performance of the stretched Ag/AgCl nanowire electrode with commonly used commercial wet electrodes to measure electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) signals. All the signal-to-noise ratios (SNRs) of the as-fabricated or stretched (50% tensile strain) Ag/AgCl nanowire electrodes are higher than that measured by commercial wet electrodes as well as other dry electrodes. The evaluation of ECG signal quality through waveform segmentation, the signal quality index (SQI), and heart rate variability (HRV) reveal that both the as-fabricated and stretched Ag/AgCl nanowire electrode produce high-quality signals similar to those obtained from commercial wet electrodes. The stretchable electrode exhibits high sensitivity and dependability in measuring EMG and EEG data, successfully capturing EMG signals associated with muscle activity and clearly recording α-waves in EEG signals during eye closure. Our stretchable dry electrode shows enhanced comfort, high sensitivity, and convenience for curved surface biosignal monitoring in clinical contexts.

A Novel Method and Python Library for ECG Signal Quality Assessment.

Electrocardiogram (ECG) is one of the reference cardiovascular diagnostic exams. However, the ECG signal is very prone to being distorted through different sources of artifacts that can later interfere with the diagnostic. For this reason, signal quality assessment (SQA) methods that identify corrupted signals are critical to improve the robustness of automatic ECG diagnostic methods. This work presents a review and open-source implementation of different available indices for SQA as well as introducing an index that considers the ECG as a dynamical system. These indices are then used to develop machine learning models which evaluate the quality of the signals. The proposed index along the designed ML models are shown to improve SQA for ECG signals.

Study on the Anti-Interference Performance of Substrate-Free PEDOT:PSS ECG Electrodes

Substrate-free electrodes are promising dry electrodes for long-term physiological electrical signal monitoring due to their ultra-thinness, conformal contact, and stable skin–electrode impedance. However, the response of substrate-free electrodes to various disturbances during electrocardiogram (ECG) monitoring and the corresponding optimization needs to be investigated. This paper investigates the specific effects of various influencing factors on skin–electrode impedance and ECG during electrocardiogram (ECG) detection. The research utilizes substrate-free poly(3,4-ethylenedioxythiophene)/poly(styrene-sulfonate) (PEDOT:PSS) electrodes. The investigation employs several methods, including skin–electrode impedance comparison, ECG waveform analysis, spectrum analysis, and signal-to-noise ratio (SNR) evaluation. To avoid the impact of physiological state differences in subjects at different times, relevant data were only compared with the same group of experiments conducted in the same period. The results demonstrate that the substrate-free conformal contact PEDOT:PSS electrode has more stable skin–electrode impedance and could obtain a more stable ECG than partial contact electrodes (the SNR of the partial contact and conformal contact electrodes are 1.2768 ± 4.0299 dB and 7.2637 ± 1.4897 dB, respectively). Furthermore, the ECG signal quality of the substrate-free conformal contact PEDOT:PSS electrode was independent of the electrode area and shape (the SNRs of the large, medium, and small electrodes are 4.0447 ± 0.4616 dB, 3.9115 ± 0.5885 dB, and 4.1556 ± 0.5557 dB, respectively; the SNRs of the circular, square, and triangular electrodes are 9.2649 ± 0.6326 dB, 9.2471 ± 0.6806 dB, and 9.1514 ± 0.6875 dB, respectively), showing high signal acquisition capability that is the same as microneedle electrodes and better than fabric electrodes. The results of clothing friction effects show that skin–electrode impedance stability was important for ECG stability, while the impedance value was not (the SNRs of friction and non-friction electrodes are 2.4128 ± 7.0784 dB and 9.2164 ± 0.6696 dB, respectively). Moreover, the skin–electrode impedance maintains stability even at a high breathing frequency, but the ECG signal fluctuates at a high breathing frequency. This experiment demonstrates that even when the skin–electrode impedance remains stable, the ECG signal can still be susceptible to interference from other factors. This study suggests that substrate-free PEDOT:PSS that could form conformal contact with the skin has higher skin–electrode impedance stability and could measure a high ECG signal even with a small electrode area, demonstrating its potential as dry ECG electrodes, but the interference from other physiological electrical signals may require better circuit design.

