High-quality Electrocardiogram Signals Research Articles

SwinDAE: Electrocardiogram Quality Assessment Using 1D Swin Transformer and Denoising AutoEncoder.

Electrocardiogram (ECG) signals have wide-ranging applications in various fields, and thus it is crucial to identify clean ECG signals under different sensors and collection scenarios. Despite the availability of a variety of deep learning algorithms for ECG quality assessment, these methods still lack generalization across different datasets, hindering their widespread use. In this paper, an effective model named Swin Denoising AutoEncoder (SwinDAE) is proposed. Specifically, SwinDAE uses a DAE as the basic architecture, and incorporates a 1D Swin Transformer during the feature learning stage of the encoder and decoder. SwinDAE was first pre-trained on the public PTB-XL dataset after data augmentation, with the supervision of signal reconstruction loss and quality assessment loss. Specially, the waveform component localization loss is proposed in this paper and used for joint supervision, guiding the model to learn key information of signals. The model was then fine-tuned on the finely annotated BUT QDB dataset for quality assessment. SwinDAE achieved 0.02-0.13 mean F1 score improvement on the BUT QDB dataset compared to multiple deep learning methods, and demonstrated applicability on two other datasets. The proposed SwinDAE shows strong generalization ability on different datasets, and surpasses other state-of-the-art deep learning methods on multiple evaluation metrics. In addition, the statistical analysis for SwinDAE prove the significance of the performance and the rationality of the prediction. SwinDAE can learn the commonality between high-quality ECG signals, exhibiting excellent performance in the application of cross-sensors and cross-collection scenarios.

Enhancing Digital Filtering to Electrocardiogram Analysis: Butterworth Filter Application

The electrocardiogram (ECG) of the human body is an important basis in heart function as well as the diagnosis of cardiovascular diseases, which has a very vital role in clinical diagnosis. Obtaining high-quality ECG signals with a portable remote ECG acquisition system is a big challenge given limited resources. According to the World Health Organization (WHO), disorders of the cardiovascular system still rank high, causing about 31% of deaths globally. This is because the symptoms of cardiovascular disease cannot be seen directly, but rather by conducting an electrocardiograph (ECG) examination. The purpose of this research is to record the electrical signals of the heart by comparing the 2nd order AD8232 module analogue filter with the 8th order Butterworth digital filter which is a better signal for easier reading. This research uses a multiplexer circuit for switching leads, AD8232 ECG module, 50Hz notch filter circuit, non-inverting amplifier, adder, Arduino Mega 2560, USB module, and an application to display digital signals, namely Delphi 7. Signal acquisition is done by monitoring for one minute. Data collection was carried out with 5 respondents 5 times on each lead. The results of the data collection can be concluded that 80% of digital filters display smoother signals for ECG signals than analogue filters

RMKnet: A novel and parameter-efficient architecture for robust r-peak detection

The Electrocardiogram (ECG) has become the most important diagnostic criteria in the non-invasive diagnosis of heart disease. The automatic detection of r-peaks in an electrocardiogram signal is also crucial in a all kinds of applications about heart disease. Recently, lots of algorithms have been proposed to automatically detect r-peaks in ECG signals. Traditional detection methods have high detection accuracy on high quality ECG signals which don’t have so much noise, but they perform poorly on low quality ECG signal. Most of the deep learning methods are more robust and have higher accuracy than the traditional methods, but they are usually computationally expensive and difficult to implement and customize. In this paper, we proposed a novel and robust model architecture based on Unet[17] to detect r-peaks. After training, we use re-parameterization greatly accelerating the inference process and reducing the inference memory utilization. The proposed network is evaluated on two open-access ECG databases: CPSC-DB, MIT-BIH and achieves high F1 scores which showing good generalization on different databases comparing to all competing methods.

Development of a model of electric impedance in the contact between the skin and a capacitive active electrode when measuring electrocardiogram in combustiology

