GABC based neuro-fuzzy classifier with hybrid features for ECG Beat classification

Muthuvel, K.; Anto, S.; Alexander, T. Jerry

doi:10.1007/s11042-019-08132-9

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…The noise was separated from the speech signal and converted into time-frequency units [54,55]. A neural network was used for channel frequency estimation in [56].…”

Section: Literature Reviewmentioning

confidence: 99%

Marathi Speech Intelligibility Enhancement Using I-AMS Based Neuro-Fuzzy Classifier Approach for Hearing Aid Users

et al. 2022

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Globally, 1.6 billion individuals suffered from hearing disability in 2019. According to the World Health Organization, by 2050, the number of people with hearing impairments will rise to 2.5 billion. Speech perception in noisy surroundings is a challenge for hearing aid users. This study aimed to design a novel methodology to improve the speech recognition ability of hearing aid users from various backgrounds. To improve speech enhancement, we propose a discrete cosine transform (DCT)-based improved amplitude-magnitude spectrogram (I-AMS) algorithm with a fuzzy classifier. First, the I-AMS approach disintegrates speech signals containing noise into time-frequency units and eliminates the noise present in the signal. Next, the time frequency units (t-f units), modulation frequency (fm), and centre frequency (fc) are extracted from the denoised signal. A neuro-fuzzy classifier was used to classify the background speech environment into three different classes. The proposed I-AMS algorithm was tested, achieved improvements in terms of sensitivity (+1.02%) and accuracy (+11.80%). Speech denoising revealed a 1.27% improvement in speech recognition performance.

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“…The noise was separated from the speech signal and converted into time-frequency units [54,55]. A neural network was used for channel frequency estimation in [56].…”

Section: Literature Reviewmentioning

confidence: 99%

Marathi Speech Intelligibility Enhancement Using I-AMS Based Neuro-Fuzzy Classifier Approach for Hearing Aid Users

et al. 2022

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“…The layer-by-layer ECG signal had segmented to extract the beats for further analysis. The ECG-based disease detection using hybrid feature extraction and classifiers had proposed in [32]. They extracted features from input signals such as tri-spectrum features, morphological features, and haar wavelet features.…”

Section: Heart Beats Segmentation and Features Extractionmentioning

confidence: 99%

“…• The adaptive and accurate ECG wave segmentation is still a research problem considering the robustness and accuracy of beats extraction [26][27][28][29][30][31][32][33]. The beats segmentation in the recent automatic deep learning-based methods were poorly designed [34][35][36][37][38][39][40][41][42][43][44][45][46][47].…”

Section: Research Gap Analysismentioning

confidence: 99%

Automatic Diagnosis of Cardiovascular Diseases from ECG Signals using Convolutional Neural Network and Soft Computing Methods

Goyal

2022

Preprint

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Since the last decade, the Electrocardiogram (ECG) tool has received medical experts and researchers' attention for accurate and fast diagnosis of cardiovascular diseases (CVD). The automatic detection and classification of 1D ECG-based heart disease have become more realistic and efficient solutions using the deep learning technique such as Convolutional Neural Network (CNN). As CNN is designed mainly for 2D or 3D applications therefore, designing CNN to process 1D ECG becomes challenging. We proposed a novel framework for automatic CVD detection and classification from the raw ECG signals using 1D-CNN deep learning technique. The framework consists of pre-processing, automatic feature extraction, feature optimization, and classification. In pre-processing, raw ECG signals are filtered to remove the baseline drift and power line interference using median and notch filters respectively. The pre-processed ECG signals are then used to extract the QRS and ST waves using the dynamic thresholding in the wavelet transfer domain. The fusion of QRS and ST waves have fed to automatic 1D-CNN that consists of layers i.e,1D convolutional layer, ReLU layer, and max-pooling layers. The 1D-CNN is proposed in this paper to extract features automatically with little computing complexity. The high-dimensional raw CNN features are optimized by applying a feature selection and scaling approach. For classification, different soft computing techniques such as Long- Short Term Memory (LSTM), Support Vector Machine (SVM), Naïve Bays (NB), Artificial Neural Network (ANN), and k-nearest neighbor (KNN) are applied. The experimental performances of the proposed model have been investigated on a publicly available research dataset and outperformed recent CNN-based techniques.

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The Self-discipline Learning Model with Imported Backpropagation Algorithm

Gu¹,

Sun²,

Sun³

2022

Lecture Notes in Networks and Systems

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GABC based neuro-fuzzy classifier with hybrid features for ECG Beat classification

Cited by 7 publications

References 25 publications

Marathi Speech Intelligibility Enhancement Using I-AMS Based Neuro-Fuzzy Classifier Approach for Hearing Aid Users

Marathi Speech Intelligibility Enhancement Using I-AMS Based Neuro-Fuzzy Classifier Approach for Hearing Aid Users

Automatic Diagnosis of Cardiovascular Diseases from ECG Signals using Convolutional Neural Network and Soft Computing Methods

The Self-discipline Learning Model with Imported Backpropagation Algorithm

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