Towards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method

NSICA: Multi-objective imperialist competitive algorithm for feature selection in arrhythmia diagnosis

This study proposes a multi-objective, non-dominated, imperialist competitive algorithm (NSICA) to solve optimal feature selection problems. The NSICA is a multi-objective and discrete version of the original Imperialist Competitive Algorithm (ICA) that utilizes the competition between colonies and imperialists to solve optimization problems. This study focused on solving challenges such as discretization and elitism by modifying the original operations and using a non-dominated sorting approach. The proposed algorithm is independent of the application, and with customization, it could be employed to solve any feature selection problem. We evaluated the algorithm's efficiency using it as a feature selection system for diagnosing cardiac arrhythmias. The Pareto optimal selected features from NSICA were utilized to classify arrhythmias in binary and multi-class forms based on three essential objectives: accuracy, number of features, and false negativity. We applied NSICA to an ECG-based arrhythmia classification dataset from the UCI machine learning repository. The evaluation results indicate the efficiency of the proposed algorithm compared to other state-of-the-art algorithms.

Covid19-Mexican-Patients' Dataset (Covid19MPD) Classification and Prediction Using Feature Importance

Coronavirus disease, Covid19, pandemic has a great effect on human heath worldwide since it was first detected in late 2019. A clear understanding of the structure of the available Covid19 datasets might give the healthcare provider a better understanding of identifying some of the cases at an early stage. In this article, we will be looking into a Covid19 Mexican Patients' Dataset (Covid109MPD), and we will apply number of machine learning algorithms on the dataset to select the best possible classification algorithm for the death and survived cases in Mexico, then we will study the performance of the enhancement of the specified classifiers in term of their features selection in order to be able to predict sever, and or death, cases from the available dataset. Results show that J48 classifier gives the best classification accuracy with 94.41% and RMSE = 0.2028 and ROC = 0.919, compared to other classifiers, and when using feature selection method, J48 classifier can predict a surviving Covid19MPD case within 94.88% accuracy, and by using only 10 out of the total 19 features.

Classification of arrhythmia’s ECG signal using cascade transparent classifier

Arrhythmia is an abnormality often encountered in patients with cardiac problems. The presence of arrhythmia can be detected by an electrocardiogram (ECG) test. Automatic observation based on machine learning has been developed for long time. Unfortunately, only few of them have capability of explaining the knowledge inside themselves. Thus, transparency is important to improve human understanding of knowledge. To achieve this goal, a method based on cascaded transparent classifier is proposed. Firstly, ECG signals were separated and every single signal was extracted using feature extraction method. Several of extracted feature’s attributes were selected, and the final step was classifying data using cascade classifier which consists of decision tree and the rule based classifier. Classification performance was evaluated with publicly available dataset, the MIT-BIH Physionet Dataset. The methods were tested using 10-fold cross validation. The average of both accuracy and number of rules generated was considered. The best result using rule-based classifier achieves the accuracy and the number of rules 92.40% and 40, respectively. And the best result using cascade classifier achieves the accuracy and the number of rules 92.84% and 80, respectively. As a conclusion, transparent classifier shows a competitive performance with reasonable accuracy compared with previous research and promising in addressing the need for interpretability model.

PVC arrhythmia classification based on fractional order system modeling

It is well known that many physiological phenomena are modeled accurately and effectively using fractional operators and systems. This type of modeling is due mainly to the dynamical link between fractional-order systems and the fractal structures of the physiological systems. The automatic characterization of the premature ventricular contraction (PVC) is very important for early diagnosis of patients with different life-threatening cardiac diseases. In this paper, a classification scheme of normal and PVC beats of the electrocardiogram (ECG) signal is proposed. The clustering features used for normal and PVC beats discrimination are the parameters of the commensurate order linear fractional model of the frequency content of the QRS complex of the ECG signal. A series of tests and comparisons have been performed to evaluate and validate the efficiency of the proposed PVC classification algorithm using the MIT-BIH arrhythmia database. The proposed PVC classification method has achieved an overall accuracy of 94.745%, a specificity of 95.178% and a sensitivity of 90.021% using all the 48 records of the database.

