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Keyword: ECG (57) | 18 Feb 2025 | Run |
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In the recent past, numerous frameworks have been designed to take decision support from samples for analyzing ECG signal data classification with wearable devices to prevent health risks in sports. As various frameworks permit a distinctive set of results, assessing the framework’s classification control in examination with other order frameworks or in correlation with human specialists is hard. The order precision is generally utilized as a measure of classification execution in this research. A novel hybrid Improved Monkey-based search (IMS) and support vector machine (SVM) technique have been designed and developed in this research for the health risk identification in ECGs. It incorporates handling of noise, extraction of signals, rule-based beat classification, and sliding window arrangement using a wearable device for the sportsperson. It can be executed continuously and can give clarifications to the analytic choices, and maximum scores have been acquired in terms of sensitivity and specificity (98.1% and 98.5% correspondingly using collective accuracy gross information, and 98.8% using aggregate average statistics, which has been shown in this research. Finally, experimental analysis has exposed that the hybrid Improved Monkey-based search (IMS) and support vector machine (SVM) technique achieve high precision (99.01%) in analyses of the heart rate for the sportsperson.
Driver fatigue can be detected by constructing a discriminant mode using some features obtained from physiological signals. There exist two major challenges of this kind of methods. One is how to collect physiological signals from subjects while they are driving without any interruption. The other is to find features of physiological signals that are of corresponding change with the loss of attention caused by driver fatigue. Driving fatigue is detected based on the study of surface electromyography (EMG) and electrocardiograph (ECG) during the driving period. The noncontact data acquisition system was used to collect physiological signals from the biceps femoris of each subject to tackle the first challenge. Fast independent component analysis (FastICA) and digital filter were utilized to process the original signals. Based on the statistical analysis results given by Kolmogorov–Smirnov Z test, the peak factor of EMG (p < 0.001) and the maximum of the cross-relation curve of EMG and ECG (p < 0.001) were selected as the combined characteristic to detect fatigue of drivers. The discriminant criterion of fatigue was obtained from the training samples by using Mahalanobis distance, and then the average classification accuracy was given by 10-fold cross-validation. The results showed that the method proposed in this paper can give well performance in distinguishing the normal state and fatigue state. The noncontact, onboard vehicle drivers' fatigue detection system was developed to reduce fatigue-related risks.
Echo state networks (ESNs), belonging to the family of recurrent neural networks (RNNs), are suitable for addressing complex nonlinear tasks due to their rich dynamic characteristics and easy implementation. The reservoir of the ESN is composed of a large number of sparsely connected neurons with randomly generated weight matrices. How to set the structural parameters of the ESN becomes a difficult problem in practical applications. Traditionally, the design of the parameters of the ESN structure is performed manually. The manual adjustment of the ESN parameters is not convenient since it is an extremely challenging and time-consuming task. This paper proposes an ensemble of five particle swarm optimization (PSO) strategies to design the structure of ESN and then reduce the manual intervention in the design process. An adaptive selection mechanism is used for each particle in the evolution to select a strategy from the strategy candidate pool for evolution. In addition, leaky integration neurons are used as reservoir internal neurons, which are added within the adaptive mechanism for optimization. The root mean squared error (RMSE) is adopted as the evaluation criterion. The experimental results on Mackey–Glass time series benchmark dataset show that the proposed method outperforms other traditional evolutionary methods. Furthermore, experimental results on electrocardiogram dataset show that the proposed method on the ensemble of PSO displays an excellent performance on real-world problems.
Accurate detection of arrhythmia signal types is of great significance for the early detection of heart disease and its subsequent treatment. The primary purpose of this study is to explore an electrocardiogram (ECG) classification system to improve its performance and achieve excellent computing performance, especially for large sample datasets. We classified ECG signals using the Hurst exponent, which is an ECG feature extracted by multifractal detrended moving average cross-correlation analysis (MF-XDMA). In addition, we used multifractal methods such as multifractal detrended fluctuation analysis (MF-DFA), multifractal detrended cross-correlation analysis (MF-DCCA) and multifractal detrended moving average (MF-DMA) to extract the features of ECG signals, and we used a support vector machine (SVM) to classify the four types of feature data. The experimental results show that MF-XDMA-SVM has the best classification performance for atrial premature beat (APB) and bigeminy signals, which indicates that MF-XDMA-SVM is the most effective for the extraction of ECG signal sequence features among the four multifractal models.
