scholarly journals RESEARCH OF THE CETLIN METHOD OF AUTOMATIC ARRHYTHMIA DETECTION BY ECG SIGNALS FROM MIT-BIH

2021 ◽  
Vol 63 (1) ◽  
pp. 98-109
Author(s):  
Aleksei K. Khalaidzhi ◽  
Keyword(s):  
Real Time ◽  
Quality Metrics ◽  
False Positives ◽  
Ecg Signal ◽  
Rr Intervals ◽  
Arrhythmia Detection ◽  
Ecg Signals ◽  
Method Paper ◽  
Best Parameters

This article presents and solves the problem of evaluating the quality of the Cetlin method, which classifies the sequence of RR-intervals by the recordings of ECG signals from MIT-BIH, which have labels on R-peaks. To solve this problem, author proposes new quality metrics and describes developed algorithms for calculating them in real time with taking into account segmentation errors. Author analyzes the influence of the accuracy of the segmentation procedure for extracting the positions of R-peaks from ECG signal on the proposed quality metrics. Paper compares the quality of the Cetlin method and other existing algorithms for arrhythmia detection that analyze the duration of RR-intervals in accordance with a set of rules in real time. Article reveals advantages and limitations of the method. Paper shows that the method successfully detects SVEB and VEB arrhythmias. but has inertia, that leads to false positives, and is immune to morphological abnormalities that do not change the duration of RR-intervals. Author analyzes the influence of parameters of the Cetlin method on its quality according to the proposed metrics. Paper describes the procedure for searching the best parameters configuration. In conclusion, author reveals that there is no the only configuration, that achieves the best quality for each signal from MIT-BIH.

STRONG REAL-TIME QRS COMPLEX DETECTION

2017 ◽  
Vol 17 (08) ◽  
pp. 1750111 ◽  
Author(s):  
M. M. BENOSMAN ◽  
F. BEREKSI-REGUIG ◽  
E. GORAN SALERUD
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Trunk Muscles ◽  
Computation Method ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Rr Intervals ◽  
Ecg Signals ◽  
Qrs Complex Detection ◽  
Complex Detection

Heart rate variability (HRV) analysis is used as a marker of autonomic nervous system activity which may be related to mental and/or physical activity. HRV features can be extracted by detecting QRS complexes from an electrocardiogram (ECG) signal. The difficulties in QRS complex detection are due to the artifacts and noises that may appear in the ECG signal when subjects are performing their daily life activities such as exercise, posture changes, climbing stairs, walking, running, etc. This study describes a strong computation method for real-time QRS complex detection. The detection is improved by the prediction of the position of [Formula: see text] waves by the estimation of the RR intervals lengths. The estimation is done by computing the intensity of the electromyogram noises that appear in the ECG signals and known here in this paper as ECG Trunk Muscles Signals Amplitude (ECG-TMSA). The heart rate (HR) and ECG-TMSA increases with the movement of the subject. We use this property to estimate the lengths of the RR intervals. The method was tested using famous databases, and also with signals acquired when an experiment with 17 subjects from our laboratory. The obtained results using ECG signals from the MIT-Noise Stress Test Database show a QRS complex detection error rate (ER) of 9.06%, a sensitivity of 95.18% and a positive prediction of 95.23%. This method was also tested against MIT-BIH Arrhythmia Database, the result are 99.68% of sensitivity and 99.89% of positive predictivity, with ER of 0.40%. When applied to the signals obtained from the 17 subjects, the algorithm gave an interesting result of 0.00025% as ER, 99.97% as sensitivity and 99.99% as positive predictivity.

scholarly journals Template Matching and Matrix Profile for Signal Quality Assessment of Carotid and Femoral Laser Doppler Vibrometer Signals

2022 ◽  
Vol 12 ◽  
Author(s):  
Silvia Seoni ◽  
Simeon Beeckman ◽  
Yanlu Li ◽  
Soren Aasmul ◽  
Umberto Morbiducci ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Quality Assessment ◽  
Real Time ◽  
Template Matching ◽  
Quality Metrics ◽  
Laser Doppler ◽  
Signal Quality ◽  
Laser Doppler Vibrometry ◽  
Quality Metric

