Stability and Phase Response Analysis of Optimum Reduced-Order IIR Filter Designs for ECG R-Peak Detection

Amhia, Hemant; Wadhwani, A. K.

doi:10.1155/2022/9899899

“…In order to establish a machine-learning model for the LVH detection system, the first step was to ensure that all the R peaks and S valleys of an ECG sequence could be extracted automatically. In wave detection methods of ECG signals, the R peak is often detected first, and then the relative positions and characteristics of other waves are used to detect other wave peaks and valleys [37][38][39][40][41] . For example, an S valley is usually the first lowest voltage point following a R peak.…”

Section: Ecg Signal Processing Detection Of R Peaks and S Valleysmentioning

confidence: 99%

Left ventricular hypertrophy detection using electrocardiographic signal

Li

¹

,

Wu

²

,

Hu

³

et al. 2023

Sci Rep

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Left ventricular hypertrophy (LVH) indicates subclinical organ damage, associating with the incidence of cardiovascular diseases. From the medical perspective, electrocardiogram (ECG) is a low-cost, non-invasive, and easily reproducible tool that is often used as a preliminary diagnosis for the detection of heart disease. Nowadays, there are many criteria for assessing LVH by ECG. These criteria usually include that voltage combination of RS peaks in multi-lead ECG must be greater than one or more thresholds for diagnosis. We developed a system for detecting LVH using ECG signals by two steps: firstly, the R-peak and S-valley amplitudes of the 12-lead ECG were extracted to automatically obtain a total of 24 features and ECG beats of each case (LVH or non-LVH) were segmented; secondly, a back propagation neural network (BPN) was trained using a dataset with these features. Echocardiography (ECHO) was used as the gold standard for diagnosing LVH. The number of LVH cases (of a Taiwanese population) identified was 173. As each ECG sequence generally included 8 to 13 cycles (heartbeats) due to differences in heart rate, etc., we identified 1466 ECG cycles of LVH patients after beat segmentation. Results showed that our BPN model for detecting LVH reached the testing accuracy, precision, sensitivity, and specificity of 0.961, 0.958, 0.966 and 0.956, respectively. Detection performances of our BPN model, on the whole, outperform 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH.

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“…In wave detection methods of ECG signals, the R peak is often detected rst, and then the relative positions and characteristics of other waves are used to detect other wave peaks and valleys (Ref. [37][38][39][40][41]). For example, an S valley is usually the rst lowest voltage point following a R peak.…”

Section: Lvh Criteriamentioning

confidence: 99%

Left Ventricular Hypertrophy Detection Using Electrocardiographic Signal

Li

¹

,

Wu²,

Hu

³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Left ventricular hypertrophy (LVH) indicates subclinical organ damage, associating with the incidence of cardiovascular diseases. From the medical perspective, electrocardiogram (ECG) is a low-cost, non-invasive, and easily reproducible tool that is often used as a preliminary diagnosis for the detection of heart disease. Nowadays, there are many criteria for assessing LVH by ECG. These criteria usually include that voltage combination of RS peaks in multi-lead ECG must be greater than one or more thresholds for diagnosis. We developed a system for detecting LVH using ECG signals by two steps: firstly, the R-peak and S-valley amplitudes of the 12-lead ECG were extracted to automatically obtain a total of 24 features and ECG beats of each case (LVH or non-LVH) were segmented; secondly, a back propagation neural network (BPN) was trained using a dataset with these features. Echocardiography (ECHO) was used as the gold standard for diagnosing LVH. The number of LVH cases (of a Taiwanese population) identified was 173. As each ECG sequence generally included 8 to 13 cycles (heartbeats) due to differences in heart rate, etc., we identified 1466 ECG cycles of LVH patients after beat segmentation. Results showed that our BPN model for detecting LVH reached the testing accuracy, precision, sensitivity, and specificity of 0.961, 0.958, 0.966 and 0.956, respectively. Detection performances of our BPN model, on the whole, outperform 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH.

show abstract