Electrocardiogram lead selection for intelligent screening of patients with systolic heart failure

Chiou, Yu An; Syu, Jhen Yang; Wu, Sz Ying; Lin, Lian Yu; Yi, Li Tzu; Lin, Ting Tse; Lin, Shien Fong

doi:10.1038/s41598-021-81374-6

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…Studies show excellent performance metrics with these (Table 3). [21][22][23][24][25][26][27][28][29][30][31][32][33][34] By detecting subtle changes in cardiac structure and function over time, ML algorithms can assist in identifying early indicators of HF. This early detection can facilitate timely interventions and preventative measures, thereby potentially reducing the burden of HF and enhancing patient outcomes.…”

Section: Heart Failure Diagnosismentioning

confidence: 99%

Artificial intelligence and heart failure: A state‐of‐the‐art review

Khan

Arshad

Greene

et al. 2023

European J of Heart Fail

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Heart failure (HF) is a heterogeneous syndrome affecting more than 60 million individuals globally. Despite recent advancements in understanding of the pathophysiology of HF, many issues remain including residual risk despite therapy, understanding the pathophysiology and phenotypes of patients with HF and preserved ejection fraction (HFpEF), and the challenges related to integrating a large amount of disparate information available for risk stratification and management of these patients. Risk prediction algorithms based on artificial intelligence (AI) may have superior predictive ability compared to traditional methods in certain instances. AI algorithms can play a pivotal role in the evolution of HF care by facilitating clinical decision making to overcome various challenges such as allocation of treatment to patients who are at highest risk or are more likely to benefit from therapies, prediction of adverse outcomes, and early identification of patients with subclinical disease or worsening HF. With the ability to integrate and synthesize large amounts of data with multidimensional interactions, AI algorithms can supply information with which physicians can improve their ability to make timely and better decisions. In this review, we provide an overview of the AI algorithms that have been developed for establishing early diagnosis of HF, phenotyping HF with preserved ejection fraction, and stratifying HF disease severity. This review also discusses the challenges in clinical deployment of AI algorithms in HF, and the potential path forward for developing future novel learning‐based algorithms to improve HF care.This article is protected by copyright. All rights reserved.

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Section: Heart Failure Diagnosismentioning

confidence: 99%

Artificial intelligence and heart failure: A state‐of‐the‐art review

Khan

Arshad

Greene

et al. 2023

European J of Heart Fail

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“…In a recent explosion in popularity, NNs are being widely used in cardiac EP, from predicting AF or congestive heart failure from heart rate variability biomarkers, to predicting the presence of acute myocardial infarction from an ECG. [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] NN models have been preferred in these studies because they can practically operate on any data type, and they inherently learn feature importance and the relationships between features. These studies have demonstrated the advantages of NN in handling raw ECG data, raw LGE-MRI signals, electroanatomic mapping (EAM) data, and overall, in learning from high dimensional complex data.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Primer on Machine Learning in Electrophysiology

Loeffler

Trayanova

2023

Arrhythm Electrophysiol Rev

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Artificial intelligence has become ubiquitous. Machine learning, a branch of artificial intelligence, leads the current technological revolution through its remarkable ability to learn and perform on data sets of varying types. Machine learning applications are expected to change contemporary medicine as they are brought into mainstream clinical practice. In the field of cardiac arrhythmia and electrophysiology, machine learning applications have enjoyed rapid growth and popularity. To facilitate clinical acceptance of these methodologies, it is important to promote general knowledge of machine learning in the wider community and continue to highlight the areas of successful application. The authors present a primer to provide an overview of common supervised (least squares, support vector machine, neural networks and random forest) and unsupervised (k-means and principal component analysis) machine learning models. The authors also provide explanations as to how and why the specific machine learning models have been used in arrhythmia and electrophysiology studies.

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“…Following a pre-processing procedure, the signals were transformed into a bidimensional spectrum using continuous wavelet transform [46] for presentation to the deep learning system, developed to discriminate 31 and later 30 different ECG and RRI classes as responses to different types of HMI and noise. The system used the ResNet-18 [47] and GoogLeNet [48] pre-trained convolutional neural networks; deep learning allows for dealing with several dependencies at once, such as all classes of stimuli in the study.…”

Section: Introductionmentioning

confidence: 99%

Heart Response to Harmonic Music Interval Stimuli Via Deep Learning Structures

Idrobo-Ávila¹,

Correa²,

Muñoz-Bolaños³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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The effect of music on the heart is reflected in variables such as heart rate and electrocardiographic (ECG) signals. ECG is a record of heart electrical activity and is a useful tool in diagnosing various cardiopathies. Artificial intelligence techniques have recently been implemented to analyze ECG and RR-interval data and are used thus in the present study to examine the influence on the heart of harmonic musical intervals and colored noise. Harmonic intervals were chosen because of their emotional response, while noise has been linked to positive responses such as improved sleep quality. A deep learning system was implemented, employing the ResNet-18 and GoogLeNet pre-trained networks to discriminate 31 different classes of ECG and RR-interval responses to the sound stimuli. Following an exploratory approach, deep learning was selected as an alternative to traditional analysis with the expectation that it could be incorporated into future music perception research. Classification revealed the ability of the implemented system to demonstrate heart response to the stimuli. ECG signals performed best, with 97% accuracy and Matthew's coefficient of 0.97, while RR-interval achieved a 93% accuracy and Matthews coefficient of 0.93, suggesting that the considered stimuli of harmonic musical intervals and noise produced different responses in the heart. Moreover, the Matthews coefficient values above 0.7 and close to 1 imply a correlation between the two types of stimuli and the heart response, as measured by ECG and RR-interval signals.

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Electrocardiogram lead selection for intelligent screening of patients with systolic heart failure

Cited by 9 publications

References 19 publications

Artificial intelligence and heart failure: A state‐of‐the‐art review

Artificial intelligence and heart failure: A state‐of‐the‐art review

Primer on Machine Learning in Electrophysiology

Heart Response to Harmonic Music Interval Stimuli Via Deep Learning Structures

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