An Advanced Two-Step DNN-Based Framework for Arrhythmia Detection
ABSTRACT: Heart arrhythmia is a severe heart problem. Automated heartbeat classification provides a cost-effective screening for heart arrhythmia and allows at-risk patients to receive timely treatments, which is a highly demanded but challenging task. Recent works have brought visible improvements to this area, but to identify the problematic supraventricular ectopic (S-type) heartbeats is still a bottleneck in most existing studies. This paper presents a two-step DNN-based framework to identify arrhythmia-related heartbeats. In the first step, a deep dual-channel convolutional neural network (DDCNN) is proposed to classify all heartbeat classes, except for the normal and S-type heartbeats. In the second stage, a central-towards LSTM supportive model (CLSM) is specially designed to distinguish S-type heartbeats from the normal ones. By processing heart rhythms in central-towards directions, CLSM learns and abstracts hidden temporal information between a heartbeat and its neighbors to reveal the deep differences between the two heartbeat types. As an improvement, we also propose a rule-based data augmentation method to solve the training data imbalance problem. The proposed framework is evaluated over three real-world ECG databases. The results show that our method outperforms the baselines in most evaluation metrics.
Project description:Heartbeat classification is an important step in the early-stage detection of cardiac arrhythmia, which has been identified as a type of cardiovascular diseases (CVDs) affecting millions of people around the world. The current progress on heartbeat classification from ECG recordings is facing a challenge to achieve high classification sensitivity on disease heartbeats with a satisfied overall accuracy. Most of the work take individual heartbeats as independent data samples in processing. Furthermore, the use of a static feature set for classification of all types of heartbeats often causes distractions when identifying supraventricular (S) ectopic beats. In this work, a pyramid-like model is proposed to improve the performance of heartbeat classification. The model distinguishes the classification of normal and S beats and takes advantage of the neighbor-related information to assist identification of S bests. The proposed model was evaluated on the benchmark MIT-BIH-AR database and the St. Petersburg Institute of Cardiological Technics(INCART) database for generalization performance measurement. The results reported prove that the proposed pyramid-like model exhibits higher performance than the state-of-the-art rivals in the identification of disease heartbeats as well as maintains a reasonable overall classification accuracy.
Project description:BACKGROUND AND OBJECTIVE:Cardiac arrhythmia, which is an abnormal heart rhythm, is a common clinical problem in cardiology. Detection of arrhythmia on an extended duration electrocardiogram (ECG) is done based on initial algorithmic software screening, with final visual validation by cardiologists. It is a time consuming and subjective process. Therefore, fully automated computer-assisted detection systems with a high degree of accuracy have an essential role in this task. In this study, we proposed an effective deep neural network (DNN) model to detect different rhythm classes from a new ECG database. METHODS:Our DNN model was designed for high performance on all ECG leads. The proposed model, which included both representation learning and sequence learning tasks, showed promising results on all 12-lead inputs. Convolutional layers and sub-sampling layers were used in the representation learning phase. The sequence learning part involved a long short-term memory (LSTM) unit after representation of learning layers. RESULTS:We performed two different class scenarios, including reduced rhythms (seven rhythm types) and merged rhythms (four rhythm types) according to the records from the database. Our trained DNN model achieved 92.24% and 96.13% accuracies for the reduced and merged rhythm classes, respectively. CONCLUSION:Recently, deep learning algorithms have been found to be useful because of their high performance. The main challenge is the scarcity of appropriate training and testing resources because model performance is dependent on the quality and quantity of case samples. In this study, we used a new public arrhythmia database comprising more than 10,000 records. We constructed an efficient DNN model for automated detection of arrhythmia using these records.
Project description:The automatic interpretation of electrocardiography (ECG) data can provide continuous analysis of heart activity, allowing the effective use of wireless devices such as the Holter monitor.We propose an intelligent heartbeat monitoring system to detect the possibility of arrhythmia in real time. We detected heartbeats and extracted features such as the QRS complex and P wave from ECG signals using the Pan-Tompkins algorithm, and the heartbeats were then classified into 16 types using a decision tree.We tested the sensitivity, specificity, and accuracy of our system against data from the MIT-BIH Arrhythmia Database. Our system achieved an average accuracy of 97% in heartbeat detection and an average heartbeat classification accuracy of above 96%, which is comparable with the best competing schemes.This work provides a guide to the systematic design of an intelligent classification system for decision support in Holter ECG monitoring.
