Patient-specific Electrocardiogram Research Articles

An automated patient-specific ECG beat classification using LSTM-based recurrent neural networks

Customized electrocardiogram analysis is inevitable in the cardiac functionality assessment of every single individual patient. In this paper, we firmly introduce a novel framework for patient-specific electrocardiogram (ECG) beat classification characterized by the long-short term memory (LSTM) based recurrent neural networks (RNNs). A novel architecture is developed by governing two LSTM models connected in parallel, and each of them operates on the single-channel ECG input. The proposed model customizes the classification process and imbibes the trait of patient-specific analysis by training the model with the first 5-min segment of heartbeats of the respective patient’s ECG record and tests the same for the rest of the ECG record. The proposed framework undergoes five stages viz; pre-processing, QRS complex detection, segmentation, feature extraction, and LSTM-based RNNs for classification. The efficiency of the proposed framework is deeply examined by the support of various metrics viz; accuracy (Acc), sensitivity (Sen), specificity (Spe), positive predictivity (Ppr), F1 score, and G score and the performance of the proposed method is compared to that of some popular state-of-the-art techniques. The proposed framework is exposed to a highly popular and reliable MIT-BIH arrhythmia database for training and testing purposes. The obtained results are found promising and yield superior classification performance in recognizing ventricular ectopic beats (VEB) and supra-ventricular ectopic beats (SVEB). In addition, the proposed framework is recognized as simple, lightweight yet efficient which in turn makes it suitable for continuous monitoring applications in clinical settings for automated ECG analysis to assist the cardiologist.

Towards Automated Optimization of Residual Convolutional Neural Networks for Electrocardiogram Classification.

The interpretation of biological data such as theElectroCardioGram (ECG) signalgives clinical information and helps to assess theheart function. There are distinct ECG patterns associated with a specific class of arrhythmia. The convolutional neural network, inspired by findings in the study of biological vision, is currently one of the most commonly employed deep neural network algorithms for ECG processing. However, deep neural network models require many hyperparameters to tune. Selecting the optimal or thebest hyperparameter for the convolutional neural network algorithm is a highly challenging task. Often, we end up tuning the model manually with different possible ranges of values until a best fit model is obtained. Automatic hyperparameters tuning using Bayesian Optimization (BO) and evolutionary algorithms can provide an effective solution to current labour-intensive manual configuration approaches. In this paper, we propose to optimize the Residual one Dimensional Convolutional Neural Network model (R-1D-CNN) at two levels. At the first level, a residual convolutional layer and one-dimensional convolutional neural layers are trained to learn patient-specific ECG features over which multilayer perceptron layers can learn to produce the final class vectors of each input. This level is manual and aims to limit the search space and select the most important hyperparameters to optimize. The second level is automatic and based on our proposed BO-based algorithm. Our optimized proposed architecture (BO-R-1D-CNN) is evaluated on two publicly available ECG datasets. Comparative experimental results demonstrate that our BO-based algorithm achieves an optimal rate of 99.95% for the MIT-BIH database to discriminate between five kinds of heartbeats, including normal heartbeats, left bundle branch block, atrial premature, right bundle branch block, and premature ventricular contraction. Moreover, experiments demonstrate that the proposed architecture fine-tuned with BO achieves a higher accuracy tested on the10,000 ECG patients datasetcompared to the other proposed architectures. Our optimized architecture achieves excellent results compared to previous works on the two benchmark datasets.

Global ECG Classification by Self-Operational Neural Networks With Feature Injection.

Global (inter-patient) ECG classification for arrhythmia detection over Electrocardiogram (ECG) signal is a challenging task for both humans and machines. Automating this process with utmost accuracy is, therefore, highly desirable due to the advent of wearable ECG sensors. However, even with numerous deep learning approaches proposed recently, there is still a notable gap in the performance of global and patient-specific ECG classification performance. In this study, we propose a novel approach for inter-patient ECG classification using a compact 1D Self-ONN by exploiting morphological and timing information in heart cycles. We used 1D Self-ONN layers to automatically learn morphological representations from ECG data, enabling us to capture the shape of the ECG waveform around the R peaks. We further inject temporal features based on RR interval for timing characterization. The classification layers can thus benefit from both temporal and learned features for the final arrhythmia classification. Using the MIT-BIH arrhythmia benchmark database, the proposed method achieves the highest classification performance ever achieved, i.e., 99.21% precision, 99.10% recall, and 99.15% F1-score for normal (N) segments; 82.19% precision, 82.50% recall, and 82.34% F1-score for the supra-ventricular ectopic beat (SVEBs); and finally, 94.41% precision, 96.10% recall, and 95.2% F1-score for the ventricular-ectopic beats (VEBs). As a pioneer application, the results show that compact and shallow 1D Self-ONNs with the feature injection can surpass all state-of-the-art deep models with a significant margin and with minimal computational complexity. This study has demonstrated that using a compact and superior network model, a global ECG classification can still be achieved with an elegant performance level even when no patient-specific information is used.

