scholarly journals Capillary Wave-Detection Algorithm Based on Cylindrical Solitary Waves

2021 ◽  
Vol 1945 (1) ◽  
pp. 012037
Author(s):  
I Mizeva
Keyword(s):  
Solitary Waves ◽  
Capillary Wave ◽  
Detection Algorithm ◽  
Wave Detection

An automated Rayleigh-wave detection algorithm

1997 ◽  
Vol 87 (1) ◽  
pp. 157-163
Author(s):  
Eric P. Chael
Keyword(s):  
Rayleigh Wave ◽  
High Reliability ◽  
Detection Algorithm ◽  
Arrival Times ◽  
Long Period ◽  
Wave Detection ◽  
Linearly Polarized ◽  
Automated Data Processing ◽  
Component Wave ◽  
Elliptically Polarized

Abstract The desire to operate denser networks in order to monitor seismic activity at lower thresholds leads to greater emphasis on automated data processing. An algorithm for detecting and characterizing long-period Rayleigh-wave arrivals has been developed and tested. The routine continuously monitors all directions of approach to a station, in a manner similar to beamforming. The detector is based on cross-powers between the Hilbert-transformed vertical and rotated horizontal signals, so it is sensitive to both the power and polarization properties of the three-component wave field. Elliptically polarized Rayleigh arrivals are enhanced, while linearly polarized Love waves and body phases are suppressed. A test using one month of data from station ANMO demonstrated that this technique can, with high reliability, detect Rayleigh arrivals that are visible on the records. The measured arrival times and azimuths are accurate enough to permit automated association of the detections to events in a bulletin.

A New Method to Detect P-Wave Based on Quadratic Function

2011 ◽  
Vol 267 ◽  
pp. 462-467
Author(s):  
Nan Quan Zhou
Keyword(s):  
Quadratic Function ◽  
Least Square Method ◽  
Detection Algorithm ◽  
Least Square ◽  
P Wave ◽  
Good Effect ◽  
Rate Variation ◽  
Optimal Interval ◽  
Wave Detection ◽  
Wide Range

The paper presents a P-wave detection algorithm based on fitting function in the optimal interval. In the algorithm we used quadratic function to fit the P wave by this means of least square method in every interval, which was shifted in local range. Then we found the optimal fitting interval of P wave by comparing the error of fitting. Finally, we obtained the characteristic points of P wave by using the fitting function to fit P wave in the optimal interval. The performance of the algorithm tested using the records of the MIT-BIH database was effective and accurate. The algorithm on the wide range of heart rate variation and small P wave of ECG P-wave detection has good effect. Also it has some capabilities of anti-interference, particularly the false dismissal probability is quite low.

A REAL-TIME QT INTERVAL DETECTION ALGORITHM

2008 ◽  
Vol 08 (02) ◽  
pp. 251-263 ◽  
Author(s):  
Z. E. HADJ SLIMANE ◽  
F. BEREKSI REGUIG
Keyword(s):  
Real Time ◽  
Qt Interval ◽  
Detection Algorithm ◽  
Qrs Detection ◽  
Signal To Noise ◽  
Ecg Signals ◽  
Wave Detection ◽  
T Wave ◽  
Electrocardiogram Ecg ◽  
Ventricular Depolarization

The QT interval is the electrocardiographic representation of the duration of ventricular depolarization and repolarization. In this paper, we have developed a new real-time QT interval detection algorithm for automatically locating the onset of QRS and the end of the T wave. The algorithm consists of several steps: signal-to-noise enhancement, QRS detection, QRS onset, and T-wave end definition. The detection algorithm is tested on electrocardiogram (ECG) signals from the universal MIT-BIH Arrhythmia Database. The resulting QRS detection algorithm has a sensitivity of 99.79% and a specificity of 99.72%. The QRS onset and T-wave detection algorithm is tested using several data records from the MIT/BIH Arrhythmia Database. The results obtained are shown to be highly satisfactory.

scholarly journals Evaluation of Percutaneous Coronary Intervention for Acute ST-Segment Elevation Myocardial Infarction with Hypertension by Dynamic Electrocardiogram Feature Data

