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

Saenz-Cogollo, Jose Francisco; Agelli, Maurizio

doi:10.3390/a13040075

Cited by 21 publications

(27 citation statements)

References 35 publications

(75 reference statements)

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“…Jose did an investigation of feature selection for heartbeat classification. He suggests that using normalized RR intervals could increase the classifier’s performance [ 25 ]. Mondejar demonstrates using several features such as RR interval, normalized RR interval, high order statistic, HBF coefficients, and wavelet transform, thus using a support vector machine (SVM) to classify each feature [ 26 ].…”

Section: Automated Heartbeats Classificationmentioning

confidence: 99%

“…Mondejar demonstrates using several features such as RR interval, normalized RR interval, high order statistic, HBF coefficients, and wavelet transform, thus using a support vector machine (SVM) to classify each feature [ 26 ]. Developing automatic heartbeat classification systems on resource-constrained devices is challenging, e.g., discovering an optimal mixture of features and classifiers [ 25 ].…”

Section: Automated Heartbeats Classificationmentioning

confidence: 99%

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A Heartbeat Classifier for Continuous Prediction Using a Wearable Device

Pramukantoro

Gofuku

2022

Sensors

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Heartbeat monitoring may play an essential role in the early detection of cardiovascular disease. When using a traditional monitoring system, an abnormal heartbeat may not appear during a recording in a healthcare facility due to the limited time. Thus, continuous and long-term monitoring is needed. Moreover, the conventional equipment may not be portable and cannot be used at arbitrary times and locations. A wearable sensor device such as Polar H10 offers the same capability as an alternative. It has gold-standard heartbeat recording and communication ability but still lacks analytical processing of the recorded data. An automatic heartbeat classification system can play as an analyzer and is still an open problem in the development stage. This paper proposes a heartbeat classifier based on RR interval data for real-time and continuous heartbeat monitoring using the Polar H10 wearable device. Several machine learning and deep learning methods were used to train the classifier. In the training process, we also compare intra-patient and inter-patient paradigms on the original and oversampling datasets to achieve higher classification accuracy and the fastest computation speed. As a result, with a constrain in RR interval data as the feature, the random forest-based classifier implemented in the system achieved up to 99.67% for accuracy, precision, recall, and F1-score. We are also conducting experiments involving healthy people to evaluate the classifier in a real-time monitoring system.

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Section: Automated Heartbeats Classificationmentioning

confidence: 99%

Section: Automated Heartbeats Classificationmentioning

confidence: 99%

A Heartbeat Classifier for Continuous Prediction Using a Wearable Device

Pramukantoro

Gofuku

2022

Sensors

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“…In recent studies, researchers have proved that performance of the base classifier can be improved with ensemble classification method. Jose et al, [27] proposed random forest ensemble classification technique to diagnose cardiac arrhythmia. In this model the more informative features were selected using ranking criteria on training dataset.…”

Section: Motivation Towards Ensemble Learningmentioning

confidence: 99%

An Outlier Detection and Feature Ranking based Ensemble Learning for ECG Analysis

Ardeti¹,

Kolluru²,

Varghese³

et al. 2022

IJACSA

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Automated classification of each heartbeat class from the ECG signal is important to diagnose cardiovascular diseases (CVDs) more quickly. ECG data acquired from the realtime or clinical databases contains exceptional values or extreme values called outliers. The separation and removal of outliers is very much useful for improving the data quality. The presence of outliers will influence the results of machine learning (ML) methods such as classification and regression. Outlier identification and removal plays a significant role in this area of research and is a part of signal denoising. Also, most of the traditional ECG-signal processing methods are facing the difficulty in finding the essential key features of recorded signal. In this work, an extreme outlier detection technique known as improved inter quartile range (IIQR) filtering method is used to find the outliers of the signal for the feature ranking process. In addition, an optimized random forest (ORF) based heterogenous ensemble classification model is proposed to improve the true positive and runtime on the ECG data. The classification of each heartbeat type is classified with majority voting technique. Ensemble learning and majority voting rule is used to enhance the accuracy of heart disease prediction. The proposed feature ranking based ORF ensemble classification model (LR + SVM + ORF + XGBoost + KNN) is evaluated on the MITBIH arrhythmia database and produces an overall accuracy of 99.45% which significantly outperforms the state-of-the-art methods such as, (LR + SVM + RF + XGBoost + KNN) with 96.17% accuracy, ensemble deep learning accuracy of 95.81% and ensemble SVM accuracy of 94.47%.

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“…Besides those two categories, some studies proposed their novel features, such as Sparse Representation [6] . Regarding feature selection, methods like principal component analysis [7] , Mutual Information (MI) score [8] , and genetic algorithm [9] are employed. As to model selection, mainstream models are: Support Vector Machine (SVM) [3] , [10] , Linear Discriminants (LDs) [11] , XGBoost [12] , Neural Network (NN) [13] and Random Forest (RF) [4] , [8] , which is employed in our work.…”

Section: Introductionmentioning

confidence: 99%

Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label

Zou

Muller

Utschick

et al. 2022

IEEE J. Transl. Eng. Health Med.

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Objective: Physicians use electrocardiograms (ECG) to diagnose cardiac abnormalities. Sometimes they need to take a deeper look at abnormal heartbeats to diagnose the patients more precisely. The objective of this research is to design a more accurate heartbeat classification algorithm to assist physicians in identifying specific types of the heartbeat. Methods and procedures: In this paper, we propose a novel feature called a segment label, to improve the performance of a heartbeat classifier. This feature, provided by a Convolutional Neural Network, encodes the information surrounding the particular heartbeat. The random forest classifier is trained based on this new feature and other traditional features to classify the heartbeats. Results: We validate our method on the MIT-BIH Arrhythmia dataset following the inter-patient evaluation paradigm. The proposed method is competitive with other similar works. It achieves an accuracy of 0.96, and F1-scores for normal beats, ventricular ectopic beats, and Supra-Ventricular Ectopic Beats (SVEB) of 0.98, 0.93, and 0.74, respectively. The precision and sensitivity for SVEB are 0.76 and 0.78, which outperforms the state-of-the-art methods. Conclusion: This study demonstrates that the segment label can contribute to precisely classifying heartbeats, especially those that require rhythm information as context information (e.g. SVEB). Clinical impact: Using a medical devices embedding our algorithm could ease the physicians’ processes of diagnosing cardiovascular diseases, especially for SVEB, in clinical implementation.

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

Cited by 21 publications

References 35 publications

A Heartbeat Classifier for Continuous Prediction Using a Wearable Device

A Heartbeat Classifier for Continuous Prediction Using a Wearable Device

An Outlier Detection and Feature Ranking based Ensemble Learning for ECG Analysis

Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label

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