A deep neural network electrocardiogram analysis framework for left ventricular hypertrophy prediction

Wu, Jimmy Ming‐Tai; Tsai, Meng‐Hsiun; Xiao, Sheng-Han; Liaw, Yung‐Po

doi:10.1007/s12652-020-01826-1

Cited by 18 publications

(15 citation statements)

References 30 publications

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“…On the other hand, machine learning models for detecting LVH using ECG signals reported in previous studies (Ref. [20][21][22][24][25][26][27]) are also summarized in Tab. 6.…”

Section: Feature Importancementioning

confidence: 99%

“…[25]), a model of ensemble neural network (ENN) which integrated the convolution neural network (CNN) and DNN was proposed in predicting LVH. [20][21][22][24][25][26][27]) and listed in Tab. 6, in detecting LVH using ECG signals.…”

Section: Feature Importancementioning

confidence: 99%

“…Furthermore, in (Ref. [12,[20][21][22][24][25][26]), they concluded that machine learning methods outperform some methods of classical ECG criteria in detecting LVH. Basically, our BPN model also outperforms the methods of criteria reported in previous studies listed in Tab.…”

Section: Feature Importancementioning

confidence: 99%

“…have also been proposed in recent years, however, the overall detection performances still need to be improved (Ref. [20][21][22][23][24][25][26][27]).…”

Section: Introductionmentioning

confidence: 99%

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Left Ventricular Hypertrophy Detection Using Electrocardiographic Signal

Wu²,

et al. 2022

Preprint

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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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“…On the other hand, machine learning models for detecting LVH using ECG signals reported in previous studies (Ref. [20][21][22][24][25][26][27]) are also summarized in Tab. 6.…”

Section: Feature Importancementioning

confidence: 99%

Section: Feature Importancementioning

confidence: 99%

Section: Feature Importancementioning

confidence: 99%

“…have also been proposed in recent years, however, the overall detection performances still need to be improved (Ref. [20][21][22][23][24][25][26][27]).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Left Ventricular Hypertrophy Detection Using Electrocardiographic Signal

Wu²,

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…To assess overfitting, a data set is usually divided into a training data set, a validation data set and a test data set, or resampling methods are used, such as crossvalidation or bootstrapping. 24 To train and test ML algorithms, particularly DNNs, it is preferable to use a large data set, known as big data. Performance of highly dimensional algorithms -e.g.…”

mentioning

confidence: 99%

Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology

Leur

Boonstra

Bagheri

et al. 2020

Arrhythm Electrophysiol Rev

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The combination of big data and artificial intelligence (AI) is having an increasing impact on the field of electrophysiology. Algorithms are created to improve the automated diagnosis of clinical ECGs or ambulatory rhythm devices. Furthermore, the use of AI during invasive electrophysiological studies or combining several diagnostic modalities into AI algorithms to aid diagnostics are being investigated. However, the clinical performance and applicability of created algorithms are yet unknown. In this narrative review, opportunities and threats of AI in the field of electrophysiology are described, mainly focusing on ECGs. Current opportunities are discussed with their potential clinical benefits as well as the challenges. Challenges in data acquisition, model performance, (external) validity, clinical implementation, algorithm interpretation as well as the ethical aspects of AI research are discussed. This article aims to guide clinicians in the evaluation of new AI applications for electrophysiology before their clinical implementation.

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A Novel Data‐Driven Approach to Lithium‐ion Battery Dynamic Charge State Capture for New Energy Electric Vehicles

Zheng,

Huang,

Liu

et al. 2024

Advcd Theory and Sims

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As lithium‐ion batteries are the main power source of new energy vehicles, making accurate predictions of unknown State of Charge (SOC) during vehicle operation for vehicle data monitoring is vital to the advancement of intelligent new energy vehicles. In this manuscript, an expression tree‐based genetic programming regression model (ETGPR) is proposed to estimate the real‐time SOC of lithium‐ion batteries. The proposed model mainly adopts the symbolic regression technique. In addition to the current–voltage curves being fed into the model, an additional approach is designed to ensure real‐time model predictions in dynamic situations, which includes the previous moment's power in the input parameters. Different seed hyperparameters in the model are set, and the model automatically performs evolutionary calculations. Subsequently, each parameter of the model is optimally adjusted to obtain a set of regression expressions that accurately reflect the relationship between the SOC and each parameter after a specified number of iterations. Finally, the generated expression is proven to perform better in terms of its ability to capture the nonlinear relationship between SOC and battery variables. Also, the model demonstrates excellent robustness in the presence of notable noise from input‐independent features compared to other models, a root mean square error (RMSE) of less than 0.3% and a mean absolute error (MAE) of less than 0.2% are achieved. Furthermore, the potential of the model's implement‐ability under variable temperature and real driving data conditions is verified.

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A deep neural network electrocardiogram analysis framework for left ventricular hypertrophy prediction

Cited by 18 publications

References 30 publications

Left Ventricular Hypertrophy Detection Using Electrocardiographic Signal

Left Ventricular Hypertrophy Detection Using Electrocardiographic Signal

Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology

A Novel Data‐Driven Approach to Lithium‐ion Battery Dynamic Charge State Capture for New Energy Electric Vehicles

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