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Electrocardiogram (ECG) is a noninvasive, effective and economical biomedical signal that is vital in diagnosing cardiovascular diseases. However, the acquiring process contaminates the ECG signal with several types of noises like Motion Artifacts, Power Line Interference and Baseline Wander. Hence, this paper proposes a new approach to detect and suppress the noises from ECG signals. The complete methodology comprises two stages: noise detection and noise suppression. The former stage applies Improved Complete Ensemble Empirical Mode Decomposition (CEEMD) to decompose the noisy ECG into Intrinsic Mode Functions (IMFs). Next, Maximum Absolute Amplitude (MAA) and Auto-Correlation Maximum Amplitude (AMA) are extracted and used to classify the type of noises from ECG. Then, the noisy ECG segments are processed through the second stage and decomposed into sub-bands through Discrete Wavelet Transform (DWT). Then, the sub-bands are categorized into noise-dominant and signal-dominant frequency bins, and only noise-dominant frequency bins are subjected to noise suppression through a newly proposed adaptive soft thresholding mechanism. The effectiveness of the proposed method is assessed by contaminating the ECG signals acquired from the MIT-BIH arrhythmia database with different noises at different Signal-Noise Ratios (SNRs). Three performance metrics, namely Output SNR, Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC), are employed to explore the superiority of the proposed method over state-of-the-art methods, which considered EMD and CEEMD as decomposition filters. The proposed method improved by an average of 3.5 dB in output SNR and 0.0290 in RMSE.

Cardiovascular disease (CVD) is the leading cause of global mortality in the modern world. This situation is difficult to predict and requires a combination of advanced techniques and specialist knowledge. Healthcare systems have recently adopted the Internet of Things (IoT) to collect critical sensor data to diagnose and predict CVD. Predictive models can be made more accurate and effective through such integration, which could radically change how we manage cardiovascular health. This study presents an improved squirrel search optimization algorithm for searching vital indications of CVD. To address the issue of low-cardiac diagnostic accuracy, the proposed IoT system uses enhanced squirrel search optimization with deep convolutional neural networks (SSO-DCNN). This new approach uses data from smartwatches and cardiac devices, which monitor patients’ electrocardiogram (ECG) and blood pressure readings. The proposed SSO-DCNN performs well compared to well-known deep learning networks such as logistic regression. The findings show an accuracy of 99.1% over current classifiers, suggesting effectiveness in the CVD prediction.

In recent years, the prevalence of mental disorders, such as depression and stress, has been on the rise, yet a large number of individuals do not receive timely treatment. Addressing mental health concerns involves the evaluation of an individual’s mental state, which can be influenced by a variety of factors. Technological advancements have introduced smart wearable devices that enable real-time monitoring of vital signs, offering potential applications for self-care in mental health. However, the current methodology utilized by most of these devices relies on hand-crafted features and demands time-consuming pre-processing. To address this limitation, our research aims to develop a pre-processing-free model for real-world application, focusing on noisy electrocardiograph (ECG) signals for four-class mental state detection. For this purpose, we used an available wearable stress and affect detection dataset. We took raw ECG signals and transformed them into two-second plots, which were then fed into seven pre-trained convolutional neural networks (AlexNet, GoogLeNet, EfficientNetB0, VGG16, VGG19, XceptionNet, and InceptionV3). Through our experimentation, the fine-tuned VGG16 model emerged as the most effective, outperforming other techniques in accurately detecting baseline, stress, amusement, and meditation states, achieving an impressive accuracy of 99.35%. This achievement stands significantly higher than existing literature, making our model a suitable option for classifying mental states even in noisy raw ECG signals. Furthermore, it exhibits reduced computational complexity when compared to other state-of-the-art studies.

Background: Both electrocardiogram (ECG) and heart rate variability (HRV) are hallmark markers of cardiovascular patho-physiology, and rats are readily used for understanding various cardiovascular abnormalities ailing humans. Aims: The cur-rent research has two proposals: testing the non-invasive, reusable limb electrodes made from stainless steel paper binder clip for recording rat ECG, which is like clinical clamp electrodes used for humans, and analyzing the correlation amongst HRV parameters in both rats and humans thereby evaluating whether rat and human cardiac physiology shares analogy in electrical conduction and sympathovagal modulation. Methods: Single-channel digital bipolar ECG signals (250 sam-ples/second) from rats and human subjects were recorded with the help of a two-channel Biopac amplifier and its associated software (Biopac Inc., USA). The ECG signals of rats were recorded with the laboratory-made stainless steel clip electrodes and the conventional needle electrodes. In contrast, the surface ECG was recorded from human subjects with the clinical clamp electrodes. Results: Smooth rat ECG signals with well-demarcated cliffs and troughs having patterns resembling human ECG waveforms were obtained using novel clip electrodes. In addition, the correlation amongst several HRV parameters in rats, like RR interval with heart rate and SD2 with RMSSD, agreed with the one seen in humans. Conclusion: The ECG waveform acquisitions uphold the utility of novel clip electrodes. The HRV correlation matrix heatmap of rats, when compared to humans, showcased reasonable similarity. Thus, the utility of rats as model animals and the use of ethical laboratory procedures in understanding human cardiac pathophysiology is reaffirmed.