Project description:Arrhythmia detection by classifying ECG heartbeats is an important research topic for healthcare. Recently, deep learning models have been increasingly applied to ECG classification. Among them, most methods work in three steps: preprocessing, heartbeat segmentation and beat-wise classification. However, this methodology has two drawbacks. First, explicit heartbeat segmentation can undermine model simplicity and compactness. Second, beat-wise classification risks losing inter-heartbeat context information that can be useful to achieving high classification performance. Addressing these drawbacks, we propose a novel deep learning model that can simultaneously conduct heartbeat segmentation and classification. Compared to existing methods, our model is more compact as it does not require explicit heartbeat segmentation. Moreover, our model is more context-aware, for it takes into account the relationship between heartbeats. To achieve simultaneous segmentation and classification, we present a Faster R-CNN based model that has been customized to handle ECG data. To characterize inter-heartbeat context information, we exploit inverted residual blocks and a novel feature fusion subroutine that combines average pooling with max-pooling. Extensive experiments on the well-known MIT-BIH database indicate that our method can achieve competitive results for ECG segmentation and classification.
Project description:Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to minimize infectious disease contagions. However, in this system, it is difficult to immediately act against infectious disease because of missing and delayed reports. Moreover, infectious disease trends are not known, which means prediction is not easy. This study predicts infectious diseases by optimizing the parameters of deep learning algorithms while considering big data including social media data. The performance of the deep neural network (DNN) and long-short term memory (LSTM) learning models were compared with the autoregressive integrated moving average (ARIMA) when predicting three infectious diseases one week into the future. The results show that the DNN and LSTM models perform better than ARIMA. When predicting chickenpox, the top-10 DNN and LSTM models improved average performance by 24% and 19%, respectively. The DNN model performed stably and the LSTM model was more accurate when infectious disease was spreading. We believe that this study's models can help eliminate reporting delays in existing surveillance systems and, therefore, minimize costs to society.
Project description:Mounting evidence suggests that interoceptive signals are fundamentally important for the experience of the self. Thus far, studies on interoception have mainly focused on the ability to monitor the timing of ongoing heartbeats and on how these influence emotional and self-related processes. However, cardiac afferent signalling is not confined to heartbeat timing and several other cardiac parameters characterize cardiodynamic functioning. Building on the fact that each heart has its own self-specific cardio-dynamics, which cannot be expressed uniquely by heart rate, we devised a novel task to test whether people could recognize the sound of their own heart even when perceived offline and thus not in synchrony with ongoing heartbeats. In a forced-choice paradigm, participants discriminated between sounds of their own heartbeat (previously recorded with a Doppler device) versus another person's heart. Participants identified the sound of their own heart above chance, whereas their metacognition of performance - as calculated by contrasting performance against ratings of confidence - was considerably poorer. These results suggest an implicit access to fine-grained neural representations of elementary cardio-dynamic parameters beyond heartbeat timing.
Project description:Emotional trauma and psychological stress can precipitate cardiac arrhythmia and sudden death through arrhythmogenic effects of efferent sympathetic drive. Patients with preexisting heart disease are particularly at risk. Moreover, generation of proarrhythmic activity patterns within cerebral autonomic centers may be amplified by afferent feedback from a dysfunctional myocardium. An electrocortical potential reflecting afferent cardiac information has been described, reflecting individual differences in interoceptive sensitivity (awareness of one's own heartbeats). To inform our understanding of mechanisms underlying arrhythmogenesis, we extended this approach, identifying electrocortical potentials corresponding to the cortical expression of afferent information about the integrity of myocardial function during stress. We measured changes in cardiac response simultaneously with electroencephalography in patients with established ventricular dysfunction. Experimentally induced mental stress enhanced cardiovascular indices of sympathetic activity (systolic blood pressure, heart rate, ventricular ejection fraction, and skin conductance) across all patients. However, the functional response of the myocardium varied; some patients increased, whereas others decreased, cardiac output during stress. Across patients, heartbeat-evoked potential amplitude at left temporal and lateral frontal electrode locations correlated with stress-induced changes in cardiac output, consistent with an afferent cortical representation of myocardial function during stress. Moreover, the amplitude of the heartbeat-evoked potential in the left temporal region reflected the proarrhythmic status of the heart (inhomogeneity of left ventricular repolarization). These observations delineate a cortical representation of cardiac function predictive of proarrhythmic abnormalities in cardiac repolarization. Our findings highlight the dynamic interaction of heart and brain in stress-induced cardiovascular morbidity.