Background: Laser-Doppler Vibrometry (LDV) is a laser-based technique that allows measuring the motion of moving targets with high spatial and temporal resolution. To demonstrate its use for the measurement of carotid-femoral pulse wave velocity, a prototype system was employed in a clinical feasibility study. Data were acquired for analysis without prior quality control. Real-time application, however, will require a real-time assessment of signal quality. In this study, we (1) use template matching and matrix profile for assessing the quality of these previously acquired signals; (2) analyze the nature and achievable quality of acquired signals at the carotid and femoral measuring site; (3) explore models for automated classification of signal quality.Methods: Laser-Doppler Vibrometry data were acquired in 100 subjects (50M/50F) and consisted of 4–5 sequences of 20-s recordings of skin displacement, differentiated two times to yield acceleration. Each recording consisted of data from 12 laser beams, yielding 410 carotid-femoral and 407 carotid-carotid recordings. Data quality was visually assessed on a 1–5 scale, and a subset of best quality data was used to construct an acceleration template for both measuring sites. The time-varying cross-correlation of the acceleration signals with the template was computed. A quality metric constructed on several features of this template matching was derived. Next, the matrix-profile technique was applied to identify recurring features in the measured time series and derived a similar quality metric. The statistical distribution of the metrics, and their correlates with basic clinical data were assessed. Finally, logistic-regression-based classifiers were developed and their ability to automatically classify LDV-signal quality was assessed.Results: Automated quality metrics correlated well with visual scores. Signal quality was negatively correlated with BMI for femoral recordings but not for carotid recordings. Logistic regression models based on both methods yielded an accuracy of minimally 80% for our carotid and femoral recording data, reaching 87% for the femoral data.Conclusion: Both template matching and matrix profile were found suitable methods for automated grading of LDV signal quality and were able to generate a quality metric that was on par with the signal quality assessment of the expert. The classifiers, developed with both quality metrics, showed their potential for future real-time implementation.

scholarly journals ECG signal classification based on Deep Learning by using Convolutional Neural Network (CNN)

2020 ◽  
Vol 3 (3) ◽  
pp. 12-23
Author(s):  
Aqeel M. Hamad alhussainy ◽  
Ammar D. Jasim
Keyword(s):  
Deep Learning ◽  
Classification System ◽  
World Health ◽  
Geometric Transformation ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Result Show ◽  
Automatic Methods ◽  
Health Organization

Cardiovascular diseases (CVDs) are consider  the main cause  of death today According to World Health Organization (WHO),and because that ECG signal is very important tool in monitoring and diagnosis of these disease , different automatic methods were proposed based on this signal. [1]. The manual analysis of ECG signals is suffered different challenges such as differeculty of detecting and classify waveform of this signal, So, many machine learning methods  are  explored to describe  the anomalies ECG signal accurately . Deep learning (DL) can be used in ECG classification, it can improve the quality of the automatic classification system. In this paper , we have proposed a deep learning classification system by using  different layers of convolution, rectifier and pooling operations  that can be used to increase feature extraction of ECG signal.        We have proposed two models, one is used for input signal of 1-D, in which we designed model for classification csv type of data for ECG signal, while in the second proposed system, we used model for 2-D signal after convert it from its csv type .  2-D signal (ECG image) is used in order to augment the two dimensional signal with different methods to increase the accuracy of the model by training it with geometric transformation of the original input images such as rotation, shearing etc.The results are compared with AlexNet and other  models  based on the metrics, which are    used to measure the performance of the proposed work, the result show that, the proposed models improve the efficiency of the classification  in the two systems.

scholarly journals TWO-LEAD JOINT DETECTION ALGORITHM OF QRS COMPLEXES IN ECG SIGNAL

2021 ◽  
Author(s):  
WANSONG XU ◽  
TIANWU CHEN ◽  
FANYU DU
Keyword(s):  
Detection Algorithm ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Joint Detection ◽  
Ecg Signals ◽  
Detection Algorithms ◽  
Threshold Adjustment ◽  
Current Algorithm ◽  
Electrocardiogram Ecg

