Abnormal Heartbeats Research Articles

부정맥 판독을 위한 형태변형 및 간격변형 기반 특이심박 검출

부정맥 판독을 위한 형태변형 및 간격변형 기반 특이심박 검출

Enhancement in the Detection of Atrial Fibrillation Arrhythmia For Health Monitoring System

In day to day life, continuous monitoring of medical data is essential for many individuals including patients with severe health risks and elderly people. In this paper, one such system is developed for continuous monitoring of Atrial Fibrillation abnormality. Electrocardiographic (ECG) signal monitoring plays a vital role in the management of patients with atrial fibrillation (AF). Atrial Fibrillation (AF) is a type of abnormality in heart, it causes during the AF electrical discharges in the atrium are rapid and results in an abnormal heartbeat. In this paper, ECG signals taken from the MIT-BIH arrhythmia database. After the signal is acquired, the hybrid filtering technique is used to remove the artifacts. Naturally, the ECG signal gets distorted by different types of artifacts which must be removed from the signal otherwise it will convey incorrect information regarding the patient’s heart condition. Efficient LMS and Normalized LMS adaptive filters are computationally used for cancellation of noise. Analyzing functions of the filtered signal is Peak Signal to Noise Ratio (PSNR), Mean Square Normalized Error Performance (MSE), Maximum Square Error (MAXERR), the ratio of Squared Norms (L2RAT). The continuous health statistics will be given to individuals and caretaker in the remote location so that they can take necessary action to prevent from health issues. The paper will provide primitive solutions in the field of telemedicine using continuous health monitoring and medical data analysis of a particular individual.In future work, the ECG sensor will acquire the ECG signal and the acquired signal processed through the classification algorithm for classifying the signal into different categories. The biosensor is a combination of the biological element with the physiochemical transducer to produce an electronic signal which can be further converted, processed and transmitted for data analytics, processing, validation, visualization, interpretation, and data logging.

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

Abnormal heart rhythms are one of the significant health concerns worldwide. The current state-of-the-art to recognize and classify abnormal heartbeats is manually performed by visual inspection by an expert practitioner. This is not just a tedious task; it is also error prone and, because it is performed, post-recordings may add unnecessary delay to the care. The real key to the fight to cardiac diseases is real-time detection that triggers prompt action. The biggest hurdle to real-time detection is represented by the rare occurrences of abnormal heartbeats and even more are some rare typologies that are not fully represented in signal datasets; the latter is what makes it difficult for doctors and algorithms to recognize them. This work presents an automated heartbeat classification based on nonlinear morphological features and a voting scheme suitable for rare heartbeat morphologies. Although the algorithm is designed and tested on a computer, it is intended ultimately to run on a portable i.e., field-programmable gate array (FPGA) devices. Our algorithm tested on Massachusetts Institute of Technology- Beth Israel Hospital(MIT-BIH) database as per Association for the Advancement of Medical Instrumentation(AAMI) recommendations. The simulation results show the superiority of the proposed method, especially in predicting minority groups: the fusion and unknown classes with 90.4% and 100%.

Decomposition of higher-order spectra for blind multiple-input deconvolution, pattern identification and separation

Like the ordinary power spectrum, higher-order spectra (HOS) describe signal properties that are invariant under translations in time. Unlike the power spectrum, HOS retain phase information from which details of the signal waveform can be recovered. Here we consider the problem of identifying multiple unknown transient waveforms which recur within an ensemble of records at mutually random delays. We develop a new technique for recovering filters from HOS whose performance in waveform detection approaches that of an optimal matched filter, requiring no prior information about the waveforms. Unlike previous techniques of signal identification through HOS, the method applies equally well to signals with deterministic and non-deterministic HOS. In the non-deterministic case, it yields an additive decomposition, introducing a new approach to the separation of component processes within non-Gaussian signals having non-deterministic higher moments. We show a close relationship to minimum-entropy blind deconvolution (MED), which the present technique improves upon by avoiding the need for numerical optimization, while requiring only numerically stable operations of time shift, element-wise multiplication and averaging, making it particularly suited for real-time applications. The application of HOS decomposition to real-world signals is demonstrated with blind denoising, detection and classification of normal and abnormal heartbeats in electrocardiograms.