Wearable devices are increasingly gaining more attentions in healthcare and fitness industry due to their potentials to measure valuable physiological signals on the move. There are many researchers who have proposed different types of designs that embed biosensors into miniature wearable devices. In this paper, we present a wearable companion that monitors the cardiac activities of a wearer with smartphone. The device makes use of a single, integrated biosensor that is designed with a unique analog front-end circuitry and a dedicated signal processing pipeline. In order to meet the requirements of possible but different user scenarios, three types of product forms are presented. The experimental results show that electrocardiogram (ECG) signals collected are valid and consistent through the systems. Future topics include adding extra algorithms to remove motion artifacts in order to achieve better signal quality in various settings and include wireless communication through 4G.
The research of ECG diagnosis based on deep learning is a hot topic at present. ECG signals are collected from human body surface electrodes and electrocardiograms are obtained. The research process is to integrate engineering technology into the medical field, reflecting the new direction of interdisciplinary combination. This paper introduces the basic principle of ECG signal and the basic analysis method of ECG. The experimental results show that the application of one-dimensional convolutional neural network is more effective and accurate than the traditional methods. The design of the theoretical method has provided the technical support and theoretical basis for the further study of electrophysiological signals and the clinical diagnosis.
An integrated current mode high impedance input stage designed for Electrocardiography (ECG) systems (or low frequency general applications) is presented. This feature becomes necessary when a two-electrodes ECG apparatus is used (e.g., in fetal ECG or heart monitoring in extreme sports) and a good response of the system to common mode signals is required. The proposed input stage, based on a bootstrap topology that simultaneously increases the ECG electrodes input impedance (from 5 ÷ 50 kΩ to about 50 MΩ) and amplifies the applied signal, is implemented by using a configuration that employs only two second generation current conveyors for each electrode. Post-layout simulations have proved that the proposed system is quite not sensible to electrodes mismatch and battery discharge.
A new approach to the design of a comb filter using current conveyor is proposed to eliminate the undesired harmonic interference from biomedical signal. In this approach, a number of inverted band pass filters are used to construct a comb filter. The components used are second generation current conveyor (CCII), capacitor and resistor. To verify the performance of the proposed circuit, the comb filter is designed to eliminate the unwanted power line frequency of 60Hz and its odd harmonics such as 180, 300 and 420Hz. The proposed circuit is simulated by implementing CCII± using a macro model of commercially available CFOA IC AD844 as well as 0.18μm CMOS technology. The circuit is also verified experimentally by using commercially available IC AD844. The effectiveness of harmonic removal has also been tested.
This paper presents a fully integrated front-end, low noise amplifier (LNA), dedicated to the processing of various types of bio-medical signals, such as Electrocardiogram (ECG), Electroencephalography (EEG), Axon Action Potential (AAP). A novel noise reduction technique, for an operational transconductance amplifier (OTA), has been proposed. This adds a current steering branch parallel to the differential pair, with a view to reducing the noise contribution by the cascode current sources. Hence, this reduces the overall input-referred noise of the LNA, without adding any additional power. The proposed technique implemented in 65nm CMOS technology achieves 30 dB closed-loop voltage gain, 0.05Hz lower cut-off frequency and 100 MHz 3-dB bandwidth. It operates at 1.2V power supply and draws 1μA static current. The prototype described in this paper occupies 3300μm2 silicon area.
Electrocardiogram (ECG) is a graphical visualization of the electrical activity of the human heart that is recorded by placing a surface electrode at standardized position on a person’s chest. ECG signals suffer from artifacts/noises due to baseline wander (BW), electrode artifacts, muscle artifacts, power-line interference and channel noises during acquisition and transmission of the ECG signals. Reduction of these artifacts is crucial for efficient diagnosis and interpretation of the human heart condition. In this paper, an effective adaptive noise canceller (ANC) based on empirical mode decomposition (EMD)-Jaya algorithm is proposed for denoising electrocardiogram. In this approach, intrinsic mode functions (IMFs) produced by EMD are used as reference and Jaya algorithm is used to calculate optimum weights of finite impulse response (FIR) filter. This scheme is compared with EMD, wavelet transform (WT) thresholding, and hybrid EMD-least mean square (LMS) approaches through extensive simulation on noise corrupted ECG besides verifying the robustness with real ECG signals. The performance of the proposed technique is assessed using standard metric signal-to-noise ratio (SNR) with different contamination levels. The results obtained demonstrate the superiority of the hybrid when compared to other competing approaches.