Objective: The detection of QRS complexes is an important part of computer-aided analysis of electrocardiogram (ECG). However, most of the existing detection algorithms are mainly for single-lead ECG signals, which requires high quality of signal. If the signal quality decreases suddenly due to some interference, then the current algorithm is easy to cause misjudgment or missed detection. To improve the detection ability of QRS complexes under sudden interference, we study the QRS complexes information on multiple leads in-depth, and propose a two-lead joint detection algorithm of QRS complexes. Methods: Firstly, the suspected QRS complexes are screened on the main lead. For the suspected QRS complexes with low confidence and the complexes that may be missed, further accurate detection and joint judgment shall be carried out at the corresponding position of the auxiliary lead. At the same time, the adaptive threshold adjustment algorithm and backtracking mechanism are used to modify the detection results. Results: The proposed detection algorithm is validated using 48 ECG records of the MIT-BIH arrhythmia database, and achieves average detection accuracy of 99.71%, sensitivity of 99.88% and positive predictivity of 99.81%. Conclusion: The proposed algorithm has high accuracy, which can effectively deal with the sudden interference of ECG signal. Meanwhile, the algorithm requires small amount of computation, and can be embedded into hardware for real-time detection.

scholarly journals An Optimized FPGA Based System Design for the Arrhythmia Detection using ECG

2019 ◽  
Vol 9 (1) ◽  
pp. 2808-2818
Keyword(s):  
System Design ◽  
Detection System ◽  
Ecg Signal ◽  
Noise Elimination ◽  
Is Success ◽  
Arrhythmia Detection ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Reconstructed Signal ◽  
Signal Peak

Electrocardiogram signals are highly susceptible to interferences caused due to various kinds of noises including artefacts’, disruptions in power lines attained from the human interferences and device disturbances. These noise signals tend to lower the quality of signals that result in crucial environment for detecting and diagnosing different types of arrhythmia. In order to avoid this issue, multiple filtering techniques are being incorporated out of all Gaussian filters with Haar DWT portray better outcomes in noise elimination and smoothening of signal. The process of ECG signal filtering allows performing the testing and validation of in the actual world emulation. Enhancement in PSNR ratio is observed by using the ECG signal filters along the reconstructed signal. For a given input ECG signal, the levels of the signal peak decide if the patient is suffering from arrhythmia or not. If peak is low, patient is detected with the arrhythmia disease, if high patient is normal. The results can be observed in simulation. FPGA prototyping of the design is carried out along the hardware debugging in chip scope pro tool. The design is realized using Verilog coding with the technique of morphological filtering. For the purpose of debugging the hardware device used is Artix-7. The FPGA methodology is success full in a position to detect arrhythmia. The framework based on FPGA is structured and executed in the paper which can detect a type of arrhythmia which indicates Atrio Ventricular block along with all the noises removed. The simulation results are obtained by taking ECG signals from MIT-BIH arrhythmia database. The proposed FPGA based system design is proven to be optimized as it showed very less utilization of resources when compared to previous arrhythmia detection system designs.

Quality Assessment of ECG Signal Based on Wavelet Energy Ratio and Wavelet Energy Entropy

2014 ◽  
Vol 530-531 ◽  
pp. 577-580 ◽  
Author(s):  
Ai Hua Zhang ◽  
Ming Chun Kou ◽  
Chen Diao ◽  
Dong Mei Lin
Keyword(s):  
Quality Assessment ◽  
Wavelet Decomposition ◽  
Energy Ratio ◽  
Ecg Signal ◽  
Quality Indices ◽  
Ecg Signals ◽  
Wavelet Energy ◽  
Energy Entropy ◽  
Wavelet Energy Entropy

ECG signal is affected by many factors such as noise and interference in the process of acquisition, which make it difficult for clinicians to interpret the ECG signal precisely and effectively. In order to detect whether an ECG signal is worthy to be interpreted by clinicians, an algorithm was proposed to assess the quality of ECG signal based on wavelet energy ratio and wavelet energy entropy. After wavelet decomposition, the ECG signals wavelet energy ratio and wavelet energy entropy were calculated in three different frequency bands, and we defined them as the quality indices to evaluate the quality of ECG signal. Experimental results show that we can achieve an accuracyof 95.2%.

