Detection of life-threatening arrhythmias using feature selection and support vector machines

Ventricular Arrhythmia Classification Using Similarity Maps and Hierarchical Multi-Stream Deep Learning

OBJECTIVE

Ventricular arrhythmias are the primary arrhythmias that cause sudden cardiac death. We address the problem of classification between ventricular tachycardia (VT), ventricular fibrillation (VF) and non-ventricular rhythms (NVR).

METHODS

To address the challenging problem of the discrimination between VT and VF, we develop similarity maps - a novel set of features designed to capture regularity within an ECG trace. These similarity maps are combined with features extracted through learnable Parzen band-pass filters and derivative features to discriminate between VT, VF, and NVR. To combine the benefits of these different features, we propose a hierarchical multi-stream ResNet34 architecture.

RESULTS

Our empirical results demonstrate that the similarity maps significantly improve the accuracy of distinguishing between VT and VF. Overall, the proposed approach achieves an average class sensitivity of 89.68%, and individual class sensitivities of 81.46% for VT, 89.29% for VF, and 98.28% for NVR.

CONCLUSION

The proposed method achieves a high accuracy of ventricular arrhythmia detection and classification.

SIGNIFICANCE

Correct detection and classification of ventricular fibrillation and ventricular tachycardia are essential for effective intervention and for the development of new therapies and translational medicine.

Advanced integration of 2DCNN-GRU model for accurate identification of shockable life-threatening cardiac arrhythmias: a deep learning approach

Cardiovascular diseases remain one of the main threats to human health, significantly affecting the quality and life expectancy. Effective and prompt recognition of these diseases is crucial. This research aims to develop an effective novel hybrid method for automatically detecting dangerous arrhythmias based on cardiac patients' short electrocardiogram (ECG) fragments. This study suggests using a continuous wavelet transform (CWT) to convert ECG signals into images (scalograms) and examining the task of categorizing short 2-s segments of ECG signals into four groups of dangerous arrhythmias that are shockable, including ventricular flutter (C1), ventricular fibrillation (C2), ventricular tachycardia torsade de pointes (C3), and high-rate ventricular tachycardia (C4). We propose developing a novel hybrid neural network with a deep learning architecture to classify dangerous arrhythmias. This work utilizes actual electrocardiogram (ECG) data obtained from the PhysioNet database, alongside artificially generated ECG data produced by the Synthetic Minority Over-sampling Technique (SMOTE) approach, to address the issue of imbalanced class distribution for obtaining an accuracy-trained model. Experimental results demonstrate that the proposed approach achieves high accuracy, sensitivity, specificity, precision, and an F1-score of 97.75%, 97.75%, 99.25%, 97.75%, and 97.75%, respectively, in classifying all the four shockable classes of arrhythmias and are superior to traditional methods. Our work possesses significant clinical value in real-life scenarios since it has the potential to significantly enhance the diagnosis and treatment of life-threatening arrhythmias in individuals with cardiac disease. Furthermore, our model also has demonstrated adaptability and generality for two other datasets.

Second-Order Level-Crossing Sampling Analog to Digital Converter for Electrocardiogram Delineation and Premature Ventricular Contraction Detection

This article presents an electrocardiogram (ECG) delineation and arrhythmia heartbeat detection system using a novel second-order level-crossing sampling analog to digital converter (ADC) for real-time data compression and feature extraction. The proposed system consists of the front-end integrated circuit of the data converter, the delineation algorithm, and the arrhythmia detection algorithm. Compared with conventional level-sampling ADCs, the proposed circuit updates tracking thresholds using linear extrapolation, which forms a second-order level-crossing sampling ADC that has sloped sampling levels. The computing is done digitally and is implemented by modifying the digital control logic of a conventional Successive-approximation-register (SAR) ADC. The system separates the sampling and quantization processes and only selects the turning points in the input waveform for quantization. The output of the proposed data converter consists of both the digital value of the selected sampling points and the timestamp between the selected sampling points. The main advantages are data savings for the data converter and the following digital signal processing or communication circuits, which are ideal for low-power sensors. The test chip was fabricated using a 180 nm CMOS process. When sensing sparse signals such as ECG signals the proposed ADC achieves a compression factor of 8.33. The delineation algorithm uses a triangle filter method to locate the fiducial points and measures the intervals, slopes, and morphology of the QRS complex and the P/T waves. Those extracted features are then used in the arrhythmia heartbeat detection algorithm to identify Premature Ventricular Contraction (PVC). The overall performance of the system is evaluated using the MIT-BIH database and the QT database, which is also compared with the recently reported systems. The accuracy, sensitivity, specificity, PPV, and F1 score are 97.3%, 89.6%, 97.8%, 73.3%, and 0.81 for detecting PVC.

