Original Electrocardiogram Signal Research Articles

Comparative analysis of parametric B-spline and Hermite cubic spline based methods for accurate ECG signal modeling

Analyzing Electrocardiogram (ECG) signals is imperative for diagnosing cardiovascular diseases. However, evaluating ECG analysis techniques faces challenges due to noise and artifacts in actual signals. Machine learning for automatic diagnosis encounters data acquisition hurdles due to medical data privacy constraints. Addressing these issues, ECG modeling assumes a crucial role in biomedical and parametric spline-based methods have garnered significant attention for their ability to accurately represent the complex temporal dynamics of ECG signals. This study conducts a comparative analysis of two parametric spline-based methods—B-spline and Hermite cubic spline—for ECG modeling, aiming to identify the most effective approach for accurate and reliable ECG representation. The Hermite cubic spline serves as one of the most effective interpolation methods, while B-spline is an approximation method. The comparative analysis includes both qualitative and quantitative evaluations. Qualitative assessment involves visually inspecting the generated spline-based models, comparing their resemblance to the original ECG signals, and employing power spectrum analysis. Quantitative analysis incorporates metrics such as root mean square error (RMSE), Percentage Root Mean Square Difference (PRD) and cross correlation, offering a more objective measure of the model's performance. Preliminary results indicate promising capabilities for both spline-based methods in representing ECG signals. However, the analysis unveils specific strengths and weaknesses for each method. The B-spline method offers greater flexibility and smoothness, while the cubic spline method demonstrates superior waveform capturing abilities with the preservation of control points, a critical aspect in the medical field. Presented research provides valuable insights for researchers and practitioners in selecting the most appropriate method for their specific ECG modeling requirements. Adjustments to control points and parameterization enable the generation of diverse ECG waveforms, enhancing the versatility of this modeling technique. This approach has the potential to extend its utility to other medical signals, presenting a promising avenue for advancing biomedical research.

Detection of congestive heart failure based on Gramian angular field and two-dimensional symbolic phase permutation entropy

Congestive heart failure (CHF) is a serious threat to human health. Electrocardiogram (ECG) signals have been proven to be useful in the detection of CHF. However, the low amplitude and short duration of the ECG signals, as well as the superimposed noise during the real-time acquisition of the signal, seriously affect the CHF detection. To improve the detection rate of CHF, this paper proposes a congestive heart failure detection method based on Gramian angular field (GAF) and two-dimensional symbolic phase permutation entropy (SPPE2D). The significant advantage of this method is that it reduces the sensitivity to noise, and good performance can be obtained without denoising using raw ECG signals. We segment the original ECG signals into 2 s non-overlapping segments and convert them into images using the GAF method. Then, the SPPE2D algorithm is proposed to measure the complexity between normal sinus rhythm (NSR) and CHF, and analyze the anti-noise performance of the algorithm. Finally, the SPPE2D features of GAF images are computed and input into a support vector machine (SVM) for CHF detection. Classification accuracy on the Massachusetts Institute of Technology − Beth Israel Hospital Normal Sinus Rhythm Database and Beth Israel Deaconess Medical Center Congestive Heart Failure Database is 99.59%, sensitivity is 99.42%, specificity is 99.80%, and F1-score is 99.62%. The accuracy of detecting CHF reach more than 97.75% in the other five CHF databases. The experimental results show that the method based on GAF and SPPE2D can effectively detect CHF by images of ECG signals and has good robustness. CHF can be detected using the 2 s sample lengths of ECG signals recording with high sensitivity, giving clinicians ample time to treat patients with CHF.

