Algorithm of Automatic Differentiation of Myocardial Infarction from Cardiomyopathy based on Electrocardiogram

Nasimov, Rashid; Muminov, Bahodir; Mirzahalilov, Sanjar; Nasimova, Nigorakhon

doi:10.1109/aict50176.2020.9368738

Cited by 9 publications

(1 citation statement)

References 14 publications

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“…Heart abnormalities can be detected by examining the structural characteristics of an ECG signal that is described by some featured impulses (P, Q, R, S, and T waves) [3,4]. It can be used for detecting diseases such as hypertension [5], cardiac arrhythmia [6,7], coronary artery disease [8], heart attack [9], and cardiomyopathy [10].…”

Section: Introductionmentioning

confidence: 99%

Subject-Independent per Beat PPG to Single-Lead ECG Mapping

et al. 2023

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In this paper, a beat-based autoencoder is proposed for mapping photoplethysmography (PPG) to a single-lead electrocardiogram (single-lead ECG) signal. The main limiting factors represented in uncleaned data, subject dependency, and erroneous beat segmentation are regarded. The dataset is cleaned by a two-stage clustering approach. Rather than complete single–lead ECG signal reconstruction, a beat-based PPG-to-single-lead-ECG (PPG2ECG) conversion is introduced for providing a simple lightweight model that meets the computational capabilities of wearable devices. In addition, peak-to-peak segmentation is employed for alleviating errors in PPG onset detection. Furthermore, subject-dependent training is highlighted as a critical factor in training procedures because most existing work includes different beats/signals from the same subject’s record in both training and testing sets. So, we provide a completely subject-independent model where the testing subjects’ records are hidden in the training stage entirely, i.e., a subject record appears once either in the training or testing set, but testing beats/signals belong to records that never appear in the training set. The proposed deep learning model is designed for providing efficient feature extraction that attains high reconstruction quality over subject-independent scenarios. The achieved performance is about 0.92 for the correlation coefficient and 0.0086 for the mean square error for the dataset extracted/cleaned from the MIMIC II dataset.

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