Classification Methodology of CVD with Localized Features Analysis Using Phase Space Reconstruction Targeting Personalized Remote Health Monitoring

Vemishetty, Naresh; Acharyya, Amit; Das, Saptarshi; Ayyagari, Shivteja; Jana, Soumya; Maharatna, Koushik; Puddu, Paolo

doi:10.22489/cinc.2016.126-512

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Machine learning methods are already being used in a variety of applications such as the classification and the prediction of various cardiovascular diseases through ECG data analysis (Fan et al, 2018; Kiranyaz et al, 2016; Lih et al, 2020; Pourbabaee et al, 2018; Roberts et al, 2001; Rocha et al, 2008; Vemishetty et al, 2016; Vemishetty et al, 2019; Zhang et al, 2020). A well‐recognized technique for preprocessing ECG data is to create its phase space reconstruction matrix (PSR).…”

Section: Artificial Intelligence and Neural Networking Modelmentioning

confidence: 99%

Role of deep learning methods in screening for subcutaneous implantable cardioverter defibrillator in heart failure

ElRefai

Abouelasaad

Wiles

et al. 2022

Noninvasive Electrocardiol

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Section: Artificial Intelligence and Neural Networking Modelmentioning

confidence: 99%

Role of deep learning methods in screening for subcutaneous implantable cardioverter defibrillator in heart failure

ElRefai

Abouelasaad

Wiles

et al. 2022

Noninvasive Electrocardiol

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show abstract

“…Machine learning methods have been used for ECG analysis in a variety of applications. There has been a wealth of work in the classification of various Cardiovascular Diseases (CVDs) from ECG data [30,29,27,26,31,25,14,8,33]. Other applications of machine learning in ECG analysis include detecting seizures and heart attacks [18,19], predicting patients' blood pressure [24], detecting a patients facial expressions [6] and analysis of ECG of the brain has been used for creating brain computer interfaces (BCI) capable of detecting which body Figure 2.…”

Section: Related Workmentioning

confidence: 99%

“…Box counting as well as column and row statistics are features o en extracted from the PSR matrix of ECG data. These methods have been used in the prediction of CVD [30,29,27,26], creating BCIs [5,7], and detecting facial expressions [6]. These approaches all centre around manually selecting features to extract from the PSR matrix.…”

Section: Related Workmentioning

confidence: 99%

Deep learning methods for screening patients' S-ICD implantation eligibility

Dunn¹,

ElRefai²,

Roberts³

et al. 2021

Preprint

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Subcutaneous Implantable Cardioverter-Defibrillators (S-ICDs) are used for prevention of sudden cardiac death triggered by ventricular arrhythmias. T Wave Over Sensing (TWOS) is an inherent risk with S-ICDs which can lead to inappropriate shocks. A major predictor of TWOS is a high T:R ratio (the ratio between the amplitudes of the T and R waves). Currently patients' Electrocardiograms (ECGs) are screened over 10 seconds to measure the T:R ratio, determining the patients' eligibility for S-ICD implantation. Due to temporal variations in the T:R ratio, 10 seconds is not long enough to reliably determine the normal values of a patient's T:R ratio. In this paper, we develop a convolutional neural network (CNN) based model utilising phase space reconstruction matrices to predict T:R ratios from 10-second ECG segments without explicitly locating the R or T waves, thus avoiding the issue of TWOS. This tool can be used to automatically screen patients over a much longer period and provide an in-depth description of the behaviour of the T:R ratio over that period. The tool can also enable much more reliable and descriptive screenings to better assess patients' eligibility for S-ICD implantation.

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Deep learning and hyperparameter optimization for assessing one’s eligibility for a subcutaneous implantable cardioverter-defibrillator

Dunn¹,

Coniglio

ElRefai

et al. 2023

Ann Oper Res

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It is standard cardiology practice for patients suffering from ventricular arrhythmias (the main cause of sudden cardiac death) belonging to high risk populations to be treated via the implantation of Subcutaneous Implantable cardioverter-defibrillators (S-ICDs). S-ICDs carry a risk of so-called T wave over sensing (TWOS), which can lead to inappropriate shocks that carry an inherent health risk. For this reason, according to current practice patients’ Electrocardiograms (ECGs) are manually screened by a cardiologist over 10 s to assess the T:R ratio—the ratio between the amplitudes of the T and R waves which is used as a marker for the likelihood of TWOS—with a plastic template. Unfortunately, the temporal variability of a patient’ T:R ratio can render such a screening procedure, which relies on an inevitably short ECG segment due to its manual nature, unreliable. In this paper, we propose and investigate a tool based on deep learning for the automatic prediction of the T:R ratios from multiple 10-second segments of ECG recordings capable of carrying out a 24-hour automated screening. Thanks to the significantly increased screening window, such a screening would provide far more reliable T:R ratio predictions than the currently utilized 10-second, template-based, manual screening is capable of. Our tool is the first, to the best of our knowledge, to fully automate such an otherwise manual and potentially inaccurate procedure. From a methodological perspective, we evaluate different deep learning model architectures for our tool, assess a range of stochastic-gradient-descent-based optimization methods for training their underlying deep-learning model, perform hyperparameter tuning, and create ensembles of the best performing models in order to identify which combination leads to the best performance. We find that the resulting model, which has been integrated into a prototypical tool for use by clinicians, is able to predict T:R ratios with very high accuracy. Thanks to this, our automated T:R ratio detection tool will enable clinicians to provide a completely automated assessment of whether a patient is eligible for S-ICD implantation which is more reliable than current practice thanks to adopting a significantly longer ECG screening window which better and more accurately captures the behavior of the patient’s T:R ratio than the current manual practice.

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Classification Methodology of CVD with Localized Features Analysis Using Phase Space Reconstruction Targeting Personalized Remote Health Monitoring

Abstract: Abstract

Cited by 4 publications

References 8 publications

Role of deep learning methods in screening for subcutaneous implantable cardioverter defibrillator in heart failure

Role of deep learning methods in screening for subcutaneous implantable cardioverter defibrillator in heart failure

Deep learning methods for screening patients' S-ICD implantation eligibility

Deep learning and hyperparameter optimization for assessing one’s eligibility for a subcutaneous implantable cardioverter-defibrillator

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