Original Electrocardiogram Signal Research Articles

Convolutional squeeze-and-excitation network for ECG arrhythmia detection

Automatic detection of arrhythmia through an electrocardiogram (ECG) is of great significance for the prevention and treatment of cardiovascular diseases. In Convolutional neural network, the ECG signal is converted into multiple feature channels with equal weights through the convolution operation. Multiple feature channels can provide richer and more comprehensive information, but also contain redundant information, which will affect the diagnosis of arrhythmia, so feature channels that contain arrhythmia information should be paid attention to and given larger weight. In this paper, we introduced the Squeeze-and-Excitation (SE) block for the first time for the automatic detection of multiple types of arrhythmias with ECG. Our algorithm combines the residual convolutional module and the SE block to extract features from the original ECG signal. The SE block adaptively enhances the discriminative features and suppresses noise by explicitly modeling the interdependence between the channels, which can adaptively integrate information from different feature channels of ECG. The one-dimensional convolution operation over the time dimension is used to extract temporal information and the shortcut connection of the Se-Residual convolutional module in the proposed model makes the network easier to optimize. Thanks to the powerful feature extraction capabilities of the network, which can effectively extract discriminative arrhythmia features in multiple feature channels, so that no extra data preprocessing including denoising in other methods are need for our framework. It thus improves the working efficiency and keeps the collected biological information without loss. Experiments conducted with the 12-lead ECG dataset of the China Physiological Signal Challenge (CPSC) 2018 and the dataset of PhysioNet/Computing in Cardiology (CinC) Challenge 2017. The experiment results show that our model gains great performance and has great potential in clinical.

Sleep apnea automatic detection method based on convolutional neural network

Sleep apnea (SA) detection method based on traditional machine learning needs a lot of efforts in feature engineering and classifier design. We constructed a one-dimensional convolutional neural network (CNN) model, which consists in four convolution layers, four pooling layers, two full connection layers and one classification layer. The automatic feature extraction and classification were realized by the structure of the proposed CNN model. The model was verified by the whole night single-channel sleep electrocardiogram (ECG) signals of 70 subjects from the Apnea-ECG dataset. Our results showed that the accuracy of per-segment SA detection was ranged from 80.1% to 88.0%, using the input signals of single-channel ECG signal, RR interval (RRI) sequence, R peak sequence and RRI sequence + R peak sequence respectively. These results indicated that the proposed CNN model was effective and can automatically extract and classify features from the original single-channel ECG signal or its derived signal RRI and R peak sequence. When the input signals were RRI sequence + R peak sequence, the CNN model achieved the best performance. The accuracy, sensitivity and specificity of per-segment SA detection were 88.0%, 85.1% and 89.9%, respectively. And the accuracy of per-recording SA diagnosis was 100%. These findings indicated that the proposed method can effectively improve the accuracy and robustness of SA detection and outperform the methods reported in recent years. The proposed CNN model can be applied to portable screening diagnosis equipment for SA with remote server.

Interpretable Feature Generation in ECG Using a Variational Autoencoder.

We propose a method for generating an electrocardiogram (ECG) signal for one cardiac cycle using a variational autoencoder. Our goal was to encode the original ECG signal using as few features as possible. Using this method we extracted a vector of new 25 features, which in many cases can be interpreted. The generated ECG has quite natural appearance. The low value of the Maximum Mean Discrepancy metric, 3.83 × 10−3, indicates good quality of ECG generation too. The extracted new features will help to improve the quality of automatic diagnostics of cardiovascular diseases. Generating new synthetic ECGs will allow us to solve the issue of the lack of labeled ECG for using them in supervised learning.

Design and Analysis of a Signal Denoising and Estimation System

Heart disease is one of the most common causes of death worldwide and electrocardiogram (ECG) signals are among the best tools currently available for diagonising heart disease by offering accurate data about heart performance and the circulatory system. Unfortunately, the linear-quadratic problem often arises, which is a serious problem affecting the estimation of any linear dynamic system’s immediate state due to disruption with white noise due to the linear employment of information about state and white noise corruption. This can theoritecally be addressed by using a Kalman filter as a recursive estimator. However, all biomedical applications that generate electrical signals are disposed to creating different types of noise with fixed coefficients filters, due to the random nature of these signals, and these can cause inaccuracies over time. In this paper, a double Kalman filter is suggested for ECG signal denoising and estimation. Additive White Noise added to the original ECG signal to form a noisy signal, and Kalman filter was used initially to improve the quality of the ECG signal and to offer high performance convergence while increasing the SNR. The second step involved employing Kalman filter to estimate the new denoised ECG signal during a given time period, as accuracy in time is very important in medical applications. The results were satisfactory, offering highly recognisable estimations.