The methods of nonlinear dynamics are applied to reveal the pathologies of patients with different heart failures. Our approach is based on the analysis of the correlation and embedding dimensions of the RR-intervals of ECGs. We demonstrate that these characteristics are quite convenient tools for the initial diagnosis. Advantages and disadvantages of the method are discussed.
Tele-medical systems have proven to be very useful to improve patient outcomes. However, they suffered from drawbacks such as insufficient functionality, prohibitive cost, the lack of connectivity and many other factors. To solve these problems, medical experts were consulted and an embedded tele-medical system was developed that allows a doctor to analyze and predict the ailment of a patient and direct the paramedic on the scene to perform potentially life-saving corrective actions or even promote recovery. The system would capture, process and interpret ECG data from the patient using the low cost hardware which was designed.
Standard Electrocardiogram (ECG) database is created for validating and comparing different algorithms on feature detection and disease classification. At present, there are four frequently used standard databases: MIT-BIH arrhythmia database, QT database, CSE multi-lead database and AHA database. With the development in equipment and diagnosis approach, severe deficiencies are discovered and a new modern ECG database is needed for further research. So Chinese Cardiovascular Disease Database (CCDD or CCD database), which contains 12-Lead ECG data, detailed annotation features and beat diagnosis result is proposed. It is advanced for not only improving the raw ECG data's technical parameters, but also introducing valuable morphology features which are utilized by experienced cardiologists effectively. CCDD is employed by our group as well as aiming for supporting other research groups that work in automated ECG analysis.
A general technique for representing quasi-periodic oscillations, typical of biomedical signals, is described. Using energy thresholding and Gaussian kernels, in conjunction with a nonlinear gradient descent optimization, it is shown that significant noise reduction, compression and turning point location is possible. As such, the signal representation model can be considered a form of correlated source separation. Applications to filtering, modelling and robust ECG QT-analysis are described.
In this paper, automatic electrocardiogram (ECG) recognition and classification algorithms based on multifractal detrended fluctuation analysis (MF-DFA) and multifractal detrended cross-correlation analysis (MF-DXA) were studied. As human heart is a complex, nonlinear, chaotic system, using multifractal analysis to analyze chaotic systems is also a trend. We performed a comparison study of the multifractal nature of the healthy subjects and that of the cardiac dysfunctions ones. To analyze multifractal property quantitatively, the ranges of the Hurst exponent (Δh) are computed by MF-DFA and MF-DXA. We found that for MF-DFA, the area of Hurst exponents for atrial premature beat (APB) people was narrower than normal sinus rhythm (NSR) subjects, and for MF-DXA, the difference of Δh (Δ(Δh)) of NSR and APB subjects was larger than that of MF-DFA. We then regarded the Hurst exponents (h) as the input vectors and took them into support vector machine (SVM) for classification. The results showed that h obtained from MF-DXA led to a higher classification accuracy than that of MF-DFA. This is related to the widening of the difference in the values of Hurst exponents in MF-DFA and MF-DXA. The proposed MF-DFA-SVM and MF-DXA-SVM systems achieved classification accuracy of 86.54%±0.068% and 98.63%±0.0644%, achieved classification sensitivity of 75.03%±0.1323% and 90.77%±0.1309%, achieved classification specificity of 86.66%±0.1131% and 96.47%±0.0891%, respectively. In general, the Hurst exponents obtained from MF-DXA played an important role in classifying ECG of the healthy and that of the cardiac dysfunctions subjects. Moreover, MF-DXA was more accurate than MF-DFA in the classification of ECG studied in this paper. The research in automatic medical diagnosis and early warning of major diseases has very important practical value.