Comparison of various approaches to combine logistic regression with genetic algorithms in survival prediction of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common liver cancer in adults. Many different factors make it difficult to diagnose in humans.. In this paper, a novel diagnostics approach based on machine learning techniques is presented. Logistic regression is one of the most classic machine learning models used to solve the problem of binary classification. In typical implementations, logistic regression coefficients are optimized using iterative methods. Additionally, parameters such as solver, C - a regularization parameter or the number of iterations of the algorithm operation should be selected. In our research, we propose a combination of logistic regression with genetic algorithms. We present three experiments showing the fusion of those methods. In the first experiment, we genetically select the logistic regression parameters, while the second experiment extends this approach by including a genetic selection of features. The third experiment presents a novel approach to train the logistic regression model - the genetic selection of coefficients (weights). Our models are tested for the survival prediction of hepatocellular carcinoma based on patient data collected at Coimbra's Hospital and Universitary Center (CHUC), Portugal. The model we proposed achieved a classification accuracy of 94.55% and an f1-score of 93.56%. Our algorithm shows that machine learning techniques optimized by the proposed concept can bring a new and accurate approach in HCC diagnosis with high accuracy.

Real-Time Heart Arrhythmia Detection Using Apache Spark Structured Streaming

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.

A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network

Cardiovascular diseases (CVDs) are the leading cause of death today. The current identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a medical monitoring technology recording cardiac activity. Unfortunately, looking for experts to analyze a large amount of ECG data consumes too many medical resources. Therefore, the method of identifying ECG characteristics based on machine learning has gradually become prevalent. However, there are some drawbacks to these typical methods, requiring manual feature recognition, complex models, and long training time. This paper proposes a robust and efficient 12-layer deep one-dimensional convolutional neural network on classifying the five micro-classes of heartbeat types in the MIT- BIH Arrhythmia database. The five types of heartbeat features are classified, and wavelet self-adaptive threshold denoising method is used in the experiments. Compared with BP neural network, random forest, and other CNN networks, the results show that the model proposed in this paper has better performance in accuracy, sensitivity, robustness, and anti-noise capability. Its accurate classification effectively saves medical resources, which has a positive effect on clinical practice.

Classifying a Lending Portfolio of Loans with Dynamic Updates via a Machine Learning Technique

Bankruptcy prediction has been broadly investigated using financial ratios methodologies. One involved factor is the quality of the portfolio of loans which is given. Hence, having a model to classify/predict position of each loan candidate based on several features is important. In this work, an application of machine learning approach in mathematical finance and banking is discussed. It is shown how we can classify some lending portfolios of banks under several features such as rating categories and various maturities. Dynamic updates of the portfolio are also given along with the top probabilities showing how the financial data of this type can be classified. The discussions and results reveal that a good algorithm for doing such a classification on large economic data of such type is the k-nearest neighbors (KNN) with k=1 along with parallelization even over the support vector machine, random forest, and artificial neural network techniques to save as much as possible on computational time.

Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics

Although biometrics systems using an electrocardiogram (ECG) have been actively researched, there is a characteristic that the morphological features of the ECG signal are measured differently depending on the measurement environment. In general, post-exercise ECG is not matched with the morphological features of the pre-exercise ECG because of the temporary tachycardia. This can degrade the user recognition performance. Although normalization studies have been conducted to match the post- and pre-exercise ECG, limitations related to the distortion of the P wave, QRS complexes, and T wave, which are morphological features, often arise. In this paper, we propose a method for matching pre- and post-exercise ECG cycles based on time and frequency fusion normalization in consideration of morphological features and classifying users with high performance by an optimized system. One cycle of post-exercise ECG is expanded by linear interpolation and filtered with an optimized frequency through the fusion normalization method. The fusion normalization method aims to match one post-exercise ECG cycle to one pre-exercise ECG cycle. The experimental results show that the average similarity between the pre- and post-exercise states improves by 25.6% after normalization, for 30 ECG cycles. Additionally, the normalization algorithm improves the maximum user recognition performance from 96.4 to 98%.