Electrocardiogram signal reconstruction based on mode component identification by heartbeat physical feature in improved empirical mode decomposition domain

Electrocardiogram (ECG) diagnosis is based on the waveform, duration and amplitude of characteristic wave, which are required to have a high accuracy for ECG signal reconstruction. As an effective nonlinear signal processing method, empirical mode decomposition (EMD) has been widely used for diagnosing and reconstructing the ECG signal, but there are two problems arising here. One is the mode mixing, and the other is that the mode components used in reconstruction are identified by experience. Therefore, the method of reconstruction is not adaptive and universal, and reconstructed ECG signal loses accuracy. Firstly, we propose an improved EMD method, which is called integral mean mode decomposition (IMMD). The analysis of 5000 samples of Gaussian white noise shows that IMMD has better multi-resolution analysis ability than EMD, and it can effectively alleviate mode mixing consequently. Secondly, based on the inherent physical characteristics of ECG signal, cardiac cycle or heart rate (HR), it has practical physical significance to identify the mode components used in ECG signal reconstruction. The cardiac cycle feature acts as the intrinsic mode function (IMF) component through two modes. 1) For the low-order IMF that belongs to the ECG signal, the cardiac cycle feature acts as the amplitude modulation. The envelope of the IMF component has the characteristics of the cardiac cycle, and the frequency corresponding to the maximum amplitude in the spectrum of the envelope is equal to HR. 2) For the high-order IMF that belongs to the ECG signal, the cardiac cycle feature acts as frequency modulation. Those IMF components have the harmonic characteristics of periodic heartbeats, and the maximum amplitude in the spectrum corresponds to an integral multiple of HR (usually 1－3 times). The noise attributed to IMF component cannot show the above two cardiac cycle characteristics. Thus the proposed method is adaptive and universal. The 47 ECG signals with baseline drift and muscle artifact noise are tested. The results show that the proposed method is more effective than the variational mode decomposition (VMD), Haar wavelet with soft threshold, ensemble empirical mode decomposition (EEMD) and EMD. Among the 47 correlation coefficients between reconstructed and original ECG signals, the proposed method has 31 better than VMD, 33 better than Haar wavelet, 42 better than EEMD and 45 better than EMD. The mean of 47 correlation coefficients from the proposed method is 0.8904, and the variance is 0.0071, which shows that the proposed method has good performance and stability.

An efficient reversible ECG steganography by adaptive LSB approach based on 1D FDCT domain

Based on the processing of one-dimensional fast discrete cosine transform (1D FDCT) coefficients, we present an efficient reversible data hiding method for electrocardiogram (ECG) signal. The proposed method is implemented in two phases. The purpose of phase-I is to classify the FDCT (host) bundles, where each input bundle will be attributed to one of four different bundles. The aim of phase-II is to embed data bits in the selected coefficients of the classified bundles according to a predetermined bit-index table (which generated from phase-I) via adaptive least significant bit (LSB) technique. Simulations confirmed that hidden bits were extracted without distortion and the original ECG signal can be completely recovered. Furthermore, a good perceived quality and a hiding capacity superior to existing techniques were achieved. Moreover, our method is robust against attacks such as noise addition, inversion, truncation, translations, and so on. Notice that the robustness is rarely seen in conventional reversible ECG steganography methods. Since this method has a fast computation speed, it is feasible for real-time applications and can be installed in the health care devices such as the wearable ECG measure equipment.

Accurate classification of ECG arrhythmia using MOWPT enhanced fast compression deep learning networks

Accurate classification of electrocardiogram (ECG) signals is of significant importance for automatic diagnosis of heart diseases. In order to enable intelligent classification of arrhythmias with high accuracy, an accurate classification method based intelligent ECG classifier using the fast compression residual convolutional neural networks (FCResNet) is proposed. In the proposed method, the maximal overlap wavelet packet transform (MOWPT), which provides a comprehensive time-scale paving pattern and possesses the time-invariance property, was utilized for decomposing the original ECG signals into sub-signal samples of different scales. Subsequently, the samples of the five arrhythmia types were utilized as input to the FCResNet such that the ECG arrhythmia types were identified and classified. In the proposed FCResNet model, a fast down-sampling module and several residual block structural units were incorporated. The proposed deep learning classifier can substantially alleviate the problems of low computational efficiency, difficult convergence and model degradation. Parameter optimizations of the FCResNet were investigated via single-factor experiments. The datasets from MIT-BIH arrhythmia database were employed to test the performance of the proposed deep learning classifier. An averaged accuracy of 98.79% was achieved when the number of the wide-stride convolution in fast down-sampling module was set as 2, the batch size parameter was set as 20 and wavelet subspaces of low frequency bands in MOWPT were selected as input of the classifier. These analysis results were compared with those generated by some comparison methods to validate the superiorities and enhancements of the proposed method.