Semi-Supervised Domain Adaptation for Individual Identification from Electrocardiogram Signals

When acquiring electrocardiogram (ECG) signals, the placement of electrode patches is crucial for acquiring electrocardiographic signals. Constant displacement positions are essential for ensuring the consistency of the ECG signal when used for individual identification. However, achieving constant placement of ECG electrode patches in every trial for data acquisition is challenging. This is because different individuals may attach patches, and even when the same person attaches them, it may be difficult to specify the exact position. Therefore, gathering ECG data from various locations is necessary. However, this process requires a substantial amount of labor and time, owing to the requirement for multiple attempts. Nonetheless, persisting with these efforts enables the endurance of some ECG differences. To reduce labor and time, we propose a semi-supervised domain adaptation for individual identification using ECG signals. The method operates with a full set of original ECG signals and a small set of ECG signals from different placements to account for the differences between the signals in the generative adversarial network (CycleGAN). Specifically, to train the CycleGAN, the ECG signals were transformed into time–frequency representations, and the trained generator was used to generate ECG signals to expand the small set of ECG signals from different placements. Subsequently, both the original and generated signals were used to train the classifier for individual identification. This scenario can also be applied to the classification of ECG signals from different sensors. The PTB-ECG dataset was used for this experiment. We found that the proposed method showed higher accuracy than when only the original ECG signals were used for the training classifier.

An error-bounded median filter for correcting ECG baseline wander.

The baseline wander (BLW) in electrocardiogram (ECG) is a common disturbance that has a significant influence on the ECG wave pattern recognition. Many methods, such as IIR filter, mean filter, etc., can be used to correct BLW; However, most of them work on the original ECG signals. Compressed ECG data are economic for data storage and transmission, and if the baseline correction can be processed on them, it will be more efficient than we decompress them first and then do such correction. In this paper, we propose a new type of median filter CM_Filter, which works on the synopses of straight lines achieved from ECG by piecewise linear approximation (PLA) under maximum error bound. In CM_Filter, a heuristic strategy "Quick-Finding" is deduced by a property of straight lines in order to get the quality-assured median values from the synopses. The extended experimental tests demonstrate that the proposed filter is very efficient in execution time, and effective for correcting both slow and abrupt ECG baseline wander.

Washable and Flexible Screen-Printed Ag/AgCl Electrode on Textiles for ECG Monitoring.

Electrocardiogram (ECG) electrodes are important sensors for detecting heart disease whose performance determines the validity and accuracy of the collected original ECG signals. Due to the large drawbacks (e.g., allergy, shelf life) of traditional commercial gel electrodes, textile electrodes receive widespread attention for their excellent comfortability and breathability. This work demonstrated a dry electrode for ECG monitoring fabricated by screen printing silver/silver chloride (Ag/AgCl) conductive ink on ordinary polyester fabric. The results show that the screen-printed textile electrodes have good and stable electrical and electrochemical properties and excellent ECG signal acquisition performance. Furthermore, the resistance of the screen-printed textile electrode is maintained within 0.5 Ω/cm after 5000 bending cycles or 20 washing and drying cycles, exhibiting excellent flexibility and durability. This research provides favorable support for the design and preparation of flexible and wearable electrophysiological sensing platforms.

Security enhancement of the access control scheme in IoMT applications based on fuzzy logic processing and lightweight encryption

Security of Internet-of-Medical-Things (IoMT) networks has evolved as a vital issue in recent years. The IoMT networks are designed to link patients with caregivers. All reports, data, and medical signals are transferred over these networks. Hence, these networks require robust and secure access strategies for patients to send their vital data or reports. Hence, hacking of these networks may lead to harmful effects on patients. One of the vulnerable points to hacking in these networks is the access point. Access to these networks could be performed with biometrics. The popular biometric traits for this purpose are biomedical signals such as Electrocardiogram (ECG) signals, as they are continuously monitored and measured for patients. A common thread between all biometric authentication systems is the possibility of losing the biometric traits forever if hacking attempts manage to concur the biometric template storage. A new trend in the field of biometric authentication is to avoid the utilization of original biometrics in access control processes. A possible alternative is to use cancelable biometrics instead. Cancelable biometrics can be generated through encryption schemes or non-invertible transforms. This paper adopts both strategies in a unified framework for cancelable ECG signal recognition that can be used in the access step of IoMT networks. The proposed framework begins with applying a non-invertible transformation on the ECG signals through fuzzy logic to change the dynamic range of the signals. As this process is non-invertible in nature, it prevents the recovery of the original ECG signals from the processed versions, which is the main target of cancelable biometric systems. After that, lightweight encryption through XOR operation with user-specific patterns is implemented. Here, the high complexity of full encryption schemes that need a large processing burden is eliminated. The addition of the encryption stage enhances the security of cancelable biometric traits, allowing a hybrid nature of the proposed cancelable biometric framework through the merging of non-invertible transforms and encryption algorithms. Moreover, an FPGA hardware implementation is introduced for real implementation of the proposed ECG-based cancelable biometric recognition framework. This hardware can accompany the user to allow access of the IoMT network when requested. Experimental results show a promising performance of the proposed framework with a large Area under the Receiver Operating Characteristic curve (AROC) of 99.5% and an Equal Error Rate (EER) of 0.058%.

