Heartbeat Sequences Research Articles

Anthropomorphic diagnosis of runtime hidden behaviors in OpenMP multi-threaded applications

Extreme-scale computing involves hundreds of millions of threads with multi-level parallelism running on large-scale hierarchical and heterogeneous hardware. Some OpenMP multi-threaded applications increasingly suffer from runtime hidden behaviors owning to shared resource contention as well as software- and hardware-related problems. Such hidden behaviors can result in failure and inefficiencies and are among the main challenges in system resiliency. To minimize the impact of hidden behaviors, one must quickly and accurately detect and diagnose the hidden behaviors that cause the failures. However, it is difficult to identify hidden behaviors in the dynamic and noisy data collected by OpenMP multi-threaded monitoring infrastructures. This paper presents an anthropomorphic diagnosis framework for hidden behaviors of OpenMP multi-threaded applications. In the framework, we first design injected heartbeat functions for OpenMP multi-threaded applications. Then, we leverage the heartbeat sequences to extract features of hidden behaviors. Finally, we develop a feature learning-based algorithm using heartbeat analysis, namely HSA, to diagnose hidden behaviors. To evaluate our framework, the NAS Parallel NPB benchmark, EPCC OpenMP micro-benchmark suite, and Jacobi benchmark are used to test the performance of our proposed framework. The experimental results demonstrate that our framework successfully identifies 90.3% of the injected hidden behaviors of OpenMP multi-threaded applications while acquiring low overhead.

A training pipeline of an arrhythmia classifier for atrial fibrillation detection using Photoplethysmography signal.

Photoplethysmography (PPG) signal is potentially suitable in atrial fibrillation (AF) detection for its convenience in use and similarity in physiological origin to electrocardiogram (ECG). There are a few preceding studies that have shown the possibility of using the peak-to-peak interval of the PPG signal (PPIp) in AF detection. However, as a generalized model, the accuracy of an AF detector should be pursued on the one hand; on the other hand, its generalizability should be paid attention to in view of the individual differences in PPG manifestation of even the same arrhythmia and the existence of sub-types. Moreover, a binary classifier for atrial fibrillation and normal sinus rhythm is not convincing enough for the similarity between AF and ectopic beats. In this study, we project the atrial fibrillation detection as a multiple-class classification and try to propose a training pipeline that is advantageous both to the accuracy and generalizability of the classifier by designing and determining the configurable options of the pipeline, in terms of input format, deep learning model (with hyperparameter optimization), and scheme of transfer learning. With a rigorous comparison of the possible combinations of the configurable components in the pipeline, we confirmed that first-order difference of heartbeat sequence as the input format, a 2-layer CNN-1-layer Transformer hybridR model as the learning model and the whole model fine-tuning as the implementing scheme of transfer learning is the best combination for the pipeline (F1 value: 0.80, overall accuracy: 0.87)R.

An explainable attention-based TCN heartbeats classification model for arrhythmia detection

Background and Objective:Electrocardiogram (ECG) is a non-invasive tool to measure the heart’s electrical activity. ECG signal based automatic heartbeat classification is a critical task for arrhythmia detection and continues to be challenging. While diverse automated classification methods have been developed, they still cannot provide acceptable performance in classifying different heartbeats because of their poor ability to extract abstract patterns comprehensively. Besides, the performance of previous work drops sharply when dealing with imbalanced datasets and lacks interpretability. Methods:This paper proposes a novel, explainable attention-based temporal convolutional network(TCN) heartbeat classification method. The first contribution of our approach is that we fuse the TCN architecture and self-attention mechanism to encode the ECG heartbeat sequences. Specifically, TCN and the self-attention block are designed to capture global variation tends and local features, respectively, to best serve the classification. Meanwhile, multi-class focal loss helps model training overcome the class imbalance problem. In the end, the dynamic perturbation based high-fidelity explanation module was introduced to understand the AI-based model and enhance the model’s transparency to clinicians. Conclusions:Experiments on the MIT-BIH-AD dataset demonstrate that our model with a simpler architecture can achieve 99.84% accuracy, 99.90% specificity and 99.60% precision for the intra-patient scheme and 87.81% accuracy, 91.85% sensitivity and 89.81% precision for the inter-patient scheme, which outperforms most of the state-of-the-art(SOTA) works, especially for minority classes.