ECG Heartbeat Classification Using CONVXGB Model

ELECTROCARDIOGRAM (ECG) signals are reliable in identifying and monitoring patients with various cardiac diseases and severe cardiovascular syndromes, including arrhythmia and myocardial infarction (MI). Thus, cardiologists use ECG signals in diagnosing cardiac diseases. Machine learning (ML) has also proven its usefulness in the medical field and in signal classification. However, current ML approaches rely on hand-crafted feature extraction methods or very complicated deep learning networks. This paper presents a novel method for feature extraction from ECG signals and ECG classification using a convolutional neural network (CNN) with eXtreme Gradient Boosting (XBoost), ConvXGB. This model was established by stacking two convolutional layers for automatic feature extraction from ECG signals, followed by XGBoost as the last layer, which is used for classification. This technique simplified ECG classification in comparison to other methods by minimizing the number of required parameters and eliminating the need for weight readjustment throughout the backpropagation phase. Furthermore, experiments on two famous ECG datasets–the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) and Physikalisch-Technische Bundesanstalt (PTB) datasets–demonstrated that this technique handled the ECG signal classification issue better than either CNN or XGBoost alone. In addition, a comparison showed that this model outperformed state-of-the-art models, with scores of 0.9938, 0.9839, 0.9836, 0.9837, and 0.9911 for accuracy, precision, recall, F1-score, and specificity, respectively.

Ventricular Fibrillation and Tachycardia Detection Using Features Derived from Topological Data Analysis

A rapid and accurate detection of ventricular arrhythmias is essential to take appropriate therapeutic actions when cardiac arrhythmias occur. Furthermore, the accurate discrimination between arrhythmias is also important, provided that the required shocking therapy would not be the same. In this work, the main novelty is the use of the mathematical method known as Topological Data Analysis (TDA) to generate new types of features which can contribute to the improvement of the detection and classification performance of cardiac arrhythmias such as Ventricular Fibrillation (VF) and Ventricular Tachycardia (VT). The electrocardiographic (ECG) signals used for this evaluation were obtained from the standard MIT-BIH and AHA databases. Two input data to the classify are evaluated: TDA features, and Persistence Diagram Image (PDI). Using the reduced TDA-obtained features, a high average accuracy near 99% was observed when discriminating four types of rhythms (98.68% to VF; 99.05% to VT; 98.76% to normal sinus; and 99.09% to Other rhythms) with specificity values higher than 97.16% in all cases. In addition, a higher accuracy of 99.51% was obtained when discriminating between shockable (VT/VF) and non-shockable rhythms (99.03% sensitivity and 99.67% specificity). These results show that the use of TDA-derived geometric features, combined in this case this the k-Nearest Neighbor (kNN) classifier, raises the classification performance above results in previous works. Considering that these results have been achieved without preselection of ECG episodes, it can be concluded that these features may be successfully introduced in Automated External Defibrillation (AED) and Implantable Cardioverter Defibrillation (ICD) therapies.

Role of artificial intelligence in defibrillators: a narrative review

Automated external defibrillators (AEDs) and implantable cardioverter defibrillators (ICDs) are used to treat life-threatening arrhythmias. AEDs and ICDs use shock advice algorithms to classify ECG tracings as shockable or non-shockable rhythms in clinical practice. Machine learning algorithms have recently been assessed for shock decision classification with increasing accuracy. Outside of rhythm classification alone, they have been evaluated in diagnosis of causes of cardiac arrest, prediction of success of defibrillation and rhythm classification without the need to interrupt cardiopulmonary resuscitation. This review explores the many applications of machine learning in AEDs and ICDs. While these technologies are exciting areas of research, there remain limitations to their widespread use including high processing power, cost and the ‘black-box’ phenomenon.

Similarity Maps for Ventricular Arrhythmia Classification

Ventricular arrhythmias are the primary arrhythmias that cause sudden cardiac death. In current clinical and preclinical research, the discovery of new therapies and their translation is hampered by the lack of consistency in diagnostic criteria for distinguishing between ventricular tachycardia (VT) and ventricular fibrillation (VF). This study develops a new set of features, similarity maps, for discrimination between VT and VF using deep neural network architectures. The similarity maps are designed to capture the similarity and the regularity within an ECG trace. Our experiments show that the similarity maps lead to a substantial improvement in distinguishing VT and VF.

DE-PNN: Differential Evolution-Based Feature Optimization with Probabilistic Neural Network for Imbalanced Arrhythmia Classification

In this research, a heartbeat classification method is presented based on evolutionary feature optimization using differential evolution (DE) and classification using a probabilistic neural network (PNN) to discriminate between normal and arrhythmic heartbeats. The proposed method follows four steps: (1) preprocessing, (2) heartbeat segmentation, (3) DE feature optimization, and (4) PNN classification. In this method, we have employed direct signal amplitude points constituting the heartbeat acquired from the ECG holter device with no secondary feature extraction step usually used in case of hand-crafted, frequency transformation or other features. The heartbeat types include normal, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature, ventricular escape, ventricular flutter and paced beat. Using ECG records from the MIT-BIH, heartbeats are identified to start at 250 ms before and end at 450 ms after the respective R-peak positions. In the next step, the DE method is applied to reduce and optimize the direct heartbeat features. Although complex and highly computational ECG heartbeat classification algorithms have been proposed in the literature, they failed to achieve high performance in detecting some minority heartbeat categories, especially for imbalanced datasets. To overcome this challenge, we propose an optimization step for the deep CNN model using a novel classification metric called the Matthews correlation coefficient (MCC). This function focuses on arrhythmia (minority) heartbeat classes by increasing their importance. Maximum MCC is used as a fitness function to identify the optimum combination of features for the uncorrelated and non-uniformly distributed eight beat class samples. The proposed DE-PNN scheme can provide better classification accuracy considering 8 classes with only 36 features optimized from a 253 element feature set implying an 85.77% reduction in direct amplitude features. Our proposed method achieved overall 99.33% accuracy, 94.56% F1, 93.84% sensitivity, and 99.21% specificity.

