Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

Denysyuk, Hanna Vitaliyivna; Pinto, Rui João; Silva, Pedro Miguel; Duarte, Rui Pedro; Marinho, Francisco Alexandre; Pimenta, Luís; Gouveia, António Jorge; Gonçalves, Norberto Jorge; Coelho, Paulo; Zdravevski, Eftim; Lameski, Petre; Leithardt, Valderi Reis Quietinho; García, Nuno M.; Pires, Ivan Miguel

doi:10.1016/j.heliyon.2023.e13601

Cited by 14 publications

(6 citation statements)

References 141 publications

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“…Various methods have been published in recent years for the automatic diagnosis of ECG data (e.g., [1][2][3]) or other feature-based heart data (e.g., [8]). In [9], Stracina and coworkers give a good overview of the developments and possibilities in ECG recordings that have been made since the first successful recording of the electrical activity of the human heart in 1887.…”

Section: Related Workmentioning

confidence: 99%

“…Automated classification (and diagnostics) of ECG data has been a field of active research over the past years, and various methods have been proposed to solve this task (see, e.g., [1][2][3][4][5]). However, most of these approaches require that not only the ECG data themselves have very good quality but also that the data labels for each episode are available and correct.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Electrocardiogram (ECG) Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification

Bleich,

Linnemann,

Jaidi

et al. 2023

MAKE

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Implantable Cardiac Monitor (ICM) devices are demonstrating, as of today, the fastest-growing market for implantable cardiac devices. As such, they are becoming increasingly common in patients for measuring heart electrical activity. ICMs constantly monitor and record a patient’s heart rhythm, and when triggered, send it to a secure server where health care professionals (HCPs) can review it. These devices employ a relatively simplistic rule-based algorithm (due to energy consumption constraints) to make alerts for abnormal heart rhythms. This algorithm is usually parameterized to an over-sensitive mode in order to not miss a case (resulting in a relatively high false-positive rate), and this, combined with the device’s nature of constantly monitoring the heart rhythm and its growing popularity, results in HCPs having to analyze and diagnose an increasingly growing number of data. In order to reduce the load on the latter, automated methods for ECG analysis are nowadays becoming a great tool to assist HCPs in their analysis. While state-of-the-art algorithms are data-driven rather than rule-based, training data for ICMs often consist of specific characteristics that make their analysis unique and particularly challenging. This study presents the challenges and solutions in automatically analyzing ICM data and introduces a method for its classification that outperforms existing methods on such data. It carries this out by combining high-frequency noise detection (which often occurs in ICM data) with a semi-supervised learning pipeline that allows for the re-labeling of training episodes and by using segmentation and dimension-reduction techniques that are robust to morphology variations of the sECG signal (which are typical to ICM data). As a result, it performs better than state-of-the-art techniques on such data with, e.g., an F1 score of 0.51 vs. 0.38 of our baseline state-of-the-art technique in correctly calling atrial fibrillation in ICM data. As such, it could be used in numerous ways, such as aiding HCPs in the analysis of ECGs originating from ICMs by, e.g., suggesting a rhythm type.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing Electrocardiogram (ECG) Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification

Bleich,

Linnemann,

Jaidi

et al. 2023

MAKE

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“…The electrocardiogram (ECG) is a fundamental diagnostic tool used in medicine to monitor and evaluate the electrical activity of the heart [ [1] , [2] , [3] ]. It records the electrical impulses generated by the heart during each heartbeat cycle.…”

Section: Introductionmentioning

confidence: 99%

Robust QRS detection based on simulated degenerate optical parametric oscillator-assisted neural network

Liao,

Shi,

Sarker

et al. 2024

Heliyon

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“…Therefore, the early detection of anomalies in ECG signals, possibly using computeraided diagnosis as a prediction mechanism for the occurrence of CVD, is a powerful tool for healthcare systems worldwide as it may help healthcare professionals in their decision-making processes or possibly to prevent dangerous clinical events [4,5,8]. Different approaches can be followed in order to predict or detect issues regarding ECG signals [3,9,10].…”

Section: Introductionmentioning

confidence: 99%

ECG Forecasting System Based on Long Short-Term Memory

Zacarias,

Marques,

Felizardo

et al. 2024

Bioengineering

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Worldwide, cardiovascular diseases are some of the primary causes of death; yet the early detection and diagnosis of such diseases have the potential to save many lives. Technological means of detection are becoming increasingly essential and numerous techniques have been created for this purpose, such as forecasting. Of these techniques, the time series forecasting technique seeks to predict future events. The long-term time series forecasting of physiological data could assist medical professionals in predicting and treating patients based on very early diagnosis. This article presents a model that utilizes a deep learning technique to predict long-term ECG signals. The forecasting model can learn signals’ nonlinearity, nonstationarity, and complexity based on a long short-term memory architecture. However, this is not a trivial task as the correct forecasting of a signal that closely resembles the original complex signal’s structure and behavior while minimizing any differences in amplitude continues to pose challenges. To achieve this goal, we used a dataset available on the Physio net database, called MIT-BIH, with 48 ECG recordings of 30 min each. The developed model starts with pre-processing to reduce interference in the original signals, then applies a deep learning algorithm, based on a long short-term memory (LTSM) neural network with two hidden layers. Next, we applied the root mean square error (RMSE) and mean absolute error (MAE) metrics to evaluate the performance of the model and obtained an average RMSE of 0.0070±0.0028 and an average MAE of 0.0522±0.0098 across all simulations. The results indicate that the proposed LSTM model is a promising technique for ECG forecasting, considering the trends of the changes in the original data series, most notably in R-peak amplitude. Given the model’s accuracy and the features of the physiological signals, the system could be used to improve existing predictive healthcare systems for cardiovascular monitoring.

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Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

Cited by 14 publications

References 141 publications

Enhancing Electrocardiogram (ECG) Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification

Enhancing Electrocardiogram (ECG) Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification

Robust QRS detection based on simulated degenerate optical parametric oscillator-assisted neural network

ECG Forecasting System Based on Long Short-Term Memory

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