A Real Time QRS Detection Algorithm Based on ET and PD Controlled Threshold Strategy

Chen, Aiyun; Zhang, Yidan; Zhang, Mengxin; Liu, Wenhan; Chang, Sheng; He, Jin; Huang, Qijun

doi:10.3390/s20144003

Cited by 25 publications

(16 citation statements)

References 26 publications

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“…This code is an implementation of the Pan–Tompkins algorithm 25 that is one of the most widely used algorithms and most frequently cited approaches for the extraction of QRS complexes from an ECG signal. 27 , 28 The MATLAB subroutine used to identify the R‐peaks was tested using 10 different known ECG signals (including signals with VT episodes) randomly selected from the widely used MIT‐BIH Arrhythmia Database. 29 The subroutine was able to correctly identify the R‐peaks of the recorded ECG signals, and values of cardiac cycles and heart rates calculated based on R–R intervals were within the expected range provided in the database.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the feasibility of cardiac gating for SBRT of ventricular tachycardia based on real‐time ECG signal acquisition

Reis

Robar

2022

J Applied Clin Med Phys

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Purpose To investigate the feasibility of cardiac synchronized gating in stereotactic body radiation therapy (SBRT) of ventricular tachycardia (VT) using a real‐time electrocardiogram (ECG) signal acquisition. Methods and materials Stability of beam characteristics during simulated ECG gating was examined by developing a microcontroller interface to a Varian Clinac iX linear accelerator allowing gating at frequencies and duty cycles relevant to cardiac rhythm. Delivery accuracy was evaluated by measuring dose linearity with an ionization chamber, and flatness and symmetry with a two‐dimensional detector array, for different gating windows within typical human cardiac cycle periods. To establish a practical method of gating based on actual ECG signals, an AD8232 Heart Monitor board was used to acquire the ECG signal and synchronize the beam delivery. Real‐time cardiac gated delivery measurements were performed for a single 10 × 10 cm 2 field and for a VT‐SBRT plan using intensity‐modulated radiation therapy (IMRT). Results and discussion Dose per monitor unit (MU) values were found to be linear within most gating windows investigated with maximum differences relative to non‐gated delivery of <2% for gating windows ≥200 ms and for >10 MUs. Beam profiles for both gated and non‐gated modes were also found to agree with maximum differences of 0.5% relative to central axis dose for all sets of beam‐on/beam‐off combinations. Comparison of dose distributions for intensity‐modulated SBRT plans between non‐gating and cardiac gating modes provided a gamma passing rate of 97.2% for a 2% 2 mm tolerance. Conclusions Beam output is stable with respect to linearity, flatness, and symmetry for gating windows within cardiac cycle periods. Agreement between dose distributions for VT‐SBRT using IMRT in non‐gated and cardiac cycle gated delivery modes shows that the proposed methodology is feasible. Technically, gating for delivery of SBRT for VT is possible with regard to beam stability.

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Section: Methodsmentioning

confidence: 99%

Evaluation of the feasibility of cardiac gating for SBRT of ventricular tachycardia based on real‐time ECG signal acquisition

Reis

Robar

2022

J Applied Clin Med Phys

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“…In this paper, the proposed algorithm for the feature extraction and classification is as shown in Figure 1. The dataset for evaluating the suggested technique in the research is the well-known MIT-BIH arrhythmia database [18]. The ECG signal pre-processing contains denoising of the signal and heartbeat segmentations, R-R interval extractions [19].…”

Section: Methodsmentioning

confidence: 99%

Electrocardiograph signal recognition using wavelet transform based on optimized neural network

Jawad¹,

Abdul-Zahra²,

Gheni

et al. 2022

IJECE

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<span lang="EN-US">Due to the growing number of cardiac patients, an automatic detection that detects various heart abnormalities has been developed to relieve and share physicians’ workload. Many of the depolarization of ventricles complex waves (QRS) detection algorithms with multiple properties have recently been presented; nevertheless, real-time implementations in low-cost systems remain a challenge due to limited hardware resources. The proposed algorithm finds a solution for the delay in processing by minimizing the input vector’s dimension and, as a result, the classifier’s complexity. In this paper, the wavelet transform is employed for feature extraction. The optimized neural network is used for classification with 8-classes for the electrocardiogram (ECG) signal this data is taken from two ECG signals (ST-T and MIT-BIH database). The wavelet transform coefficients are used for the artificial neural network’s training process and optimized by using the invasive weed optimization (IWO) algorithm. The suggested system has a sensitivity of over 70%, a specificity of over 94%, a positive predictive of over 65%, a negative predictive of more than 93%, and a classification accuracy of more than 80%. The performance of the classifier improves when the number of neurons in the hidden layer is increased.</span>

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“…Specifically, the proposed method first applies the R-peak detection algorithm to locate the R-peak on the ECG. Because the existing R-peak detection algorithm [ 40 , 41 , 42 , 43 ] performs very well in accuracy and real-time, for example, Pan et al designed an algorithm that can correctly detect 99.3% of the R-peak for the MIT-BIH Arrhythmia Database. This study directly used the MIT-BIH Arrhythmia Database’s R-peak position.…”

Section: Methodsmentioning

confidence: 99%

Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN

Wang

Chen

et al. 2021

Biosensors

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Premature ventricular contractions (PVCs), common in the general and patient population, are irregular heartbeats that indicate potential heart diseases. Clinically, long-term electrocardiograms (ECG) collected from the wearable device is a non-invasive and inexpensive tool widely used to diagnose PVCs by physicians. However, analyzing these long-term ECG is time-consuming and labor-intensive for cardiologists. Therefore, this paper proposed a simplistic but powerful approach to detect PVC from long-term ECG. The suggested method utilized deep metric learning to extract features, with compact intra-product variance and separated inter-product differences, from the heartbeat. Subsequently, the k-nearest neighbors (KNN) classifier calculated the distance between samples based on these features to detect PVC. Unlike previous systems used to detect PVC, the proposed process can intelligently and automatically extract features by supervised deep metric learning, which can avoid the bias caused by manual feature engineering. As a generally available set of standard test material, the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) Arrhythmia Database is used to evaluate the proposed method, and the experiment takes 99.7% accuracy, 97.45% sensitivity, and 99.87% specificity. The simulation events show that it is reliable to use deep metric learning and KNN for PVC recognition. More importantly, the overall way does not rely on complicated and cumbersome preprocessing.

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A Real Time QRS Detection Algorithm Based on ET and PD Controlled Threshold Strategy

Cited by 25 publications

References 26 publications

Evaluation of the feasibility of cardiac gating for SBRT of ventricular tachycardia based on real‐time ECG signal acquisition

Evaluation of the feasibility of cardiac gating for SBRT of ventricular tachycardia based on real‐time ECG signal acquisition

Electrocardiograph signal recognition using wavelet transform based on optimized neural network

Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN

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