DCDA-Net: Dual-convolutional dual-attention network for obstructive sleep apnea diagnosis from single-lead electrocardiograms

Obstructive sleep apnea (OSA) is a breathing-related chronic disease in which the soft palate and tongue collapse and block the upper airway for at least 10 s during sleep. It can lead to many heart diseases such as hypertension, myocardial infarction, and coronary heart syndrome if not detected early. Artificial intelligence has facilitated the diagnosis of many diseases in healthcare. Polysomnography is a widely used but unpleasant, time-consuming, technically demanding, and financially expensive procedure to detect OSA. Some previous methods have detected OSA using time-domain information from an electrocardiogram (ECG), whereas others have used frequency-domain information. The limitations of these two approaches can be handled using the data’s time–frequency representation. Nevertheless, there is room for enhancing the detection accuracy of OSA using the time–frequency representation approach. Therefore, we propose a novel technique that takes the ECG signal and detects R-peaks from the QRS complexes. Afterward, we interpolate those R-peaks by linear interpolation and get an interpolated-R signal. Then we magnify the interpolated-R signal corresponding to the apnea and normal frequency ranges. After magnification in the time domain, we transformed the magnified version into a scalogram. We also transformed the original one-minute ECG signal into a spectrogram after denoising. Overall, we used ECG signals to generate scalograms and spectrograms for 2 dimensional convolutional neural network (2D CNN) to classify obstructive sleep apnea. For apnea classification, we proposed a dual convolutional dual attention network (DCDA-Net) that includes a dual convolutionally modified inception module, a spatial attention module, and a channel attention module. Finally, we apply a support vector machine to the probability scores obtained from DCDA-Net based on the scalogram and spectrogram. Extensive experimental results using the open PhysioNet apnea ECG dataset confirm the effectiveness of our method in terms of accuracy and F1 score of 98% and 97.5%, respectively, which outperforms state-of-the-art methods.

Multimodal multi-instance learning for long-term ECG classification

Recently, deep learning-based models have been widely used for electrocardiogram (ECG) classification tasks. Most ECG signals are long-term time series that contain a large number of sample points. However, existing deep learning-based models resize or crop the original long-term ECG signal due to the limitation of input size and hardware, which results in information loss. To address this issue, a multimodal multi-instance learning neural network (MAMIL) is proposed for long-term ECG classification. The proposed MAMIL has three major components. First, the original ECG signal and Gramian Angular Field (GAF) image converted from the ECG signal are utilized as multimodal inputs, which enables the model to learn complementary information between different modalities. Second, multi-instance learning (MIL) is introduced to avoid information loss. Each long-term ECG signal and GAF image are treated as bags, and each heartbeat from a long-term ECG signal and each patch from a GAF image are treated as instances. Convolutional neural networks (CNNs) are utilized to extract instance features from different modalities. Third, a novel attention mechanism-based feature fusion method is proposed to aggregate the instance features from multiple modalities to obtain the bag feature for final classification. Our feature fusion method adopts pooling to obtain positive instances, which can effectively eliminate redundant information and achieve low computational complexity. The proposed MAMIL is evaluated on both intrapatient and interpatient patterns of two commonly used ECG datasets. Experimental results show that our model not only outperforms common deep learning-based models, but also outperforms previous MIL-based models.

Artificial intelligence-enabled electrocardiographic screening for left ventricular systolic dysfunction and mortality risk prediction.

