ECG arrhythmia detection in an inter-patient setting using Fourier decomposition and machine learning.

ECG beat classification or arrhythmia detection through artificial intelligence (AI) is an active topic of research. It is vital to recognize and detect the type of arrhythmia for monitoring cardiac abnormalities. The AI-based ECG beat classification algorithms proposed in the literature suffer from two main drawbacks. Firstly, some of the works have not considered any unseen test data to validate the performance of their algorithms. Secondly, the accuracy of detecting superventricular ectopic beats (SVEB) needs to be improved. In this work, we address these issues by considering an inter-patient paradigm where the test dataset is collected from a different set of subjects than the training data. Also, the proposed methodology detects SVEB with an F1 score of 89.35%, which is better than existing algorithms. We have used the Fourier decomposition method (FDM) for multi-scale analysis of ECG signals and extracted time-domain and statistical features from the narrow-band signal components obtained using FDM. Feature selection techniques, including the Kruskal-Wallis test and minimum redundancy maximum relevance (mRMR) have been used to select only the relevant features and rank these features to remove any redundancy. Since the dataset used is highly imbalanced, Mathew's correlation coefficient (MCC) has also been used to analyze the performance of the proposed method. Support vector machine classifier with linear kernel achieves an overall 98.03% accuracy and 91.84% MCC for the MIT-BIH arrhythmia dataset.

A multi-modal assessment of sleep stages using adaptive Fourier decomposition and machine learning.

Healthy sleep is essential for the rejuvenation of the body and helps in maintaining good health. Many people suffer from sleep disorders that are characterized by abnormal sleep patterns. Automated assessment of such disorders using biomedical signals has been an active subject of research. Electroencephalogram (EEG) is a popular diagnostic used in this regard. We consider a widely-used publicly available database and process the signals using the Fourier decomposition method (FDM) to obtain narrowband signal components. Statistical features extracted from these components are passed on to machine learning classifiers to identify different stages of sleep. A novel feature measuring the non-stationarity of the signal is also used to capture salient information. It is shown that classification results can be improved by using multi-channel EEG instead of single-channel EEG data. Simultaneous utilization of multiple modalities, such as Electromyogram (EMG), Electrooculogram (EOG) along with EEG data leads to further enhancement in the obtained results. The proposed method can be efficiently implemented in real-time using fast Fourier transform (FFT), and it provides better classification results than the other algorithms existing in the literature. It can assist in the development of low-cost sensor-based setups for continuous patient monitoring and feedback.

A comparative study for predictive monitoring of COVID-19 pandemic.

COVID-19 pandemic caused by novel coronavirus (SARS-CoV-2) crippled the world economy and engendered irreparable damages to the lives and health of millions. To control the spread of the disease, it is important to make appropriate policy decisions at the right time. This can be facilitated by a robust mathematical model that can forecast the prevalence and incidence of COVID-19 with greater accuracy. This study presents an optimized ARIMA model to forecast COVID-19 cases. The proposed method first obtains a trend of the COVID-19 data using a low-pass Gaussian filter and then predicts/forecasts data using the ARIMA model. We benchmarked the optimized ARIMA model for 7-days and 14-days forecasting against five forecasting strategies used recently on the COVID-19 data. These include the auto-regressive integrated moving average (ARIMA) model, susceptible-infected-removed (SIR) model, composite Gaussian growth model, composite Logistic growth model, and dictionary learning-based model. We have considered the daily infected cases, cumulative death cases, and cumulative recovered cases of the COVID-19 data of the ten most affected countries in the world, including India, USA, UK, Russia, Brazil, Germany, France, Italy, Turkey, and Colombia. The proposed algorithm outperforms the existing models on the data of most of the countries considered in this study.

COVID-19 image classification using deep learning: Advances, challenges and opportunities.

Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. Among AI methodologies, Deep Learning (DL) algorithms, particularly Convolutional Neural Networks (CNN), have gained significant popularity for the classification of COVID-19. This paper summarizes and reviews a number of significant research publications on the DL-based classification of COVID-19 through CXR and CT images. We also present an outline of the current state-of-the-art advances and a critical discussion of open challenges. We conclude our study by enumerating some future directions of research in COVID-19 imaging classification.