An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers

Pandit, Diptangshu; Zhang, Li; Aslam, Nauman; Liu, Chengyu; Hossain, Alamgir; Chattopadhyay, Samiran

doi:10.1109/skima.2014.7083561

Cited by 13 publications

(3 citation statements)

References 31 publications

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“…Rules are derived based on the ST-segment value depending on the time between the R-peak and the start of the ST-segment slope. In Reference [51], the authors have used an ensemble learning technique called Adaptive Boosting (AdaBoost) also known as meta-learning, used to enhance binary classification efficiency in detecting abnormal beats from the ECG signal and have evaluated on three databases of MITDB, QT [52], and ESCDB. Studies have shown that Artificial Neural Networks (ANN) are powerful data analysis tools.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling

et al. 2021

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Cardiovascular diseases have been reported to be the leading cause of mortality across the globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of main interest among researchers, as its early diagnosis can prevent life threatening cardiac conditions and potentially save human lives. Analyzing the Electrocardiogram (ECG) can provide valuable diagnostic information to detect different types of cardiac arrhythmia. Real-time ECG monitoring systems with advanced machine learning methods provide information about the health status in real-time and have improved user’s experience. However, advanced machine learning methods have put a burden on portable and wearable devices due to their high computing requirements. We present an improved, less complex Convolutional Neural Network (CNN)-based classifier model that identifies multiple arrhythmia types using the two-dimensional image of the ECG wave in real-time. The proposed model is presented as a three-layer ECG signal analysis model that can potentially be adopted in real-time portable and wearable monitoring devices. We have designed, implemented, and simulated the proposed CNN network using Matlab. We also present the hardware implementation of the proposed method to validate its adaptability in real-time wearable systems. The European ST-T database recorded with single lead L3 is used to validate the CNN classifier and achieved an accuracy of 99.23%, outperforming most existing solutions.

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Section: Machine Learning Approachesmentioning

confidence: 99%

Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling

et al. 2021

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“…Ahmed et al categorized the features for classification in time-, frequency-, and time-frequency-based features [19]. In the literature, the variety of classifiers ranges from neural networks [27] to k-nearest neighbors [28]. In 2021, Hua et al developed an approach for information divergences based on divergence-based matrix information geometry detectors [29].…”

Section: Introductionmentioning

confidence: 99%

Printed and Flexible ECG Electrodes Attached to the Steering Wheel for Continuous Health Monitoring during Driving

Warnecke

Ganapathy

Koch

et al. 2022

Sensors

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Continuous health monitoring in a vehicle enables the earlier detection of symptoms of cardiovascular diseases. In this work, we designed flexible and thin electrodes made of polyurethane for long-term electrocardiogram (ECG) monitoring while driving. We determined the time for reliable ECG recording to evaluate the effectiveness of the electrodes. We recorded data from 19 subjects under four scenarios: rest, city, highway, and rural. The recording time was five min for rest and 15 min for the other scenarios. The total recording (950 min) is publicly available under a CC BY-ND 4.0 license. We used the simultaneous truth and performance level estimation (STAPLE) algorithm to detect the position of R-waves. Then, we derived the RR intervals to compare the estimated heart rate with the ground truth, which we obtained from ECG electrodes on the chest. We calculated the signal-to-noise ratio (SNR) and averaged it for the different scenarios. Highway had the lowest SNR (−6.69 dB) and rural had the highest (−6.80 dB). The usable time of the steering wheel was 42.46% (city), 46.67% (highway), and 47.72% (rural). This indicates that steering-wheel-based ECG recording is feasible and delivers reliable recordings from about 45.62% of the driving time. In summary, the developed electrodes allow continuous in-vehicle heart rate monitoring, and our publicly available recordings provide the opportunity to apply more sophisticated data analytics.

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“…The constructed features convey a subset of medically important information which are used for identification of abnormal heartbeats. Previously, we have proposed multiple standalone ECG processing schemes such as [28], [29], however, in this work, we intend to propose a lightweight system and benchmark it using multiple databases and classification techniques.…”

Section: Introductionmentioning

confidence: 99%

Automated Hertbeat Abnormality Detection Using Realtime R-Assisted Lightweight Feature Extraction Algorithm

Pandit¹,

Liu²,

Zhang³

2018

Preprint

Self Cite

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Abstract-Automated Electrocardiogram (ECG) processing is an important technique which helps in identifying abnormalitiesin the heart before any formal diagnosis. This research presents a real-time and lightweight R-assisted feature extraction algorithm and a heartbeat classification scheme which achieves highly accurate abnormality detection. In the proposed algorithm, we extract fifteen features from each heartbeat taken from raw Lead-II ECG signals. The features carry medically valuable information such as locations, amplitude and energy of ECG waves (P, Q, R, S, T waves) which are then used for detection of any abnormality that might be present in the heartbeat using various classification algorithms. We have used four popular databases from Physionet and extracted ten thousand ECG signals from each for training the models and benchmarking results. Four classification models i.e. Naïve Bays, k-Nearest Neighbor, Neural Network, Decision Tree were used for abnormality detection validating the efficiency of the system.

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An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers

Cited by 13 publications

References 31 publications

Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling

Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling

Printed and Flexible ECG Electrodes Attached to the Steering Wheel for Continuous Health Monitoring during Driving

Automated Hertbeat Abnormality Detection Using Realtime R-Assisted Lightweight Feature Extraction Algorithm

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