Left ventricular systolic dysfunction (LVSD) characterized by a reduced left ventricular ejection fraction (LVEF) is associated with adverse patient outcomes. We aimed to build a deep neural network (DNN)-based model using standard 12-lead electrocardiogram (ECG) to screen for LVSD and stratify patient prognosis. This retrospective chart review study was conducted using data from consecutive adults who underwent ECG examinations at Chang Gung Memorial Hospital in Taiwan between October 2007 and December 2019. DNN models were developed to recognize LVSD, defined as LVEF <40%, using original ECG signals or transformed images from 190,359 patients with paired ECG and echocardiogram within 14 days. The 190,359 patients were divided into a training set of 133,225 and a validation set of 57,134. The accuracy of recognizing LVSD and subsequent mortality predictions were tested using ECGs from 190,316 patients with paired data. Of these 190,316 patients, we further selected 49,564 patients with multiple echocardiographic data to predict LVSD incidence. We additionally used data from 1,194,982 patients who underwent ECG only to assess mortality prognostication. External validation was performed using data of 91,425 patients from Tri-Service General Hospital, Taiwan. The mean age of patients in the testing dataset was 63.7 ± 16.3 years (46.3% women), and 8,216 patients (4.3%) had LVSD. The median follow-up period was 3.9 years (interquartile range 1.5-7.9 years). The area under the receiver-operating characteristic curve (AUROC), sensitivity, and specificity of the signal-based DNN (DNN-signal) to identify LVSD were 0.95, 0.91, and 0.86, respectively. DNN signal-predicted LVSD was associated with age- and sex-adjusted hazard ratios (HRs) of 2.57 (95% confidence interval [CI], 2.53-2.62) for all-cause mortality and 6.09 (5.83-6.37) for cardiovascular mortality. In patients with multiple echocardiograms, a positive DNN prediction in patients with preserved LVEF was associated with an adjusted HR (95% CI) of 8.33 (7.71 to 9.00) for incident LVSD. Signal- and image-based DNNs performed equally well in the primary and additional datasets. Using DNNs, ECG becomes a low-cost, clinically feasible tool to screen LVSD and facilitate accurate prognostication.

An efficient ECG denoising method by fusing ECA-Net and CycleGAN.

For wearable electrocardiogram (ECG) acquisition, it was easy to infer motion artifices and other noises. In this paper, a novel end-to-end ECG denoising method was proposed, which was implemented by fusing the Efficient Channel Attention (ECA-Net) and the cycle consistent generative adversarial network (CycleGAN) method. The proposed denoising model was optimized by using the ECA-Net method to highlight the key features and introducing a new loss function to further extract the global and local ECG features. The original ECG signal came from the MIT-BIH Arrhythmia Database. Additionally, the noise signals used in this method consist of a combination of Gaussian white noise and noises sourced from the MIT-BIH Noise Stress Test Database, including EM (Electrode Motion Artifact), BW (Baseline Wander) and MA (Muscle Artifact), as well as mixed noises composed of EM+BW, EM+MA, BW+MA and EM+BW+MA. Moreover, corrupted ECG signals were generated by adding different levels of single and mixed noises to clean ECG signals. The experimental results show that the proposed method has better denoising performance and generalization ability with higher signal-to-noise ratio improvement (SNRimp), as well as lower root-mean-square error (RMSE) and percentage-root-mean-square difference (PRD).

A unique cardiac electrocardiographic 3D model. Toward interpretable AI diagnosis.

Mathematical models of cardiac electrical activity are one of the most important tools for elucidating information about heart diagnostics. In this paper, we present an efficient mathematical formulation for this modeling simple enough to be easily parameterized and rich enough to provide realistic signals. It relies on a five dipole representation of the cardiac electric source, each one associated with the well-known waves of the electrocardiogram signal. Beyond the physical basis of the model, the parameters are physiologically interpretable as they characterize the wave shape, similar to what a physician would look for in signals, thus making them very useful in diagnosis. The model accurately reproduces the electrocardiogram signals of any diseased or healthy heart. This new discovery represents a significant advance in electrocardiography research. It is especially useful for diagnosis, patient follow-up or decision-making on new therapies; is also a promising tool for well-performing, transparent and interpretable AI approaches.

