Classification of cardiac arrhythmia using hybrid genetic algorithm optimisation for multi-layer perceptron neural network

Reseek-Arrhythmia: Empirical Evaluation of ResNet Architecture for Detection of Arrhythmia

“…For a concise summary and comparison of the methods and results of the various studies discussed, refer to Table 1. [4] CNN 93.19 Optimal multi-stage arrhythmia classification approach (2020) [14] Extreme gradient boosting tree 97 Low-power ECG arrhythmia detection SoC with STT-MRAM and LDMAC unit (2021) [15] STT-MRAM 85.1 Classification of Obstructive Sleep Apnoea from single-lead ECG signals using convolutional neural and Long Short Term Memory networks (2021) [5] CNN, LSTM 90.92 Cardiac arrhythmia detection using deep learning (2017) [6] DCNN 92 Multiresolution wavelet transform-based feature extraction and ECG classification to detect cardiac abnormalities (2017) [16] SVM 98.9 High-performance personalized heartbeat classification model for long-term ECG signal (2017) [17] GRNN 88 A new personalized ECG signal classification algorithm using block-based neural network and particle swarm optimiza-tion (2016) [18] BBNN 97 An approach for ECG beats classification using adaptive neuro-fuzzy inference system (2016) [19] ANFIS 96 An automated ECG beat classification system using deep neural networks with an unsupervised feature extraction technique (2019) [20] DL 99.73 Arrhythmic heartbeat classification using ensemble of random forest and support vector machine algorithm (2021) [21] SVM, RF 98.21 Electrocardiogram soft computing using hybrid deep learning CNN-ELM (2020) [22] CNN + EML 97.50 ECG beat classification using PCA, LDA, ICA and discrete wavelet transform (2013) [23] SVM 99.28 Application of higher-order cumulant features for cardiac health diagnosis using ECG signals (2013) [24] NN, LS-SVM 94.52 Cardiac arrhythmia prediction using improved multilayer perceptron neural network (2013) [25] MLPNN 95.1 DWT-based feature extraction from ECG signal (2013) [26] MLPNN 85 Heartbeat classification using particle swarm optimization (2013) [27] BMLPNN 76 Artificial neural network models based cardiac arrhythmia disease diagnosis from ECG signal data (2012) [28] MNN-generalized FFNN 86.67 An effective ECG arrhythmia classification algorithm (2011) [29] PNN 99.71 In this study, the performance of neural networks in identifying electrocardiogram (ECG) patterns as normal or abnormal is investigated. With 91.9% accuracy in binary classification and 75.7% accuracy in multi-class classification, the neural network (NN) model, including support vector machines (SVMs), random forests, and logistic regression, outperforms.…”

Genetic Algorithm for Biomarker Search Problem and Class Prediction

“…Card iac arrhythmia early detection is another field where the genetic algorith m has been used for optimization of learning rate and mo mentu m in the neural network classifier. Here symmetric uncertainty provides reduced feature set and Simulated Annealing refines the population [19].…”