Dynamic Electrocardiogram Signal Quality Assessment Method Based on Convolutional Neural Network and Long Short-Term Memory Network

Dynamic Electrocardiogram Signal Quality Assessment Method Based on Convolutional Neural Network and Long Short-Term Memory Network

Recent Progress in the Development of Flexible Wearable Electrodes for Electrocardiogram Monitoring During Exercise

Electrocardiogram (ECG) monitoring has recently been an important indicator of cardiac health diagnosis. In the past, ECG could be measured under limited conditions in hospitals with 12‐lead electrode systems. Recently, portable and wearable devices have offered continuous, real‐time monitoring of ECG signals in real life. However, developing wearable ECG sensors that provide low‐motion artifacts and high‐quality signals during exercise conditions is still challenging. Herein, this review reports a systematic summary of the key characteristics, properties, and requirements of flexible wearable ECG devices for the early diagnosis of heart dysfunction in dynamic motions, including exercise. In addition, the recent progress in controlling sensor adhesion and novel materials for designing dry electrodes are discussed to improve ECG signal quality in exercise. Finally, various aspects of electrode developmental challenges and limitations are reviewed, and research directions for future studies are discussed.

Fast CNN-Based Electrocardiogram Signal Quality Assessment Using Fourier Magnitude Spectrum for Resource-Constrained ECG Diagnosis Devices

Fast CNN-Based Electrocardiogram Signal Quality Assessment Using Fourier Magnitude Spectrum for Resource-Constrained ECG Diagnosis Devices

Wearable Technology for Monitoring Electrocardiograms (ECGs) in Adults: A Scoping Review.

In the rapidly evolving landscape of continuous electrocardiogram (ECG) monitoring systems, there is a heightened demand for non-invasive sensors capable of measuring ECGs and detecting heart rate variability (HRV) in diverse populations, ranging from cardiovascular patients to sports enthusiasts. Challenges like device accuracy, patient privacy, signal noise, and long-term safety impede the use of wearable devices in clinical practice. This scoping review aims to assess the performance and safety of novel multi-channel, sensor-based biopotential wearable devices in adults. A comprehensive search strategy was employed on four databases, resulting in 143 records and the inclusion of 12 relevant studies. Most studies focused on healthy adult subjects (n = 6), with some examining controlled groups with atrial fibrillation (AF) (n = 3), long QT syndrome (n = 1), and sleep apnea (n = 1). The investigated bio-sensor devices included chest-worn belts (n = 2), wrist bands (n = 2), adhesive chest strips (n = 2), and wearable textile smart clothes (n = 4). The primary objective of the included studies was to evaluate device performance in terms of accuracy, signal quality, comparability, and visual assessment of ECGs. Safety findings, reported in five articles, indicated no major side effects for long-term/continuous monitoring, with only minor instances of skin irritation. Looking forward, there are ample opportunities to enhance and test these technologies across various physical activity intensities and clinical conditions.

Variational autoencoder–based neural electrocardiogram synthesis trained by FEM-based heart simulator

BackgroundFor comprehensive electrocardiogram (ECG) synthesis, a recent promising approach has been based on a heart model with physical and chemical cardiac parameters. However, the problem is that such approach requires a high-cost and limited environment using supercomputers due to the massive computation. ObjectiveThe purpose of this study is to develop an efficient method for synthesizing 12-lead ECG signals from cardiac parameters. MethodsThe proposed method is based on a variational autoencoder (VAE). The encoder and decoder of the VAE are conditioned by the cardiac parameters so that it can model the relationship between the ECG signals and the cardiac parameters. The training data are produced by a comprehensive, Finite Element Method (FEM)-based heart simulator. New ECG signals can then be synthesized by inputting the cardiac parameters into the trained VAE decoder without relying on enormous computational resources. We used two metrics to evaluate the quality of ECG signals synthesized by the proposed model. ResultsExperimental results showed that the proposed model synthesized adequate ECG signals while preserving empirically important feature points and the overall signal shapes. We also explored the optimal model by varying the number of layers and the size of latent variables in the proposed model that balances the model complexity and the simulation accuracy. ConclusionThe proposed method has the potential to become an alternative to computationally-expensive FEM-based heart simulators. It is able to synthesize ECGs from various cardiac parameters within seconds on a personal laptop computer.

