Heart and brain traumatic stress biomarker analysis with and without machine learning: A scoping review

Rountree-Harrison, Darius; Berkovsky, Shlomo; Kangas, Maria

doi:10.1016/j.ijpsycho.2023.01.009

Cited by 3 publications

(1 citation statement)

References 196 publications

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“…One of the most popular machine learning tools in recent years is eXtreme Gradient Boosting (XGBoost) ( Chen & Guestrin, 2016 ), an improved optimization of the gradient boosting technique that incorporates various algorithmic and system improvements ( Li et al, 2019 ; Shi et al, 2019 ). Latent growth mixture modeling (LGMM) is used by Rountree-Harrison, Berkovsky & Kangas (2023) to look at biomarkers in the heart and brain that show high-stress levels. In a recent study, Varalakshmi & Sankaran (2022) used the Bagging classifier to categorize arrhythmias.…”

Section: Related Workmentioning

confidence: 99%

Arrhythmia classification for non-experts using infinite impulse response (IIR)-filter-based machine learning and deep learning models of the electrocardiogram

Syed

2024

PeerJ Computer Science

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Arrhythmias are a leading cause of cardiovascular morbidity and mortality. Portable electrocardiogram (ECG) monitors have been used for decades to monitor patients with arrhythmias. These monitors provide real-time data on cardiac activity to identify irregular heartbeats. However, rhythm monitoring and wave detection, especially in the 12-lead ECG, make it difficult to interpret the ECG analysis by correlating it with the condition of the patient. Moreover, even experienced practitioners find ECG analysis challenging. All of this is due to the noise in ECG readings and the frequencies at which the noise occurs. The primary objective of this research is to remove noise and extract features from ECG signals using the proposed infinite impulse response (IIR) filter to improve ECG quality, which can be better understood by non-experts. For this purpose, this study used ECG signal data from the Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) database. This allows the acquired data to be easily evaluated using machine learning (ML) and deep learning (DL) models and classified as rhythms. To achieve accurate results, we applied hyperparameter (HP)-tuning for ML classifiers and fine-tuning (FT) for DL models. This study also examined the categorization of arrhythmias using different filters and the changes in accuracy. As a result, when all models were evaluated, DenseNet-121 without FT achieved 99% accuracy, while FT showed better results with 99.97% accuracy.

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