Feeling the pulse on the wrist is the regular diagnostic method in traditional Chinese medicine. However it is natural to ask whether there is any difference between feeling the pulse on the wrist or at any other part of the body: such as the fingertips at which it is easily measured by electronic devices. We employ a series of neural networks to model blood pressure propagation from the wrist to the fingertip. In order to avoid the problem of over-fitting we apply information theoretic criterion to determine the optimal model in these networks and then apply surrogate data method to the residuals in this model. We demonstrate the application of this method to recordings of human pulse in six subjects. Our result indicates that there is no significant difference between pulse waveform measure on the lateral arterial artery (wrist) and at the fingertip.

Epilepsy is a chronic brain disorder which manifests as recurrent seizures. Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of epileptic seizures. In this work, we propose a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform (CWT), Higher Order Spectra (HOS) and textures. First the CWT plot was obtained for the EEG signals and then the HOS and texture features were extracted from these plots. Then the statistically significant features were fed to four classifiers namely Decision Tree (DT), K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to select the best classifier. We observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results with an average accuracy of 96%, average sensitivity of 96.9% and average specificity of 97% for 23.6 s duration of EEG data. Our proposed technique can be used as an automatic seizure monitoring software. It can also assist the doctors to cross check the efficacy of their prescribed drugs.

Electrocardiogram (ECG) is the electrical activity of the heart indicated by P, Q-R-S and T wave. The minute changes in the amplitude and duration of ECG depicts a particular type of cardiac abnormality. It is very difficult to decipher the hidden information present in this nonlinear and nonstationary signal. An automatic diagnostic system that characterizes cardiac activities in ECG signals would provide more insight into these phenomena thereby revealing important clinical information. Various methods have been proposed to detect cardiac abnormalities in ECG recordings. Application of higher order spectra (HOS) features is a seemingly promising approach because it can capture the nonlinear and dynamic nature of the ECG signals. In this paper, we have automatically classified five types of beats using HOS features (higher order cumulants) using two different approaches. The five types of ECG beats are normal (N), right bundle branch block (RBBB), left bundle branch block (LBBB), atrial premature contraction (APC) and ventricular premature contraction (VPC). In the first approach, cumulant features of segmented ECG signal were used for classification; whereas in the second approach cumulants of discrete wavelet transform (DWT) coefficients were used as features for classifiers. In both approaches, the cumulant features were subjected to data reduction using principal component analysis (PCA) and classified using three layer feed-forward neural network (NN) and least square — support vector machine (LS-SVM) classifiers. In this study, we obtained the highest average accuracy of 94.52%, sensitivity of 98.61% and specificity of 98.41% using first approach with NN classifier. The developed system is ready clinically to run on large datasets.

Automated seizure detection in a home environment has been of increased interest the last couple of decades. The electrocardiogram is one of the signals that is suited for this application. In this paper, a new method is described that classifies different heart rate characteristics in order to detect seizures from temporal lobe epilepsy patients. The used support vector machine classifier is trained on data from other patients, so that the algorithm can be used directly from the start of each new recording. The algorithm was tested on a dataset of more than 918h of data coming from 17 patients containing 127 complex partial and generalized partial seizures. The algorithm was able to detect 81.89% of the seizures, with on average 1.97 false alarms per hour. These results show a strong drop in the number of false alarms of more than 50% compared to other heart rate-based patient-independent algorithms from the literature, at the expense of a slightly higher detection delay of 17.8s on average.