Project description:Arrhythmia constitutes a problem with the rate or rhythm of the heartbeat, and an early diagnosis is essential for the timely inception of successful treatment. We have jointly optimized the entire multi-stage arrhythmia classification scheme based on 12-lead surface ECGs that attains the accuracy performance level of professional cardiologists. The new approach is comprised of a three-step noise reduction stage, a novel feature extraction method and an optimal classification model with finely tuned hyperparameters. We carried out an exhaustive study comparing thousands of competing classification algorithms that were trained on our proprietary, large and expertly labeled dataset consisting of 12-lead ECGs from 40,258 patients with four arrhythmia classes: atrial fibrillation, general supraventricular tachycardia, sinus bradycardia and sinus rhythm including sinus irregularity rhythm. Our results show that the optimal approach consisted of Low Band Pass filter, Robust LOESS, Non Local Means smoothing, a proprietary feature extraction method based on percentiles of the empirical distribution of ratios of interval lengths and magnitudes of peaks and valleys, and Extreme Gradient Boosting Tree classifier, achieved an F1-Score of 0.988 on patients without additional cardiac conditions. The same noise reduction and feature extraction methods combined with Gradient Boosting Tree classifier achieved an F1-Score of 0.97 on patients with additional cardiac conditions. Our method achieved the highest classification accuracy (average 10-fold cross-validation F1-Score of 0.992) using an external validation data, MIT-BIH arrhythmia database. The proposed optimal multi-stage arrhythmia classification approach can dramatically benefit automatic ECG data analysis by providing cardiologist level accuracy and robust compatibility with various ECG data sources.
Project description:In the mammalian embryo, the primitive tubular heart starts beating during the first trimester of gestation. These early heartbeats originate from calcium-induced contractions of the developing heart muscle cells. To explain the initiation of this activity, two ideas have been presented. One hypothesis supports the role of spontaneously activated voltage-gated calcium channels, whereas the other emphasizes the role of Ca(2+) release from intracellular stores initiating spontaneous intracellular calcium oscillations. We show with experiments that both of these mechanisms coexist and operate in mouse cardiomyocytes during embryonic days 9-11. Further, we characterize how inositol-3-phosphate receptors regulate the frequency of the sarcoplasmic reticulum calcium oscillations and thus the heartbeats. This study provides a novel view of the regulation of embryonic cardiomyocyte activity, explaining the functional versatility of developing cardiomyocytes and the origin and regulation of the embryonic heartbeat.
Project description:Accurate quantification of heartbeats in fish models is an important readout to study cardiovascular biology, disease states and pharmacology. However, dependence on anaesthesia, laborious sample orientation or requirement for fluorescent reporters have hampered the use of high-throughput heartbeat analysis. To overcome these limitations, we established an efficient screening assay employing automated label-free heart rate determination of randomly oriented, non-anesthetized medaka (Oryzias latipes) and zebrafish (Danio rerio) embryos in microtiter plates. Automatically acquired bright-field data feeds into an easy-to-use HeartBeat software with graphical user interface for automated quantification of heart rate and rhythm. Sensitivity of the assay was demonstrated by profiling heart rates during entire embryonic development. Our analysis revealed rapid adaption of heart rates to temperature changes, which has implications for standardization of experimental layout. The assay allows scoring of multiple embryos per well enabling a throughput of >500 embryos per 96-well plate. In a proof of principle screen for compound testing, we captured concentration-dependent effects of nifedipine and terfenadine over time. Our novel assay permits large-scale applications ranging from phenotypic screening, interrogation of gene functions to cardiovascular drug development.