Implementation of The Variable Data Transmission System of Abnormal ECG with Activity State

The cause of death among modern people are the highest death rate from heart disease and monitoring and management of ECG signals to cope with these heart diseases is necessary. ECG is a bio-signal that is an important criterion for determining the presence or absence of cardiac activity states. Recently, an attempt has been made to analyze and compare biological signals and physical activity information for accurate analysis and diagnosis. However, in order to measure the ECG data for a long time, a storage space of several Mbytes and a wide bandwidth for wireless transmission are required. To solve this problem, cost, time, and high-performance systems are additional required. The implemented system minimizes the amount of packets generated during wireless data transmission as well as abnormal heartbeat detection and activity information, and enables monitoring of heart activity status and activity information in real time through a smart phone. In order to evaluate the data packet transmission and restoration performance of the system implemented in this research, the MIT / BIH Arrhythmia Database 100 record was embedded in the system controller section and the packet was transmitted to the smartphone. In addition, ECG evaluation experiments were conducted according to the activity status during daily life. As a result of the performance evaluation, both experiments confirmed the data packet generated and signal restoration performance.

Electrocardiogram Feature Recognition Algorithm with Windowing and Adaptive Thresholding

Heartbeat abnormalities of human body can be diagnosed by observing electrocardiogram (ECG) signal. Traditional methods of analyzing ECG signals to determine a person’s abnormality are based on the expertise of cardiologists, where sometimes multiple interpretations or misinterpretations of the disorder occur. The development of pattern recognition methods nowadays have rapidly advanced so that make it possible to be applied to ECG signal. Certain feature of ECG required for pattern recognition are P, Q, R, S and T signals. In this paper, we propose a pattern recognition method for ECG features by using adaptive threshold to find P, Q, R, S, and T position. First, we find R signal defined by local peaks, P and T signal which are defined by maximum value from a specific window, S signal defined by local valleys of the ECG signal and the rest Q signal which is defined by minimum value between P and R signal. Then based on those information, we use 48 ECG signals that contain abnormality and 18 normal ECG signals from physionet database. Experimental results show that the accuracy level of our method to recognize P, Q, R, S and T signals are 96,52%, 95,88%, 96,56%, 98,35%, and 95,88% respectively for both normal and abnormal ECG signal.

Noise-tolerant modular neural network system for classifying ECG signal

Millions of electrocardiograms (ECG) are interpreted every year, requiring specialized training for accurate interpretation. Because automated and accurate classification ECG signals will improve early diagnosis of heart condition, several neural network (NN) approaches have been proposed for classifying ECG signals. Current strategies for a critical step, the preprocessing for noise removal, still are unsatisfactory. We propose a modular NN approach based on artificial noise injection, to improve the generalization capability of the resulting model. The NN classifier initially performed a fairly accurate recognition of four types of cardiac anomalies in simulated ECG signals with minor, moderate, severe, and extreme noise, with an average accuracy of 99.2%, 95.1%, 91.4%, and 85.2% respectively. Ultimately we discriminated normal and abnormal heartbeat patterns for single lead of raw ECG signals, obtained 95.7% of overall accuracy and 99.5% of Precision. Therefore, is a useful tool for the detection and diagnosis of cardiac abnormalities.

Multi-lead model-based ECG signal denoising by guided filter

The electrocardiogram (ECG) denoising is of paramount importance for accurate disease diagnosis, but individual differences bring great difficulties for ECG denoising, especially for Dynamic Electrocardiography (DCG). In this paper, a multi-lead model-based ECG signal denoising method is proposed, in which a guided filter is inherently adapted to denoise ECG signal. For each person, a patient-specific statistical model will be constructed by sparse autoencoder (SAE) which can effectively preserve the detailed signal features. Thus, the guided signal producing by the statistical model can perform well in the guided filter. Especially, even the sudden morphological changes, the denoised ECG signals can still be conserved. The results on the 12-lead Arrhythmia Database and the MIT-BIH Arrhythmia Database demonstrate that the signal-to-noise ratio (SNR) improvement of the proposed method can reach as high as 21.54 dB, and the mean squared error (MSE) is less than 0.0401. Besides achievement of minimum signal distortion in comparisons with the major of the current denoising algorithms for complex noise environment, the proposed method demonstrate robustness in the complex interferences, especially in tracing the sudden morphological changes of ECG signals. Due to the remarkable superiority in preserving diagnostic and detail features of ECG signals, the proposed method can handle ECG signals with abnormal heart beats, and then can improve the accuracy detection of the disease.