An Innovative Machine Learning Approach for Classifying ECG Signals in Healthcare Devices

An ECG is a diagnostic technique that examines and records the heart's electrical impulses. It is easy to categorise and prevent computational abstractions in the ECG signal using the conventional method for obtaining ECG features. It is a significant issue, but it is also a difficult and time-consuming chore for cardiologists and medical professionals. The proposed classifier eliminates all of the following limitations. Machine learning in healthcare equipment reduces moral transgressions. This study's primary purpose is to calculate the R-R interval and analyze the blockage utilising simple algorithms and approaches that give high accuracy. The MIT-BIH dataset may be used to rebuild the data. The acquired data may include both normal and abnormal ECGs. A Gabor filter is employed to generate a noiseless signal, and DCT-DOST is used to calculate the signal's amplitude. The amplitude is computed to detect any cardiac anomalies. A genetic algorithm derives the main highlights from the R peak and cycle segment length underlying the ECG signal. So, combining data with specific qualities maximises identification. The genetic algorithm aids in hereditary computations, which aids in multitarget improvement. Finally, Radial Basis Function Neural Network (RBFNN) is presented as an example. An efficient feedforward neural network lowers the number of local minima in the signal. It shows progress in identifying both normal and abnormal ECG signals.

A review of progress and an advanced method for shock advice algorithms in automated external defibrillators

Shock advice algorithm plays a vital role in the detection of sudden cardiac arrests on electrocardiogram signals and hence, brings about survival improvement by delivering prompt defibrillation. The last decade has witnessed a surge of research efforts in racing for efficient shock advice algorithms, in this context. On one hand, it has been reported that the classification performance of traditional threshold-based methods has not complied with the American Heart Association recommendations. On the other hand, the rise of machine learning and deep learning-based counterparts is paving the new ways for the development of intelligent shock advice algorithms. In this paper, we firstly provide a comprehensive survey on the development of shock advice algorithms for rhythm analysis in automated external defibrillators. Shock advice algorithms are categorized into three groups based on the classification methods in which the detection performance is significantly improved by the use of machine learning and/or deep learning techniques instead of threshold-based approaches. Indeed, in threshold-based shock advice algorithms, a parameter is calculated as a threshold to distinguish shockable rhythms from non-shockable ones. In contrast, machine learning-based methods combine multiple parameters of conventional threshold-based approaches as a set of features to recognize sudden cardiac arrest. Noticeably, those features are possibly extracted from stand-alone ECGs, alternative signals using various decomposition techniques, or fully augmented ECG segments. Moreover, these signals can be also used directly as the input channels of deep learning-based shock advice algorithm designs. Then, we propose an advanced shock advice algorithm using a support vector machine classifier and a feature set extracted from a fully augmented ECG segment with its shockable and non-shockable signals. The relatively high detection performance of the proposed shock advice algorithm implies a potential application for the automated external defibrillator in the practical clinic environment. Finally, we outline several interesting yet challenging research problems for further investigation.

Detection of fetal arrhythmias in non-invasive fetal ECG recordings using data-driven entropy profiling

Objective:Fetal arrhythmias are a life-threatening disorder occurring in up to 2% of pregnancies. If identified, many fetal arrhythmias can be effectively treated using anti-arrhythmic therapies. In this paper, we present a novel method of detecting fetal arrhythmias in short length non-invasive fetal electrocardiography (NI-FECG) recordings.Approach:Our method consists of extracting a fetal heart rate (FHR) time series from each NI-FECG recording and computing an entropy profile using a data-driven range of the entropy tolerance parameter r. To validate our approach, we apply our entropy profiling method to a large clinical data set of 318 NI-FECG recordings.Main Results:We demonstrate that our method (TotalSampEn) provides strong performance for classifying arrhythmic fetuses (AUC of 0.83) and outperforms entropy measures such as SampEn (AUC of 0.68) and FuzzyEn (AUC of 0.72). We also find that NI-FECG recordings incorrectly classified using the investigated entropy measures have significantly lower signal quality, and that excluding recordings of low signal quality (13.5% of recordings) increases the classification accuracy of TotalSampEn (AUC of 0.90).Significance:The superior performance of our approach enables automated detection of fetal arrhythmias and warrants further investigation in a prospective clinical trial.

Combining Rhythm Information between Heartbeats and BiLSTM-Treg Algorithm for Intelligent Beat Classification of Arrhythmia

Arrhythmia is a cardiovascular disease that seriously affects human health. The identification and diagnosis of arrhythmia is an effective means of preventing most heart diseases. In this paper, a BiLSTM-Treg algorithm that integrates rhythm information is proposed to realize the automatic classification of arrhythmia. Firstly, the discrete wavelet transform is used to denoise the ECG signal, based on which we performed heartbeat segmentation and preserved the timing relationship between heartbeats. Then, different heartbeat segment lengths and the BiLSTM network model are used to conduct multiple experiments to select the optimal heartbeat segment length. Finally, the tree regularization method is used to optimize the BiLSTM network model to improve classification accuracy. And the interpretability of the neural network model is analyzed by analyzing the simulated decision tree generated in the tree regularization method. This method divides the heartbeat into five categories (nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), fused heartbeats (F), and unknown heartbeats (Q)) and is validated on the MIT-BIH arrhythmia database. The results show that the overall classification accuracy of the algorithm is 99.32%. Compared with other methods of classifying heartbeat, the BiLSTM-Treg network model algorithm proposed in this paper not only improves the classification accuracy and obtains higher sensitivity and positive predictive value but also has higher interpretability.