A real-time embedded system to detect QRS-complex and arrhythmia classification using LSTM through hybridized features

The electrocardiogram (ECG) is an extremely valuable medical examination for monitoring cardiac disorders. The QRS waves on the ECG signal are essential in diagnosing these disorders. While numerous algorithms for detecting R-peaks/QRS complexes are developed, most are focused on complex computations that need off-line execution on a PC. However, advancements in telemedicine and wearable devices require an algorithm that runs effectively on an embedded system. This paper aims to design and develop an embedded system to detect the QRS complex and arrhythmia classification based on the patient-specific ECG data. The proposed model is based on the Discrete Wavelet Transform (DWT), Delta Sigma Modulation (DSM) with local maximum/minimum point algorithm to detect R peak/QRS complex. It extracts several R peaks/QRS complex features, such as the waves peak, onset, offset, and duration between consecutive R peaks (RR interval), and uses these to improve classification accuracy. We proposed Long Short Term Memory (LSTM) neural network for arrhythmia classification. First, the ECG signal is extracted through the embedded system and used for further processes. Second, the QRS complex/R peak is detected using modulated bitstreams, threshold level through DSM and DWT, respectively. Thirdly, the extracted features are hybridized and input into an LSTM for arrhythmia classification. The MIT-BIH database was used to evaluate the algorithm’s performance, and the accuracy, positive predictivity, sensitivity, and F1 score were evaluated as performance metrics. The algorithm achieved 99.64 %, 99.15 %, 99.87 %, and 98.18 % for all four metrics, respectively. The algorithm was then executed on an embedded system, and its run time and power consumption were examined. The DSM algorithm detects QRS waves in 17.2 ms, while the DWT method detects R peak in 14.02 ms. The proposed LSTM algorithm takes 58 ms for classification. The DSM chip (MCP3008 ADC) consumes 680 nW of power at a sampling rate of 500 Hz. Additionally, the algorithm’s performance was compared to those of other widely used algorithms. The suggested approach holds considerable promise for long-term monitoring in wearable systems.

SpEC: A system for patient specific ECG beat classification using deep residual network

Electrocardiogram (ECG) is a non-invasive technique used to detect various cardiac disorders. One of the major causes of cardiac arrest is an arrhythmia. Furthermore, ECG beat classification is essential to detect life-threatening cardiac arrhythmias. The major limitations of the traditional ECG beat classification systems are the requirement of an extensive training dataset to train the model and inconsistent performance for the detection of ventricular and supraventricular ectopic (V and S) beats. To overcome these limitations, a system denoted as SpEC is proposed in this work based on Stockwell transform (ST) and two-dimensional residual network (2D-ResNet) for improvement of ECG beat classification technique with a limited amount of training data. ST, which is used to represent the ECG signal into a time-frequency domain, provides frequency invariant amplitude response and dynamic resolution. The resultant ST images are applied as input to the proposed 2D-ResNet to classify five different types of ECG beats in a patient-specific way as recommended by the Association for the Advancement of Medical Instrumentation (AAMI). The proposed SpEC system achieved an overall accuracy (Acc) of 99.73%, sensitivity (Sen)=98.84%, Specificity (Spe)=99.50%, Positive predictivity (Ppr)=98.20% on MIT-BIH arrhythmia database, and shows an overall Acc of 89.87% on real-time acquired ECG dataset with classification time of single ECG beat image=0.2365 (s) in detecting of five arrhythmia classes. The proposed method shows better performance on both the database compared to the earlier reported state-of-art techniques.

