Accurate RR-Interval Detection with Daubechies Filtering and Adaptive Thresholding

2019 ◽  
pp. 59-67
Author(s):  
Mochammad Rif’an ◽  
Robert Rieger ◽  
Chua-Chin Wang
Keyword(s):  
Adaptive Thresholding ◽  
Rr Interval

Adaptive Thresholding for Signal De-Noising for Power-Line Communications

2017 ◽  
Vol E100.A (12) ◽  
pp. 3041-3044
Author(s):  
Yu Min HWANG ◽  
Gyeong Hyeon CHA ◽  
Jong Kwan SEO ◽  
Jae-Jo LEE ◽  
Jin Young KIM
Keyword(s):  
Power Line ◽  
Adaptive Thresholding ◽  
Power Line Communications

An Adaptive Thresholding-Based Movement Epenthesis Detection Technique Using Hybrid Feature Set for Continuous Fingerspelling Recognition

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Ananya Choudhury ◽  
Anjan Kumar Talukdar ◽  
Kandarpa Kumar Sarma ◽  
M. K. Bhuyan
Keyword(s):  
Detection Technique ◽  
Adaptive Thresholding

scholarly journals Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 119
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Yining Sun ◽  
Mei Yang ◽  
Chun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Ecg Signals ◽  
Rr Interval ◽  
Frequency Components ◽  
Fully Connected

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

Adaptive thresholding and windowing in real-time multispectral correlation

1992 ◽  
Author(s):  
Gary V. Stephenson ◽  
John J. Winterberg ◽  
Bart A. Bartel ◽  
Richard L. Hedden
Keyword(s):  
Real Time ◽  
Adaptive Thresholding

scholarly journals Heartbeat Classification Using Normalized RR Intervals and Morphological Features

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Chun-Cheng Lin ◽  
Chun-Min Yang
Keyword(s):  
Feature Extraction ◽  
Classification System ◽  
Linear Prediction ◽  
Morphological Features ◽  
Rr Intervals ◽  
Heartbeat Classification ◽  
Linear Discriminant ◽  
Rr Interval ◽  
Signal Preprocessing ◽  
Ectopic Beats

This study developed an automatic heartbeat classification system for identifying normal beats, supraventricular ectopic beats, and ventricular ectopic beats based on normalized RR intervals and morphological features. The proposed heartbeat classification system consists of signal preprocessing, feature extraction, and linear discriminant classification. First, the signal preprocessing removed the high-frequency noise and baseline drift of the original ECG signal. Then the feature extraction derived the normalized RR intervals and two types of morphological features using wavelet analysis and linear prediction modeling. Finally, the linear discriminant classifier combined the extracted features to classify heartbeats. A total of 99,827 heartbeats obtained from the MIT-BIH Arrhythmia Database were divided into three datasets for the training and testing of the optimized heartbeat classification system. The study results demonstrate that the use of the normalized RR interval features greatly improves the positive predictive accuracy of identifying the normal heartbeats and the sensitivity for identifying the supraventricular ectopic heartbeats in comparison with the use of the nonnormalized RR interval features. In addition, the combination of the wavelet and linear prediction morphological features has higher global performance than only using the wavelet features or the linear prediction features.

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