An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques

The new advances in multiple types of devices and machine learning models provide opportunities for practical automatic computer-aided diagnosis (CAD) systems for ECG classification methods to be practicable in an actual clinical environment. This imposes the requirements for the ECG arrhythmia classification methods that are inter-patient. We aim in this paper to design and investigate an automatic classification system using a new comprehensive ECG database inter-patient paradigm separation to improve the minority arrhythmical classes detection without performing any features extraction. We investigated four supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), Random Forest (RF), and the ensemble of these three methods. We test the performance of these techniques in classifying: Normal beat (NOR), Left Bundle Branch Block Beat (LBBB), Right Bundle Branch Block Beat (RBBB), Premature Atrial Contraction (PAC), and Premature Ventricular Contraction (PVC), using inter-patient real ECG records from MIT-DB after segmentation and normalization of the data, and measuring four metrics: accuracy, precision, recall, and f1-score. The experimental results emphasized that with applying no complicated data pre-processing or feature engineering methods, the SVM classifier outperforms the other methods using our proposed inter-patient paradigm, in terms of all metrics used in experiments, achieving an accuracy of 0.83 and in terms of computational cost, which remains a very important factor in implementing classification models for ECG arrhythmia. This method is more realistic in a clinical environment, where varieties of ECG signals are collected from different patients.

An ECG Delineation and Arrhythmia Classification System Using Slope Variation Measurement by Ternary Second-Order Delta Modulators for Wearable ECG Sensors

This paper presents a system for electrocardiogram (ECG) delineation and arrhythmia classification. The proposed system consists of a front-end integrated circuit, a delineation algorithm implemented on an FPGA board, and an arrhythmia classification algorithm. The front-end circuit applies a ternary second-order Delta modulator to measure the slope variation of the input analog ECG signal. The circuit converts the analog inputs into a pulse density modulated bitstream, whose pulse density is proportional to the slope variation of the input analog signal regardless of the instantaneous amplitude. The front-end chip can detect the minimum slope variation of 3.2 mV/ms ² within a 3 ms timing error. The front-end integrated circuit was fabricated with a 180 nm CMOS process occupying a 0.25 mm ² area with a 151 nW power consumption at the sampling rate of 1 kS/s. Based on the slope variation obtained from the front-end circuit, a delineation algorithm is designed to detect fiducial points in the ECG waveform. The delineation algorithm was tested on a Spartan-6 FPGA. The delineation system can detect the intervals, slopes, and morphology of the QRS/PT waves and form a feature set that contains 22 features. Based on these features, a rotate linear kernel support vector machine (SVM) is applied for patient-specific arrhythmia classification of the ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), and heartbeats originating in sinus node. The performance of the proposed system is comparable to the recently published methods while providing a promising solution for the low-complexity implementation of future wearable ECG monitoring systems.

Reconstructed State Space Features for Classification of ECG Signals

Background:

Cardiac arrhythmias are considered as one of the most serious health conditions; therefore, accurate and quick diagnosis of these conditions is highly paramount for the electrocardiogram (ECG) signals. Moreover, are rather difficult for the cardiologists to diagnose with unaided eyes due to a close similarity of these signals in the time domain.

Objective:

In this paper, an image-based and machine learning method were presented in order to investigate the differences between the three cardiac arrhythmias of VF, VT, SVT and the normal signal.

Material and Methods:

In this simulation study, the ECG data used are collected from 3 databases, including Boston Beth University Arrhythmias Center, Creighton University, and MIT-BIH. The proposed algorithm was implemented using MATLAB R2015a software and its simulation. At first, the signal is transmitted to the state space using an optimal time delay. Then, the optimal delay values are obtained using the particle swarm optimization algorithm and normalized mutual information criterion. Furthermore, the result is considered as a binary image. Then, 19 features are extracted from the image and the results are presented in the multilayer perceptron neural network for the purpose of training and testing.

Results:

In order to classify N-VF, VT-SVT, N-SVT, VF-VT, VT-N-VF, N-SVT-VF, VT-VF-SVT and VT-VF-SVT-N in the conducted experiments, the accuracy rates were determined at 99.5%, 100%, 94.98%, 100%,100%, 100%, 99.5%, 96.5% and 95%, respectively.

Conclusion:

In this paper, a new approach was developed to classify the abnormal signals obtained from an ECG such as VT, VF, and SVT compared to a normal signal. Compared to Other related studies, our proposed system significantly performed better

Artificial intelligence in the diagnosis and management of arrhythmias

The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI) methodologies for decades. Recent renewed interest in deep learning techniques has opened new frontiers in electrocardiography analysis including signature identification of diseased states. Artificial intelligence advances coupled with simultaneous rapid growth in computational power, sensor technology, and availability of web-based platforms have seen the rapid growth of AI-aided applications and big data research. Changing lifestyles with an expansion of the concept of internet of things and advancements in telecommunication technology have opened doors to population-based detection of atrial fibrillation in ways, which were previously unimaginable. Artificial intelligence-aided advances in 3D cardiac imaging heralded the concept of virtual hearts and the simulation of cardiac arrhythmias. Robotics, completely non-invasive ablation therapy, and the concept of extended realities show promise to revolutionize the future of EP. In this review, we discuss the impact of AI and recent technological advances in all aspects of arrhythmia care.