A Framework Analysing Patient Adherence Level in Cardiovascular Disease in Delhi-NCR Region

The deficit in the patient adherence level is considered to be the most problematic factor in chronic diseases. The cross-sectional study on cardiovascular disease related patients was conducted in hospitals of Delhi-NCR region in the month of July 2018-August 2018. The total respondents taken were about 144 and were selected from cardiovascular diseases such as coronary artery disease, heart attack, abnormal heart beat, heart valve disease, congenital heart disease, aortic disease, vascular disease etc. The respondents were from various age groups and the results were concluded on the basis of the treatment and medications. The reliability of Morisky Medication Adherence Scale-8 was upto 0.78 which is considered to be good and on the basis of adherence scale category, the widely held of patients were categorized under medium adherence level. The chief factors which seems to be responsible for low adherence level were-monotonous life schedule, long waiting times in hospitals, irregular visits, high cost of treatment, forgetfulness etc.

Caffeinated beverages and energy drink: pattern, awareness and health side effects among Omani university students.

Objectives: Due to lack of scientific evidence in Oman regarding caffeine and energy drink consumption, the study aims to demonstrate its prevalence, awareness, and side effects. Methods: Cross-sectional study including apparently healthy 365 (18-35 years) university students using self-administered questionnaire. Results: The prevalence of caffeinated beverage consumption was high among participants (97%). Males preferred coffee (p<0.001), soda (p<0.008) while females consumed tea (p<0.029). Females were more aware about caffeine as ingredients of energy drink (p<0.036) and its adverse health effects (p<0.002). The prevalence of energy drink consumption was high in males (75%, p<0.001) with early age of first use 11-15 years (33.3%) as compared to females 16-20 years (51.1%). The major source of information was family and friends (58.3%). Red Bull was the preferred choice (55.5%) among participants for common reasons such as energy boost (68.4 %), taste (62.9%), reduce fatigue (52.1%), and better performance (47.3%). Females reported breathing problem, abnormal heart beat (p<0.004, 0.054, respectively), while males reported irritability (p<0.052). Conclusion: The prevalence of caffeinated beverage and energy drink consumption is high with lack of knowledge and awareness among participants.

Automatic Classification of CAD ECG Signals With SDAE and Bidirectional Long Short-Term Network

Coronary artery disease (CAD) has been one of main causes of heart diseases globally. The electrocardiogram (ECG) is a widely used diagnostic tool to monitor patients’ heart activities, and medical personnel need to judge whether there are abnormal heartbeats according to captured results. Therefore, it is significant to identify ECG signals accurately and fast. In this paper, a fast and accurate electrocardiogram (ECG) classification system based on deep learning is proposed. In our model, stacked denoising autoencoders (SDAE), as encoder, automatically learns semantic encoding of heartbeats without any complex feature extraction in unsupervised way. Then bidirectional LSTM (Bi-LSTM) classifier achieves classification of heartbeats with semantic encoding. SDAE implements noise-reduction while Bi-LSTM takes full advantage of temporal information in data. At the same time, this method relieves impacts from unbalanced data by employing cost-sensitive loss function. We validate our model on MIT-BIH Arrhythmias Database, SVDB and NSTDB respectively. Compared with state-of-art methods, the final result verify that this newly proposed method not only has high accuracy but also boosts classifying efficiency.

Automated heartbeat classification and detection of arrhythmia using optimal orthogonal wavelet filters