Multifractal Detrended Fluctuation Analysis of Congestive Heart Failure Disease Based on Constructed Heartbeat Sequence

Heart rate variability (HRV) can be used as a common detection method for congestive heart failure (CHF). Existing researches regarding HRV, including both linear indicators and nonlinear characteristics, are mostly based on the RR intervals of the ECG signal. This article proposed a sequence that can reflect the regulation of sympathetic and parasympathetic nerve on heart rate, and on this basis, conducted multifractal detrended fluctuation analysis (MFDFA). We extracted multifractal features to quantitatively compare the complexity of proposed sequence between the healthy and CHF groups. Results showed that abnormal physiological and pathological conditions due to the weakening of autonomic nerve control can reduce the complexity of the heartbeat signal. Estimate the separation performance of all features, the best discrimination is obtained for the area under the mass index spectrum S1 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">τ</sub> as providing 100% accuracy in separating the Healthy Young and CHF groups, and 90.93% separation accuracy between the Healthy Elderly and CHF groups. This work provide a good basis for the diagnosis of CHF with a novel perspective.

A convolutional neural network approach to detect congestive heart failure

Congestive Heart Failure (CHF) is a severe pathophysiological condition associated with high prevalence, high mortality rates, and sustained healthcare costs, therefore demanding efficient methods for its detection. Despite recent research has provided methods focused on advanced signal processing and machine learning, the potential of applying Convolutional Neural Network (CNN) approaches to the automatic detection of CHF has been largely overlooked thus far. This study addresses this important gap by presenting a CNN model that accurately identifies CHF on the basis of one raw electrocardiogram (ECG) heartbeat only, also juxtaposing existing methods typically grounded on Heart Rate Variability. We trained and tested the model on publicly available ECG datasets, comprising a total of 490,505 heartbeats, to achieve 100% CHF detection accuracy. Importantly, the model also identifies those heartbeat sequences and ECG’s morphological characteristics which are class-discriminative and thus prominent for CHF detection. Overall, our contribution substantially advances the current methodology for detecting CHF and caters to clinical practitioners’ needs by providing an accurate and fully transparent tool to support decisions concerning CHF detection.

Fertility Detection of Hatching Eggs Based on a Convolutional Neural Network

In order to achieve the goal of detecting the fertility of hatching eggs which are divided into fertile eggs and dead eggs more accurately and effectively, a novel method combining a convolution neural network (CNN) and a heartbeat signal of the hatching eggs is proposed in this paper. Firstly, we collected heartbeat signals of 9-day-later hatching eggs by the method of PhotoPlethysmoGraphy (PPG), which is a non-invasive method to detect the change of blood volume in living tissues by photoelectric means. Secondly, a sequential convolutional neural network E-CNN, which was used to analyze heartbeat sequence of hatching eggs, was designed. Thirdly, an end-to-end trainable convolutional neural network SR-CNN, which was used to process heartbeat waveform images of hatching eggs, was designed to improve the classification performance in this paper. Key to improving the classification performance of SR-CNN is the SE-Res module, which combines the channel weighting unit “Squeeze-and-Excitation” (SE) block and the residual structure. The experimental results show that two models trained on our dataset, with E-CNN and SR-CNN, are able to achieve the fertility detection of the hatching eggs with superior identification accuarcy, up to 99.50% and 99.62% respectively, on our test set. It is demonstrated that the proposed method is feasible for identifying and classifying the survival of hatching eggs accurately and effectively.

Localization of Myocardial Infarction With Multi-Lead Bidirectional Gated Recurrent Unit Neural Network

Myocardial infarction (MI) is an acute disease. Early detection and early treatment are of great significance for improving the health of people. In order to reduce the misdiagnosis rate of MI diseases, this paper proposes a multi-lead bidirectional gated recurrent unit neural network (ML-BiGRU) learning algorithm based on current research status in the field of intelligent medical diagnosis, combined with the timing and multi-lead correlation characteristics of the electrocardiogram (ECG) signals. At first, the original ECG signal is denoised and preprocessed and then segmented into heartbeats. After that, the heartbeat sequence is sent to the deep neural network training model to learn the classification. Lastly, the Physikalisch-Technische Bundesanstalt (PTB) ECG database is used to verify the multi-lead BiGRU algorithm. The verification results demonstrate that the accuracy of the algorithm for MI localization is 99.84%, which outperform the other algorithms. The experimental results also show that the algorithm is obviously superior to the traditional localization algorithm in improving the localization accuracy, which is of great significance for improving the correct diagnosis rate of MI.