Research on time series characteristics of sports training effect based on support vector machine

Sports athletes not only exercise fast, but also suffer from the surrounding complex environment. Therefore, the video needs to be sequenced to improve processing efficiency. From the perspective of machine learning, this paper designs a spatial feature extractor based on CNN to extract time series features of sports. Moreover, this paper uses the support vector machine as the basis of the construction model to construct a feature extraction model based on support vector machine and random forest based on different situations. At the same time, this paper collects test data through the sports database and uses the swimming project as an example to analyze the model performance. Finally, the paper verifies the validity of the model by comparing and verifying methods. The research indicates that the proposed method has certain effectiveness and can provide theoretical reference for subsequent related research.

Image invariant features and SVM techniques for college level English learning platform

Under the Internet information network environment, College English teaching mode is faced with a new choice and transformation. SPOC and STEAM classes have multiple advantages, which can make up for the lack of a single teaching model and provide new ideas for teaching reform. It is a practical application of statistical method to optimize the model of artificial neural network by machine learning method of statistics. The application of mathematical statistics can solve some related problems of artificial perception. Therefore, artificial neural network has the same simple decision ability and judgment ability as human beings. In this paper, the authors analyze the image invariant features and SVM algorithms application in college English education platform. The results show that this method has a positive effect on learners’ English proficiency and learning effect. Teachers also avoid paying a lot of labor, which is very beneficial to the implementation of innovative teaching. However, compared with the traditional teaching, the phenomenon of student achievement differentiation is very serious, and teaching is facing great pressure. Therefore, improving students’ autonomous learning ability and teachers’ information literacy is still very helpful to improve the teaching effect.

A Novel 1-D CCANet for ECG Classification

This paper puts forward a 1-D convolutional neural network (CNN) that exploits a novel analysis of the correlation between the two leads of the noisy electrocardiogram (ECG) to classify heartbeats. The proposed method is one-dimensional, enabling complex structures while maintaining a reasonable computational complexity. It is based on the combination of elementary handcrafted time domain features, frequency domain features through spectrograms and the use of autoregressive modeling. On the MIT-BIH database, a 95.52% overall accuracy is obtained by classifying 15 types, whereas a 95.70% overall accuracy is reached when classifying 7 types from the INCART database.

A machine learning method for acute hypotensive episodes prediction using only non-invasive parameters

BACKGROUND AND OBJECTIVES

Accurate prediction of acute hypotensive episodes (AHE) is fundamentally important for timely and appropriate clinical decision-making, as it can provide medical professionals with sufficient time to accurately select more efficient therapeutic interventions for each specific condition. However, existing methods are invasive, easily affected by artifacts and can be difficult to acquire in a pre-hospital setting.

METHODS

In this study, 1055 patients' records were extracted from the Multiparameter Intelligent Monitoring in Intensive Care II database (MIMIC II), comprising of 388 AHE records and 667 non-AHE records. Six commonly used machine learning algorithms were selected and used to develop an AHE prediction model based on features extracted from seven types of non-invasive physiological parameters.

RESULTS

The optimal observation window and prediction gap were selected as 300 minutes and 60 minutes, respectively. For GBDT, XGB and AdaBoost, the optimal feature subsets contained only 39% of the overall features. An ensemble prediction model was developed using the voting method to achieve a more robust performance with an accuracy (ACC) of 0.822 and area under the receiver operating characteristic curve (AUC) of 0.878.

CONCLUSION

A novel machine learning method that uses only noninvasive physiological parameters offers a promising solution for easy and prompt AHE prediction in widespread scenario applications, including pre-hospital and in-hospital care.

A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal

Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model’s classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models’ effectiveness.

Detection of ventricular arrhythmia using hybrid time-frequency-based features and deep neural network

Sudden cardiac death (SCD) is a major cause of death among patients with heart diseases. It occurs mainly due to ventricular tachyarrhythmia (VTA) which includes ventricular tachycardia (VT) and ventricular fibrillation (VF) conditions. The main challenging task is to predict the VTA condition at a faster rate and timely application of automatic external defibrillator (AED) for saving lives. In this study, a VF/VT classification scheme has been proposed using a deep neural network (DNN) approach using hybrid time-frequency-based features. Two annotated public domain ECG databases (CUDB and VFDB) were used as training, test, and validation of datasets. The main motivation of this study was to implement a deep learning model for the classification of the VF/VT conditions and compared the results with other standard machine learning algorithms. The signal is decomposed with the wavelet transform, empirical mode decomposition (EMD) and variable mode decomposition (VMD) approaches and twenty-four are extracted to form a hybrid model from a window of length 5 s length. The DNN classifier achieved an accuracy (Acc) of 99.2%, sensitivity (Se) of 98.8%, and specificity (Sp) of 99.3% which is comparatively better than the results of the standard classifier. The proposed algorithm can detect VTA conditions accurately, hence could reduce the rate of misinterpretations by human experts and improves the efficiency of cardiac diagnosis by ECG signal analysis.