A Framework to Measure and Estimate Video Quality in SVC Real-Time Adaptive Systems

2014 ◽  
Vol 10 (1) ◽  
pp. 47-64
Author(s):  
Alberto Álvarez ◽  
Laura Pozueco ◽  
Sergio Cabrero ◽  
Xabiel G. Pañeda ◽  
Roberto García ◽  
...  
Keyword(s):  
Real Time ◽  
Adaptive Systems ◽  
Scalable Video Coding ◽  
Video Quality ◽  
Estimation Method ◽  
Quality Metrics ◽  
Reference Information ◽  
Video Quality Metrics ◽  
Interesting Alternative

Effectively adapting the content to network conditions in real-time is an important matter in best-effort networks like the Internet. Scalable Video Coding (SVC) is an interesting alternative to implement such systems. However, some problems of the performance evaluation of SVC based adaptive systems have not been solved. The authors review the main efforts directed to measure video quality on SVC related systems and discuss the limitations of each one. This paper elaborates a framework to measure video quality metrics in real adaptive SVC based streams. An estimation method for full reference video quality metrics is proposed. This method reduces reference information required and it is able to provide real-time accurate results simply using metadata regarding the video quality of the reference layers. The video quality of several streams that have been generated using a real-time adaptive system is first measured with the elaborated framework and then estimated with the proposed method.

scholarly journals Automated Classification of Atrial Fibrillation Using Artificial Neural Network for Wearable Devices

2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Fengying Ma ◽  
Jingyao Zhang ◽  
Wei Liang ◽  
Jingyu Xue
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Artificial Neural Network ◽  
Real Time ◽  
Classification Model ◽  
Ecg Monitoring ◽  
Rr Intervals ◽  
Ecg Signals ◽  
Artificial Neural

Atrial fibrillation (AF), as one of the most common arrhythmia diseases in clinic, is a malignant threat to human health. However, AF is difficult to monitor in real time due to its intermittent nature. Wearable electrocardiogram (ECG) monitoring equipment has flourished in the context of telemedicine due to its real-time monitoring and simple operation in recent years, providing new ideas and methods for the detection of AF. In this paper, we propose a low computational cost classification model for robust detection of AF episodes in ECG signals, using RR intervals of the ECG signals and feeding them into artificial neural network (ANN) for classification, to compensate the defect of the computational complexity in traditional wearable ECG monitoring devices. In addition, we compared our proposed classifier with other popular classifiers. The model was trained and tested on the AF Termination Challenge Database and MIT-BIH Arrhythmia Database. Experimental results achieve the highest sensitivity of 99.3%, specificity of 97.4%, and accuracy of 98.3%, outperforming most of the others in the recent literature. Accordingly, we observe that ANN using RR intervals as an input feature can be a suitable candidate for automatic classification of AF.

Development of medical capacitive coupling electrodes using the skin-electrode contact control

Sensor Review ◽  
2020 ◽  
Vol 40 (3) ◽  
pp. 347-354
Author(s):  
Gennadiy Evtushenko ◽  
Inna A. Lezhnina ◽  
Artem I. Morenetz ◽  
Boris N. Pavlenko ◽  
Arman A. Boyakhchyan ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
The Body ◽  
Capacitive Coupling ◽  
Ecg Signal ◽  
Significant Deterioration ◽  
Content Type ◽  
Ecg Signals ◽  
Skin Electrode ◽  
Electrode Contact

Purpose The purpose of this paper is the development and study of capacitive coupling electrodes with the ability to monitor the quality of the skin–electrode contact in the process of electrocardiogram (ECG) diagnostics. The study’s scope embraces experimental identification of distortions contributed into the recorded ECG signal at various degrees of disturbance of the skin–electrode contact. Design/methodology/approach A capacitive coupling electrode is designed and manufactured. A large number of experiments was carried out to record ECG signals with different quality of the skin–electrode contact. Using spectral analysis, the characteristic distortions of the ECG signals in the event of contact disturbance are revealed. Findings It was found that the violation of the skin–electrode contact leads to significant deterioration in the recorded signal. In this case, the most severe distortions appear with various violations of the skin–electrode contact of two sensors in one lead. It has been experimentally shown that the developed sensor allows monitoring the quality of the contact, and therefore, improvement of the quality of signal registration, enabled by the use of bespoke processing algorithms. Practical implications These sensors will be used in personalized medicine devices and tele-ECG devices. Originality/value In this work, authors studied the effect of the skin–electrode contact of a capacitive electrode with the body on the quality of the recorded ECG signal. Based on the studies, the necessity of monitoring contact was shown to improve the quality of diagnostics provided by personalized medicine devices; the capacitive sensor with contact feedback was developed.

Sign in / Sign up

Export Citation Format

Share Document