Abstract Electrocardiogram (ECG) is commonly used to analyze heartbeats. Arrhythmia is a disturbance of the heartbeat that can potentially be lethal. Arrhythmia can be detected by identifying an individual abnormal heartbeat, which can occur in isolation or sequentially. Heartbeats can be classified into five types: non-ectopic (N), supra ventricular ectopic (S), ventricular ectopic (V), fusion (F) and unknown beats (Q). Automated classification of the incidence and pattern of abnormal heartbeats enables arrhythmia detection, triage and life-threatening arrhythmia. Therefore, it is vital to develop a tool for the classification of these classes. ECG signals are contaminated by noise rendering difficulty and challenging to discriminate between different types of heartbeats. In this work, we used noisy as well as denoised (clean) ECG signals to classify heartbeats. Using stop-band energy (SBE) minimized dyadic orthogonal filter bank, wavelet decomposition of the ECG signals was performed. After this, fuzzy entropy, Renyi entropy and fractal dimension features were extracted for easier and accurate classification into the five classes. These features were then fed to classifiers, achieving maximum accuracy (MAAC) of 98%, maximum sensitivity (MASE) of 85.33% and maximum specificity (MASP) of 98.22% for noisy data, and MAAC of 98.1%, MASE of 85.63% and MASP of 98.27% for clean data, with KNN classifier. Our developed system can be used in intensive care units to assist the clinicians to aid in their diagnosis.

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.

A robust deep convolutional neural network with batch-weighted loss for heartbeat classification

The early detection of abnormal heart rhythm has become crucial due to the spike in the rate of deaths caused by cardiovascular diseases. While many existing works tried to classify heartbeats accurately, they suffered from the imbalance between heartbeat classes in the available ECG datasets since abnormal heartbeats appear much less frequently than normal ones. In addition, most of existing methods heavily rely on data preprocessing such as noise removal and feature extraction, which is computationally expensive, thus limits their use on low-cost portable ECG devices.We present a novel deep convolutional neural network based on state-of-the-art deep learning techniques for accurate heartbeat classification. We suggest a batch-weighted loss function to better quantify the loss in order to overcome the imbalance between classes. The loss weights dynamically change as the distribution of classes in each batch changes. Also, we propose to use multiple heartbeats for more effective heartbeat classification.Even though we use ECG signal from one lead only without any data preprocessing, our method consistently outperforms existing methods of 5-class heartbeat classification. Our accuracy, positive productivity, sensitivity and specificity under intra-patient paradigm are 99.48%, 98.83%, 96.97% and 99.87%, and those under inter-patient paradigm are 88.34%, 48.25%, 90.90% and 88.51% respectively.

Phonocardiographic Sensing Using Deep Learning for Abnormal Heartbeat Detection

Deep learning-based cardiac auscultation is of significant interest to the healthcare community as it can help reducing the burden of manual auscultation with automated detection of abnormal heartbeats. However, the problem of automatic cardiac auscultation is complicated due to the requirement of reliable and highly accurate systems, which are robust to the background noise in the heartbeat sound. In this paper, we propose a Recurrent Neural Networks (RNNs)-based automated cardiac auscultation solution. Our choice of RNNs is motivated by their great success of modeling sequential or temporal data even in the presence of noise. We explore the use of various RNN models, and demonstrate that these models significantly outperform the best reported results in the literature. We also present the run-time complexity of various RNNs, which provides insight about their complexity versus performance trade-offs.

An Early Drowning Detection System for Internet of Things (IoT) Applications

Drowning is the leading cause of injury or death for children and teenagers. Designing a drowning detection device by implementing an Internet of Thing (IoT) is needed. An Early Drowning Detection System (EDDS) is a system that gives an early alarm to the guardians (parents and lifeguard) if the detector triggered an abnormal heartbeat and the victims are submerged under the water for a long time. A microcontroller was used to control the signal received from a pulse sensor and time for the signal lost under the water before it is transmitted to the access point. The access point acts as a data forwarding to the database via an internet connection. Universal Asynchronous Receiver/Transmitter (UART) 433MHz radio frequency transceiver has been used to create the wireless communication between drowning detection device and monitoring hub. A triggered warning signal will be transmitted to the guardians via Android apps and web page.

The Influence of Vitamin E on Change Degrees of Dismenorhea in Midwifery Students of Muslim University of Indonesia

Dysmenorrhea or menstrual pain is the most common symptom in women of reproductive age. This condition is often associated with various other symptoms that occur before menstruation, such as headache, nausea, vomiting, diarrhea, abnormal heartbeat, and sweating. Dysmenorrhea is associated with increased prostaglandin levels before menstruation. Vitamin E may reduce menstrual pain, through inhibition of prostaglandin biosynthesis. The purpose of this research is to influence the effect of Vitamin E on the change of degrees of dysmenorrhea in the students of Prodi DIII Kebidanan UMI. The research used was experimental research with one group pretest posttest design with 29 research subjects. All subjects were assessed with NRS to assess the degree of dysmenorrhea. Giving Vitamin E 400 IU was given 3 consecutive days before the first menstruation in the second cycle. The results of this study showed that twenty students who experienced dysmenorrhea, after being given vitamin E, there were 82.75% of respondents experienced a decrease in the degree of dysmenorrhea. There was a significant influence on the degree of dysmenorrhea and after the administration of vitamin E dose of 400 IU in the students of Prodi DIII Midwifery UMI (p = 0,000).