Long-term ECG (electrocardiogram) measurement in patients with burns is a complicated problem since the overlapping of surface contact electrodes can lead to additional injuries. The possibility of ECG recording in patients with burns using capacitive electrodes was not proved, and there are no models of the electrode contact with a patient’s body while rehabilitation means are used. In this paper, the model of the contact between capacitive electrodes and the skin was modified and the circuit model of the contact: skin – bandages (saline solution) – film – active capacitive electrode, was described. The influence of the parameters of a capacitive electrode on the amplitude-frequency characteristics (AFC) of the contact of an electrode with skin was assessed. It was found that contact capacitance is crucial to obtain a high-quality ECG signal. The parameters of the impedance of bandages, saline solution, a dielectric film were calculated, and their effect on the AFC was studied. Based on the modified model, the AFC contact was modeled taking into consideration all the calculated parameters; it was found that the resulting AFC of the contact corresponds to the frequency range of the ECG signal. Analysis of the calculations proves the possibility of using capacitive electrodes when applying various rehabilitation means. It was found that at a change in the impedance of the saline solution from 0.1 gigaohms to 1 gigaohm, the changes in the AFC of the contact are not crucial for the final quality of the received signal. All calculations were carried out by modeling in the Qucs environment (ngspice SPICE). Simulation results can be used in the development of new types of capacitive electrocardiographic electrodes. The proposed model can be used to study other wound covers, as well as to model physiological processes when putting artificial skin and wound covers

Two-Electrode ECG for Ambulatory Monitoring with Minimal Hardware Complexity.

This article introduces a two-electrode ground-free electrocardiogram (ECG) with minimal hardware complexity, which is ideal for wearable battery-powered devices. The main issue of ground-free measurements is the presence of noise. Therefore, noise suppression methods that can be employed for a two-electrode ECG acquisition system are discussed in detail. Experimental measurements of a living subject and patient simulator are used to investigate and compare the performance of the three proposed methods utilizing the ADS1191 analogue front-end for biopotential measurements. The resulting signals recorded for the simulator indicate that all three methods should be suitable for suppressing power-line noise. The Power Spectral Density (PSD) of the signals measured for a subject exhibits differences across methods; the signal power at 50 Hz is −28, −24.8, and −26 dB for the first, second, and third method, respectively. The digital postprocessing of measured signals acquired a high-quality ECG signal comparable to that of three-electrode sensing. The current consumption measurements demonstrate that all proposed two-electrode ECG solutions are appropriate as a battery-powered device (current consumption < 1.5 mA; sampling rate of 500 SPS). The first method, according to the results, is considered the most effective method in the suppression of power-line noise, current consumption, and hardware complexity.

Noise Reduction in ECG Signal Using an Effective Hybrid Scheme

Electrocardiogram (ECG) is a critical biological signal, which usually carries a great deal of essential information about patients. The high quality ECG signals are always required for a proper diagnosis of cardiac disorders. However, the raw ECG signals are highly noisy in nature. In the paper, we propose a hybrid denoising scheme to enhance ECG signals by combining high-order synchrosqueezing transform (FSSTH) with non-local means (NLM). With this method, a noisy ECG signal is first decomposed into an ensemble of intrinsic mode functions (IMFs) by FSSTH. Then, some noise is removed by eliminating a set of noisy IMFs that are determined by a scaling exponent obtained by the detrended fluctuation analysis (DFA); while the remaining IMFs are filtered by NLM. Finally, the denoised ECG signal is obtained by reconstructing the processed IMFs. Experiments are carried out using the simulated ECG signals and real ones from the MIT-BIH database, and the denoising performances are evaluated in terms of signal to noise ratio (SNR), root mean squared error (RMSE) and percent root mean square difference (PRD). Results show that the hybrid denoising scheme involving both FSSTH and NLM is able to suppress complex noise from ECG signals more effectively while preserving the details well.

Evaluation of patient electrocardiogram datasets using signal quality indexing

Electrocardiogram (ECG) is widely used in the hospital emergency rooms for detecting vital signs, such as heart rate variability and respiratory rate. However, the quality of the ECGs is inconsistent. ECG signals lose information because of noise resulting from motion artifacts. To obtain an accurate information from ECG, signal quality indexing (SQI) is used where acceptable thresholds are set in order to select or eliminate the signals for the subsequent information extraction process. A good evaluation of SQI depends on the R-peak detection quality. Nevertheless, most R-peak detectors in the literature are prone to noise. This paper assessed and compared five peak detectors from different resources. The two best peak detectors were further tested using MIT-BIH arrhythmia database and then used for SQI evaluation. These peak detectors robustly detected the R-peak for signals that include noise. Finally, the overall SQI of three patient datasets, namely, Fantasia, CapnoBase, and MIMIC-II, was conducted by providing the interquartile range (IQR) and median SQI of the signals as the outputs. The evaluation results revealed that the R-peak detectors developed by Clifford and Behar showed accuracies of 98% and 97%, respectively. By introducing SQI and choosing only high-quality ECG signals, more accurate vital sign information will be achieved.