Electroencephalography (EEG) has been used to forecast seizures with varying success. There is an increasing interest to use electrocardiogram (ECG) to help with seizure forecasting. The neural and cardiovascular systems may exhibit critical slowing, which is measured by an increase in variance and autocorrelation of the system, when change from a normal state to an ictal state. To forecast seizures, the variance and autocorrelation of long-term continuous EEG and ECG data from 16 patients were used for analysis. The average period of recordings was 161.9 h, with an average of 9 electrographic seizures in an individual patient. The relationship between seizure onset times and phases of variance and autocorrelation in EEG and ECG data was investigated. The results of forecasting models using critical slowing features, seizure circadian features, and combined critical slowing and circadian features were compared using the receiver-operating characteristic curve. The results demonstrated that the best forecaster was patient-specific and the average area under the curve (AUC) of the best forecaster across patients was 0.68. In 50% of patients, circadian forecasters had the best performance. Critical slowing forecaster performed best in 19% of patients. Combined forecaster achieved the best performance in 31% of patients. The results of this study may help to advance the field of seizure forecasting and lead to the improved quality of life of people who suffer from epilepsy.

This research evaluates the effect of physiological responses during electrical acupuncture (EA) stimulation on specific acupuncture points (APs: PC5 and PC6). A variety of special responses in the human body were determined by electroencephalogram (EEG), heart rate (HR) in an electrocardiogram (ECG), and skin impedance test for 2 groups, sham group as a control and a group under acupuncture stimulation. The total stimulation time in this study was set for 5 min since the effect of EA on all recorded parameters became stable within this period. According to the experiments, during EA stimulation of PC5 and PC6, the power spectrum of EEG showed that the number of low frequency waves was increased in all lobes. Heart rate variability measures of 10 subjects stimulation trials at PC6 and PC5 were compared to 10 subjects who received no stimulation treatment. In both the AP and the sham groups, the mean R-R interval increased significantly during EA stimulation. A comparison between the AP and non-AP group in terms of skin resistance measurement experiments revealed no difference in skin resistance.

The results of this study verified that EA stimulation of APs (PC5, PC6) causes EEG changes, and ECG heart rate changes. However, from human skin impedance measurements, the beneficial effects were not sustained. These results may be helpful in the understanding of the mechanism underlying the effect of electrical acupuncture on PC6 and PC5.

This paper introduces an efficient and robust method for heartbeat detection based on the calculated angles between the successive samples of electrocardiogram (ECG) signal. The proposed approach involves three stages: filtering, computing the angles of the signal and thresholding. The suggested method is applied to two different types of ECG databases (QTDB and MIT-BIH). The results were compared with the other algorithms suggested in previous works. The proposed approach outperformed the other algorithms, in spite of its simplicity and their fast calculations. These features make it applicable in real-time ECG diagnostics systems. The suggested method was implemented in real-time using a low cost ECG acquisition system and it shows excellent performance.

Nowadays, a computer-aided diagnosis system is required to monitor the cardiac patients continuously and detecting the heart diseases automatically. In this paper, a new field programmable gate array-based morphological feature extraction approach is proposed for electrocardiogram signal analysis. The proposed architecture is mainly based on the Generalized Synchrosqueezing transform but a detrended fluctuation analyzer is applied in the reconstruction stage for capturing the maximum information of QRS complexes and P-waves by eliminating a set of noisy intrinsic modes. Then, a correntropy envelope is determined from the QRS enhanced signal for localizing the QRS region accurately. Also, an adaptive heuristic framework is introduced to detect the true P-wave from the P-wave enhanced reconstructed signal by analyzing both the positive and negative amplitudes. In addition, a root mean square Error estimation-based adaptive thresholding approach is used to estimate the T-wave after removing the P-QRS complexes. The proposed architecture has been implemented on field programmable gate array using the Xilinx Vertex 7 platform. The performance of the proposed architecture is validated by performing a comparative study between the resultant performances and those attained with state-of-the-art feature descriptors, in terms of Sensitivity, accuracy, positive prediction, error rate and field programmable gate array resources estimation. The proposed sensitivity, accuracy and positive prediction are 99.84%, 99.85% and 99.86% for QRS detection approach. The proposed sensitivity, accuracy and positive prediction are 99.45%, 99.23% and 99.78% for P-wave detection approach. The proposed sensitivity, accuracy and positive prediction are 99.58%, 99.65% and 100% for T-wave detection approach. The simulation results show that the proposed architecture overtakes existing designs and minimizes hardware complexity, which proves the suitability of this approach on real-time applications of electrocardiogram signals.