Very deep feature extraction and fusion for arrhythmias detection

The electrocardiogram (ECG) is a picture of heart electrical conduction, which is widely used to diagnose many types of diseases such as abnormal heartbeat rhythm (arrhythmia). However, it is very difficult to detect the abnormal ECG characteristics because of the nonlinearity and the complexity of ECG signals from one side, and the noise effect of these signals from the other side, which make it very difficult to perform direct information extraction. Therefore, in this study we propose a very deep convolutional neural network (VDCNN) by using small filters throughout the whole net to reduce the noise affect and improve the performance. Our approach introduces multi-canonical correlation analysis (MCCA), a method to learn selective adaptive layer’s features such that the resulting representations are highly linearly correlated and speed up the training task. Moreover, the Q-Gaussian multi-class support vector machine (QG-MSVM) is introduced for classification, an algorithm which has a better learning performance and generalization ability on ECG signals processing. As a result, we come up with expressively more accurate architecture which is able to differentiate between the normal (NSR) heartbeats and three common types of arrhythmia atrial fibrillation (A-Fib), atrial flutter (AFL), and paroxysmal supraventricular tachycardia (PSVT) without performing any noise filtering or pre-processing techniques. Experimental results show that the proposed algorithm outperforms the state-of-the-art methods.

Detection of abnormal heart conditions based on characteristics of ECG signals

Heart diseases are one of the most important death causes across the globe. Therefore, early detection of heart diseases is crucial to reduce the rising death rate. Electrocardiogram (ECG) is widely used to diagnose many types of heart diseases such as abnormal heartbeat rhythm (arrhythmia). However, the non-linearity and the complexity of the abnormal ECG signals make it very difficult to detect its characteristics. Besides, it may be time-consuming to check these ECG signals manually. To overcome these limitations, we have proposed fast and accurate classifier that simulates the diagnosis of the cardiologist to classify the ECG signals into normal and abnormal from a single lead ECG signal and better than other well-known classifiers. First, an accurate algorithm is used for correcting the ECG signals from noise and extracting the major features of each ECG signal. After that, we simulated the characteristics of the ECG signals and created the proposed classifier from these characteristics. Two Neural Network (NN) classifiers, four Support Vector Machine (SVM) classifiers and K-Nearest Neighbor (KNN) classifier are employed to classify the ECG signals and compared with the proposed classifier. The total 13 features extracted from each ECG signal used in the proposed algorithm and set as input to the other classifiers. Our algorithm is validated using all records of MIT-BIH arrhythmia database. Experimental results show that the proposed classifier demonstrates better performance than other classifiers and yielded the highest average classification accuracy of 99%. Thus, our algorithm has the possibility to be implemented in clinical settings.

A deep learning approach for ECG-based heartbeat classification for arrhythmia detection

Classification is one of the most popular topics in healthcare and bioinformatics, especially in relation to arrhythmia detection. Arrhythmias are irregularities in the rate or rhythm of the heartbeat which, in some cases, may occur sporadically in a subject’s daily life. To capture these infrequent events, a Holter device is usually employed to record long-term ECG data. Therefore, the automatic recognition of abnormal heartbeats from a large amount of ECG data is an important and essential task. In the last two decades, a huge number of methods have been proposed to address the problem of ECG beat classification. At the same time, deep learning has advanced rapidly since the early 2000s and now demonstrates a state-of-the-art performance in various fields. In this paper, we propose a novel deep learning approach for ECG beat classification. We have conducted the experiments on the well-known MIT–BIH Arrhythmia Database, and compared our results with the scientific literature. The final results show that our model is not only more efficient than the state of the art in terms of accuracy, but also competitive in terms of sensitivity and specificity.