Comparison of Digital Filter Orders in Single Lead Electrocardiographic Design for Reducing Interference Motion Artifact

Developed a new method to overcome motion artifacts in Electrocardiogram (ECG) signals, which often interfere with accurate clinical analysis. Motion artifacts, such as body movements, can cause significant distortions in the ECG signal, resulting in incorrect interpretation and affecting medical diagnosis. The main objective of this research is to design and implement an infinite impulse response (IIR) filter with a predetermined sequence, namely orders 2, 4, 6, and 8 to reduce motion artifacts in the ECG signal. We aim to improve ECG signal quality by preserving important ECG signal information and reducing noise caused by motion artifacts. This research contributes to developing more precise and reliable ECG signal processing techniques. The proposed method provides an effective approach to handling motion artifacts, enabling more accurate and reliable ECG interpretation by medical professionals. We used an ECG simulator that provides body movement simulation as a basis for experiments. The detected ECG signal is processed with a predetermined order IIR filter. We compare the filtered signal to the original signal to measure the effectiveness of reducing motion artifacts. Experimental results show that the applied IIR filter efficiently reduces motion artifacts in the ECG signal. The SNR assessment showed a significant improvement, proving the success of this method in maintaining ECG signal quality. The result is that in the 2nd order, the SNR value is 22.25 dB, in the 4th order the SNR value is 22.75 dB, in the 6th order the SNR value is 22.99 dB, in the 6th order the SNR value is 22.99 dB. 8 obtained an SNR value of 23dB. This study successfully demonstrated that using IIR filters in a specified order effectively reduces motion artifacts in the ECG signal, increases SNR, and maintains the integrity of clinical information in the ECG signal. The implications of this research extend to medical technology development and clinical applications, providing a strong foundation for continued research in more efficient and reliable ECG signal processing.

Patient Confidential Data Hiding and Transmission System Using Amplitude Quantization in the Frequency Domain of ECG Signals.

The transform domain provides a useful tool in the field of confidential data hiding and protection. In order to protect and transmit patients' information and competence, this study develops an amplitude quantization system in a transform domain by hiding patients' information in an electrocardiogram (ECG). In this system, we first consider a non-linear model with a hiding state switch to enhance the quality of the hidden ECG signals. Next, we utilize particle swarm optimization (PSO) to solve the non-linear model so as to have a good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative root mean square error (rRMSE). Accordingly, the distortion of the shape in each ECG signal is tiny, while the hidden information can fulfill the needs of physiological diagnostics. The extraction of hidden information is reversely similar to a hiding procedure without primary ECG signals. Preliminary outcomes confirm the effectiveness of our proposed method, especially an Amplitude Similarity of almost 1, an Interval RMSE of almost 0, and SNRs all above 30.

Factors for incorrect automated atrial fibrillation identification in large-scale screening

