scholarly journals An effective frequency-domain feature of atrial fibrillation based on time-frequency analysis

2020 ◽  
Author(s):  
Yusong Hu ◽  
Yantao Zhao ◽  
Jihong Liu ◽  
Jin Pang ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Decision Tree ◽  
Sinus Rhythm ◽  
Frequency Domain ◽  
Frequency Spectrum ◽  
Detection Method ◽  
Maximum Amplitude ◽  
Time Frequency ◽  
Electrocardiogram Signals ◽  
Cart Algorithm

Abstract Background: Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect. Methods: This paper proposed a high distinguishable frequency feature - the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R-R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features - the maximum amplitude in the frequency spectrum and R-R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm. Results: The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%. Conclusions: The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity and specificity of our method were pretty good by comparison with other studies.

scholarly journals An effective frequency-domain feature of atrial fibrillation based on time-frequency analysis

2020 ◽  
Author(s):  
Yusong Hu ◽  
Yantao Zhao ◽  
Jihong Liu ◽  
Jin Pang ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Decision Tree ◽  
Sinus Rhythm ◽  
Frequency Domain ◽  
Frequency Spectrum ◽  
Detection Method ◽  
Maximum Amplitude ◽  
Time Frequency ◽  
Electrocardiogram Signals ◽  
Cart Algorithm

Abstract Background: Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect. Methods: This paper proposed a high distinguishable frequency feature - the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R-R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features - the maximum amplitude in the frequency spectrum and R-R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm. Results: The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%. Conclusions: The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity and specificity of our method were pretty good by comparison with other studies.

scholarly journals An effective frequency-domain feature of atrial fibrillation based on time–frequency analysis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yusong Hu ◽  
Yantao Zhao ◽  
Jihong Liu ◽  
Jin Pang ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Decision Tree ◽  
Sinus Rhythm ◽  
Frequency Domain ◽  
Frequency Spectrum ◽  
Detection Method ◽  
Maximum Amplitude ◽  
Time Frequency ◽  
Electrocardiogram Signals ◽  
Cart Algorithm

Abstract Background Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect. Methods This paper proposed a high distinguishable frequency feature—the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R–R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features—the maximum amplitude in the frequency spectrum and R–R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm. Results The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%. Conclusions The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity and specificity of our method were pretty good by comparison with other studies.

scholarly journals An effective frequency-domain feature of atrial fibrillation based on time-frequency analysis

2020 ◽  
Author(s):  
Yusong Hu ◽  
Yantao Zhao ◽  
Jihong Liu ◽  
Jin Pang ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Decision Tree ◽  
Sinus Rhythm ◽  
Frequency Domain ◽  
Frequency Spectrum ◽  
Detection Method ◽  
Maximum Amplitude ◽  
Time Frequency ◽  
Electrocardiogram Signals ◽  
Cart Algorithm

Abstract Background: Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect. Methods: This paper proposed a high distinguishable frequency feature - the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R-R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features - the maximum amplitude in the frequency spectrum and R-R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm. Results: The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%. Conclusions: The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity and specificity of our method were pretty good by comparison with other studies.

scholarly journals An effective frequency domain feature of atrial fibrillation based on time-frequency analysis

2020 ◽  
Author(s):  
Yusong Hu ◽  
Yantao Zhao ◽  
Jihong Liu ◽  
Jin Pang ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Decision Tree ◽  
Frequency Domain ◽  
Frequency Spectrum ◽  
Sampling Rate ◽  
Maximum Amplitude ◽  
Decision Tree Algorithm ◽  
Transform Method ◽  
Tree Algorithm ◽  
Time Frequency

Abstract Background: Atrial fibrillation(AF) is a kind of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram (ECG) has great importance on further treatment. The practical ECG signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the AF signal features extracted by algorithm. But some of the discovered AF features are not well distinguishable, resulting in poor classification effect. Methods: This paper proposed a high distinguishable atrial fibrillation feature - the frequency corresponding to the maximum amplitude in the frequency spectrum (MAiFS). We used the R-R interval detection method optimized with mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features - the MAiFS and R-R interval irregular, we can recognize AF in ECG signal by decision tree classification algorithm. Results: The data used in the experiment comes from the MIT-BIH database [16] , which is publicly accessible via the web and with ethics approval and consent. The dataset contains 23 annotated ECG records, each of which is approximately 10 hours with a sampling rate of 250Hz and a 12-bit resolution with a range of 10mv. Based on the input of time-domain and frequency-domain features, a supervised classifier is constructed by using decision tree algorithm, and the data obtained from the above experiments are brought in to carry out a 5-fold cross validation test, the accuracy of classification reaches 98.9%. Conclusions: The frequency corresponding to the maximum amplitude in frequency spectrum in the normal signal is concentrated and the fluctuation is weak. But the frequency corresponding to the maximum amplitude in frequency spectrum in the atrial fibrillation signal is divergent and irregular. The decision tree algorithm can detect the normal signal and AF signal with 98.9% accuracy.

