Patient Specific Machine Learning Models for ECG Signal Classification

Pandey, Saroj Kumar; Janghel, Rekh Ram; Vani, V.

doi:10.1016/j.procs.2020.03.269

Cited by 57 publications

(29 citation statements)

References 32 publications

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“…Proposed method outperforms the other state-of-art approaches viz. KICA + LIBSVM 88 , 1-D CNN 65 , PCAnet + SVM 89 , CNN + LSTM 90 , Evolutionary-Neural System based on SVM 64 , WT-HMM model 91 , Ensemble SVM 92 . All these approaches used MLII database with 4 or 5 classes.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier

Dalal

Vishwakarma

2021

Sci Rep

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Every human being has a different electro-cardio-graphy (ECG) waveform that provides information about the well being of a human heart. Therefore, ECG waveform can be used as an effective identification measure in biometrics and many such applications of human identification. To achieve fast and accurate identification of human beings using ECG signals, a novel robust approach has been introduced here. The databases of ECG utilized during the experimentation are MLII, UCI repository arrhythmia and PTBDB databases. All these databases are imbalanced; hence, resampling techniques are helpful in making the databases balanced. Noise removal is performed with discrete wavelet transform (DWT) and features are obtained with multi-cumulants. This approach is mainly based on features extracted from the ECG data in terms of multi-cumulants. The multi-cumulants feature based ECG data is classified using kernel extreme learning machine (KELM). The parameters of multi-cumulants and KELM are optimized using genetic algorithm (GA). Excellent classification rate is achieved with 100% accuracy on MLII and UCI repository arrhythmia databases, and 99.57% on PTBDB database. Comparison with existing state-of-art approaches has also been performed to prove the efficacy of the proposed approach. Here, the process of classification in the proposed approach is named as evolutionary hybrid classifier.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier

Dalal

Vishwakarma

2021

Sci Rep

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“…Therefore, SVM is generally known as a linear classifier. Researchers have detected arrhythmias using SVM [96], [98], [101] with Sequential Minimal Optimization-SVM (SMO-SVM)) [102], Multi-class Support Vector Machine (MSVM)/Complex Support Vector Machine (CSVM) [104] and in conjunction with other ML methods such as Ensemble-SVM [97]. Even though SVM is a linear classifier, it can still capture nonlinear relationships in the cardiovascular functionalities, often making highly accurate predictions such as classifying ECG as Normal versus Abnormal [99], [100] and detecting different heartbeats [103].…”

Section: ) Traditional Ecg Classification Approachesmentioning

confidence: 99%

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

et al. 2020

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Electrocardiogram (ECG) gives essential information about different cardiac conditions of the human heart. Its analysis has been the main objective among the research community to detect and prevent life threatening cardiac circumstances. Traditional signal processing methods, machine learning and its subbranches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications for early detection and treatment of cardiac conditions and arrhythmias. A detailed literature survey regarding ECG signal analysis is presented in this paper. We first introduce a stages-based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. The model describes both traditional time/frequency-domain and advanced machine learning techniques reported in the published literature at every stage of analysis, starting from ECG data acquisition to its classification for both simulations and real-time monitoring systems. We present a comprehensive literature review of real-time ECG signal acquisition, prerecorded clinical ECG data, ECG signal processing and denoising, detection of ECG fiducial points based on feature engineering and ECG signal classification along with comparative discussions among the reviewed studies. This study also presents a detailed literature review of ECG signal analysis and feature engineering for ECG-based body sensor networks in portable and wearable ECG devices for real-time cardiac status monitoring. Additionally, challenges and limitations are discussed and tools for research in this field as well as suggestions for future work are outlined. INDEX TERMS ECG analysis, cardiac arrhythmias, QRS and ST detection, ECG classification, deep learning.

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“…Javadi et al [ 38 ] integrated a multiple neural network model based on a stacking algorithm for ECG classification, which reduced the classification error rate. Pandey et al [ 39 ] employed an ensemble of SVMs to classify heartbeats into four classes. Rajesh et al [ 40 ] used intrinsic mode functions to get the final features, and the AdaBoost classifier was employed to classify heartbeats.…”

Section: Related Workmentioning

confidence: 99%

Heartbeat Classification Based on Multifeature Combination and Stacking-DWKNN Algorithm

Shen

et al. 2021

Journal of Healthcare Engineering

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Arrhythmia is one of the most common abnormal symptoms that can threaten human life. In order to distinguish arrhythmia more accurately, the classification strategy of the multifeature combination and Stacking-DWKNN algorithm is proposed in this paper. The method consists of four modules. In the preprocessing module, the signal is denoised and segmented. Then, multiple different features are extracted based on single heartbeat morphology, P length, QRS length, T length, PR interval, ST segment, QT interval, RR interval, R amplitude, and T amplitude. Subsequently, the features are combined and normalized, and the effect of different feature combinations on heartbeat classification is analyzed to select the optimal feature combination. Finally, the four types of normal and abnormal heartbeats were identified using the Stacking-DWKNN algorithm. This method is performed on the MIT-BIH arrhythmia database. The result shows a sensitivity of 89.42% and a positive predictive value of 94.90% of S-type beats and a sensitivity of 97.21% and a positive predictive value of 97.07% of V-type beats. The obtained average accuracy is 99.01%. Compared to other models with the same features, this method can improve accuracy and has a higher positive predictive value and sensitivity, which is important for clinical decision-making.

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Patient Specific Machine Learning Models for ECG Signal Classification

Cited by 57 publications

References 32 publications

Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier

Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

Heartbeat Classification Based on Multifeature Combination and Stacking-DWKNN Algorithm

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