Integrating Patient-Specific Electrocardiogram Signals and Image-Based Computational Fluid Dynamics Method to Analyze Coronary Blood Flow in Patients during Cardiac Arrhythmias

PurposeThe aim of this study was to use the computational fluid dynamics (CFD) method, patient-specific electrocardiogram (ECG) signals, and computed tomography three-dimensional image reconstruction technique to investigate the blood flow in coronary arteries during cardiac arrhythmia.MethodsTwo patients with premature ventricular contraction-type cardiac arrhythmia and one with atrial fibrillation-type cardiac arrhythmia were investigated. The inlet velocity of the coronary artery in simulation was applied with the measured velocity profile of the left ventricular outflow tract (LVOT) from the Doppler echocardiography. The measured patient central aortic blood pressure waveform was employed for the coronary artery outlet in simulation. The no-slip boundary condition was applied to the arterial wall.ResultsFor the patient with irregular cardiac rhythms (Case I), the coronary blood flow rate under the shortened and lengthened cardiac rhythms were 0.66 and 0.96 mL/s, respectively. In Case II, the maximum velocity at the LVOT under a normal heartbeat was found to be 101 cm/s, whereas the average value was 73 cm/s. In Case III, the patient was also diagnosed with a congenital stenosis problem at the myocardial bridge (MCB) at the LAD. The measured blood flow rate at the MCB of the LAD for the three heartbeats in Case III was found to be 0.68, 1.08, and 1.14 mL/s.ConclusionThe integration of patient-specific ECG signals and image-based CFD methods can clearly analyze hemodynamic information for patients during cardiac arrhythmia. The cardiac arrhythmia can reduce the blood flow in the coronary arteries.

PGANs: Personalized Generative Adversarial Networks for ECG Synthesis to Improve Patient-Specific Deep ECG Classification

The Electrocardiogram (ECG) is performed routinely by medical personnel to identify structural, functional and electrical cardiac events. Many attempts were made to automate this task using machine learning algorithms including classic supervised learning algorithms and deep neural networks, reaching state-of-the-art performance. The ECG signal conveys the specific electrical cardiac activity of each subject thus extreme variations are observed between patients. These variations are challenging for deep learning algorithms, and impede generalization. In this work, we propose a semisupervised approach for patient-specific ECG classification. We propose a generative model that learns to synthesize patient-specific ECG signals, which can then be used as additional training data to improve a patient-specific classifier performance. Empirical results prove that the generated signals significantly improve ECG classification in a patient-specific setting.

I-Vector-Based Patient Adaptation of Deep Neural Networks for Automatic Heartbeat Classification.

Automatic classification of electrocardiogram (ECG) signals is important for diagnosing heart arrhythmias. A big challenge in automatic ECG classification is the variation in the waveforms and characteristics of ECG signals among different patients. To address this issue, this paper proposes adapting a patient-independent deep neural network (DNN) using the information in the patient-dependent identity vectors (i-vectors). The adapted networks, namely i-vector adapted patient-specific DNNs (iAP-DNNs), are tuned toward the ECG characteristics of individual patients. For each patient, his/her ECG waveforms are compressed into an i-vector using a factor analysis model. Then, this i-vector is injected into the middle hidden layer of the patient-independent DNN. Stochastic gradient descent is then applied to fine-tune the whole network to form a patient-specific classifier. As a result, the adaptation makes use of not only the raw ECG waveforms from the specific patient but also the compact representation of his/her ECG characteristics through the i-vector. Analysis on the hidden-layer activations shows that by leveraging the information in the i-vectors, the iAP-DNNs are more capable of discriminating normal heartbeats against arrhythmic heartbeats than the networks that use the patient-specific ECG only for the adaptation. Experimental results based on the MIT-BIH database suggest that the iAP-DNNs perform better than existing patient-specific classifiers in terms of various performance measures. In particular, the sensitivity and specificity of the existing methods are all under the receiver operating characteristic curves of the iAP-DNNs.