A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals

Atrial fibrillation (AF), a common abnormal heartbeat rhythm, is a life-threatening recurrent disease that affects older adults. Automatic classification is one of the most valuable topics in medical sciences and bioinformatics, especially the detection of atrial fibrillation. However, it is difficult to accurately explain the local characteristics of electrocardiogram (ECG) signals by manual analysis, due to their small amplitude and short duration, coupled with the complexity and non-linearity. Hence, in this paper, we propose a novel deep arrhythmia-diagnosis method, named deep CNN-BLSTM network model, to automatically detect the AF heartbeats using the ECG signals. The model mainly consists of four convolution layers: two BLSTM layers and two fully connected layers. The datasets of RR intervals (called set A) and heartbeat sequences (P-QRS-T waves, called set B) are fed into the above-mentioned model. Most importantly, our proposed approach achieved favorable performances with an accuracy of 99.94% and 98.63% in the training and validation set of set A, respectively. In the testing set (unseen data sets), we obtained an accuracy of 96.59%, a sensitivity of 99.93%, and a specificity of 97.03%. To the best of our knowledge, the algorithm we proposed has shown excellent results compared to many state-of-art researches, which provides a new solution for the AF automatic detection.

Permutation entropy improves fetal behavioural state classification based on heart rate analysis from biomagnetic recordings in near term fetuses

The relevance of the complexity of fetal heart rate fluctuations with regard to the classification of fetal behavioural states has not been satisfyingly clarified so far. Because of the short behavioural states, the permutation entropy provides an advantageous complexity estimation leading to the Kullback-Leibler entropy (KLE). We test the hypothesis that parameters derived from KLE can improve the classification of fetal behaviour states based on classical heart rate fluctuation parameters (SDNN, RMSSD, ln(LF), ln(HF)). From measured heartbeat sequences (35 healthy fetuses at a gestational age between 35 and 40 completed weeks) representative intervals of 256 heartbeats were visually preclassified into fetal behavioural states. Employing discriminant analysis to separate the states 1F, 2F and 4F, the best classification result by classical parameters was 80.0% (SDNN). After additionally considering KLE parameters it was improved significantly (p<0.0005) to 94.3% (ln(LF), KLE_Mean). It could be confirmed that KLE can improve the state classification. This might reflect the consideration of different physiological aspects by classical and complexity measures.

Long- and short-time analysis of heartbeat sequences: Correlation with mortality risk in congestive heart failure patients

We analyze RR heartbeat sequences with a dynamic model that satisfactorily reproduces both the long- and the short-time statistical properties of heart beating. These properties are expressed quantitatively by means of two significant parameters, the scaling delta concerning the asymptotic effects of long-range correlation, and the quantity 1-pi establishing the amount of uncorrelated fluctuations. We find a correlation between the position in the phase space (delta, pi) of patients with congestive heart failure and their mortality risk.

ASYMMETRIC ANOMALOUS DIFFUSION: AN EFFICIENT WAY TO DETECT MEMORY IN TIME SERIES

We study time series concerning rare events. The occurrence of a rare event is depicted as a jump of constant intensity always occurring in the same direction, thereby generating an asymmetric diffusion process. We consider the case where the waiting time distribution is an inverse power law with index μ. We focus our attention on μ&lt;3, and we evaluate the scaling exponent δ of the time in the resulting diffusion process. We prove that δ gets its maximum value, δ=1, corresponding to the ballistic motion, at μ=2. We study the resulting diffusion process by means of joint use of the continuous time random walk and of the generalized central limit theorem (CLT), as well as adopting a numerical treatment. We show that rendering the diffusion process to be asymmetric yields the significant benefit of enhancing the value of the scaling parameter δ. Furthermore, this scaling parameter becomes sensitive to the power index μ in the whole region 1&lt;μ&lt;3. Finally, we show our method in action on real data concerning human heartbeat sequences.

Receiver-Operating-Characteristic Analysis Reveals Superiority of Scale-Dependent Wavelet and Spectral Measures for Assessing Cardiac Dysfunction

Receiver-operating-characteristic analysis was used to assess the suitability of various heart-rate variability (HRV) measures for correctly classifying electrocardiogram records of varying lengths as normal or revealing the presence of heart failure. Scale-dependent HRV measures were found to be substantially superior to scale-independent measures (scaling exponents) for discriminating the two classes of data over a broad range of record lengths (hours to minutes). A jittered integrate-and-fire model built around a fractal Gaussian-noise kernel provides a realistic, though not perfect, simulation of heartbeat sequences.

A New Tool for Nonlinear Dynamical Analysis of Heart Rate Variability

Abstract In this paper we analyse the sequences of the time intervals between heart-beats-the RR intervals-by means of AIP (artificial insymmetration patterns) diagrams. The sequences were produced by artificial heartbeat sequences generated numerically and compared with sequences obtained from real heart activity. We hope that the AIP diagrams method will prove useful for a rapid qualitative assessment of dynamics from nonlinear time series, and that it is able to distinguish various types of heart dynamics (regular and pathological), while other diagnostical methods fail.