scholarly journals Comparison of Baseline Wander Removal Techniques considering the Preservation of ST Changes in the Ischemic ECG: A Simulation Study

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Gustavo Lenis ◽  
Nicolas Pilia ◽  
Axel Loewe ◽  
Walther H. W. Schulze ◽  
Olaf Dössel
Keyword(s):  
Simulation Study ◽  
Cardiac Myocyte ◽  
Correct Diagnosis ◽  
Ground Truth ◽  
Realistic Model ◽  
Pass Filter ◽  
St Segment ◽  
Surface Ecg ◽  
Baseline Wander ◽  
High Pass

The most important ECG marker for the diagnosis of ischemia or infarction is a change in the ST segment. Baseline wander is a typical artifact that corrupts the recorded ECG and can hinder the correct diagnosis of such diseases. For the purpose of finding the best suited filter for the removal of baseline wander, the ground truth about the ST change prior to the corrupting artifact and the subsequent filtering process is needed. In order to create the desired reference, we used a large simulation study that allowed us to represent the ischemic heart at a multiscale level from the cardiac myocyte to the surface ECG. We also created a realistic model of baseline wander to evaluate five filtering techniques commonly used in literature. In the simulation study, we included a total of 5.5 million signals coming from 765 electrophysiological setups. We found that the best performing method was the wavelet-based baseline cancellation. However, for medical applications, the Butterworth high-pass filter is the better choice because it is computationally cheap and almost as accurate. Even though all methods modify the ST segment up to some extent, they were all proved to be better than leaving baseline wander unfiltered.

scholarly journals High-Bandpass Filters in Electrocardiography: Source of Error in the Interpretation of the ST Segment

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
F. Buendía-Fuentes ◽  
M. A. Arnau-Vives ◽  
A. Arnau-Vives ◽  
Y. Jiménez-Jiménez ◽  
J. Rueda-Soriano ◽  
...  
Keyword(s):  
Real Time ◽  
Fourier Transforms ◽  
Pass Filter ◽  
Discrete Fourier Transforms ◽  
Real Time Mode ◽  
Asymptomatic Patients ◽  
St Segment ◽  
Coronary Syndromes ◽  
Clinically Significant ◽  
High Pass

Introduction. Artifactual variations in the ST segment may lead to confusion with acute coronary syndromes. Objective. To evaluate how the technical characteristics of the recording mode may distort the ST segment. Material and Method. We made a series of electrocardiograms using different filter configurations in 45 asymptomatic patients. A spectral analysis of the electrocardiograms was made by discrete Fourier transforms, and an accurate recomposition of the ECG signal was obtained from the addition of successive harmonics. Digital high-pass filters of 0.05 and 0.5 Hz were used, and the resulting shapes were compared with the originals. Results. In 42 patients (93%) clinically significant alterations in ST segment level were detected. These changes were only seen in “real time mode” with high-pass filter of 0.5 Hz. Conclusions. Interpretation of the ST segment in “real time mode” should only be carried out using high-pass filters of 0.05 Hz.

A V-Band Two Pole High-Pass Filter for Frequency Quadrupler Design

2016 ◽  
Vol 6 (1) ◽  
pp. 56
Author(s):  
Maryam Abata ◽  
Mahmoud Mehdi ◽  
Said Mazer ◽  
Moulhime El Bekkali ◽  
Catherine Algani
Keyword(s):  
Pass Filter ◽  
High Pass Filter ◽  
V Band ◽  
High Pass

Baseline-free sensor fault detection for piezoelectric array

2021 ◽  
pp. 1045389X2110188
Author(s):  
Qibo Mao ◽  
Yuande Wang ◽  
Shizuo Huang
Keyword(s):  
Prior Knowledge ◽  
Frequency Response Function ◽  
Sensor Array ◽  
Piezoelectric Element ◽  
Piezoelectric Sensor ◽  
Steel Beam ◽  
Sensor Fault ◽  
Pass Filter ◽  
Sensor Fault Detection ◽  
High Pass

