Ventricular fibrillation detection from ECG surface electrodes using different filtering techniques, window length and artificial neural networks

Cortina, Alejandro; Mjahad, Azeddine; Rosado, A.; Bataller, María Querol; Francés-Víllora, Jose V.; Dutta, Malay Kishore; Vyas, Garima

doi:10.1109/icetcct.2017.8280303

Cited by 2 publications

(4 citation statements)

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“…The workflow starts with signal filtering and QRS and R-peak detection algorithms methods, followed by feature extraction and simple classification with a classifier or classification fusion methods with multiple classifiers. A broad set of handcrafted features for ECG analysis, such as temporal relationships between waves, morphological descriptors, state-space features, linear transform, spectral representation, wavelet analysis, etc., have been described as well [11,12,[18][19][20][21][22][23][24]. Among the temporal features, a wide assortment of QRS morphological descriptors was mentioned, including QRS width, positive and negative peak amplitudes, QRS slopes, and cardiogram vector descriptors.…”

Section: Literature Reviewmentioning

confidence: 99%

Human-Assisted vs. Deep Learning Feature Extraction: An Evaluation of ECG Features Extraction Methods for Arrhythmia Classification Using Machine Learning

et al. 2022

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The success of arrhythmia classification tasks with Machine Learning (ML) algorithms is based on the handcrafting extraction of features from Electrocardiography (ECG) signals. However, feature extraction is a time-consuming trial-and-error approach. Deep Neural Network (DNN) algorithms bypass the process of handcrafting feature extraction since the algorithm extracts the features automatically in their hidden layers. However, it is important to have access to a balanced dataset for algorithm training. In this exploratory research study, we will compare the evaluation metrics among Convolutional Neural Networks (1D-CNN) and Support Vector Machines (SVM) using a dataset based on the merged public ECG signals database TNMG and CINC17 databases. Results: Both algorithms showed good performance using the new, merged ECG database. For evaluation metrics, the 1D-CNN algorithm has a precision of 93.04%, an accuracy of 93.07%, a recall of 93.20%, and an F1-score of 93.05%. The SVM classifier (λ = 10, C = 10e9) achieved the best classification metrics with two combined, handcrafted feature extraction methods: Wavelet transforms and R-peak Interval features, which achieved an overall precision of 89.04%, accuracy of 92.00%, recall of 94.20%, and F1-score of 91.54%. As an unique input feature and SVM (λ=10,C=100), wavelet transforms achieved precision, accuracy, recall, and F1-score metrics of 86.15%, 85.33%, 81.16%, and 83.58%. Conclusion: Researchers face a challenge in finding a broad dataset to evaluate ML models. One way to solve this problem, especially for deep learning models, is to combine several public datasets to increase the amount of data. The SVM and 1D-CNN algorithms showed positive results with the merge of databases, showing similar F1-score, precision, and recall during arrhythmia classification. Despite the favorable results for both of them, it should be considered that in the SVM, feature selection is a time-consuming trial-and-error process; meanwhile, CNN algorithms can reduce the workload significantly. The disadvantage of CNN algorithms is that it has a higher computational processing cost; moreover, in the absence of access to powerful computational processing, the SVM can be a reliable solution.

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Section: Literature Reviewmentioning

confidence: 99%

Human-Assisted vs. Deep Learning Feature Extraction: An Evaluation of ECG Features Extraction Methods for Arrhythmia Classification Using Machine Learning

et al. 2022

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“…• MIT-BIH & AHA. The MIT-BIH Malignant Ventricular Arrhythmia [41] and AHA (2000 series) [42] databases were processed as in [43], [44] to obtain 15 features. One output class identify two different types of rhythms (normal and abnormal).…”

Section: Algorithm 3 Output Computation Control Sequencementioning

confidence: 99%

“…The aim of this application is to discern between the normal function of the heart and several pathologies as Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF), amongst others. To feed the classifier, the input data (ECG signal) were preprocessed in several stages [43], [44]. The first step consisted of a baseline wandering removal (denoising), Fig.…”

Section: Real Case Applicationmentioning

confidence: 99%

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A Novel Systolic Parallel Hardware Architecture for the FPGA Acceleration of Feedforward Neural Networks

et al. 2019

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New chips for machine learning applications appear, they are tuned for a specific topology, being efficient by using highly parallel designs at the cost of high power or large complex devices. However, the computational demands of deep neural networks require flexible and efficient hardware architectures able to fit different applications, neural network types, number of inputs, outputs, layers, and units in each layer, making the migration from software to hardware easy. This paper describes novel hardware implementing any feedforward neural network (FFNN): multilayer perceptron, autoencoder, and logistic regression. The architecture admits an arbitrary input and output number, units in layers, and a number of layers. The hardware combines matrix algebra concepts with serial-parallel computation. It is based on a systolic ring of neural processing elements (NPE), only requiring as many NPEs as neuron units in the largest layer, no matter the number of layers. The use of resources grows linearly with the number of NPEs. This versatile architecture serves as an accelerator in real-time applications and its size does not affect the system clock frequency. Unlike most approaches, a single activation function block (AFB) for the whole FFNN is required. Performance, resource usage, and accuracy for several network topologies and activation functions are evaluated. The architecture reaches 550 MHz clock speed in a Virtex7 FPGA. The proposed implementation uses 18-bit fixed point achieving similar classification performance to a floating point approach. A reduced weight bit size does not affect the accuracy, allowing more weights in the same memory. Different FFNN for Iris and MNIST datasets were evaluated and, for a real-time application of abnormal cardiac detection, a ×256 acceleration was achieved. The proposed architecture can perform up to 1980 Giga operations per second (GOPS), implementing the multilayer FFNN of up to 3600 neurons per layer in a single chip. The architecture can be extended to bigger capacity devices or multi-chip by the simple NPE ring extension.INDEX TERMS Feedforward neural networks -FFNN, systolic hardware architecture, FPGA implementation, neural network acceleration, deep neural networks.

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Ventricular fibrillation detection from ECG surface electrodes using different filtering techniques, window length and artificial neural networks

Cited by 2 publications

References 15 publications

Human-Assisted vs. Deep Learning Feature Extraction: An Evaluation of ECG Features Extraction Methods for Arrhythmia Classification Using Machine Learning

Human-Assisted vs. Deep Learning Feature Extraction: An Evaluation of ECG Features Extraction Methods for Arrhythmia Classification Using Machine Learning

A Novel Systolic Parallel Hardware Architecture for the FPGA Acceleration of Feedforward Neural Networks

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