Ensemble Learning for Detection of Short Episodes of Atrial Fibrillation

Peimankar, Abdolrahman; Puthusserypady, Sadasivan

doi:10.23919/eusipco.2018.8553253

Cited by 11 publications

(3 citation statements)

References 34 publications

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“…Zhao et al measured atrial fibrillation entropy for atrial fibrillation detection in the time window of a short RR time series [16]. Peimankar and Puthusserypady used a random forest classification algorithm based on the RRI, RMSSD, nRMSSD, and other features extracted from the RR interval to achieve an accuracy rate of 97.86% in 300 heartbeats [17]. However, many methods based on ventricular activity require long pieces of data to identify long AF events (20-s) and are limited in dealing with very short AF events [18].…”

Section: Related Workmentioning

confidence: 99%

SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal

Zhang

Dai

et al. 2020

Journal of Healthcare Engineering

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Atrial fibrillation is the most common arrhythmia and is associated with high morbidity and mortality from stroke, heart failure, myocardial infarction, and cerebral thrombosis. Effective and rapid detection of atrial fibrillation is critical to reducing morbidity and mortality in patients. Screening atrial fibrillation quickly and efficiently remains a challenging task. In this paper, we propose SS-SWT and SI-CNN: an atrial fibrillation detection framework for the time-frequency ECG signal. First, specific-scale stationary wavelet transform (SS-SWT) is used to decompose a 5-s ECG signal into 8 scales. We select specific scales of coefficients as valid time-frequency features and abandon the other coefficients. The selected coefficients are fed to the scale-independent convolutional neural network (SI-CNN) as a two-dimensional (2D) matrix. In SI-CNN, a convolution kernel specifically for the time-frequency characteristics of ECG signals is designed. During the convolution process, the independence between each scale of coefficient is preserved, and the time domain and the frequency domain characteristics of the ECG signal are effectively extracted, and finally the atrial fibrillation signal is quickly and accurately identified. In this study, experiments are performed using the MIT-BIH AFDB data in 5-s data segments. We achieve 99.03% sensitivity, 99.35% specificity, and 99.23% overall accuracy. The SS-SWT and SI-CNN we propose simplify the feature extraction step, effectively extracts the features of ECG, and reduces the feature redundancy that may be caused by wavelet transform. The results shows that the method can effectively detect atrial fibrillation signals and has potential in clinical application.

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Section: Related Workmentioning

confidence: 99%

SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal

Zhang

Dai

et al. 2020

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…Thus, the performance of ensemble learning models is generally higher than single classification algorithms [ 27 ]. There are various applications in which ensemble learning methods are utilized such as cyber security [ 28 , 29 , 30 , 31 , 32 , 33 ], energy [ 34 , 35 , 36 , 37 ], and health informatics [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders

Peimankar

Winther

Ebrahimi

et al. 2023

Sensors

Self Cite

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Walking ability of elderly individuals, who suffer from walking difficulties, is limited, which restricts their mobility independence. The physical health and well-being of the elderly population are affected by their level of physical activity. Therefore, monitoring daily activities can help improve the quality of life. This becomes especially a huge challenge for those, who suffer from dementia and Alzheimer’s disease. Thus, it is of great importance for personnel in care homes/rehabilitation centers to monitor their daily activities and progress. Unlike normal subjects, it is required to place the sensor on the back of this group of patients, which makes it even more challenging to detect walking from other activities. With the latest advancements in the field of health sensing and sensor technology, a huge amount of accelerometer data can be easily collected. In this study, a Machine Learning (ML) based algorithm was developed to analyze the accelerometer data collected from patients with walking difficulties, who live in one of the municipalities in Denmark. The ML algorithm is capable of accurately classifying the walking activity of these individuals with different walking abnormalities. Various statistical, temporal, and spectral features were extracted from the time series data collected using an accelerometer sensor placed on the back of the participants. The back sensor placement is desirable in patients with dementia and Alzheimer’s disease since they may remove visible sensors to them due to the nature of their diseases. Then, an evolutionary optimization algorithm called Particle Swarm Optimization (PSO) was used to select a subset of features to be used in the classification step. Four different ML classifiers such as k-Nearest Neighbors (kNN), Random Forest (RF), Stacking Classifier (Stack), and Extreme Gradient Boosting (XGB) were trained and compared on an accelerometry dataset consisting of 20 participants. These models were evaluated using the leave-one-group-out cross-validation (LOGO-CV) technique. The Stack model achieved the best performance with average sensitivity, positive predictive values (precision), F1-score, and accuracy of 86.85%, 93.25%, 88.81%, and 93.32%, respectively, to classify walking episodes. In general, the empirical results confirmed that the proposed models are capable of classifying the walking episodes despite the challenging sensor placement on the back of the patients, who suffer from walking disabilities.

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“…For example, HR increase will shorten the duration of the ventricular depolarization period, leading to T wave shift. In other ECG applications, such as atrial fibrillation (AF) [13], the HR variability can be used to distinguish the AF episode from normal sinus rhythm. However, in ECG biometric, this variation will result in low identification task and make the identification become far more difficult without appropriate processing [14].…”

Section: Introductionmentioning

confidence: 99%

A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification

Wang

Yang

et al. 2019

Applied Sciences

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In the past decades, the electrocardiogram (ECG) has been investigated as a promising biometric by exploiting the subtle discrepancy of ECG signals between subjects. However, the heart rate (HR) for one subject may vary because of physical activities or strong emotions, leading to the problem of ECG signal variation. This variation will significantly decrease the performance of the identification task. Particularly for short-term ECG signal without many heartbeats, the hardly measured HR makes the identification task even more challenging. This study aims to propose a novel method suitable for short-term ECG signal identification. In particular, an improved HR-free resampling strategy is proposed to minimize the influence of HR variability during heartbeat processing. For feature extraction, the Principal Component Analysis Network (PCANet) is implemented to determine the potential difference between subjects. The proposed method is evaluated using a public ECG-ID database that contains various HR data for some subjects. Experimental results show that the proposed method is robust to HR change and can achieve high subject identification accuracy (94.4%) on ECG signals with only five heartbeats. Thus, the proposed method has the potential for application to systems that use short-term ECG signals for identification (e.g., wearable devices).

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Ensemble Learning for Detection of Short Episodes of Atrial Fibrillation

Cited by 11 publications

References 34 publications

SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal

SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal

A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders

A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification

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