Systematic Approach to Processing and Analysis Diagnostic Indicators of Electrocardiograms Based on Labview

Introduction. Cardiovascular disease occupies an important place throughout the world, which necessitates the development of more effective modern means of diagnosis and treatment. The primary diagnosis of heart disease is based on analysis and processing of an electrocardiogram (ECG). Despite the fact that there are many methods and algorithms for ECG analysis and processing, one of the urgent problems of cardiology remains to obtain the most complete information about heart electric potential, respectively, the behavior of the waves P, Q, R, S and T.Aim. Development of algorithms and software for processing and analysis of electrocardiograms (ECGs), as well as calculation of heart rate and detection of arrhythmias based on Labview.Materials and methods. The methods for removing noise using the wavelet transform method to eliminate baseline deviation ,to extract ECG signs ,to calculate heart rate and to detect arrhythmias based on Labview have been adopted as a mathematical apparatus for processing and analyzing ECGs.Results. Organizing of the ECG database, developing algorithms for converting the ECG file of the database into a useful format for Labview, processing of the ECG signal with removing noise from the original ECG signal, extracting signs for obtaining ECG diagnostic indicators, calculating heart rate and detecting arrhythmias.Conclusion. An analysis of the results demonstrates that systematic approaches to evaluating ECG signals allow to avoid one-way decisions and to integrate different methods into an integrated system of ideas of the state. The implementation of the proposed algorithms using Labview programming system ensures the removal of noise and artifacts, the extraction of the necessary ECG signs, the calculation of heart contractions and the detection of arrhythmias.

An Artifact Elimination in Cardiac Signal using Circular Leaky Normalized LMS Adaptive Algorithm through Iot

In distant cardiac care observing purposes, Electrocardiogram (ECG) waves are infected by artifacts while gaining data and broadcast of signals. The elimination of the noise is a predominant mission for accurate analysis. In this paper, an effort has been made to eliminate the artifacts, mainly baseline wander (BW), muscle artifacts (MA), power line interface (PLI), and Electrode motion (EM) using a Normalized Least mean square error (NLMS) algorithm. Later on to recover the filtering capacity and speed up the convergence process, data normalization is used. The above algorithm is normalized with reference to maximum data normalization which results in lessened computational difficulty in the denominator. Based on the above algorithms, a variety of adaptive signal enhancers (ASE’s) are improved. To decrease the computational difficulty of the signal enhancer, the designed ASE’s are united with sign-based algorithms. The designed ASE’s are analyzed on original ECG signals gained from the MIT-BIH database to evaluate the performance. The reproduction outcomes gained demonstrates that the block based algorithms are finer than NLMS in terms of the signal to noise ratio (SNR), excess mean square error and computational difficulty. Among the NLMS alternatives, the VSS-CLNLMS (Variable Stepsize Circular Leaky Normalized Least Mean Square error) based ASE’s have fine filtering capacity with a lessened number of computations. The development of the SNR obtained in the progression with the use of VSS-CLNLMS-based ASE’s are calculated as 13.2945 dBs, 12.4589 dBs, 15.6179 dBs and 14.0881 dBs respectively for BW, MA, PLI and EM artifacts.

A dictionary matrix generation based compression and bitwise embedding mechanisms for ECG signal classification