Multi-information fusion neural networks for arrhythmia automatic detection

BACKGROUND AND OBJECTIVES

. The electrocardiograms (ECGs) are widely used to diagnose a variety of arrhythmias. Generally, the abnormalities of ECG signals mainly consist of ill-shaped ECG beat morphologies and irregular intervals. The ill-shaped ECG beat morphologies represent morphological information, while the irregular intervals denote the temporal information of ECG signals. But it is difficult to utilize morphological information and temporal information simultaneously when dealing with single ECG heartbeats, because RR interval is not contained in a single short heartbeat. Therefore, to handle this problems, a novel Multi-information Fusion Convolutional Bidirectional Recurrent Neural Network (MF-CBRNN) is proposed for arrhythmia automatic detection.

METHODS

. The MF-CBRNN is designed with two parallel hybrid branches that can simultaneously focus on the beat-based information in the ECG beats and the segment-based information in the adjacent segments of the beats. A single ECG beat provides the morphological information. At the same time, the adjacent segment of the ECG beat enriches the temporal information, so the two branches are designed to exploit the multiple information contained in ECGs. Furthermore, a combination of convolutional neural networks (CNNs) and a bidirectional long short memory (BLSTM) in each branch is utilized to capture the information from the two inputs. And all the features extracted from the two branches are fused for information aggregation.

RESULTS

. To evaluate the performance of the proposed model, the ECG signals from MIT-BIH databases are used for intra-patient and inter-patient paradigms. The proposed model yields an accuracy of 99.56% and an F1-score of 96.40% under the intra-patient paradigm. And it obtains an overall accuracy of 96.77% and F1-score of 77.83% under the inter-patient paradigm.

CONCLUSIONS

. Compared with other studies on arrhythmia detection, our method achieves a state-of-the-art performance. It indicates that the proposed model is a promising arrhythmia detection algorithm for computer-aided diagnostic systems.

Prediction of heart disease and classifiers' sensitivity analysis

Background

Heart disease (HD) is one of the most common diseases nowadays, and an early diagnosis of such a disease is a crucial task for many health care providers to prevent their patients for such a disease and to save lives. In this paper, a comparative analysis of different classifiers was performed for the classification of the Heart Disease dataset in order to correctly classify and or predict HD cases with minimal attributes. The set contains 76 attributes including the class attribute, for 1025 patients collected from Cleveland, Hungary, Switzerland, and Long Beach, but in this paper, only a subset of 14 attributes are used, and each attribute has a given set value. The algorithms used K- Nearest Neighbor (K-NN), Naive Bayes, Decision tree J48, JRip, SVM, Adaboost, Stochastic Gradient Decent (SGD) and Decision Table (DT) classifiers to show the performance of the selected classifications algorithms to best classify, and or predict, the HD cases.

Results

It was shown that using different classification algorithms for the classification of the HD dataset gives very promising results in term of the classification accuracy for the K-NN (K = 1), Decision tree J48 and JRip classifiers with accuracy of classification of 99.7073, 98.0488 and 97.2683% respectively. A feature extraction method was performed using Classifier Subset Evaluator on the HD dataset, and results show enhanced performance in term of the classification accuracy for K-NN (N = 1) and Decision Table classifiers to 100 and 93.8537% respectively after using the selected features by only applying a combination of up to 4 attributes instead of 13 attributes for the predication of the HD cases.

Conclusion

Different classifiers were used and compared to classify the HD dataset, and we concluded the benefit of having a reliable feature selection method for HD disease prediction with using minimal number of attributes instead of having to consider all available ones.

Feature rearrangement based deep learning system for predicting heart failure mortality

BACKGROUND AND OBJECTIVE

Heart Failure is a clinical syndrome commonly caused by any structural or functional impairment. Fast and accurate mortality prediction for Heart Failure is essential to improve the health care of patients and prevent them from death. However, due to the imbalance problem and poor feature representation in Heart Failure data, mortality prediction of Heart Failure is difficult with some simple models. To handle these problems, this study is focused on proposing a fast and accurate Heart Failure mortality prediction framework.

METHODS

This paper proposes a feature rearrangement based deep learning system for heart failure mortality prediction. The proposed framework improves the performance of predicting heart failure mortality by handling imbalance problem and achieving better feature representation. This paper also proposes a method named Feature rearrangement based convolutional layer, which demonstrates that the order of the input features is essential for the convolutional network.