Electrocardiography (ECG) signals provides assistance to the cardiologists for identification of various cardiovascular diseases (CVD). ECG machine records the electrical activity of the heart with the assistance of electrodes placed on the patient’s body. Qualitative characterization of ECG signal reflects its sensitiveness towards distinct artifacts that resulted in low diagnostic accuracy and may lead to incorrect decision of the clinician. The artifacts are removed utilizing a robust noise estimator employing DTCWT using various threshold values and functions. The segments and intervals of ECG signals are calculated using the peak detection algorithm followed by particle swarm optimization (PSO) and the proposed optimization technique to select the best features from a considerable pool of features. Out of the 12 features, the best four features are selected using PSO and the proposed optimization technique. Comparative analysis with other feature selection methods and state-of-the-art techniques demonstrated that the proposed algorithm precisely selects principle features for handling the ECG signal and attains better classification utilizing distinctive machine learning algorithms. The obtained accuracy using our proposed optimization technique is 95.71% employing k-NN and neural networks. Also, 4% and 10% improvements have been observed while using k-NN over ANN and SVM, respectively, when the PSO technique is executed. Similarly, a 14.16% improvement is achieved while using k-NN and ANN over the SVM machine learning technique for the proposed optimization technique. Heart rate is calculated using the proposed estimator and optimization technique, which is in consensus with the gold standard.
Rheumatic Heart Disease (RHD) is a disorder of heart caused by streptococcal throat infection followed by the organ damage, irreversible valve damage and heart failure. Acute Rheumatic Fever (ARF) is a precursor to the disease. Sometimes, RHD can occur without any signs or symptoms, and if there are any symptoms, they occur with the infection in the heart valves and fever. Due to these issues, respiratory problems occur with chest pain and tremors. Additionally, the symptoms include faint, heart murmurs, stroke and unexpected collapse. The techniques available try to detect the RHD as early as possible. Although the recent medical health care department uses crucial techniques, they are not accurate in terms of symptom classification, precision and prediction. On the scope, we are developing Multi-Layered Acoustic Neural (MLAN) Networks to detect the RHD symptoms using heart beat sound and Electrocardiogram (ECG) measurements. In this proposed MLAN system, the novel techniques such as multi-attribute acoustic data sampling model, heart sound sampling procedures, ECG data sampling model, RHD Recurrent Convolutional Network (RRCN) and Acoustic Support Vector Machine (ASVM) are used for increasing the accuracy. In the implementation section, the proposed model has been compared to the Long Short-Term Memory-based Cardio (LSTC) data analysis model, Cardio-Net and Video-Based Deep Learning (VBDL) techniques. In this comparison, the proposed system has 10%–17% higher accuracy in RHD detection than existing techniques.
The RR and RT time intervals extracted from the electrocardiogram measure respectively the duration of cardiac cycle and repolarization. The series of these intervals recorded during the exercise test are characterized by two trends: A decreasing one during the stress phase and an increasing one during the recovery, separated by a global minimum. We model these series as a sum of a deterministic trend and random fluctuations, and estimate the trend using methods of curve extraction: Running mean, polynomial fit, multi scale wavelet decomposition. We estimate the minimum location from the trend. Data analysis performed on a group of 20 healthy subjects provides evidence that the minimum of the RR series precedes the minimum of the RT series, with a time delay of about 19 seconds.
The electrocardiographic (ECG) signal is a major artifact during recording the surface electromyography (SEMG). Removal of this artifact is one of the important tasks before SEMG analysis for biomedical goals. In this paper, the application of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for elimination of ECG artifact from SEMG is investigated. The focus of this research is to reach the optimized number of decomposed levels using mean power frequency (MPF) by both techniques. In order to implement the proposed methods, ten simulated and three real ECG contaminated SEMG signals have been tested. Signal-to-noise ratio (SNR) and mean square error (MSE) between the filtered and the pure signals are applied as the performance indexes of this research. The obtained results suggest both techniques could remove ECG artifact from SEMG signals fair enough, however, DWT performs much better and faster in real data.
Electrocardiogram (ECG) signals might be affected by various artifacts and noises that have biological and external sources. Baseline wander (BW) is a low-frequency artifact that may be caused by breathing, body movements and loose sensor contact. In this paper, a novel method based on empirical mode decomposition (EMD) for removal of baseline noise from ECG is presented. When compared to other EMD-based methods, the novelty of this research is to reach the optimized number of decomposed levels for ECG BW de-noising using mean power frequency (MPF), while the reduction of processing time is considered. To evaluate the performance of the proposed method, a fifth-order Butterworth high pass filtering (BHPF) with cut-off frequency at 0.5Hz and wavelet approach are applied. Three performance indices, signal-to-noise ratio (SNR), mean square error (MSE) and correlation coefficient (CC), between pure and filtered signals have been utilized for qualification of presented techniques. Results suggest that the EMD-based method outperforms the other filtering method.
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