Deterministic Learning-Based WEST Syndrome Analysis and Seizure Detection on ECG

Chen, Shiyao; Zheng, Runze; Wang, Tianlei; Jiang, Tiejia; Gao, Feng; Wang, Danping; Cao, Jiuwen

doi:10.1109/tcsii.2022.3188162

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…ECG data may be analyzed using AI algorithms to find patterns connected to epilepsy. These patterns can be utilized to create AI-based tools that assist physicians in making earlier and more accurate diagnoses of epilepsy [137,138]. The combination of ML/DL and ECG has the potential to completely transform healthcare by enabling the early and precise identification of a wide range of medical disorders outside of heart diseases.…”

Section: Use Of Ml/dl With Ecg For Other Health Issuesmentioning

confidence: 99%

AI-Enabled Electrocardiogram Analysis for Disease Diagnosis

Khan Mamun,

Elfouly

2023

ASI

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Contemporary methods used to interpret the electrocardiogram (ECG) signal for diagnosis or monitoring are based on expert knowledge and rule-centered algorithms. In recent years, with the advancement of artificial intelligence, more and more researchers are using deep learning (ML) and deep learning (DL) with ECG data to detect different types of cardiac issues as well as other health problems such as respiration rate, sleep apnea, and blood pressure, etc. This study presents an extensive literature review based on research performed in the last few years where ML and DL have been applied with ECG data for many diagnoses. However, the review found that, in published work, the results showed promise. However, some significant limitations kept that technique from implementation in reality and being used for medical decisions; examples of such limitations are imbalanced and the absence of standardized dataset for evaluation, lack of interpretability of the model, inconsistency of performance while using a new dataset, security, and privacy of health data and lack of collaboration with physicians, etc. AI using ECG data accompanied by modern wearable biosensor technologies has the potential to allow for health monitoring and early diagnosis within reach of larger populations. However, researchers should focus on resolving the limitations.

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Section: Use Of Ml/dl With Ecg For Other Health Issuesmentioning

confidence: 99%

AI-Enabled Electrocardiogram Analysis for Disease Diagnosis

Khan Mamun,

Elfouly

2023

ASI

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Dynamical pattern recognition for univariate time series and its application to an axial compressor

Hu,

Wu,

Zhu

et al. 2024

Control Theory Technol.

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An interpretable ensemble trees method with joint analysis of static and dynamic features for myocardial infarction detection

Liang,

Sun,

et al. 2024

Physiol. Meas.

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Objective. In recent years, artificial intelligence-based electrocardiogram (ECG) methods have been massively applied to myocardial infarction (MI). However, the joint analysis of static and dynamic features to achieve accurate and interpretable MI detection has not been comprehensively addressed. Approach. This paper proposes a simplified ensemble tree method with a joint analysis of static and dynamic features to solve this issue for MI detection. Initially, the dynamic features are extracted by modeling the intrinsic dynamics of ECG via dynamic learning in addition to extracting classical static features. Secondly, a two-stage feature selection strategy is designed to identify a few significant features, which substitute the original variables that are employed in constructing the ensemble tree. This approach enhances the discriminative ability by selecting significant static and dynamic features. Subsequently, this paper presents an interpretable classification method named StackTree by introducing a stacked ensemble scheme to modify the ensemble tree simplification algorithm. The representative rules of the raw ensemble trees are selected as the intermediate training data that is used to retrain a decision tree with performance close to that of the source ensemble model. Using this scheme, the significant precision and interpretability of MI detection are thus comprehensively addressed. Main results. The effectiveness of our method in detecting MI is evaluated using the PTB and clinical database. The findings suggest that our algorithm outperforms the traditional methods based on a single type of feature. Additionally, it is comparable to the conventional random forest, achieving 97.1% accuracy under the inter-patient framework on the PTB database. Furthermore, feature subsets trained on PTB are validated using the clinical database, resulting in an accuracy of 84.5%. The chosen important features demonstrate that both static and dynamic information have crucial roles in MI detection. Crucially, the proposed method provides clear internal workings in an easy-to-understand visual manner. 

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Deterministic Learning-Based WEST Syndrome Analysis and Seizure Detection on ECG

Cited by 4 publications

References 21 publications

AI-Enabled Electrocardiogram Analysis for Disease Diagnosis

AI-Enabled Electrocardiogram Analysis for Disease Diagnosis

Dynamical pattern recognition for univariate time series and its application to an axial compressor

An interpretable ensemble trees method with joint analysis of static and dynamic features for myocardial infarction detection

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