Patient-specific ECG classification by deeper CNN from generic to dedicated

Abstract This paper presents a new mechanism which is more effective for wearable devices to classify patient-specific electrocardiogram (ECG) heartbeats. In our method, a Generic Convolutional Neural Network (GCNN) is trained first using a large number of heartbeats without distinguishing patients. Based on the GCNN, fine-tuning technique is applied to modify the GCNN to a Tuned Dedicated CNN (TDCNN) for the corresponding individual. Notably, only the GCNN instead of common training data is required to be stored into wearable devices. Moreover, only fine-tuning with several seconds rather than dozens of minutes is needed before the TDCNN is used to monitor the long-term ECG signals in clinical. To accelerate the ECG classification, only the original ECG heartbeat is input to the CNN without other extended information from the neighbor heartbeats or FFT representation. A deeper CNN architecture with small-scale convolutional kernels is adopted to improve the speed and accuracy for classification. With deeper CNN, hierarchical features can be extracted to help improve the accuracy of ECG classification. The state-of-the-art performance on efficiency and accuracy for ECG classification over MIT-BIH dataset is achieved by the proposed method. The effectiveness and superiority for detecting ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB) events are demonstrated. The proposed mechanism of fine-tuning the GCNN to TDCNN improves the efficiency for training patient-specific CNN classifier. Because of the computational efficiency of fine-tuning, ECG diagnosis and heart monitoring can be easily implemented with popular wearable devices in practice.

Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks

This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. An adaptive implementation of 1-D convolutional neural networks (CNNs) is inherently used to fuse the two major blocks of the ECG classification into a single learning body: feature extraction and classification. Therefore, for each patient, an individual and simple CNN will be trained by using relatively small common and patient-specific training data, and thus, such patient-specific feature extraction ability can further improve the classification performance. Since this also negates the necessity to extract hand-crafted manual features, once a dedicated CNN is trained for a particular patient, it can solely be used to classify possibly long ECG data stream in a fast and accurate manner or alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. The results over the MIT-BIH arrhythmia benchmark database demonstrate that the proposed solution achieves a superior classification performance than most of the state-of-the-art methods for the detection of ventricular ectopic beats and supraventricular ectopic beats. Besides the speed and computational efficiency achieved, once a dedicated CNN is trained for an individual patient, it can solely be used to classify his/her long ECG records such as Holter registers in a fast and accurate manner. Due to its simple and parameter invariant nature, the proposed system is highly generic, and, thus, applicable to any ECG dataset.

Convolutional Neural Networks for patient-specific ECG classification.

We propose a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system using an adaptive implementation of 1D Convolutional Neural Networks (CNNs) that can fuse feature extraction and classification into a unified learner. In this way, a dedicated CNN will be trained for each patient by using relatively small common and patient-specific training data and thus it can also be used to classify long ECG records such as Holter registers in a fast and accurate manner. Alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. The experimental results demonstrate that the proposed system achieves a superior classification performance for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB).

Computational modeling of electrocardiograms: A finite element approach toward cardiac excitation

AbstractThe objective of this work is the computational simulation of a patient‐specific electrocardiogram (EKG) using a novel, robust, efficient, and modular finite element‐based simulation tool for cardiac electrophysiology. We apply a two‐variable approach in terms of a fast action potential and a slow recovery variable, whereby the latter phenomenologically summarizes the concentration of ionic currents. The underlying algorithm is based on a staggered solution scheme in which the action potential is introduced globally as nodal degree of freedom, while the recovery variable is treated locally as internal variable on the integration point level. We introduce an unconditionally stable implicit backward Euler scheme to integrate the evolution equations for both variables in time, and an incremental iterative Newton–Raphson scheme to solve the resulting nonlinear system of equations. In a straightforward post‐processing step, we calculate the flux of the action potential and integrate it over the entire domain to obtain the heart vector. The projection of the heart vector onto six pre‐defined directions in space defines a six‐lead EKG. We illustrate its generation in terms of a magnetic resonance‐based patient‐specific heart geometry and discuss the clinical implications of the computational electrocardiography. Copyright © 2009 John Wiley & Sons, Ltd.

A patient-adaptable ECG beat classifier using a mixture of experts approach.

We present a "mixture-of-experts" (MOE) approach to develop customized electrocardiogram (ECG) beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care. A small customized classifier is developed based on brief, patient-specific ECG data. It is then combined with a global classifier, which is tuned to a large ECG database of many patients, to form a MOE classifier structure. Tested with MIT/BIH arrhythmia database, we observe significant performance enhancement using this approach.