Research on English score analysis system based on improved decision tree algorithm and fuzzy set

At present, the data mining technology is introduced into the analysis of English scores, the data is deeply explored and analyzed reasonably, and the analysis results are used to guide the smooth development of teaching, which is conducive to improving the quality of English teaching. The main work of this thesis is based on the background of this study: taking the academic performance of college students as the application background, this paper first introduces the basic theoretical knowledge of data mining and the application status of data mining technology in education field. Secondly, this paper establishes a student performance database and uses data mining technology to carry out in-depth mining of the established performance database. Finally, the mining results are analyzed, and the factors affecting students’ academic performance are obtained. These analysis results have important reference value for the future improvement of teaching work in colleges and universities.

Human-computer system design of entrepreneurship education based on artificial intelligence and image feature retrieval

The key of deep learning is how to extract abstract, deep and nonlinear target features, in which algorithm plays a crucial role. In this paper, the authors analyze the intelligent system design of entrepreneurship education classroom based on artificial intelligence and image feature retrieval. Pyramid pooling is used to transform any size feature map into fixed size feature vector, which is finally sent to the full connection layer for classification and regression. Experimental results show that the algorithm accelerates the convergence of the whole network and improves the detection speed. The education taught by entrepreneurial class is not only to help college students to seek a stable career, but also to help college students develop their own potential, cultivate entrepreneurial awareness, improve entrepreneurial quality and ability. Entrepreneurship education should not only stay in the design of subject courses, but should integrate entrepreneurship education with internet entrepreneurship practice. On this basis, we provide new countermeasures and suggestions for improving the quality and ability of college students in the process of entrepreneurial activities.

Towards Interpretable Arrhythmia Classification With Human-Machine Collaborative Knowledge Representation

Arrhythmia detection and classification is a crucial step for diagnosing cardiovascular diseases. However, deep learning models that are commonly used and trained in end-to-end fashion are not able to provide good interpretability. In this paper, we address this deficiency by proposing the first novel interpretable arrhythmia classification approach based on a human-machine collaborative knowledge representation. Our approach first employs an AutoEncoder to encode electrocardiogram signals into two parts: hand-encoding knowledge and machine-encoding knowledge. A classifier then takes as input the encoded knowledge to classify arrhythmia heartbeats with or without human in the loop (HIL). Experiments and evaluation on the MIT-BIH Arrhythmia Database demonstrate that our new approach not only can effectively classify arrhythmia while offering interpretability, but also can improve the classification accuracy by adjusting the hand-encoding knowledge with our HIL mechanism.

A machine learning approach for mortality prediction only using non-invasive parameters

At present, the traditional scoring methods generally utilize laboratory measurements to predict mortality. It results in difficulties of early mortality prediction in the rural areas lack of professional laboratorians and medical laboratory equipment. To improve the efficiency, accuracy, and applicability of mortality prediction in the remote areas, a novel mortality prediction method based on machine learning algorithms is proposed, which only uses non-invasive parameters readily available from ordinary monitors and manual measurement. A new feature selection method based on the Bayes error rate is developed to select valuable features. Based on non-invasive parameters, four machine learning models were trained for early mortality prediction. The subjects contained in this study suffered from general critical diseases including but not limited to cancer, bone fracture, and diarrhea. Comparison tests among five traditional scoring methods and these four machine learning models with and without laboratory measurement variables are performed. Only using the non-invasive parameters, the LightGBM algorithms have an excellent performance with the largest accuracy of 0.797 and AUC of 0.879. There is no apparent difference between the mortality prediction performance with and without laboratory measurement variables for the four machine learning methods. After reducing the number of feature variables to no more than 50, the machine learning models still outperform the traditional scoring systems, with AUC higher than 0.83. The machine learning approaches only using non-invasive parameters achieved an excellent mortality prediction performance and can equal those using extra laboratory measurements, which can be applied in rural areas and remote battlefield for mortality risk evaluation. Graphical abstract.

A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data

The electrocardiogram records the heart's electrical activity and generates a significant amount of data. The analysis of these data helps us to detect diseases and disorders via heart bio-signal abnormality classification. In unbalanced-data contexts, where the classes are not equally represented, the optimization and configuration of the classification models are highly complex, reflecting on the use of computational resources. Moreover, the performance of electrocardiogram classification depends on the approach and parameter estimation to generate the model with high accuracy, sensitivity, and precision. Previous works have proposed hybrid approaches and only a few implemented parameter optimization. Instead, they generally applied an empirical tuning of parameters at a data level or an algorithm level. Hence, a scheme, including metrics of sensitivity in a higher precision and accuracy scale, deserves special attention. In this article, a metaheuristic optimization approach for parameter estimations in arrhythmia classification from unbalanced data is presented. We selected an unbalanced subset of those databases to classify eight types of arrhythmia. It is important to highlight that we combined undersampling based on the clustering method (data level) and feature selection method (algorithmic level) to tackle the unbalanced class problem. To explore parameter estimation and improve the classification for our model, we compared two metaheuristic approaches based on differential evolution and particle swarm optimization. The final results showed an accuracy of 99.95%, a F1 score of 99.88%, a sensitivity of 99.87%, a precision of 99.89%, and a specificity of 99.99%, which are high, even in the presence of unbalanced data.