ECG Denoising Method Based on an Improved VMD Algorithm

Electrocardiogram (ECG) acquisition is easily contaminated by interferences, and denoising is the most important task in ECG detection. The variational mode decomposition (VMD) algorithm is widely used in ECG denoising, which can overcome mode aliasing between intrinsic mode function (IMF) components that existed in the traditional empirical mode decomposition (EMD) algorithm, but the mode decomposition number <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> and penalty factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> in VMD must be optimized to obtain the best signal decomposition accuracy. This article proposes an improved VMD denoising algorithm that overcomes the shortcomings of slow parameter selection and poor generalization in the traditional VMD algorithm. The algorithm presented first adopts the EMD algorithm to remove the low-frequency baseline drift noise and then employs the adaptive particle swarm optimization (APSO) algorithm to optimize the parameter pair ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> ) for VMD. To validate the denoising performance of the improved VMD algorithm, the No. 103 record from the Massachusetts Institute of Technology (MIT) arrhythmia database is first selected as the pure ECG signal, then both 20-dB Gaussian white noises and 0.3-Hz baseline drift are added to simulate the noisy ECG signal. Second, the ECG signals of nine subjects are collected by a customized ECG detection platform based on AD8232 and ADALM1000. The ECG denoising results in simulation and actual experiments show that the improved VMD algorithm achieves the highest signal-to-noise ratio (SNR), correlation coefficient (CC), and minimum mean square error (MSE) compared with the traditional EMD and VMD algorithms, which demonstrates that the proposed denoising algorithm has stronger denoising ability and better retains morphological characteristics of the original ECG signals.

Intelligent Healthcare System Using Mathematical Model and Simulated Annealing to Hide Patients Data in the Low-Frequency Amplitude of ECG Signals.

Healthcare is an important medical topic in recent years. In this study, the novelty we propose is the intelligent healthcare system using an inequality-type optimization mathematical model with signal-to-noise ratio (SNR) and wavelet-domain low-frequency amplitude adjustment techniques to hide patients' confidential data in their electrocardiogram (ECG) signals. The extraction of the hidden patient information also utilizes the low-frequency amplitude adjustment. The detailed steps of establishing the system are as follows. To integrate confidential patient data into ECG signals, we first propose a nonlinear model to optimize the quality of ECG signals with the embedded patients' confidential data including patient name, patient birthdate, date of medical treatment, and medical history. Then, we apply Simulated Annealing (SA) to solve the nonlinear model such that the ECG signals with embedded patients' confidential data have good SNR, good root mean square error (RMSE), and high similarity. In other words, the distortion of the PQRST complexes and the ECG shape caused by the embedded patients' confidential data is very small, and thus the quality of the embedded ECG signals meets the requirements of physiological diagnostics. In the terminals, one can receive the ECG signals with the embedded patients' confidential data. In addition, the embedded patients' confidential data can be received and extracted without the original ECG signals. The experimental results confirm the efficiency that our method maintains a high quality of each ECG signal with the embedded patient confidential data. Moreover, the embedded confidential data shows a good robustness against common attacks.

Cardiac arrhythmia classification by time–frequency features inputted to the designed convolutional neural networks

The electrocardiogram (ECG) plays a vital auxiliary role in medical diagnosis, but due to the very low amplitude of the ECG signals, it is challenging and time-consuming to conduct artificial visual evaluation of the ECG signals. In recent years, medical aid research methods through ECG have emerged one after another. However, most of them have defects such as poor model generalization ability and obvious individual differences. This paper constructs two-way multiplex convolutional neural networks (CNNs) based on time–frequency features to classify normal cardiac rhythm (NOR) and seven cardiac arrhythmias including atrial premature contraction (APC), ventricular premature beat (PVC), left bundle branch block (LBBB), right bundle branch block (RBBB), signal quality change (∼), ventricular fused heart beat (FVN), and pacing heart beat (/). Firstly, the preprocessing steps of the original rough ECG signal are arranged in a unique order, including wavelet transform, threshold denoising, normalization, chopping, mel-frequency cepstral coefficients (MFCC). Secondly, a 12-layer one-dimensional CNN model with block representation and a 11-layer auxiliary-two-dimensional CNN architecture are designed for the time-domain feature and the frequency-domain feature, respectively, where the focal loss function is defined to solve the problem of data categories imbalance. Finally, the experimental results show that the proposed algorithm presents excellent performances in processing variable length ECGs, the average accuracy of time-domain model is 99.1 %, and the classification accuracy of APC in frequency-domain model is 96.3 %.