Electrocardiogram (ECG) is considered as the important diagnostic tests in medical field for detecting the cardiac anomalies. But, the ECG signals are polluted with numerous noise from power line intrusion, muscle noise, baseline wander, motion artifacts, low frequency noise signals, high frequency noise signals and T-wave, which automatically affects the QRS profile. The existing method provides the result in lesser accuracy with higher rate of error detection. To overcome these issues, QRS detector using modified maximum mean minimum (MoMaMeMi) filter optimized with mayfly optimization algorithm (QRS-MoMaMeMi-MOA) is proposed in this paper for less computational cost along with resource requirements. The proposed filter design consists of two phases for detecting QRS detector, such as filtering process associated to the enhancement and detection phase. Initially, the ECG data are taken from MIT/BIH arrhythmia dataset (MIT-AD). For eradicating the baseline wander in ECG data, MaMeMi filter is used. For expanding the performance of the modified MaMeMi filter, filter parameters, such as $\sigma$ and $\mathrm{\Delta }$ are optimized by MOA to accomplish the best values and measure the performance of the whole QRS detector. For high frequency noise suppression in ECG data, the range function, noise subtractors, modified triangular detector are used. Then, heart beat detection can be done with the help of adaptive thresholding technique. The proposed filter design is carried out in MATLAB and implemented on field programmable gate arrays (FPGAs). The proposed QRS-MoMaMeMi-MOA filter design had 0.93%, 0.12% and 0.19% higher accuracy and 89.32%, 50% and 62% low detection error rate, compared to the existing filters, like Kalman filtering based adaptive threshold algorithm for QRS complex detection (QRS-KF-ATA), QRS detection of ECG signal utilizing hybrid derivative with MaMeMi filter by efficiently removing the baseline wander (QRS-HD-MaMeMi), and knowledge-based QRS detection operated by cascade of moving average filters (QRS-CAF). Then, the device utilization of the proposed FPGA implementation of the QRS-MoMaMeMi-MOA filter provides 95.556% and 71.428% lower power usage compared with the existing algorithms, like Kalman filtering based adaptive threshold algorithm for QRS complex detection in FPGA (FPGA-QRS-KF-ATA), and efficient architecture for QRS detection in FPGA utilizing integer Haar wavelet transform (FPGA-QRS-IHWT).

Sudden cardiac death (SCD) is deemed as one of the main causes of death in humans. Therefore, the prediction of an SCD event will help people to receive timely treatment, allowing saving their life. In this sense, this paper introduces a new methodology based on the adroit fusion of fractal dimension (FD) algorithms and a fuzzy logic system for predicting an SCD event automatically. Five FD implementations are investigated in this work: Katz’s FD, Sevcik’s FD, Box’s FD, Higuchi’s FD, and Petrosian’s FD, in order to evaluate the geometrical complexity in electrocardiogram signals of 20 patients with SCD and 18 patients with a normal cardiac rhythm offered by Boston’s Beth Israel Hospital. The results indicate that the FD-based methodology can predict an SCD event up to 60min before the onset, reaching an accuracy of 91.54%.

Nowadays, the digital register process of electrocardiographical signals (ECG) constitutes a common practice for the diagnosis and controlling of patients suffering from cardiac disorders. In this paper we study the usefulness of Artificial Neural Network (ANN) for clinical diagnosis through the detection of arrythmias and the reduction of the large spaces occupied by ECG records.

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Electrocardiogram (ECG) signals are largely employed as a diagnostic tool in clinical practice in order to assess the cardiac status of a specimen. Independent component analysis (ICA) of measured ECG signals yields the independent sources, provided that certain requirements are fulfilled. Properly parametrized ECG signals provide a better view of the extracted ECG signals, while reducing the amount of ECG data. Independent components (ICs) of parametrized ECG signals may also be more readily interpretable than original ECG measurements or even their ICs. The purpose of this analysis is to evaluate the effectiveness of ICA in removing artifacts and noise from ECG signals for a clear interpretation of ECG data in diagnostic applications. In this work, ICA is tested on the Common Standards for Electrocardiography (CSE) database files corrupted by abrupt changes, high frequency noise, power line interference, etc. The joint approximation for diagonalization of eigen matrices (JADE) algorithm for ICA is applied to three-channel ECG, and the sources are separated as ICs. In this analysis, an extension is applied to the algorithm for further correction of the extracted components. The values of R-peak before and after application of ICA are found using quadratic spline wavelet, which facilitates the estimation of the reconstruction errors. The results indicate that, in most of the cases, the percentage reconstruction error is small at around 3%. The paper also highlights the advantages, limitations, and diagnostic feature extraction capability of ICA for clinicians and medical practitioners. Kurtosis is varied in the range of 3.0–7.0, and variance of variance (Varvar) is varied in the range of 0.2–0.5.

