Cardiac Severity Classification Using Pre Trained Neural Networks

Malleswari, P. Naga; Bindu, Ch. Hima; Prasad, K. Satya

doi:10.1007/s12539-021-00416-9

Cited by 8 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Discrete Wavelet Transform(DWT) requires various filtration and subsampling stages. Low Pass Filters and High Pass Filters are used to process the signal x(n), where n is the number of samples., as well as downsampling the output signal by two to make each band of frequency include n/2 samples [20]. Simultaneously, two separate filtering techniques are used.…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Detection of cardiac abnormalities from 12-lead ecg using complex wavelet sub-band features

Mondal,

Choudhary,

Rathee

2024

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

[Aim of the study: This research endeavors to optimize cardiac anomaly detection by introducing a method focused on selecting the most effective Daubechis wavelet families. The principal aim is to differentiate between cardiac states that are normal and abnormal by utilising longer electrocardiogram (ECG) signal events based on Apnea ECG dataset. Apnea ECG is often used for the detection of sleep apnea, a sleep disorder characterized by repeated interruptions in breathing during sleep. By using machine learning methods, such as Principal Component Analysis (PCA) and different classifiers, the goal is to improve the precision of cardiac irregularity identification.Used method: In order to extract important statistical and sub-band information from lengthy ECG signal episodes, the study uses a novel method that combines discrete wavelet transform with Principal Component Analysis (PCA) for dimension reduction. The methodology focuses on successfully categorising ECG signals by utilising several classifiers, including multilayer perceptron (MLP) neural network, Ensemble Subspace K-Nearest Neighbour(KNN), and Ensemble Bagged Trees, together with varied Daubechis wavelet families (db2, db3, db4, db5, db6).Brief Description of Results: The results emphasise the importance of the chosen Daubechis wavelet family, db5, and its superiority in ECG representation. The method distinguishes normal and abnormal ECG signals well on the Physionet Apnea ECG database. The Neural Network-based method accurately recognises 100% of healthy signals and 97.8% of problematic ones with 98.6% accuracy.Findings: The Ensemble Subspace K-Nearest Neighbour (KNN) and Ensemble Bagged Trees methods got 87.1% accuracy and 0.89 and 0.87 AOC curve values on this dataset, showing that the method works. Precision values of 0.96, 0.86, and 0.86 for MLP Neural Network, KNN Subspace, and Ensemble Bagged Trees confirm their robustness. These findings suggest that wavelet families and machine learning can improve cardiac abnormality detection and categorization.]

show abstract

Section: Discrete Wavelet Transformmentioning

confidence: 99%