Abstract Introduction Correct heart rhythm diagnosis in single-lead electrocardiogram (ECG) screening for atrial fibrillation (AF) is paramount for large-scale implementation. Aim Examine the accuracy of automated AF detection and identify patient factors and ECG signal quality leading to incorrect measurements based on human overread and a machine learning approach. Methods Screening for AF using a single-lead ECG device (MyDiagnostick, Biomedical Systems, Netherlands) was performed on 7295 customers in community pharmacies in Germany. ECG rhythm strips were blinded for automatic heart rhythm analysis and were analyzed by human overread for heart rhythm and signal quality from excellent to uninterpretable (Figure 1A). A machine learning-based estimation was used to score signal quality (MLSQE). Inclusion criteria were 30 seconds of consecutive ECG recording and complete data of pharmacy visit. In total, 6732 participants were eligible for analysis. Results An incorrect automated heart rhythm diagnosis was found for 206 subjects (false positive: 112; false negative: 94). By human overread 80% (N=5377) of the recorded ECGs were rated with excellent and good signal quality in which heart rhythm diagnosis based on visible atrial electrical activity (Figure 1A). The proportion of incorrect automated rhythm diagnosis was higher in ECGs with poorer signal quality. A regression analysis revealed patient characteristics prone to incorrect heart rhythm diagnosis included male sex (OR 1.61 95% confidence interval (CI) 1.22-2.13, P<0.001), age (per year OR 1.06 95%CI 1.04-1.09, P<0.001), body height (per cm OR 1.02 95%CI 1.00-1.03, P=0.03), and body weight (per kg OR 0.99 95%CI 0.98-0.99, P=0.005). A multivariate model for prediction of correct heart rhythm diagnosis showed a modest area under the curve (AUC) of 0.65 (sensitivity 61%, specificity 58%; Figure 1B). The prediction improved by adding information on signal quality rated by human overread (AUC 0.77, sensitivity 77%, specificity 62%) and even more by the machine-learning based MLSQE score (AUC 0.87, sensitivity 71%, specificity 90%). Conclusion The patient characteristics prone to incorrect automated heart rhythm diagnosis of male sex, older age, greater height, and lower body weight probably reflect the target population of many AF screening cohorts. Automated MLSQE scoring for ECG signal quality could improve accuracy in clinical use and potentially reduces the need for cumbersome and time-consuming human overread.Figure 1A - Signal quality by human rating and proportion of incorrect automated heart rhythm diagnosis. B - Prediction of correct heart rhythm diagnosis. Blue line - Multivariate model with forward selection considering age, height, weight, body mass index, congestive heart failure, hypertension, diabetes mellitus, vascular disease, stroke/TIA and sex. Purple line - Model with human signal quality (SQ) score. Green line – Model with MLSQE score.

Enhanced Classification of Heartbeat Electrocardiogram Signals Using a Long Short-Term Memory–Convolutional Neural Network Ensemble: Paving the Way for Preventive Healthcare

In the rapidly evolving field of medical diagnosis, the accurate and prompt interpretation of heartbeat electrocardiogram (ECG) signals have become increasingly crucial. Despite the presence of recent advances, there is an exigent need to enhance the accuracy of existing methodologies, especially given the profound implications such interpretations can have on patient prognosis. To this end, we introduce a novel ensemble comprising Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models to enable the enhanced classification of heartbeat ECG signals. Our approach capitalizes on LSTM’s exceptional sequential data learning capability and CNN’s intricate pattern recognition strength. Advanced signal processing methods are integrated to enhance the quality of raw ECG signals before feeding them into the deep learning model. Experimental evaluations on benchmark ECG datasets demonstrate that our proposed ensemble model surpasses other state-of-the-art deep learning models. It achieves a sensitivity of 94.52%, a specificity of 96.42%, and an accuracy of 95.45%, highlighting its superior performance metrics. This study introduces a promising tool for bolstering cardiovascular disease diagnosis, showcasing the potential of such techniques to advance preventive healthcare.

Washing and Abrasion Resistance of Textile Electrodes for ECG Measurements

Electrocardiogram (ECG) signals are often measured for medical purposes and in sports. While common Ag/AgCl glued gel electrodes enable good electrode skin contact, even during movements, they are not comfortable and can irritate the skin during long-term measurements. A possible alternative is textile electrodes, which have been investigated extensively during the last years. These electrodes, however, are usually not able to provide reliable, constant skin contact, resulting in reduced signal quality. Another important problem is the modification of the electrode surface due to washing or abrasion, which may impede the long-term use of such textile electrodes. Here, we report a study of washing and abrasion resistance of different ECG electrodes based on an isolating woven fabric with conductive embroidery and two conductive coatings, showing unexpectedly high abrasion resistance of the silver-coated yarn and optimum ECG signal quality for an additional coating with a conductive silicone rubber. Sheet resistances of the as-prepared electrodes were in the range of 20–30 Ω, which was increased to the range of 25–40 Ω after five washing cycles and up to approximately 50 Ω after Martindale abrasion tests. ECG measurements during different movements revealed reduced motion artifacts for the electrodes with conductive silicone rubber as compared to glued electrodes, suggesting that electronic filtering of such noise may even be easier for textile electrodes than for commercial electrodes.

