Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review.

Electrocardiogram (ECG) are the physiological signals and a standard test to measure the heart's electrical activity that depicts the movement of cardiac muscles. A review study has been conducted on ECG signals analysis with the help of artificial intelligence (AI) methods over the last ten years i.e., 2012-22. Primarily, the method of ECG analysis by software systems was divided into classical signal processing (e.g. spectrograms or filters), machine learning (ML) and deep learning (DL), including recursive models, transformers and hybrid. Secondly, the data sources and benchmark datasets were depicted. Authors grouped resources by ECG acquisition methods into hospital-based portable machines and wearable devices. Authors also included new trends like advanced pre-processing, data augmentation, simulations and agent-based modeling. The study found improvement in ECG examination perfection made each year through ML, DL, hybrid models, and transformers. Convolutional neural networks and hybrid models were more targeted and proved efficient. The transformer model extended the accuracy from 90% to 98%. The Physio-Net library helps acquire ECG signals, including the popular benchmark databases such as MIT-BIH, PTB, and challenging datasets. Similarly, wearable devices have been established as a appropriate option for monitoring patient health without the time and place limitations and are also helpful for AI model calibration with so far accuracy of 82%-83% on Samsung smartwatch. In the pre-processing signals, spectrogram generation through Fourier and wavelet transformations are erected leading approaches promoting on average accuracy of 90%-95%. Likewise, data enhancement using geometrical techniques is well-considered; however, extraction and concatenation-based methods need attention. As the what-if analysis in healthcare or cardiac issues can be performed using a complex simulation, the study reviews agent-based modeling and simulation approaches for cardiovascular risk event assessment.

A Denoising and Fourier Transformation-Based Spectrograms in ECG Classification Using Convolutional Neural Network.

The non-invasive electrocardiogram (ECG) signals are useful in heart condition assessment and are found helpful in diagnosing cardiac diseases. However, traditional ways, i.e., a medical consultation required effort, knowledge, and time to interpret the ECG signals due to the large amount of data and complexity. Neural networks have been shown to be efficient recently in interpreting the biomedical signals including ECG and EEG. The novelty of the proposed work is using spectrograms instead of raw signals. Spectrograms could be easily reduced by eliminating frequencies with no ECG information. Moreover, spectrogram calculation is time-efficient through short-time Fourier transformation (STFT) which allowed to present reduced data with well-distinguishable form to convolutional neural network (CNN). The data reduction was performed through frequency filtration by taking a specific cutoff value. These steps makes architecture of the CNN model simple which showed high accuracy. The proposed approach reduced memory usage and computational power through not using complex CNN models. A large publicly available PTB-XL dataset was utilized, and two datasets were prepared, i.e., spectrograms and raw signals for binary classification. The highest accuracy of 99.06% was achieved by the proposed approach, which reflects spectrograms are better than the raw signals for ECG classification. Further, up- and down-sampling of the signals were also performed at various sampling rates and accuracies were attained.

Neutrophil to lymphocyte ratio (NLR) in COVID-19: A cheap prognostic marker in a resource constraint setting.

OBJECTIVES: To identify association of neutrophil to lymphocyte ratio with disease severity and mortality. METHODS: Total 720 Corona Virus RT-PCR positive patients were included in this cross-sectional study. Patients were admitted to KRL Hospital Islamabad from April 2020 to August 2020. Neutrophil to lymphocyte ratio (NLR) was recorded on admission and then serially. NLR cut-off was 3.0. WHO categories for disease severity (asymptomatic, mild, moderate and severe) were used. Demographic profile, symptoms and co-morbidities were recorded. RESULTS: The mean age of patients was 40 ± 12.4 years with 96% being males. Majority patients (76.5%) were asymptomatic. Amongst symptoms, fever was the most common symptom. Diabetes mellitus was most common recorded co-morbidity. The mean NLR 2.5 ± 2.78. Significant association was found between NLR and disease severity as well as mortality. Difference in mean NLR amongst disease severity categories was also significant. CONCLUSION: Results are compatible with worldwide studies and NLR is a cheap and easily available marker of disease severity and mortality.

Applications and impact of computer technologies in management of multimorbidity.

OBJECTIVE: To highlight clinical scenarios and healthcare practitioners' difficulties where computer applications can help in multimorbidity management. METHODS: The cross-sectional study was conducted from December 2017 to January 2019 in the twin cities of Rawalpindi and Islamabad, Pakistan, and comprised local physicians/practitioners. Data was collected using a self-generated questionnaire which was distributed among the subjects. It identified four problems as most commonly faced: treatment/dose management, time management, forgetting to ask necessary questions about disease, and 'others', such as bad handwriting errors and ethical issues. Data was analysed using SPSS 17. RESULTS: Of the 53 subjects, 33(62%) marked problems related to treatment management, 35(66%) marked problems related to shortage of time, 34(64%) marked those related to difficulty in asking relevant questions about disease, 15(28%) marked the 'other' option. CONCLUSIONS: Computer technologies are significantly helpful in managing the problems of treating multimorbidity by adopting standard database.

A Comparative Analysis of Data Augmentation Approaches for Magnetic Resonance Imaging (MRI) Scan Images of Brain Tumor.

INTRODUCTION: Machine Learning (ML) is a rapidly growing subfield of Artificial Intelligence (AI). It is used for different purposes in our daily life such as face recognition, speech recognition, text translation in different languages, weather prediction, and business prediction. In parallel, ML also plays an important role in the medical domain such as in medical imaging. ML has various algorithms that need to be trained with large volumes of data to produce a well-trained model for prediction. AIM: The aim of this study is to highlight the most suitable Data Augmentation (DA) technique(s) for medical imaging based on their results. METHODS: DA refers to different approaches that are used to increase the size of datasets. In this study, eight DA approaches were used on publicly available low-grade glioma tumor datasets obtained from the Tumor Cancer Imaging Archive (TCIA) repository. The dataset included 1961 MRI brain scan images of low-grade glioma patients. You Only Look Once (YOLO) version 3 model was trained on the original dataset and the augmented datasets separately. A neural network training/testing ecosystem named as supervisely with Tesla K80 GPU was used for YOLO v3 model training on all datasets. RESULTS: The results showed that the DA techniques rotate at 180o and rotate at 90o performed the best as data enhancement techniques for medical imaging. CONCLUSION: Rotation techniques are found significant to enhance the low volume of medical imaging datasets.