A Frequency-Domain-Based Algorithm for Detecting Microseismicity Using Dense Surface Seismic Arrays

2021 ◽  
Author(s):  
Maria Mesimeri ◽  
Kristine L. Pankow ◽  
James Rutledge
Keyword(s):  
Frequency Domain ◽  
Detection Method ◽  
Maximum Amplitude ◽  
High Energy ◽  
Aftershock Sequence ◽  
Anthropogenic Activity ◽  
Small Scale ◽  
Seismic Events ◽  
Small Magnitude ◽  
Seismic Arrays

ABSTRACT We propose a new frequency-domain-based algorithm for detecting small-magnitude seismic events using dense surface seismic arrays. Our proposed method takes advantage of the high energy carried by S waves, and approximate known source locations, which are used to rotate the horizontal components to obtain the maximum amplitude. By surrounding the known source area with surface geophones, we achieve a favorable geometry for locating the detected seismic events with the backprojection method. To test our new detection method, we used a dense circular array, consisting of 151 5 Hz three-component geophones, over a 5 km aperture that was in operation at the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) in southcentral Utah. We apply the new detection method during a small-scale test injection phase at FORGE, and during an aftershock sequence of an Mw 4.1 earthquake located ∼30  km north of the geophone array, within the Black Rock volcanic field. We are able to detect and locate microseismic events (Mw<0) during injections, despite the high level of anthropogenic activity, and several aftershocks that are missing from the regional catalog. By comparing our method with known algorithms that operate both in the time and frequency domain, we show that our proposed method performs better in the case of the FORGE injection monitoring, and equally well for the off-array aftershock sequence. Our new method has the potential to improve microseismic event detections even in extremely noisy environments, and the proposed location scheme serves as a direct discriminant between true and false detections.

scholarly journals Identification of Arrhythmia by Using a Decision Tree and Gated Network Fusion Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Peng Lu ◽  
Yabin Zhang ◽  
Bing Zhou ◽  
Hongpo Zhang ◽  
Liwei Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Tree ◽  
Frequency Domain ◽  
Weight Control ◽  
High Reliability ◽  
Low Frequency ◽  
Model Parameters ◽  
Fusion Model ◽  
Tree Model ◽  
Time Frequency

In recent years, deep learning (DNN) based methods have made leapfrogging level breakthroughs in detecting cardiac arrhythmias as the cost effectiveness of arithmetic power, and data size has broken through the tipping point. However, the inability of these methods to provide a basis for modeling decisions limits clinicians’ confidence on such methods. In this paper, a Gate Recurrent Unit (GRU) and decision tree fusion model, referred to as (T-GRU), was designed to explore the problem of arrhythmia recognition and to improve the credibility of deep learning methods. The fusion model multipathway processing time-frequency domain featured the introduction of decision tree probability analysis of frequency domain features, the regularization of GRU model parameters and weight control to improve the decision tree model output weights. The MIT-BIH arrhythmia database was used for validation. Results showed that the low-frequency band features dominated the model prediction. The fusion model had an accuracy of 98.31%, sensitivity of 96.85%, specificity of 98.81%, and precision of 96.73%, indicating its high reliability and clinical significance.

scholarly journals Time- and Frequency-Domain Characteristics of Atrial Electrograms During Sinus Rhythm and Atrial Fibrillation

2011 ◽  
Vol 22 (8) ◽  
pp. 851-857 ◽  
Author(s):  
SHENG-HSIUNG CHANG ◽  
MAGNUS ULFARSSON ◽  
AMAN CHUGH ◽  
KENTARO YOSHIDA ◽  
KRIT JONGNARANGSIN ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Frequency Domain ◽  
Atrial Electrograms

scholarly journals Study on the Feature Space Detection Method of DC Arc Fault for Photovoltaic system

2021 ◽  
Vol 256 ◽  
pp. 01015
Author(s):  
Liling Sun ◽  
Han Wu ◽  
Xiangdong Lu
Keyword(s):  
Frequency Domain ◽  
Detection Method ◽  
Feature Space ◽  
Current Data ◽  
Photovoltaic System ◽  
Photovoltaic Systems ◽  
Current Signal ◽  
Time Frequency ◽  
The Time Domain ◽  
Dc Arc

An arc fault on the DC side of the photovoltaic system is a potential safety hazard and is difficult to detect due to the complexity of photovoltaic systems. The detection method of series arc fault in photovoltaic systems is investigated here. The DC arc fault test platform for a photovoltaic system is established to collect the current signal under normal and fault conditions. In this study, the time domain characteristics, frequency domain characteristics, and time-frequency domain characteristics are compared by analysing the current data from the photovoltaic system in before and after fault states: corresponding feature vectors are used to construct the arc fault feature space of the system, and according to the position of the current signal in the feature space the fault is detected, so as to realise effective arc fault feature information. Then the method of establishing the arc fault feature space is introduced and key parameters of the feature space are determined. Finally, the anti-interference ability of arc fault feature space detection is verified. The results showed that the detection method is both feasible and accurate.

Study on Detection Method of Moment of Inertia with Torsion Pendulum Method in Frequency Domain Based on FFT

2015 ◽  
Vol 738-739 ◽  
pp. 826-831
Author(s):  
Xiao Lin Zhang ◽  
Lie Shan Zhang ◽  
Bin Zou ◽  
Wen Yan Tang ◽  
Qiu Feng Shao
Keyword(s):  
Frequency Domain ◽  
Frequency Spectrum ◽  
Detection Method ◽  
Damping Ratio ◽  
Peak Frequency ◽  
Moment Of Inertia ◽  
Natural Vibration Frequency ◽  
Torsion Pendulum ◽  
Method Of Moment ◽  
Spectrum Distribution

The article presents the detection method of moment of inertia in frequency domain based on the torsion pendulum method. It analyzes the frequency spectrum distribution of pendulum motion under the condition of linear damping, establishes the relationship between peak frequency and moment of inertia; it proposes the detection method of moment of inertia based on FFT. The frequency spectrum analysis of multiple sets of simulation sequence shows that it has mapping relationship among un-damped natural vibration frequency, frequency bandwidth and damping ratio. The experiment results verify the correctness of detection method of moment of inertia based on FFT.

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