Signal quality indices evaluation for robust ECG signal quality assessment systems

As the current healthcare system faces problems of budget, staffing, and equipment, telemedicine through wearable devices gives a means of solving them. However, their adoption by physicians is hampered by the quality of electrocardiogram (ECG) signals recorded outside the hospital setting. Due to the dynamic nature of the ECG and the noise that can occur in real-world conditions, Signal Quality Assessment (SQA) systems must use robust signal quality indices (SQIs). The aim of this study is twofold: to assess the robustness of the most commonly used SQIs and to report on their complexity in terms of computational speed. A total of 39 SQIs were explored, of which 16 were statistical, 7 were non-linear, 9 were frequency-based and 7 were based on QRS detectors. With 6 databases, we manually constructed 2 datasets containing many rhythms. Each signal was labelled as ‘acceptable’ or ‘unacceptable’ (subcategories: ‘motion artefacts’, ‘electromyogram noise’, ‘additive white Gaussian noise’, or ‘power line interference’). Our results showed that the performance of an SQI in distinguishing a good signal from a bad one depends on the type of noise. Furthermore, 23 SQIs were found to be robust. The analysis of their extraction time on 10-second signals revealed that statistics-based and frequency domain-based SQIs are the least complex with an average computational time of (mean: 1.40 ms, standard deviation: 1.30 ms), and (mean: 4.31 ms, standard deviation: 4.50 ms), respectively. Then, our results provide a basis for choosing SQIs to develop more general and faster SQAs.

FGSQA-Net: A Weakly Supervised Approach to Fine-Grained Electrocardiogram Signal Quality Assessment.

Due to the lack of fine-grained labels, current research can only evaluate the signal quality at a coarse scale. This article proposes a weakly supervised fine-grained electrocardiogram (ECG) signal quality assessment method, which can produce continuous segment-level quality scores with only coarse labels. A novel network architecture, i.e. FGSQA-Net, is developed for signal quality assessment, which consists of a feature shrinking module and a feature aggregation module. Multiple feature shrinking blocks, which combine residual CNN block and max pooling layer, are stacked to produce a feature map corresponding to continuous segments along the spatial dimension. Segment-level quality scores are obtained by feature aggregation along the channel dimension. The proposed method was evaluated on two real-world ECG databases and one synthetic dataset. Our method produced an average AUC value of 0.975, which outperforms the state-of-the-art beat-by-beat quality assessment method. The results are visualized for 12-lead and single-lead signals over a granularity from 0.64 to 1.7 seconds, demonstrating that high-quality and low-quality segments can be effectively distinguished at a fine scale. FGSQA-Net is flexible and effective for fine-grained quality assessment for various ECG recordings and is suitable for ECG monitoring using wearable devices. This is the first study on fine-grained ECG quality assessment using weak labels and can be generalized to similar tasks for other physiological signals.

Characterizing the Impedance Properties of Dry E-Textile Electrodes Based on Contact Force and Perspiration.

Biopotential electrodes play an integral role within smart wearables and clothing in capturing vital signals like electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG). This study focuses on dry e-textile electrodes (E1-E6) and a laser-cut knit electrode (E7), to assess their impedance characteristics under varying contact forces and moisture conditions. Synthetic perspiration was applied using a moisture management tester and impedance was measured before and after exposure, followed by a 24 h controlled drying period. Concurrently, the signal-to-noise ratio (SNR) of the dry electrode was evaluated during ECG data collection on a healthy participant. Our findings revealed that, prior to moisture exposure, the impedance of electrodes E7, E5, and E2 was below 200 ohm, dropping to below 120 ohm post-exposure. Embroidered electrodes E6 and E4 exhibited an over 25% decrease in mean impedance after moisture exposure, indicating the impact of stitch design and moisture on impedance. Following the controlled drying, certain electrodes (E1, E2, E3, and E4) experienced an over 30% increase in mean impedance. Overall, knit electrode E7, and embroidered electrodes E2 and E6, demonstrated superior performance in terms of impedance, moisture retention, and ECG signal quality, revealing promising avenues for future biopotential electrode designs.