The Electrocardiogram (ECG) signal processing is one of the exciting research areas in recent days. Ensuring security to the patient’s confidential information is a demanding critical task in many healthcare systems. So, the traditional works developed the security mechanisms for embedding the original ECG signal with the image, audio, or video. But, it does not focus on reducing the size of the original message before transmitting it to others. Also, it has significant limitations of inefficient security, increased complexity, and reduced classification accuracy. To rectify this issue, our research proposed the new embedding mechanism to improve the security of patient’s health information. In this system, the original ECG signals compressed at the initial stage by using the proposed Dictionary Matrix Generation (DMG) algorithm. Then, the compressed signals embedded within the cover image by using the Bitwise Embedding (BE) mechanism. At the receiver side, the bedded goal is de-embedded and decompressed by using the DMG and BE algorithms. The features such as spectral and peak values of the signal are extracted for increasing the efficiency of classification. Classification and detection of abnormality present in ECG signal of patient is the most essential part. To achieve this, we proposed the Modified Dynamic Classification (MDC) algorithm based on the features. In this work, the novelty is implemented in the compression, embedding, and classification stages. The proposed system reduces the data loss during transmission, memory storage and time complexity. The overall process evaluated by using PTB diagnostic ECG database. In experiments, the proposed classification technique provides the accuracy of 98.39% and it proved that the proposed method had highest performances than existing methods such as PNN, SVM and RF classification.

A singular spectrum analysis-based model-free electrocardiogram denoising technique

Background and objectiveAn efficient and robust electrocardiogram (ECG) denoising technique caters three-fold benefits in the subsequent processing steps: first, it helps improving the accuracy of extracted features. Second, the improved accuracy in the extracted features enhances the performance as well as the reliability of computerised cardiovascular-disease diagnosis systems, and third, it also makes the interpretation task easier for the clinicians. Albeit a number of ECG denoising techniques are proposed in the literature, but most of these techniques suffer from one or more of the following drawbacks: i) model or function dependency, ii) sampling-rate dependency, or iii) high time-complexity. MethodsThis paper presents a singular spectrum analysis (SSA)-based ECG denoising technique addressing most of these afore-mentioned shortcomings. First, a trajectory matrix of dimension K × L is formed using the original one-dimensional ECG signal of length N. In SSA operation the parameter L, which is denoted as the window-length, plays a very important role and is related to the sampling frequency of the signal. In this research the value of L is calculated dynamically based on the morphological property of the ECG signal. Then, the matrix is decomposed using singular value decomposition technique, and the principal components (PC) of the original ECG signal are computed. Next, the reconstructed components (RC) are calculated from the PCs, and all the RCs are filtered through Butterworth bandpass and notch filters. An optimum number of filtered RCs are retained based on their significance. Finally, these retained RCs are summed up to obtain the denoised ECG signal. ResultsEvaluation result shows that the proposed technique outperforms state-of-the-art ECG denoising methods; in particular, the mean opinion score of the denoised signal falls under the category ‘very good’ as per the gold standard subjective measure. ConclusionsBoth the quantitative and qualitative distortion measure metrics show that the proposed ECG denoising technique is robust enough to filter various noises present in the signal without jeopardizing the clinical content. The proposed technique could be adapted for denoising other biomedical signals exhibiting periodic or quasi-periodic nature such as photoplethysmogram and esophageal pressure signal.

Method of electrocardiogram synchronization during neonatal heart radiography

The features of heart x-ray diagnostics in newborns are considered. The necessity of synchronization of x-ray apparatus with electrocardiogram (ECG) is substantiated. Prospective and retrospective methods of ECG synchronization are described, the limits of their applicability are determined. The block diagram of the device for ECG registration in one lead is given, the functional purpose of each block of the scheme is considered. An example of an ECG recorded using the developed device is shown. Based on the recorded ECG, a method for recognizing the diastolic phase is shown. The original ECG signal is processed by the moving average filter, then its first derivative is calculated. The resulting signal also passes through the «moving average» filter, and then differentiates, becoming the second derivative of the original ECG signal. For the first and second derivatives are set thresholds, within which the first and second derivatives must be located to enable the x-ray apparatus. Averaging the diastolic phase of 20–30 cardiocycles allows you to calculate the time window in which the x-ray machine is switched on. The application of the developed method can significantly improve the quality of x-ray images by improving the accuracy of control of the x-ray apparatus by synchronizing its operation with the diastolic phase of the ECG.

Passive–active integrators chaotic oscillator with anti-parallel diodes: analysis and its chaos-based encryption application to protect electrocardiogram signals

An autonomous passive–active integrators oscillator with anti-parallel diodes is proposed and analysed in this paper. It consists of anti-parallel diodes and two main blocks: A second-order passive RLC integrator and a first-order active RC integrator. The existence of two Hopf bifurcations is established during the stability analysis of the unique equilibrium point. For a suitable choice of the circuit parameters, the proposed oscillator can generate periodic oscillations, one-scroll, bistable chaotic attractors and antimonotonicity. The electronic circuit realization of the proposed oscillator is carried out to confirm results found during the numerical simulations. A good qualitative agreement is illustrated between the numerical simulations and experimental results. In addition, chaos-based encryption application to protect electrocardiogram (ECG) signals for secure transmission of medical information is performed using the proposed oscillator in chaotic regime. The ECG signals are successfully encrypted and the original ECG signal is successfully decrypted from noisy ECG signals.