In many medical applications, feature selection is obvious; but in medical domains, selecting features and creating a feature vector may require more effort. The wavelet transform (WT) technique is used to identify the characteristic points of an electrocardiogram (ECG) signal with fairly good accuracy, even in the presence of severe high-frequency and low-frequency noise. Principal component analysis (PCA) is a suitable technique for ECG data analysis, feature extraction, and image processing — an important technique that is not based upon a probability model. The aim of the paper is to derive better diagnostic parameters for reducing the size of ECG data while preserving morphology, which can be done by PCA. In this analysis, PCA is used for decorrelation of ECG signals, noise, and artifacts from various raw ECG data sets. The aim of this paper is twofold: first, to describe an elegant algorithm that uses WT alone to identify the characteristic points of an ECG signal; and second, to use a composite WT-based PCA method for redundant data reduction and better feature extraction. PCA scatter plots can be observed as a good basis for feature selection to account for cardiac abnormalities. The study is analyzed with higher-order statistics, in contrast to the conventional methods that use only geometric characteristics of feature waves and lower-order statistics. A new algorithm — viz. PCA variance estimator — is developed for this analysis, and the results are also obtained for different combinations of leads to find correlations for feature classification and useful diagnostic information. PCA scatter plots of various chest and augmented ECG leads are obtained to examine the varying orientations of the ECG data in different quadrants, indicating the cardiac events and abnormalities. The efficacy of the PCA algorithm is tested on different leads of 12-channel ECG data; file no. 01 of the Common Standards for Electrocardiography (CSE) database is used for this study. Better feature extraction is obtained for some specific combinations of leads, and significant improvement in signal quality is achieved by identifying the noise and artifact components. The quadrant analysis discussed in this paper highlights the filtering requirements for further ECG processing after performing PCA, as a primary step for decorrelation and dimensionality reduction. The values of the parameters obtained from the results of PCA are also compared with those of wavelet methods.

Independent component analysis (ICA) is a technique capable of separating independent components (ICs) from complex electrocardiogram (ECG) signals. The basic intention behind using multidimensional independent component analysis (MICA) is to find stable higher dimensional source signal subspaces. This study highlights the ability of ICA for parametrization of ECG signals to reduce the amount of redundant ECG data if any in a data set.

The aim of this paper is to justify the underlying theory of the use of ICA and how it can be extended to for MICA separation of the ECG signals for combinational leads to attain most useful diagnostic information, which was not discussed in other some similar previous publications in this field.

It is also investigated that the value of kurtosis coefficients for the ICs, which represents the noise component, can be further reduced using parametrized multidimensional independent component analysis (PMICA) technique. The indeterminacies available in the ECG data are also analyzed using modified version of Jade algorithm for PMICA and parametrized standard independent component analysis (PSICA). For the ECG data set, Jade algorithm is applied first to find smaller subspaces for MICA analysis and can therefore be regarded as a basis algorithm for PMICA analysis. The simulation results are obtained in Matlab environment to indicate that, ICA can definitely improve signal–noise ratio (SNR) in minimizing the reconstruction errors.

The future scope of MICA expected by author is that, by reconsidering the notion of ICA, a more general perspective can be envisioned: i.e. modified multidimensional independent component analysis (MMICA). It would be based on a morphological geometric parametrization (MGP) which would further reduce the indeterminacies involved in matrix-based modeling (MBM).

Reliable and computationally efficient means of classifying electrocardiogram (ECG) signals has been the subject of considerable research effort in recent years. This paper explores the potential applications of a talented, versatile computation model called the Artificial Neural Network (ANN) in the field of ECG signal classification. Two types of ANNs: Multi-Layered Feed Forward Network (MLFFN) and Probabilistic Neural Networks (PNN) are used to classify seven types of ECG beats. It includes six types of arrhythmia data and normal data. Here, parametric modeling strategies are used in conjunction with ANN classifiers to discriminate ECG signals. Instead of giving the ECG data as such, parameters such as fourth order Auto Regressive model coefficients and Spectral Entropy of the signals has been selected. On testing with the Massachusetts Institute of Technology-Beth Israel Hospital (MIT/BIH) arrhythmia database, it has been observed that PNN has better performance than conventionally used MLFFN in ECG arrhythmia classification. MLFFN with Back Propagation Algorithm gives a classification accuracy of 97.54% and PNN gives 98.96%. The classification by PNN also has an advantage that the computation time for classification is lower than that of MLFFN.