Long-term electrocardiogram signal quality assessment pipeline based on a frequency-adaptive mean absolute deviation curve

Long-term electrocardiogram signal quality assessment pipeline based on a frequency-adaptive mean absolute deviation curve

Multi-task cascaded assessment of signal quality for long-term single-lead ECG monitoring

Background and objectivesIn long-term electrocardiogram (ECG) monitoring, the use of wearable devices for signal acquisition inevitably introduces noise, which could reduce the diagnostic capability. In order to improve the efficiency of signal analysis and the accuracy of clinical diagnosis, it is necessary to automatically evaluate the signal quality of ambulatory ECG. In this study, a novel multi-task cascaded method is proposed for signal quality assessment. MethodsBased on single-lead ECG, various signal quality indices were derived to assess three classes of signal qualities (clear ECG ideal for full wave analysis, mildly contaminated ECG available for ventricular activity-based analysis, severely contaminated ECG difficult to be analyzed) through a two-step classification corresponding to different tasks. The optimized subsets of quality indices were selected with a criterion of maximal-mean-minimal-variance. Different machine learning models were then trained and tested on public datasets. ResultsThe results show that random forest using the optimal feature subsets had the best performance, with the overall classification accuracy of 93.76%, 95.13% and 99.61% on the three unknown test sets, respectively. Meanwhile, for the identification of acceptable and unacceptable signals in the first task, the AUC on the test sets reached 0.9967, 0.9959 and 1.0000, respectively. ConclusionsThis paper presents a novel signal quality assessment framework, which could provide helpful feedback for ECG processing in specific application scenarios. It is expected to greatly promote accurate and efficient ECG-based diagnosis in long-term monitoring.

Adaptive Recursive Least Squares Denoising in Ventricular Fibrillation ECG Signals

AbstractCardiac arrest is a fatal and urgent disease in humans. A high‐quality electrocardiogram (ECG) has a positive guide to the success of defibrillation and resuscitation. However, because of artificial motion interference and ambient noise, reliable ECG signals can be obtained only during chest compression (CC) pauses. To address this issue, the adaptive recursive least squares (RLS) denoising approach is proposed. First, the ECG signals of porcine are divided into three groups: CC, without CC, and both with and without CC. Then, five Gaussian noises with different signals‐to‐noise ratios (SNR) and five noises with different distribution types are added, respectively. Furthermore, RLS is compared with six other different denoising approaches. Experimental results demonstrate significant differences between RLS and the other six algorithms in main metrics. SNR and related factors are larger, while the root mean square error is smaller. In conclusion, RLS can significantly eliminate many types of ambient noise, and improve the quality of ECG signals during cardiopulmonary resuscitation.

An Automatic ECG Signal Quality Assessment Method Based on Resnet and Self-Attention

Electrocardiogram (ECG) signals are among the significant physiological signals that indicate the essential properties of the human body. In recent years, the measurement of ECG signals has become more portable thanks to the increasing usage of wearable health testing technology. However, the enormous amount of signal data gathered over a long period of time does impose a heavy load on medical professionals. In addition, false alarms might occur due to the potential for the detected signal to become jumbled with noise and motion perturbations. Therefore, analyzing the quality of the measured raw ECG signal automatically is a valuable task. In this paper, we propose a new single-channel ECG signal quality assessment method that combines the Resnet network structure and the principle of self-attention to extract ECG signal features using the principle of similarity between individual QRS heartbeats within a time slice of ten seconds. In addition, an improved self-attention module is introduced into the deep neural network to learn the similarity between features. Finally, the network distinguishes between acceptable and unacceptable ECG segments. The model test results indicate that the F1-score can approach 0.954, which leads to a more accurate assessment of the ECG signal quality.