计算机科学
卷积神经网络
人工智能
模式识别(心理学)
相似性(几何)
分割
新颖性
信号(编程语言)
深度学习
简单(哲学)
数据挖掘
学习迁移
人工神经网络
机器学习
图像(数学)
哲学
神学
认识论
程序设计语言
作者
Muhammad Farhan Safdar,Piotr Pałka,Robert Nowak,Ahmed Al Faresi
标识
DOI:10.1016/j.bspc.2023.105114
摘要
The use of deep learning models in the classification of medical diseases has evolved drastically in recent years. One such prominent application was in the classification of ECG data with the goal of achieving high accuracy and computational efficiency. However, several challenges remain to be resolved to achieve this goal, such as finding a generalized model, data imbalance, and preserving patient privacy. This research aims to overcome the first two challenges. The novelty of the proposed work is to introduce unique data augmentation for time series ECG signal data based on simple segmentation and then re-arrangement of them. Simple in nature makes it more convenient to use minimal memory for the processing and allows to obtain well-distinguished synthetic signals. Additionally, a less computationally expensive four layers convolutional neural network (CNN) model has also been proposed. The original ECG signal wrapped in a Numpy array was divided into segments of identical length. During segment re-organization, a few section values are investigated, crucial for joining the segments back into a proper signal structure. The proposed algorithm has been evaluated in two ways, including similarity & features measure and classification accuracy through customized CNN & transfer learning model with five-fold cross-validation. The study results revealed that the proposed augmentation algorithm achieved a validation accuracy of 89.87% with 88.99% recall and 0.291 loss by the four layers CNN model. The presented work can be implemented in two ways, i.e., ECG signals dataset enhancement and better utilization of computational resources through the simple CNN model architecture.
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