Investigating Feature Selection and Random Forests for Inter-patient Heartbeat Classification

Saenz-Cogollo, Jose Francisco; Agelli, Maurizio

doi:10.20944/preprints202003.0036.v1

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…To demonstrate the effect of our proposed features, we compare them against 24 gold-standard ECG features that measure the temporal characteristics of the ventricular depolarization waves (QRS complex) [ 18 , 20 ]. It includes the following: QRS complex widths, QS width, PR width, Peak Heights (P, R, Q, S), Peak Differences (PQ, RQ, RS), and normalized heart rate features, etc.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Detection of Junctional Ectopic Tachycardia by Central Venous Pressure

Tan

Dai

Humayun

et al. 2021

Artificial Intelligence in Medicine

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Central venous pressure (CVP) is the blood pressure in the venae cavae, near the right atrium of the heart. This signal waveform is commonly collected in clinical settings, and yet there has been limited discussion of using this data for detecting arrhythmia and other cardiac events. In this paper, we develop a signal processing and feature engineering pipeline for CVP waveform analysis. Through a case study on pediatric junctional ectopic tachycardia (JET), we show that our extracted CVP features reliably detect JET with comparable results to the more commonly used electrocardiogram (ECG) features. This machine learning pipeline can thus improve the clinical diagnosis and ICU monitoring of arrhythmia. It also corroborates and complements the ECG-based diagnosis, especially when the ECG measurements are unavailable or corrupted.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…It contributes to the literature by introducing a CVP signal preprocessing and feature extraction pipeline. We then compare the machine learning model based on extracted CVP features with the one based on gold standard ECG features [ 20 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Junctional Ectopic Tachycardia by Central Venous Pressure

Tan

Dai

Humayun

et al. 2021

Artificial Intelligence in Medicine

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Using Explainable AI (XAI) for the Prediction of Falls in the Older Population

Tang

Romero-Ortuño

2022

Algorithms

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The prevention of falls in older people requires the identification of the most important risk factors. Frailty is associated with risk of falls, but not all falls are of the same nature. In this work, we utilised data from The Irish Longitudinal Study on Ageing to implement Random Forests and Explainable Artificial Intelligence (XAI) techniques for the prediction of different types of falls and analysed their contributory factors using 46 input features that included those of a previously investigated frailty index. Data of participants aged 65 years and older were fed into four random forest models (all falls or syncope, simple fall, complex fall, and syncope). Feature importance rankings were based on mean decrease in impurity, and Shapley additive explanations values were calculated and visualised. Female sex and a previous fall were found to be of high importance in all of the models, and polypharmacy (being on five or more regular medications) was ranked high in the syncope model. The more ‘accidental’ (extrinsic) nature of simple falls was demonstrated in its model, where the presence of many frailty features had negative model contributions. Our results highlight that falls in older people are heterogenous and XAI can provide new insights to help their prevention.

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An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques

Sraitih

Jabrane

Hassani

2021

JCM

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The new advances in multiple types of devices and machine learning models provide opportunities for practical automatic computer-aided diagnosis (CAD) systems for ECG classification methods to be practicable in an actual clinical environment. This imposes the requirements for the ECG arrhythmia classification methods that are inter-patient. We aim in this paper to design and investigate an automatic classification system using a new comprehensive ECG database inter-patient paradigm separation to improve the minority arrhythmical classes detection without performing any features extraction. We investigated four supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), Random Forest (RF), and the ensemble of these three methods. We test the performance of these techniques in classifying: Normal beat (NOR), Left Bundle Branch Block Beat (LBBB), Right Bundle Branch Block Beat (RBBB), Premature Atrial Contraction (PAC), and Premature Ventricular Contraction (PVC), using inter-patient real ECG records from MIT-DB after segmentation and normalization of the data, and measuring four metrics: accuracy, precision, recall, and f1-score. The experimental results emphasized that with applying no complicated data pre-processing or feature engineering methods, the SVM classifier outperforms the other methods using our proposed inter-patient paradigm, in terms of all metrics used in experiments, achieving an accuracy of 0.83 and in terms of computational cost, which remains a very important factor in implementing classification models for ECG arrhythmia. This method is more realistic in a clinical environment, where varieties of ECG signals are collected from different patients.

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Investigating Feature Selection and Random Forests for Inter-patient Heartbeat Classification

Cited by 5 publications

References 24 publications

Detection of Junctional Ectopic Tachycardia by Central Venous Pressure

Detection of Junctional Ectopic Tachycardia by Central Venous Pressure

Using Explainable AI (XAI) for the Prediction of Falls in the Older Population

An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques

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