A reversible and multipurpose ECG data hiding technique for telemedicine applications

Biomedical signals serves as a backbone of telemedicine industry and its analysis help the remote diagnosis possible. Security of biomedical data is one of the critical issues during its transmission from one storage device to another storage device. In this work, a completely reversible data hiding method is proposed for the ECG (Electrocardiogram) data. The proposed scheme is designed in such a way that it can find out false ownership claims as well as detect the tampered region of ECG data. In addition, original ECG signal is perfectly reconstructed from watermarked signal. The reversibility is tested for a large set of 460 different ECG signals generated from MIT-BIH arrhythmia database and complete reversibility (100%) is obtained each time. In proposed approach, deep neural network is used for an improved error prediction and prediction error expansion (PEE) is combined with it to ensure reversibility. Proposed method is a high capacity method and data hiding of 0.99 bps (bits per sample) is achieved with very little distortion in watermarked signal. The performance of proposed work is evaluated through percentage residual difference (PRD), signal to noise ratio (SNR) and normalized cross correlation (NCC). The most important contribution of proposed work is its multipurpose nature: ownership detection, tamper localization and 100% reversibility. Tamper detection and localization performance of the proposed method is also tested against different attacks and it is found to be quite good.

Separation of Fetal-ECG From Single-Channel Abdominal ECG Using Activation Scaled Non-Negative Matrix Factorization.

Performing a fetal electrocardiogram (ECG) analysis, which contains important information about the status of a fetal, can help to detect fetus health even before birth. Since the fetal ECG extracted from the ECG signal recorded from the mother's abdomen, this extraction problem can be seen as a source separation problem, of recovering source signals from signal mixtures. In this paper, a method for separation of fetal ECG from abdominal ECG using activation scaled non-negative matrix factorization (NMF) is proposed. The performance of the proposed method is also compared with independent component analysis. The proposed method is tested under three different scenarios. First, the original abdominal ECG signal is used for fetal separation. Second, the recovered abdominal ECG after compression is used for separation. Third, the fetal ECG is extracted from the compressed domain of the abdominal ECG. We applied scaling on the activation matrix obtained using NMF for emphasizing the fetal ECG present in abdominal ECG. The improved-regularized least-squares [Formula: see text] algorithm is used for signal reconstruction, which provides better reconstruction quality and less processing time in comparison with other existing methods. The proposed algorithm is evaluated and tested on real abdominal recordings obtained from two different datasets from Physionet. The first dataset used for this paper is Silesia dataset for abdominal and direct f-ECG, and the second dataset we considered is Set-A of the Physionet challenge. The obtained outcomes reveal that it is possible to separate fetal ECG from single-channel abdominal ECG signal, which can help us to achieve energy-efficient transmission, and cost-effective fetal ECG remote monitoring for Internet-of-Things applications, where device battery and computational capacity are limited.

Localization of Myocardial Infarction With Multi-Lead Bidirectional Gated Recurrent Unit Neural Network

Myocardial infarction (MI) is an acute disease. Early detection and early treatment are of great significance for improving the health of people. In order to reduce the misdiagnosis rate of MI diseases, this paper proposes a multi-lead bidirectional gated recurrent unit neural network (ML-BiGRU) learning algorithm based on current research status in the field of intelligent medical diagnosis, combined with the timing and multi-lead correlation characteristics of the electrocardiogram (ECG) signals. At first, the original ECG signal is denoised and preprocessed and then segmented into heartbeats. After that, the heartbeat sequence is sent to the deep neural network training model to learn the classification. Lastly, the Physikalisch-Technische Bundesanstalt (PTB) ECG database is used to verify the multi-lead BiGRU algorithm. The verification results demonstrate that the accuracy of the algorithm for MI localization is 99.84%, which outperform the other algorithms. The experimental results also show that the algorithm is obviously superior to the traditional localization algorithm in improving the localization accuracy, which is of great significance for improving the correct diagnosis rate of MI.

