One of the major causes of death in the world is cardiac arrhythmias. In the field of healthcare, physicians use the patient’s electrocardiogram (ECG) records to detect arrhythmias, which indicate the electrical activity of the patient’s heart. The problem is that the symptoms do not always appear and the physician may be mistaken in the diagnosis. Therefore, patients need continuous monitoring through real-time ECG analysis to detect arrhythmias in a timely manner and prevent an eventual incident that threatens the patient’s life. In this research, we used the Structured Streaming module built top on the open-source Apache Spark platform for the first time to implement a machine learning pipeline for real-time cardiac arrhythmias detection and evaluate the impact of using this new module on classification performance metrics and the rate of delay in arrhythmia detection. The ECG data collected from the MIT/BIH database for the detection of three class labels: normal beats, RBBB, and atrial fibrillation arrhythmias. We also developed three decision trees, random forest, and logistic regression multiclass classifiers for data classification where the random forest classifier showed better performance in classification than the other two classifiers. The results show previous results in performance metrics of the classification model and a significant decrease in pipeline runtime by using more class labels compared to previous studies.

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Extensive research has been performed on continuous and noninvasive cuff-less blood pressure (BP) measurement using artificial intelligence algorithms. This approach involves extracting certain features from physiological signals, such as ECG, PPG, ICG, and BCG, as independent variables and extracting features from arterial blood pressure (ABP) signals as dependent variables and then using machine-learning algorithms to develop a blood pressure estimation model based on these data. The greatest challenge of this field is the insufficient accuracy of estimation models. This paper proposes a novel blood pressure estimation method with a clustering step for accuracy improvement. The proposed method involves extracting pulse transit time (PTT), PPG intensity ratio (PIR), and heart rate (HR) features from electrocardiogram (ECG) and photoplethysmogram (PPG) signals as the inputs of clustering and regression, extracting systolic blood pressure (SBP) and diastolic blood pressure (DBP) features from ABP signals as dependent variables, and finally developing regression models by applying gradient boosting regression (GBR), random forest regression (RFR), and multilayer perceptron regression (MLP) on each cluster. The method was implemented using the MIMIC-II data set with the silhouette criterion used to determine the optimal number of clusters. The results showed that because of the inconsistency, high dispersion, and multitrend behavior of the extracted features vectors, the accuracy can be significantly improved by running a clustering algorithm and then developing a regression model on each cluster and finally weighted averaging of the results based on the error of each cluster. When implemented with 5 clusters and GBR, this approach yielded an MAE of 2.56 for SBP estimates and 2.23 for DBP estimates, which were significantly better than the best results without clustering (DBP: 6.27, SBP: 6.36).

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The world today faces a new challenge that is unprecedented in the last 100 years. The emergence of a new coronavirus has led to a human catastrophe. The new coronavirus is the cause of the Covid-19 disease, which kills many people in the world every day. Scientists in various sciences have been looking for solutions to this problem so far. In addition to general vaccination, maintaining social distance and hygienic principles are the most well-known strategies to prevent Covid-19 infection. In this research, we have tried to examine the symptoms of Covid-19 cases through different supervised machine learning methods. We solved the class imbalance problem using the SMOTE up-sampling method and then developed some classification models to predict the recovery or death of patients. Besides, we implemented a rule-based technique to identify important symptoms that affect patients' fate and calculate the range of values in these features that lead to recovery or death of patients. Our results showed that the random forest model with 94% accuracy, 95.2% sensitivity, 92.7% specification, 93.2% precision, and 94.2% F-score outperforms state-of-the-art classification models. Finally, we identified the ten most significant rules in the data set. The rules state that different combinations of 6 features in certain ranges of their values lead to patients' recovery with 90% confidence. In conclusion, the classification results in this study show better performance than recent researches. Besides, help physicians consider other important factors in improving health services to different groups of Covid-19 patients.