RESULTS

The proposed system is experimentally evaluated on real-world Heart Failure data collected from the EHR system of Shanghai Shuguang Hospital, where 10,198 in-patients records are extracted between March 2009 and April 2016. Internal comparison results illustrate that the proposed framework achieves the best performance for Heart Failure mortality prediction. Extensive experimental results compared with other machine learning methods demonstrate that the proposed method has the highest average accuracy and area under the curve while predicting the three goals of in-hospital mortality, 30-day mortality, and 1-year mortality. Finally, top 12 essential clinical features are mined with their chi-square scores, which can help to assist clinicians in the treatment and research of heart failure.

CONCLUSIONS

The proposed method successfully predict different target in three observation windows. Feature rearrangement based convolutional layer and Focal loss are employed into the proposed framework, which helps promote the prediction accuracy of Heart Failure death. The proposed method is fast and accurate for predicting heart failure mortality, especially for imbalance situation. This paper also provide a reasonable pipeline to model EHRs data and handle imbalance problem in medical data.

Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices

Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the "AF Classification from a Short Single Lead ECG Recording" database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1-93.0%), 90.2% (CI: 86.2-94.3%) and 90.8% (CI: 88.1-93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices.

EEG-Brain Activity Monitoring and Predictive Analysis of Signals Using Artificial Neural Networks

Predictive observation and real-time analysis of the values of biomedical signals and automatic detection of epileptic seizures before onset are beneficial for the development of warning systems for patients because the patient, once informed that an epilepsy seizure is about to start, can take safety measures in useful time. In this article, Daubechies discrete wavelet transform (DWT) was used, coupled with analysis of the correlations between biomedical signals that measure the electrical activity in the brain by electroencephalogram (EEG), electrical currents generated in muscles by electromyogram (EMG), and heart rate monitoring by photoplethysmography (PPG). In addition, we used artificial neural networks (ANN) for automatic detection of epileptic seizures before onset. We analyzed 30 EEG recordings 10 min before a seizure and during the seizure for 30 patients with epilepsy. In this work, we investigated the ANN dimensions of 10, 50, 100, and 150 neurons, and we found that using an ANN with 150 neurons generates an excellent performance in comparison to a 10-neuron-based ANN. However, this analyzes requests in an increased amount of time in comparison with an ANN with a lower neuron number. For real-time monitoring, the neurons number should be correlated with the response time and power consumption used in wearable devices.

ECG Identification For Personal Authentication Using LSTM-Based Deep Recurrent Neural Networks

Securing personal authentication is an important study in the field of security. Particularly, fingerprinting and face recognition have been used for personal authentication. However, these systems suffer from certain issues, such as fingerprinting forgery, or environmental obstacles. To address forgery or spoofing identification problems, various approaches have been considered, including electrocardiogram (ECG). For ECG identification, linear discriminant analysis (LDA), support vector machine (SVM), principal component analysis (PCA), deep recurrent neural network (DRNN), and recurrent neural network (RNN) have been conventionally used. Certain studies have shown that the RNN model yields the best performance in ECG identification as compared with the other models. However, these methods require a lengthy input signal for high accuracy. Thus, these methods may not be applied to a real-time system. In this study, we propose using bidirectional long short-term memory (LSTM)-based deep recurrent neural networks (DRNN) through late-fusion to develop a real-time system for ECG-based biometrics identification and classification. We suggest a preprocessing procedure for the quick identification and noise reduction, such as a derivative filter, moving average filter, and normalization. We experimentally evaluated the proposed method using two public datasets: MIT-BIH Normal Sinus Rhythm (NSRDB) and MIT-BIH Arrhythmia (MITDB). The proposed LSTM-based DRNN model shows that in NSRDB, the overall precision was 100%, recall was 100%, accuracy was 100%, and F1-score was 1. For MITDB, the overall precision was 99.8%, recall was 99.8%, accuracy was 99.8%, and F1-score was 0.99. Our experiments demonstrate that the proposed model achieves an overall higher classification accuracy and efficiency compared with the conventional LSTM approach.