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms

Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be learned to self-extract significant features of the electrocardiogram (ECG) and can generally provide high-output diagnostic accuracy if subjected to robust training and optimization on large datasets at high computational cost. So far, limited research and optimization of DNNs in shock advisory systems is found on large ECG arrhythmia databases from out-of-hospital cardiac arrests (OHCA). The objective of this study is to optimize the hyperparameters (HPs) of deep convolutional neural networks (CNN) for detection of shockable (Sh) and nonshockable (NSh) rhythms, and to validate the best HP settings for short and long analysis durations (2-10 s). Large numbers of (Sh + NSh) ECG samples were used for training (720 + 3170) and validation (739 + 5921) from Holters and defibrillators in OHCA. An end-to-end deep CNN architecture was implemented with one-lead raw ECG input layer (5 s, 125 Hz, 2.5 uV/LSB), configurable number of 5 to 23 hidden layers and output layer with diagnostic probability p ∈ [0: Sh,1: NSh]. The hidden layers contain N convolutional blocks × 3 layers (Conv1D (filters = Fi, kernel size = Ki), max-pooling (pool size = 2), dropout (rate = 0.3)), one global max-pooling and one dense layer. Random search optimization of HPs = {N, Fi, Ki}, i = 1, … N in a large grid of N = [1, 2, … 7], Fi = [5;50], Ki = [5;100] was performed. During training, the model with maximal balanced accuracy BAC = (Sensitivity + Specificity)/2 over 400 epochs was stored. The optimization principle is based on finding the common HPs space of a few top-ranked models and prediction of a robust HP setting by their median value. The optimal models for 1-7 CNN layers were trained with different learning rates LR = [10-5; 10-2] and the best model was finally validated on 2-10 s analysis durations. A number of 4216 random search models were trained. The optimal models with more than three convolutional layers did not exhibit substantial differences in performance BAC = (99.31-99.5%). Among them, the best model was found with {N = 5, Fi = {20, 15, 15, 10, 5}, Ki = {10, 10, 10, 10, 10}, 7521 trainable parameters} with maximal validation performance for 5-s analysis (BAC = 99.5%, Se = 99.6%, Sp = 99.4%) and tolerable drop in performance (<2% points) for very short 2-s analysis (BAC = 98.2%, Se = 97.6%, Sp = 98.7%). DNN application in future-generation shock advisory systems can improve the detection performance of Sh and NSh rhythms and can considerably shorten the analysis duration complying with resuscitation guidelines for minimal hands-off pauses.

A new method for identifying the acute respiratory distress syndrome disease based on noninvasive physiological parameters

Early diagnosis and prevention play a crucial role in the treatment of patients with ARDS. The definition of ARDS requires an arterial blood gas to define the ratio of partial pressure of arterial oxygen to fraction of inspired oxygen (PaO₂/FiO₂ ratio). However, many patients with ARDS do not have a blood gas measured, which may result in under-diagnosis of the condition. Using data from MIMIC-III Database, we propose an algorithm based on patient non-invasive physiological parameters to estimate P/F levels to aid in the diagnosis of ARDS disease. The machine learning algorithm was combined with the filter feature selection method to study the correlation of various noninvasive parameters from patients to identify the ARDS disease. Cross-validation techniques are used to verify the performance of algorithms for different feature subsets. XGBoost using the optimal feature subset had the best performance of ARDS identification with the sensitivity of 84.03%, the specificity of 87.75% and the AUC of 0.9128. For the four machine learning algorithms, reducing a certain number of features, AUC can still above 0.8. Compared to Rice Linear Model, this method has the advantages of high reliability and continually monitoring the development of patients with ARDS.

Computational approaches for detection of cardiac rhythm abnormalities: Are we there yet?

The analysis of an electrocardiogram (ECG) is able to provide vital information on the electrical activity of the heart and is crucial for the accurate diagnosis of cardiac arrhythmias. Due to the nature of some arrhythmias, this might be a time-consuming and difficult to accomplish process. The advent of novel machine learning technologies in this field has a potential to revolutionise the use of the ECG. In this review, we outline key advances in ECG analysis for atrial, ventricular and complex multiform arrhythmias, as well as discuss the current limitations of the technology and the barriers that must be overcome before clinical integration is feasible.

ECG-based Random Forest Classifier for Cardiac Arrest Rhythms

Rhythm annotation of out-of-hospital cardiac episodes (OHCA) is key for a better understanding of the interplay between resuscitation therapy and OHCA patient outcome. OHCA rhythms are classified in five categories, asystole (AS), pulseless electrical activity (PEA), pulsed rhythms (PR), ventricular fibrillation (VF) and ventricular tachycardia (VT). Manual OHCA annotation by expert clinicians is onerous and time consuming, so there is a need for accurate and automatic OHCA rhythm annotation methods. For this study 852 OHCA episodes of patients treated with Automated External Defibrillators (AED) by the Emergency Medical Services of the Basque Country were analyzed. Six expert clinicians reviewed the electrocardiogram (ECG) of 4214 AED rhythm analyses and annotated the rhythm. Their consensus decision was used as ground truth. There were a total of 2418 AS, 294 PR, 1008 PEA, 472 VF and 22 VT. The ECG analysis intervals were extracted and used to develop an automatic rhythm annotator. Data was partitioned patient-wise into training (70%) and test (30%). Performance was evaluated in terms of per class sensitivity (Se) and F-score (F1). The unweighted mean of sensitivity (UMS) and F-score were used as global performance metrics. The classification method is composed of a feature extraction and denoising stage based on the stationary wavelet transform of the ECG, and on a random forest classifier. The best model presented a per rhythm Se/F1 of 95.8/95.7, 43.3/52.2, 85.3/81.3, 94.2/96.1, 81.9/72.2 for AS, PR, PEA, VF and VT, respectively. The UMS for the test set was 80.2%, 2-points above that of previous solutions. This method could be used to retrospectively annotate large OHCA datasets and ameliorate the workload of manual OHCA rhythm annotation.

