An Efficient Algorithm for Cardiac Arrhythmia Classification Using Ensemble of Depthwise Separable Convolutional Neural Networks

Abstract

Many algorithms have been developed for automated electrocardiogram (ECG) classification. Due to the non-stationary nature of the ECG signal, it is rather challenging to use traditional handcraft methods, such as time-based analysis of feature extraction and classification, to pave the way for machine learning implementation. This paper proposed a novel method, i.e., the ensemble of depthwise separable convolutional (DSC) neural networks for the classification of cardiac arrhythmia ECG beats. Using our proposed method, the four stages of ECG classification, i.e., QRS detection, preprocessing, feature extraction, and classification, were reduced to two steps only, i.e., QRS detection and classification. No preprocessing method was required while feature extraction was combined with classification. Moreover, to reduce the computational cost while maintaining its accuracy, several techniques were implemented, including All Convolutional Network (ACN), Batch Normalization (BN), and ensemble convolutional neural networks. The performance of the proposed ensemble CNNs were evaluated using the MIT-BIH arrythmia database. In the training phase, around 22% of the 110,057 beats data extracted from 48 records were utilized. Using only these 22% labeled training data, our proposed algorithm was able to classify the remaining 78% of the database into 16 classes. Furthermore, the sensitivity ( S n ), specificity ( S p ), and positive predictivity ( P p ), and accuracy ( A c c ) are 99.03%, 99.94%, 99.03%, and 99.88%, respectively. The proposed algorithm required around 180 μs, which is suitable for real time application. These results showed that our proposed method outperformed other state of the art methods.

Development of video-based emotion recognition using deep learning with Google Colab

“…The scope for future improvements is very appealing in this field. Different multimodel deep learning techniques can be used along with different architectures to improve the performance parameters [20][21][22][23][24][25][26][27]. Apart from recognizing the emotions only, there can be further addition of intensity scale.…”

DeepArrNet: An Efficient Deep CNN Architecture for Automatic Arrhythmia Detection and Classification From Denoised ECG Beats

“…Li et al [6] and Matris et al [9] used traditional handcrafted feature-based approaches using discrete wavelet transform with traditional classifiers commonly used for shallow networks, which offers unsatisfactory performance as expected. Ihsanto et al [19] classified over a large number of arrhythmia classes. However, due to lack of augmentation methods with extremely smaller number of beats in many of the rare classes, average performance became lower for overfitting towards larger classes.…”

“…Similar to the most other established studies in arrhythmia classification [6], [9], [15], [16], five arrhythmia classes are considered in this study and very satisfactory performance is achieved. However, the proposed scheme can be extended considering more number of arrhythmia classes as considered in [19], [22]. This may increase the class imbalance problem for much smaller number of beats in some of the rare classes that will put more emphasis on the augmentation methods.…”

“…A number of deep learning-based approaches have also been adopted recently for arrhythmia classification [14]- [26]. In [14]- [19], 1D convolutional neural network (CNN) and in [23]- [25], recurrent neural network (RNN) and LSTM network are employed while in [20]- [22], 2D CNN is used by converting 1D ECG beats into 2D images. Most of the deep learning-based approaches are facing some common issues: (1) raw ECG data collected from patients are being directly fed to the deep neural network making the classification process complicated due to presence of various low and high-frequency noises, (2) for dealing with 1D ECG signal, data augmentation is not necessarily used and even if it is used, natural variational pattern of ECG isn't properly captured or preserved, and (3) most of the approaches use very deep CNNs with large number of parameters that not only increase the computational complexity but also lead to overfitting the model to training data.…”

Digital Holographic Interferometry without Phase Unwrapping by a Convolutional Neural Network for Concentration Measurements in Liquid Samples

“…CNNs are mathematical algorithms that mimic the functioning of the mammalian visual cortex using advanced operation blocks, and several layers of neurons, due to the ability to approximate any continuous function accurately [19]. CNNs have been applied to multiple tasks, including image classification, object detection, object tracking, and scene labeling [20][21][22][23][24][25][26].…”