ECG Baseline Wander Correction and Denoising Based on Sparsity

To reduce the influence of both the baseline wander (BW) and noise in the electrocardiogram (ECG) is much important for further analysis and diagnosis of heart disease. This paper presents a convex optimization method, which combines linear time-invariant filtering with sparsity for the BW correction and denoising of ECG signals. The BW signals are modeled as low-pass signals, while the ECG signals are modeled as a sequence of sparse signals and have sparse derivatives. To illustrate the positive of the ECG peaks, an asymmetric function and a symmetric function are used to punish the original ECG signals and their difference signals, respectively. The banded matrix is used to represent the optimization problem, in order to make the iterative optimization method more computationally efficient, take up the less memory, and apply to the longer data sequence. Moreover, an iterative majorization-minization algorithm is employed to guarantee the convergence of the proposed method regardless of its initialization. The proposed method is evaluated based on the ECG signals from the database of MIT-BIH Arrhythmia. The simulation results show the advantages of the proposed method compared with wavelet and median filter.

AUTOMATIC DETECTION OF CARDIOVASCULAR DISEASE USING DEEP KERNEL EXTREME LEARNING MACHINE

The electrocardiogram (ECG) is a principal signal employed to automatically diagnose cardiovascular disease in shallow and deep learning models. However, ECG feature extraction is required and this may reduce diagnosis accuracy in traditional shallow learning models, while backward propagation (BP) algorithm used by the traditional deep learning models has the disadvantages of local minimization and slow convergence rate. To solve these problems, a new deep learning algorithm called deep kernel extreme learning machine (DKELM) is proposed by combining the extreme learning machine auto-encoder (ELM-AE) and kernel ELM (KELM). In the new DKELM architecture with [Formula: see text] hidden layers, ELM-AEs are employed by the front [Formula: see text] hidden layers for feature extraction in the unsupervised learning process, which can effectively extract abstract features from the original ECG signal. To overcome the “dimension disaster” problem, the kernel function is introduced into ELM to act as classifier by the [Formula: see text]th hidden layer in the supervised learning process. The experiments demonstrate that DKELM outperforms the BP neural network, support vector machine (SVM), extreme learning machine (ELM), deep auto-encoder (DAE), deep belief network (DBN) in classification accuracy. Though the accuracy of convolutional neural network (CNN) is almost the same as DKELM, the computing time of CNN is much longer than DKELM.

Alexander fractional differential window filter for ECG denoising.

The electrocardiogram (ECG) non-invasively monitors the electrical activities of the heart. During the process of recording and transmission, ECG signals are often corrupted by various types of noises. Minimizations of these noises facilitate accurate detection of various anomalies. In the present paper, Alexander fractional differential window (AFDW) filter is proposed for ECG signal denoising. The designed filter is based on the concept of generalized Alexander polynomial and the R-L differential equation of fractional calculus. This concept is utilized to formulate a window that acts as a forward filter. Thereafter, the backward filter is constructed by reversing the coefficients of the forward filter. The proposed AFDW filter is then obtained by averaging of the forward and backward filter coefficients. The performance of the designed AFDW filter is validated by adding the various type of noise to the original ECG signal obtained from MIT-BIH arrhythmia database. The two non-diagnostic measure, i.e., SNR, MSE, and one diagnostic measure, i.e., wavelet energy based diagnostic distortion (WEDD) have been employed for the quantitative evaluation of the designed filter. Extensive experimentations on all the 48-records of MIT-BIH arrhythmia database resulted in average SNR of 22.014 ± 3.806365, 14.703 ± 3.790275, 13.3183 ± 3.748230; average MSE of 0.001458 ± 0.00028, 0.0078 ± 0.000319, 0.01061 ± 0.000472; and average WEDD value of 0.020169 ± 0.01306, 0.1207 ± 0.061272, 0.1432 ± 0.073588, for ECG signal contaminated by the power line, random, and the white Gaussian noise respectively. A new metric named as morphological power preservation measure (MPPM) is also proposed that account for the power preservance (as indicated by PSD plots) and the QRS morphology. The proposed AFDW filter retained much of the original (clean) signal power without any significant morphological distortion as validated by MPPM measure that were 0.0126, 0.08493, and 0.10336 for the ECG signal corrupted by the different type of noises. The versatility of the proposed AFDW filter is also validated by its application on the ECG signal from MIT-BIH database corrupted by the combination of the noises as well as on the real noisy ECG signals are taken from MIT-BIH ID database. Furthermore, the comparative study has also been done between the proposed AFDW filter and existing state of the art denoising algorithms. The results clearly prove the supremacy of our proposed AFDW filter.

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.