Abnormal ECG detection based on an adversarial autoencoder.

Automatic detection and alarm of abnormal electrocardiogram (ECG) events play an important role in an ECG monitor system; however, popular classification models based on supervised learning fail to detect abnormal ECG effectively. Thus, we propose an ECG anomaly detection framework (ECG-AAE) based on an adversarial autoencoder and temporal convolutional network (TCN) which consists of three modules (autoencoder, discriminator, and outlier detector). The ECG-AAE framework is trained only with normal ECG data. Normal ECG signals could be mapped into latent feature space and then reconstructed as the original ECG signal back in our model, while abnormal ECG signals could not. Here, the TCN is employed to extract features of normal ECG data. Then, our model is evaluated on an MIT-BIH arrhythmia dataset and CMUH dataset, with an accuracy, precision, recall, F1-score, and AUC of 0.9673, 0.9854, 0.9486, 0.9666, and 0.9672 and of 0.9358, 0.9816, 0.8882, 0.9325, and 0.9358, respectively. The result indicates that the ECG-AAE can detect abnormal ECG efficiently, with its performance better than other popular outlier detection methods.

Noninvasive Screening Tool for Hyperkalemia Using a Single-Lead Electrocardiogram and Deep Learning: Development and Usability Study.

BackgroundHyperkalemia monitoring is very important in patients with chronic kidney disease (CKD) in emergency medicine. Currently, blood testing is regarded as the standard way to diagnose hyperkalemia (ie, using serum potassium levels). Therefore, an alternative and noninvasive method is required for real-time monitoring of hyperkalemia in the emergency medicine department.ObjectiveThis study aimed to propose a novel method for noninvasive screening of hyperkalemia using a single-lead electrocardiogram (ECG) based on a deep learning model.MethodsFor this study, 2958 patients with hyperkalemia events from July 2009 to June 2019 were enrolled at 1 regional emergency center, of which 1790 were diagnosed with chronic renal failure before hyperkalemic events. Patients who did not have biochemical electrolyte tests corresponding to the original 12-lead ECG signal were excluded. We used data from 855 patients (555 patients with CKD, and 300 patients without CKD). The 12-lead ECG signal was collected at the time of the hyperkalemic event, prior to the event, and after the event for each patient. All 12-lead ECG signals were matched with an electrolyte test within 2 hours of each ECG to form a data set. We then analyzed the ECG signals with a duration of 2 seconds and a segment composed of 1400 samples. The data set was randomly divided into the training set, validation set, and test set according to the ratio of 6:2:2 percent. The proposed noninvasive screening tool used a deep learning model that can express the complex and cyclic rhythm of cardiac activity. The deep learning model consists of convolutional and pooling layers for noninvasive screening of the serum potassium level from an ECG signal. To extract an optimal single-lead ECG, we evaluated the performances of the proposed deep learning model for each lead including lead I, II, and V1-V6.ResultsThe proposed noninvasive screening tool using a single-lead ECG shows high performances with F1 scores of 100%, 96%, and 95% for the training set, validation set, and test set, respectively. The lead II signal was shown to have the highest performance among the ECG leads.ConclusionsWe developed a novel method for noninvasive screening of hyperkalemia using a single-lead ECG signal, and it can be used as a helpful tool in emergency medicine.

Intelligent Healthcare System Using Patients Confidential Data Communication in Electrocardiogram Signals.

