Detection of atrial fibrillation from ECG recordings using decision tree ensemble with multi-level features

Shao, Minggang; Bin, Guangyu; Wu, Shuicai; Bin, Guanghong; Huang, Jiao; Zhou, Zhuhuang

doi:10.1088/1361-6579/aadf48

Cited by 36 publications

(25 citation statements)

References 43 publications

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“…The algorithms of Zihlmann et al [26] and Xiong et al [24] were based on DNN, which is an end-to-end process without extracting hand-crafted features. Using only 30 features, our previous method [27] proposed in the Challenge achieved an overall F 1 score of 0.87 on AFDB-2017. In comparison with our previous method, the CatBoost model proposed in this work achieved an overall F 1 score of 0.91 on AFDB-2017, which increased by 0.04, while using less features.…”

Section: Discussionmentioning

confidence: 96%

“…The system could provide instant feedback from the doctor. We also improved the performance of our previous AF classification method [27] proposed in the Challenge. A wearable ECG patch device was designed to collect single-lead ECG signals and to continuously send the collected ECG data to an Android smartphone via Bluetooth.…”

Section: Introductionmentioning

confidence: 94%

“…During the Challenge, we proposed a novel ECG classification algorithm [27] using machine learning to classify a short single-lead ECG recording into one of four classes: Normal sinus rhythm, AF rhythm, Other arrhythmias, and Noisy recordings. In this work, we improved the performance of the previous algorithm from three aspects: (1) enhancing the dataset, (2) using CatBoost learning kit [29] to train a new classifier, and (3) selecting appropriate features to reduce the number of features without obviously decreasing the performance.…”

Section: Ecg Classification Algorithmmentioning

confidence: 99%

“…AF detection methods using ECG signals are usually investigated from two aspects, the absence of P waves or the presence of f-waves [13,14], and irregularity of RR intervals [15][16][17][18][19]. In the 2017 PhysioNet/Computing in Cardiology (CinC) Challenge of AF classification (termed Challenge) [20], the official algorithms [21][22][23][24][25][26][27] based on machine learning, especially deep learning methods, have achieved excellent performance on AF detection.…”

Section: Introductionmentioning

confidence: 99%

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A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection

Shao

Zhou

Bin

et al. 2020

Sensors

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In this paper we proposed a wearable electrocardiogram (ECG) telemonitoring system for atrial fibrillation (AF) detection based on a smartphone and cloud computing. A wearable ECG patch was designed to collect ECG signals and send the signals to an Android smartphone via Bluetooth. An Android APP was developed to display the ECG waveforms in real time and transmit every 30 s ECG data to a remote cloud server. A machine learning (CatBoost)-based ECG classification method was proposed to detect AF in the cloud server. In case of detected AF, the cloud server pushed the ECG data and classification results to the web browser of a doctor. Finally, the Android APP displayed the doctor’s diagnosis for the ECG signals. Experimental results showed the proposed CatBoost classifier trained with 17 selected features achieved an overall F1 score of 0.92 on the test set (n = 7270). The proposed wearable ECG monitoring system may potentially be useful for long-term ECG telemonitoring for AF detection.

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Section: Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 94%

Section: Ecg Classification Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection

Shao

Zhou

Bin

et al. 2020

Sensors

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“…HRV features have been widely used in non-arrest situations to detect and predict cardiac arrhythmias [ 20 , 28 , 29 ]. They were originally designed to analyze long intervals, minutes, or even hours, in hemodynamically stable patients.…”

Section: Discussionmentioning

confidence: 99%

Towards the Prediction of Rearrest during Out-of-Hospital Cardiac Arrest

Elola

Aramendi

Rueda

et al. 2020

Entropy

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A secondary arrest is frequent in patients that recover spontaneous circulation after an out-of-hospital cardiac arrest (OHCA). Rearrest events are associated to worse patient outcomes, but little is known on the heart dynamics that lead to rearrest. The prediction of rearrest could help improve OHCA patient outcomes. The aim of this study was to develop a machine learning model to predict rearrest. A random forest classifier based on 21 heart rate variability (HRV) and electrocardiogram (ECG) features was designed. An analysis interval of 2 min after recovery of spontaneous circulation was used to compute the features. The model was trained and tested using a repeated cross-validation procedure, on a cohort of 162 OHCA patients (55 with rearrest). The median (interquartile range) sensitivity (rearrest) and specificity (no-rearrest) of the model were 67.3% (9.1%) and 67.3% (10.3%), respectively, with median areas under the receiver operating characteristics and the precision–recall curves of 0.69 and 0.53, respectively. This is the first machine learning model to predict rearrest, and would provide clinically valuable information to the clinician in an automated way.

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State of the Art in Artificial Intelligence and Machine Learning Techniques for Improving Patient Outcomes Pertaining to the Cardiovascular and Respiratory Systems

Au‐Yeung

Sevakula

Singh

et al. 2021

Cardiac Bioelectric Therapy

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Detection of atrial fibrillation from ECG recordings using decision tree ensemble with multi-level features

Abstract: The proposed algorithm may be used as a new method for AF detection.

Cited by 36 publications

References 43 publications

A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection

A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection

Towards the Prediction of Rearrest during Out-of-Hospital Cardiac Arrest

State of the Art in Artificial Intelligence and Machine Learning Techniques for Improving Patient Outcomes Pertaining to the Cardiovascular and Respiratory Systems

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