A comfortability and signal quality study of conductive weave electrodes in long-term collection of human electrocardiographs

Long-term electrocardiogram (ECG) recording can reveal some vital cardiovascular disorders and provide warning of human sudden cerebral or vascular diseases in advance. This requires high-quality ECG skin electrodes. Gel (Ag/AgCl) electrodes were reported to have good signal quality in ECG acquisition, but easily caused human skin irritation or allergy. Consequently, textile electrodes have attracted more attention for long-term ECG acquisition. In this paper, eight woven fabrics with diverse yarns and weft densities were fabricated in plain and honeycomb structures. The fabrics were investigated in terms of comfortability, fabric–skin contact impedance and acquired bio-signal quality. Honeycomb weave electrodes were measured with a high comfort level from subjective and objective views, including pleasant tactile comfort, high visual acceptance, good air permeability and good heat transfer. Weave electrodes made of all conductive filaments in high density had low skin contact impedance and high-quality ECG signals. An increase of compression load on weave electrodes resulted in a decrease of contact impedance with a high signal quality. A conductive honeycomb weave with unit repeat of 6*6 warps*wefts presented the highest score of acquired ECG signals of all studied electrodes based on the qualities of the QRS complex, P and T waves, R peak amplitude and variation and signal-to-noise ratio. This study contributes to the future design and fabrication of textile electrodes using honeycomb weave in long-term and real-time collection of human ECGs.

SQI Quality Evaluation Mechanism of Single-Lead ECG Signal Based on Simple Heuristic Fusion and Fuzzy Comprehensive Evaluation.

For both the acquisition of mobile electrocardiogram (ECG) devices and early warning and diagnosis of clinical work, high-quality ECG signals is particularly important. We describe an effective system which could be deployed as a stand-alone signal quality assessment algorithm for vetting the quality of ECG signals. The proposed ECG quality assessment method is based on the simple heuristic fusion and fuzzy comprehensive evaluation of the SQIs. This method includes two modules, i.e., the quantification and extraction of Signal Quality Indexes (SQIs) for different features, intelligent assessment and classification. First, simple heuristic fusion is executed to extract SQIs and determine the following SQIs: R peak detection match qSQI, QRS wave power spectrum distribution pSQI, kurtosis kSQI, and baseline relative power basSQI. Then, combined with Cauchy distribution, rectangular distribution and trapezoidal distribution, the membership function of SQIs was quantified, and the fuzzy vector was established. The bounded operator was selected for fuzzy synthesis, and the weighted membership function was used to perform the assessment and classification. The performance of the proposed method was tested on the database from Physionet ECG database, with an accuracy (Acc) of 97.67%, sensitivity (Se) of 96.33% and specificity (Sp) of 98.33% on the training set. Testing against the test datasets resulted in scores of 94.67, 90.33, and 93.00%, respectively. There's no gold standard exists for determining the quality of ECGs. However, the proposed algorithm discriminates between high- and poor-quality ECGs, which could aid in ECG acquisition for mobile ECG devices, early clinical diagnosis and early warning.

Wearable Current-Based ECG Monitoring System with Non-Insulated Electrodes for Underwater Application

The second most common cause of diving fatalities is cardiovascular diseases. Monitoring the cardiovascular system in actual underwater conditions is necessary to gain insights into cardiac activity during immersion and to trigger preventive measures. We developed a wearable, current-based electrocardiogram (ECG) device in the eco-system of the FitnessSHIRT platform. It can be used for normal/dry ECG measuring purposes but is specifically designed to allow underwater signal acquisition without having to use insulated electrodes. Our design is based on a transimpedance amplifier circuit including active current feedback. We integrated additional cascaded filter components to counter noise characteristics specific to the immersed condition of such a system. The results of the evaluation show that our design is able to deliver high-quality ECG signals underwater with no interferences or loss of signal quality. To further evaluate the applicability of the system, we performed an applied study with it using 12 healthy subjects to examine whether differences in the heart rate variability exist between sitting and supine positions of the human body immersed in water and outside of it. We saw significant differences, for example, in the RMSSD and SDSD between sitting outside the water (36 ms) and sitting immersed in water (76 ms) and the pNN50 outside the water (6.4%) and immersed in water (18.2%). The power spectral density for the sitting positions in the TP and HF increased significantly during water immersion while the LF/HF decreased significantly. No significant changes were found for the supine position.

