Artificial intelligence is serving as an impetus in digital health, clinical support, and health informatics for an informed patient’s outcome. Previous studies only consider classification accuracies of cardiotocographic (CTG) datasets and disregard computational time, which is a relevant parameter in a clinical environment. This paper proposes a modified deep neural algorithm to classify untapped pathological and suspicious CTG recordings with the desired time complexity. In our newly developed classification algorithm, AlexNet architecture is merged with support vector machines (SVMs) at the fully connected layers to reduce time complexity. We used an open-source UCI (Machine Learning Repository) dataset of cardiotocographic (CTG) recordings. We divided 2126 CTG recordings into 3 classes (Normal, Pathological, and Suspected), including 23 attributes that were dynamically programmed and fed to our algorithm. We employed a deep transfer learning (TL) mechanism to transfer prelearned features to our model. To reduce time complexity, we implemented a strategy wherein layers in the convolutional base were partially trained to leave others in the frozen states. We used an ADAM optimizer for the optimization of hyperparameters. The presented algorithm also outperforms the leading architectures (RCNNs, ResNet, DenseNet, and GoogleNet) with respect to real-time accuracies, sensitivities, and specificities of 99.72%, 96.67%, and 99.6%, respectively, making it a viable candidate for clinical settings after real-time validation.
Birth asphyxia a potential cause of death is also associated with acute, and chronic morbidities. The traditional and immediate approach to monitor birth asphyxia (i.e., arterial blood gas analysis) is highly invasive and intermittent. Additionally, alternative non-invasive approaches such as pulse oximeter can be problematic, due to the possibility of false and erroneous measurements. Therefore, further research is needed to explore alternative non-invasive and accurate monitoring methods for asphyxiated neonates. This study aims to investigate prominent ECG features based on pH estimation that could potentially be used to explore non-invasive, accurate, and continuous monitoring of asphyxiated neonates. The dataset contains 274 segments of ECG and pH values recorded simultaneously. After pre-processing of data, principal component analysis and Pan-Tompkins algorithm are used for each segment, to determine the most significant ECG cycle, and to compute the ECG features. Descriptive statistics are performed to describe the main properties of the dataset. The Kruskal-Wallis nonparametric test is then used to analyse differences between the two groups. Finally, Dunn–Šidák post-hoc test is utilised for individual comparison among mean ranks of all groups. This study showed that ECG features (mainly QT, QTc, and Tslope/ T) based on pH estimation differed significantly in asphyxiated neonates.
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