In this study, a new methodology is presented to detect the sensor fault for piezoelectric array based on the filtered frequency response function (FRF) shapes. The proposed method does not require prior knowledge about healthy piezoelectric array. First, the imaginary parts of FRFs from the piezoelectric array during vibration are measured and normalized to obtain the FRF shapes in different frequencies. Then the irregularities in these FRF shapes are extracted by using high-pass filter with properly chosen cut-off frequency. These abnormal irregularities on the filtered FRF shape curves indicate the location of the faulty sensor, due to the irregularity of FRF shapes introduced by the faulty piezoelectric element. The proposed sensor fault method is experimentally demonstrated on a clamped-clamped steel beam mounted with piezoelectric buzzer array. Two common piezoelectric sensor fault types including sensor breakage and debonding are evaluated. The experimental results indicate that the proposed method has great potential in the detection of the sensor fault for piezoelectric array as it is simple and does not require the FRF data of the healthy sensor array as a baseline.

Baby steps in the path of modifying the role of cardiologists for interpreting EKG for AMI

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Mehta ◽  
S Niklitschek ◽  
F Fernandez ◽  
C Villagran ◽  
J Avila ◽  
...  
Keyword(s):  
Human Resources ◽  
Ground Truth ◽  
Cost Effective ◽  
Clinical Settings ◽  
Dense Layer ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Original Dataset ◽  
Low Pass ◽  
Normal Branch

Abstract Background EKG interpretation is slowly transitioning to a physician-free, Artificial Intelligence (AI)-driven endeavor. Our continued efforts to innovate follow a carefully laid stepwise approach, as follows: 1) Create an AI algorithm that accurately identifies STEMI against non-STEMI using a 12-lead EKG; 2) Challenging said algorithm by including different EKG diagnosis to the previous experiment, and now 3) To further validate the accuracy and reliability of our algorithm while also improving performance in a prehospital and hospital settings. Purpose To provide an accurate, reliable, and cost-effective tool for STEMI detection with the potential to redirect human resources into other clinically relevant tasks and save the need for human resources. Methods Database: EKG records obtained from Latin America Telemedicine Infarct Network (Mexico, Colombia, Argentina, and Brazil) from April 2014 to December 2019. Dataset: A total of 11,567 12-lead EKG records of 10-seconds length with sampling frequency of 500 [Hz], including the following balanced classes: unconfirmed and angiographically confirmed STEMI, branch blocks, non-specific ST-T abnormalities, normal and abnormal (200+ CPT codes, excluding the ones included in other classes). The label of each record was manually checked by cardiologists to ensure precision (Ground truth). Pre-processing: The first and last 250 samples were discarded as they may contain a standardization pulse. An order 5 digital low pass filter with a 35 Hz cut-off was applied. For each record, the mean was subtracted to each individual lead. Classification: The determined classes were STEMI (STEMI in different locations of the myocardium – anterior, inferior and lateral); Not-STEMI (A combination of randomly sampled normal, branch blocks, non-specific ST-T abnormalities and abnormal records – 25% of each subclass). Training & Testing: A 1-D Convolutional Neural Network was trained and tested with a dataset proportion of 90/10; respectively. The last dense layer outputs a probability for each record of being STEMI or Not-STEMI. Additional testing was performed with a subset of the original dataset of angiographically confirmed STEMI. Results See Figure Attached – Preliminary STEMI Dataset Accuracy: 96.4%; Sensitivity: 95.3%; Specificity: 97.4% – Confirmed STEMI Dataset: Accuracy: 97.6%; Sensitivity: 98.1%; Specificity: 97.2%. Conclusions Our results remain consistent with our previous experience. By further increasing the amount and complexity of the data, the performance of the model improves. Future implementations of this technology in clinical settings look promising, not only in performing swift screening and diagnostic steps but also partaking in complex STEMI management triage. Funding Acknowledgement Type of funding source: None

A New Stripline High Pass Filter Layout

2000 ◽  
Vol 14 (3) ◽  
pp. 423-439 ◽  
Author(s):  
F. Bilotti ◽  
L. Vegni ◽  
A. Toscano
Keyword(s):  
Pass Filter ◽  
High Pass Filter ◽  
High Pass

Ripple FPN reduced algorithm based on temporal high-pass filter and hardware implementation

2016 ◽  
Author(s):  
Yiyang Li ◽  
Shuo Li ◽  
Zhipeng Zhang ◽  
Weiqi Jin ◽  
Lei Wu ◽  
...  
Keyword(s):  
Hardware Implementation ◽  
Pass Filter ◽  
High Pass Filter ◽  
High Pass
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