A Power-Efficient Mixed-Signal Smart ADC Design With Adaptive Resolution and Variable Sampling Rate for Low-Power Applications

With the rapid development of portable electronics, wearable devices have become widely used to monitor body signals for long-term health care and home care applications. They detect vital signals through physiological sensors and then transmit them to a cloud database for evaluation and monitoring purposes through wireless communication systems. In this paper, a smart analog-to-digital converter (ADC) was realized by a mixed-signal application-specific integrated circuit (ASIC) based on adaptive resolution and lossless compression techniques for electrocardiogram (ECG) signal monitoring. The sampling clock for the ADC can be adaptively selected according to the characteristic of the signals. The lossless encoder consists of trend forecasting and entropy coding modules. The transmission data rate was decreased efficiently by adaptive resolution and lossless compression techniques. The chip aims to meet the low power consumption for the design, because it reduced the signal transmission rate and maintained high-quality ECG signal detection. The proposed mixed-signal ASIC design was realized using a 0.18-μm CMOS process with a total power consumption of 78.8 μW when operating at 1 kHz and a total chip area of 850 × 850 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

ANMCO/AIIC/SIT Consensus Information Document: definition, precision, and suitability of electrocardiographic signals of electrocardiographs, ergometry, Holter electrocardiogram, telemetry, and bedside monitoring systems.

The electrocardiogram (ECG) signal can be derived from different sources. These include systems for surface ECG, Holter monitoring, ergometric stress tests, and telemetry systems and bedside monitoring of vital parameters, which are useful for rhythm and ST-segment analysis and ECG screening of electrical sudden cardiac death predictors. A precise ECG diagnosis is based upon correct recording, elaboration, and presentation of the signal. Several sources of artefacts and potential external causes may influence the quality of the original ECG waveforms. Other factors that may affect the quality of the information presented depend upon the technical solutions employed to improve the signal. The choice of the instrumentations and solutions used to offer a high-quality ECG signal are, therefore, of paramount importance. Some requirements are reported in detail in scientific statements and recommendations. The aim of this consensus document is to give scientific reference for the choice of systems able to offer high quality ECG signal acquisition, processing, and presentation suitable for clinical use.

Design of a Real-Time ECG Filter for Portable Mobile Medical Systems

Electrocardiogram (ECG) signal is a direct and effective way to find cardiovascular disease timely, which can intuitively reflect changes of the heart beat and activities of different parts. Due to the noise interference from surroundings, acquisition of real-time and high-quality ECG signal is a big challenge for portable mobile medical systems. Integer coefficients infinite impulse response (IIR) digital filter is suitable for portable mobile platform, but it will be a little bit of distortion. So, an improved integer IIR filter for portable ECG monitors is presented in this paper. The proposed IIR filter can instantaneously and effectively eliminate baseline drift of main interference frequency band and 50-Hz power frequency interference in ECG signal. The method is verified by a specific example and MIT/BIH Arrhythmia Database. The improved integer coefficients IIR filter was applied in a portable mobile medical system. It can meet the requirements of ECG filtering in real-time and filtering performance.

MEMS‐based flexible capacitive electrode for ECG measurement

A thin and flexible capacitive electrode for recording an electrocardiogram (ECG) signal has been developed. Compared with the conventional wet electrode, the capacitive electrode can work without conductive gel and skin preparation, and serves as a tool of long-term ECG measurement. The designed electrode consists of a sensing plate, an active shield plate and two insulating layers, which is fabricated by the micro-electro-mechanical system process. The test result shows that the capacitive electrode can obtain a high-quality ECG signal that is comparable to the one recorded by the conventional wet electrode. The capacitive electrode can be arbitrarily bent to fit the human torso because of its extremely thin and flexible feature. Moreover, a simple front-end circuit is used to integrate more easily into the measurement system.

Noninvasive method for electrocardiogram recording in conscious rats: feasibility for heart rate variability analysis

Heart rate variability (HRV) analysis consists in a well-established tool for the assessment of cardiac autonomic control, both in humans and in animal models. Conventional methods for HRV analysis in rats rely on conscious state electrocardiogram (ECG) recording based on prior invasive surgical procedures for electrodes/transmitters implants. The aim of the present study was to test a noninvasive and inexpensive method for ECG recording in conscious rats, assessing its feasibility for HRV analysis. A custom-made elastic cotton jacket was developed to fit the rat's mean thoracic circumference, with two pieces of platinum electrodes attached on its inner surface, allowing ECG to be recorded noninvasively in conscious, restrained rats (n=6). Time- and frequency-domain HRV analyses were conducted, under basal and autonomic blockade conditions. High-quality ECG signals were obtained, being feasible for HRV analysis. As expected, mean RR interval was significantly decreased in the presence of atropine (p <0.05) and increased in the presence of propranolol (p<0.001). Also, reinforcing the reliability of the method, low- and high-frequency HRV spectral powers were significantly decreased in the presence of propranolol (p <0.05) and atropine (p< 0.001), respectively. In summary, the present work describes a novel, inexpensive and noninvasive method for surface ECG recording in conscious rats.