Weighted Random Forests to Improve Arrhythmia Classification

Construction of an ensemble model is a process of combining many diverse base predictive learners. It arises questions of how to weight each model and how to tune the parameters of the weighting process. The most straightforward approach is simply to average the base models. However, numerous studies have shown that a weighted ensemble can provide superior prediction results to a simple average of models. The main goals of this article are to propose a new weighting algorithm applicable for each tree in the Random Forest model and the comprehensive examination of the optimal parameter tuning. Importantly, the approach is motivated by its flexibility, good performance, stability, and resistance to overfitting. The proposed scheme is examined and evaluated on the Physionet/Computing in Cardiology Challenge 2015 data set. It consists of signals (electrocardiograms and pulsatory waveforms) from intensive care patients which triggered an alarm for five cardiac arrhythmia types (Asystole, Bradycardia, Tachycardia, Ventricular Tachycardia, and Ventricular Fultter/Fibrillation). The classification problem regards whether the alarm should or should not have been generated. It was proved that the proposed weighting approach improved classification accuracy for the three most challenging out of the five investigated arrhythmias comparing to the standard Random Forest model.

Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE

Timely prediction of cardiovascular diseases with the help of a computer-aided diagnosis system minimizes the mortality rate of cardiac disease patients. Cardiac arrhythmia detection is one of the most challenging tasks, because the variations of electrocardiogram(ECG) signal are very small, which cannot be detected by human eyes. In this study, an 11-layer deep convolutional neural network model is proposed for classification of the MIT-BIH arrhythmia database into five classes according to the ANSI-AAMI standards. In this CNN model, we designed a complete end-to-end structure of the classification method and applied without the denoising process of the database. The major advantage of the new methodology proposed is that the number of classifications will reduce and also the need to detect, and segment the QRS complexes, obviated. This MIT-BIH database has been artificially oversampled to handle the minority classes, class imbalance problem using SMOTE technique. This new CNN model was trained on the augmented ECG database and tested on the real dataset. The experimental results portray that the developed CNN model has better performance in terms of precision, recall, F-score, and overall accuracy as compared to the work mentioned in the literatures. These results also indicate that the best performance accuracy of 98.30% is obtained in the 70:30 train-test data set.

Optimal level and order detection in wavelet decomposition for PCG signal denoising

The recorded phonocardiogram (PCG) signal is often contaminated by different types of noises that can be seen in the frequency band of the PCG signal, which may change the characteristics of this signal. Discrete wavelet transform (DWT) has become one of the most important and powerful tools of signal representation, but its effectiveness is influenced by the issue of the selected mother wavelet and decomposition level (DL). The selection of the DL and the mother wavelet are the main challenges. This work proposes a new approach for finding an optimal DL and optimal mother wavelet for PCG signal denoising. Our approach consists of two algorithms designed to tackle the problems of noise and variability caused by PCG acquisition in a real clinical environment for different categories of patients. The results obtained are evaluated by examining the coherence analysie (Coh) correlation coefficient (Corr) and the mean square error (MSE) and signal-to-noise ratio (SNR) in simulated noisy PCG signals. The experimental results show that the proposed method can effectively reduce noise.

Automated detection of shockable and non-shockable arrhythmia using novel wavelet-based ECG features

Malignant arrhythmia can lead to sudden cardiac death (SCD). Shockable arrhythmia can be terminated with device electrical shock therapies. Ventricular-tachycardia (VT) and ventricular fibrillation (VF) are responsive to electrical anti-tachycardia pacing therapy and defibrillation which help to restore normal electrical and mechanical function of the heart. In contrast, non-shockable arrhythmia like asystole and bradycardia are not responsive to electric shock therapy. Distinguishing between shockable and non-shockable arrhythmia is an important diagnostic challenge that has practical clinical relevance. It is difficult to accurately differentiate between these two types of arrhythmia by manual inspection of electrocardiogram (ECG) segments within the short time duration before triggering the device for electrical therapy. Automated defibrillators are equipped with automatic shockable arrhythmia detection algorithms based on ECG morphological features, which may possess variable diagnostic performance depending on machine models. In our work, we have designed a robust system using wavelet decomposition filter banks for extraction of features from the ECG signal and then classifying the features. We believe this method will improve the accuracy of discriminating between shockable and non-shockable arrhythmia compared with existing conventional algorithms. We used a novel three channel orthogonal wavelet filter bank, which extracted features from ECG epochs of duration 2 s to distinguish between shockable and non-shockable arrhythmia. The fuzzy, Renyi and sample entropies are extracted from the various wavelet coefficients and fed to support vector machine (SVM) classifier for automated classification. We have obtained an accuracy of 98.9%, sensitivity and specificity of 99.08% and 97.11.9%, respectively, using 10-fold cross validation. The area under the receiver operating characteristic has been found to be 0.99 with F1-score of 0.994. The system developed is more accurate than the existing algorithms. Hence, the proposed system can be employed in automated defibrillators inside and outside hospitals for emergency revival of patients suffering from SCD. These automated defibrillators can also be implanted inside the human body for automatic detection of potentially fatal shockable arrhythmia and to deliver an appropriate electric shock to the heart.