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Guoqiang Wang ◽  
Yu Wang ◽  
Ru Zhao
Keyword(s):  
Myocardial Infarction ◽  
Percutaneous Coronary Intervention ◽  
Coronary Intervention ◽  
Detection Algorithm ◽  
P Wave ◽  
St Segment Elevation ◽  
Segment Elevation Myocardial Infarction ◽  
Wave Detection ◽  
St Segment ◽  
Percutaneous Coronary

This work was to study the application value of dynamic electrocardiogram (ECG) feature data in evaluating the curative effect of percutaneous coronary intervention in acute ST-segment elevation myocardial infarction with hypertension, so as to facilitate the early diagnosis and treatment of the disease. In this study, 90 patients with acute ST-segment elevation myocardial infarction accompanied by hypertension were selected as the study subjects and randomly divided into group A (oral aspirin antiplatelet therapy), group B (thrombolytic drug streptokinase (SK) therapy), and group C (percutaneous coronary intervention), with 30 cases in each group. In addition, a P-wave detection algorithm was introduced for automatic detection and analysis of electrocardiograms, and the efficacy of patients was assessed by Holter feature data based on the P-wave detection algorithm. The results showed that the diagnostic error rate, sensitivity, and predictive accuracy of the P-wave detection algorithm for ST-segment elevation myocardial infarction caused by acute occlusion of left main coronary artery (LMCA) were 0.24%, 95.41%, and 92.33%, respectively; the diagnostic error rate, sensitivity, and predictive accuracy for non-LMCA (nLMCA) ST-segment elevation myocardial infarction were 0.28%, 95.32%, and 96.07%, respectively; the proportion of patients with symptom to blood flow patency time <3 h in group C (55.3%) was significantly higher than that in groups A and B (22.1% and 22.6%) ( P  < 0.05). Compared with group A, the content of B-type natriuretic peptide (pre-proBNP) at 1 week, 2 weeks, and 3 weeks after treatment in groups B and C was significantly lower and group C was significantly lower than group B ( P  < 0.05). In summary, the P-wave detection algorithm has a high application value in the diagnosis and early prediction of acute ST-segment elevation myocardial infarction. Percutaneous coronary intervention in the treatment of acute ST-segment elevation myocardial infarction with hypertension can shorten the opening time of infarction blood flow, so as to effectively protect the heart function of patients.

scholarly journals An automatic wave detection algorithm applied to Pc1 pulsations

2007 ◽  
Vol 112 (A4) ◽  
pp. n/a-n/a ◽  
Author(s):  
J. Bortnik ◽  
J. W. Cutler ◽  
C. Dunson ◽  
T. E. Bleier
Keyword(s):  
Detection Algorithm ◽  
Wave Detection ◽  
Pc1 Pulsations

scholarly journals A NOVEL QRS DETECTION ALGORITHM APPLIED TO THE ANALYSIS FOR HEART RATE VARIABILITY OF PATIENTS WITH SLEEP APNEA

2005 ◽  
Vol 17 (05) ◽  
pp. 258-262 ◽  
Author(s):  
REN-GUEY LEE ◽  
I-CHI CHOU ◽  
CHIEN-CHIH LAI ◽  
MING-HSIU LIU ◽  
MING-JANG CHIU
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Apnea ◽  
Sleep Apnea Syndrome ◽  
Detection Algorithm ◽  
Rate Variation ◽  
Qrs Detection ◽  
Detection Techniques ◽  
Wave Detection ◽  
Obstructive Sleep

Sleep-related breathing disorders can cause heart rate changes known as cyclical variation. The heart rate variation of patients with obstructive sleep apnea syndrome (OSAS) is more prominent in sleep. For this reason, to analyze heart rate variability (HRV) of patients with sleep apnea is a very important issue that can assist physicians to diagnose and give suitable treatment for patients. In this paper, a novel QRS detection algorithm is developed and applied to the analysis for HRV of patients with sleep apnea. The advantageous of the proposed algorithm is the combination of digital filtering and reverse R wave detection techniques to enhance the accuracy of R wave detection and easily implement into portable ECG monitoring system with light complexities of computation. The proposed algorithm is verified by simulation and experimental results.

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