With the advent of the aging era, healthcare and elderly care have become the focus of medical care, especially the care of the elderly with dementia. Patients’ confidential data hiding is a useful technology for healthcare and patient information privacy. In this study, we implement an intelligent healthcare system using the multiple-coefficient quantization technology in transform domain to hide patients’ confidential data into electrocardiogram (ECG) signals obtained by ECG sensor module. In embedding patients’ confidential data, we first consider a non-linear model for optimizing the quality of the embedded ECG signals. Next, we apply simulated annealing (SA) to solve the non-linear model so as to have good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative RMSE (rRMSE). Accordingly, the distortion of the PQRST complexes and the ECG amplitude is very small so that the embedded confidential data can satisfy the requirements of physiological diagnostics. In end devices, one can receive the ECG signals with the embedded confidential data and without the original ECG signals. Experimental results confirm the effectiveness of our method, which remains high quality for each ECG signal with the embedded confidential data no matter how the quantization size Q is increased.

A Novel and Low Processing Time ECG Security Method Suitable for Sensor Node Platforms

An anonymisation of electrocardiogram (ECG) signal is essential during the distribution and storage in a public repository. In this paper, we propose a novel low processing time ECG anonymisation method based on the fast Fourier transform (FFT) algorithm that is suitable for sensor node platforms. The proposed framework was developed to address two major constraints in the Internet of Medical Thing environment, i.e., immediate need for securing ECG signal and efficient method for overcoming physical limitation of sensor nodes. Performance evaluation by way of computer simulation over normal and abnormal ECG signals showed that the proposed framework was able to conceal fiducial and non-fiducial features of the ECG signals. Additionally, it showed that the proposed framework offered flexibility in determining the secret key length of the anonymised ECG signal. Strong cross-correlation indicated close similarity between the original and the reconstructed ECG signals implying lossless reconstruction of the original ECG signal. Furthermore, the proposed method achieved a lower processing time security algorithm as compared with the recently proposed wavelet based anonymisation methods.

A novel arrhythmia classification of electrocardiogram signal based on modified HRNet and ECA

Electrocardiogram (ECG) signals have been widely used to detect cardiac arrhythmia. Visual inspection is not only time consuming, but also may lead to misdiagnosis and affect the prevention or treatment of the disease. Therefore, automatic diagnosis which can greatly improve the efficiency and accuracy of diagnosis is needed to assist doctors with arrhythmia diagnosis. Due to its capacity for high resolution, HRNet has attracted extensive attention for classification in recent years. However, HRNet is only designed for two-dimensional images, and thus is not suitable for ECG signal classification. In this paper, we propose an arrhythmia classification scheme which is based on a modified HRNet and efficient channel attention (ECA) to classify five arrhythmia types. The proposed scheme first divides the original ECG signal into 5 s segments of 1800 sampling points. Then, the segments are fed into the improved HRNet network for automatic learning and classification. Extensive simulations have been performed on the MIT-BIH database to validate the effectiveness of the proposed scheme. Experimental results have shown that the proposed scheme achieves an average accuracy of 99.86%, which is superior to the benchmarking methods.

Electrocardiogram Fiducial Point Detector Using a Bilateral Filter and Symmetrical Point-Filter Structure.

The characteristics or aspects of important fiducial points (FPs) in the electrocardiogram (ECG) signal are complicated because of various factors, such as non-stationary effects and low signal-to-noise ratio. Due to the various noises caused by the ECG signal measurement environment and by typical ECG signal deformation due to heart diseases, detecting such FPs becomes a challenging task. In this study, we introduce a novel PQRST complex detector using a one-dimensional bilateral filter (1DBF) and the temporal characteristics of FPs. The 1DBF with noise suppression and edge preservation preserves the P- or T-wave whereas it suppresses the QRS-interval. The 1DBF acts as a background predictor for predicting the background corresponding to the P- and T-waves and the remaining flat interval excluding the QRS-interval. The R-peak and QRS-interval are founded by the difference of the original ECG signal and the predicted background signal. Then, the Q- and S-points and the FPs related to the P- and T-wave are sequentially detected using the determined searching range and detection order based on the detected R-peak. The detection performance of the proposed method is analyzed through the MIT-BIH database (MIT-DB) and the QT database (QT-DB).