Robust Heartbeat Classification for Wearable Single-Lead ECG via Extreme Gradient Boosting

Zhu, Huaiyu; Zhao, Yisheng; Pan, Yun; Xie, Hanshuang; Wu, Fan; Huan, Ruohong

doi:10.3390/s21165290

Cited by 10 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Las ANN típicas están compuestas por tres tipos de capas: entrada, salida y ocultas. Cada capa está constituida por neuronas [21]. Las redes neuronales convolucionales (CNN), las redes neuronales recurrentes (RNN, recurrent neural networks en inglés) y el Naive Bayes se utilizan como clasificadores.…”

Section: Enfermedades Cardiovasculares E Inteligencia Artificialunclassified

“…Un estudio propone un clasificador de señales de ECG basado en XGBoost y un conjunto de técnicas de descomposición empírica en modos (EEMD, ensemble empirical mode decomposition en inglés) que aprovecha funciones basadas en tiempo, frecuencia y características morfológicas [38]. Otro estudio propone crear un conjunto de características morfológicas en cinco dimensiones que incluyen complejos QRS e intervalos RR, así como también coeficientes de características wavelet, para construir el vector de características para una clasificación altamente eficiente de los latidos del corazón [21]. Las medidas de desempeño son entrenadas para encontrar características que sean clasificadas correctamente; entonces, la relación de aquellas que no son bien clasificadas se utiliza para encontrar la eficiencia del clasificador.…”

Section: Enfermedades Cardiovasculares E Inteligencia Artificialunclassified

See 1 more Smart Citation

Prediction of Arrhythmias and Acute Myocardial Infarctions using Machine Learning

Patiño

Medina

Bustillos

et al. 2023

ings

View full text Add to dashboard Cite

Cardiovascular diseases such as Acute Myocardial Infarction is one of the 3 leading causes of death in the world according to WHO data, in the same way cardiac arrhythmias are very common diseases today, such as atrial fibrillation. The ECG electrocardiogram is the means of cardiac diagnosis that is used in a standardized way throughout the world. Machine learning models are very helpful in classification and prediction problems. Applied to the field of health, ANN, and CNN artificial and neural networks, added to tree-based models such as XGBoost, are of vital help in the prevention and control of heart disease. The present study aims to compare and evaluate learning based on ANN, CNN and XGBoost algorithms by using the Physionet MIT-BIH and PTB ECG databases, which provide ECGs classified with Arrhythmias and Acute Myocardial Infarctions respectively. The learning times and the percentage of Accuracy of the 3 algorithms in the 2 databases are compared separately, and finally the data are crossed to compare the validity and safety of the learning prediction.

show abstract

Section: Enfermedades Cardiovasculares E Inteligencia Artificialunclassified

Prediction of Arrhythmias and Acute Myocardial Infarctions using Machine Learning

Patiño

Medina

Bustillos

et al. 2023

ings

View full text Add to dashboard Cite

show abstract

“…After data collection, the ECG signals were preprocessed [6] to remove noise, then classified into four categories of AF, normal, other rhythms, and noise [7] by a DL model consisting of a 34-layer deep residual network (ResNet) [8]. The model was initially established on the basis of the open-source 2017 PhysioNet/CinC Challenge ECG dataset [9] and was further tuned through the transfer learning technique by using ECG recordings labeled through consensus by a committee of three experts.…”

Section: Deep Learning-based Handheld Devicementioning

confidence: 99%

Deep Learning-based Handheld Device-Enabled Symptom-driven Recording: A Pragmatic Approach for the Detection of Post-ablation Atrial Fibrillation Recurrence

Chen¹,

Jiang²

2023

CVIA

View full text Add to dashboard Cite

Objective: Symptom-driven electrocardiogram (ECG) recording plays a significant role in the detection of post-ablation atrial fibrillation recurrence (AFR). However, making timely medical contact whenever symptoms occur may not be practical. Herein, a deep learning (DL)-based handheld device was deployed to facilitate symptom-driven monitoring. Methods: A cohort of patients with paroxysmal atrial fibrillation (AF) was trained to use a DL-based handheld device to record ECG signals whenever symptoms presented after the ablation. Additionally, 24-hour Holter monitoring and 12-lead ECG were scheduled at 3, 6, 9, and 12 months post-ablation. The detection of AFR by the different modalities was explored. Results: A total of 22 of 67 patients experienced AFR. The handheld device and 24-hour Holter monitor detected 19 and 8 AFR events, respectively, five of which were identified by both modalities. A larger portion of ECG tracings was recorded for patients with than without AFR [362(330) vs. 132(133), P=0.01)], and substantial numbers of AFR events were recorded from 18:00 to 24:00. Compared to Holter, more AFR events were detected by the handheld device in earlier stages (HR=1.6, 95% CI 1.2–2.2, P<0.01). Conclusions: The DL-based handheld device-enabled symptom-driven recording, compared with the conventional monitoring strategy, improved AFR detection and enabled more timely identification of symptomatic episodes.

show abstract

“…The author in [24] employed K-Nearest Neighbour classifier on MITBIH arrhythmia database to classify five types of ECG beats and attained an accuracy of 98.40% for isolating the signals. A robust extreme gradient boosting technique is utilized in [25] to classify five ECG beat classes from both MITBIH data and self-collected single-lead wearable ECG dataset. The developed model outperforms the traditional models with an accuracy of 99.14% on MITDB and 98.68% on wearable ECG 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

View full text Add to dashboard Cite

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%.

show abstract

Robust Heartbeat Classification for Wearable Single-Lead ECG via Extreme Gradient Boosting

Cited by 10 publications

References 31 publications

Prediction of Arrhythmias and Acute Myocardial Infarctions using Machine Learning

Prediction of Arrhythmias and Acute Myocardial Infarctions using Machine Learning

Deep Learning-based Handheld Device-Enabled Symptom-driven Recording: A Pragmatic Approach for the Detection of Post-ablation Atrial Fibrillation Recurrence

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

Contact Info

Product

Resources

About