Detection Of Heart Abnormalities Research Articles

Prototype Realtime Detection Of Abnormal Heart Beat Using Multiple Back Propagation Neural Network (BPNN)

Real-time heart rate monitoring and early detection of heart abnormalities are vital to determine heart health before it worsens. To achieve this goal, this project uses the backpropagation neural network (BPNN) method including its capability to classify heartbeats into normal or abnormal by inputting heartbeat values in BPM units derived from prototypes utilizing sensors like Sensor Easy Pulse and NodeMCU, along with considerations of age and sports activity. All data from sensors will be stored in Firebase. Then Firebase will connect to Android, and the normal and abnormal heart classification results will be displayed on the Android system. Simulation results successfully examined 40 people as a sample and provided information from real-time heart rate monitoring, age, and sports activity as input. This research seeks to contribute to improving health services at various public health service centers and independently in detecting heart health early.

DAE-ConvBiLSTM: End-to-end learning single-lead electrocardiogram signal for heart abnormalities detection.

The electrocardiogram (ECG) is a widely used diagnostic that observes the heart activities of patients to ascertain a heart abnormality diagnosis. The artifacts or noises are primarily associated with the problem of ECG signal processing. Conventional denoising techniques have been proposed in previous literature; however, some lacks, such as the determination of suitable wavelet basis function and threshold, can be a time-consuming process. This paper presents end-to-end learning using a denoising auto-encoder (DAE) for denoising algorithms and convolutional-bidirectional long short-term memory (ConvBiLSTM) for ECG delineation to classify ECG waveforms in terms of the PQRST-wave and isoelectric lines. The denoising reconstruction using unsupervised learning based on the encoder-decoder process can be proposed to improve the drawbacks. First, The ECG signals are reduced to a low-dimensional vector in the encoder. Second, the decoder reconstructed the signals. The last, the reconstructed signals of ECG can be processed to ConvBiLSTM. The proposed architecture of DAE-ConvBiLSTM is the end-to-end diagnosis of heart abnormality detection. As a result, the performance of DAE-ConvBiLSTM has obtained an average of above 98.59% accuracy, sensitivity, specificity, precision, and F1 score from the existing studies. The DAE-ConvBiLSTM has also experimented with detecting T-wave (due to ventricular repolarisation) morphology abnormalities. The development architecture for detecting heart abnormalities using an unsupervised learning DAE and supervised learning ConvBiLSTM can be proposed for an end-to-end learning algorithm. In the future, the precise accuracy of the ECG main waveform will affect heart abnormalities detection in clinical practice.

QRS complex detection using stationary wavelet transform and adaptive thresholding

Purpose. Electrocardiogram (ECG) signal is a record of the electrical activity of the heart and contains important clinical data about cardiovascular-related misfunctioning. The goal of the present work is to develop an improved QRS detection algorithm for the detection of heart abnormalities. Methods. In this present work stationary wavelet transforms (SWT) based method has been proposed for precise detection of QRS complex with ‘sym2’ mother wavelet. The stationary wavelet transform is a systematic mathematical tool to decompose the signal without downsampling using scale analysis and provides high detection of QRS complex and accurate localization of signal components. In the proposed method four level of decomposition is applied and the initial thresholding value is computed by the maximum amplitude of scale one at level four in SWT coefficients without the zero-crossing amplitude detection method. The multi-layered dynamic thresholding method has been applied to detect the true R-peak values and locate the QRS complex in the ECG signal. Results. For evaluation of results, the presented methodology is assessed on MIT-BIH, QTDB, and Noise stress test databases. In MIT-BIH, the sensitivity = 99.88%, positive predictivity = 99.93%, accuracy = 99.80% and detection error rate = 0.18% is achieved. In NSTD database, sensitivity = 97.46%, positive predictivity = 94.20%, accuracy = 91.95% and detection error rate = 8.47% and in QTDB, sensitivity = 99.95%, positive predictivity = 99.90%, accuracy = 99.71% and detection error rate = 0.16% is executed. Conclusion. In the presented proposed methodology, the computation complexity is low and exhibits a simple technique rather than an empirical approach. The proposed technique corroborates the performance for the detection of QRS complex with improved accuracy.

Cloud based multivariate signal based heart abnormality detection

This paper discusses the rate of heartbeat monitoring with the help of face tracking, extraction of forehead region and separation of blind source. Separation of blind source is applied for three RGB color channel. Independent Component Analysis (ICA) is a powerful tool for such acquisitions. There are mainly four ICA algorithms and these algorithms have been described in the literature. In this paper contribution of two main common ICA algorithms has been studied. These methods are compared to each other in terms of their ability to obtain independent signal from standard RGB signal of forehead region. These methods are Joint Approximate Diagonalization of Eigen matrices (JADE) and Fixed-point ICA (FastICA). Same RGB data set have been applied to these common ICA algorithms and compared with the results of blood volume pulse (BVP) sensor readings. Both methods provide equally consistent results. However, FastICA has shown better results for heart rate measurement compared to JADE.

Deep learning approach of murmur detection using Cochleagram

Primary detection of heart abnormalities can reduce sudden death due to heart related diseases to a great extent. A low cost noninvasive phonocardiogram (PCG) signal based heart murmur detection system can serve the purpose partially. In this work, initially the heart sound signal is segmented using an unsupervised technique. After that, deep neural network (DNN) architectures with three hidden layers have been proposed to classify the input heart sound signal as normal or murmur class using 64 dimensional acoustic features. Total 3482 heart sound cycles from a standard database have been used to train and test the proposed DNN model. The usual evaluation matrices of sensitivity, specificity, precision, F1-score and accuracy have been derived to evaluate the performance of the classifier. After studying five different DNN models we have achieved the highest F1-score of 98.31% and accuracy of 98.33% for the murmur class. This murmur detection system can be extremely useful in rural health cares and small hospitals to assist the general physicians in assessing heart diseases without having expertise in cardiology.

Can 2D speckle tracking echocardiography be useful in venesection therapy monitoring in hereditary hemochromatosis patients?

Abstract Funding Acknowledgements Type of funding sources: None. Background. Hereditary hemochromatosis (HH) is a widespread genetic disease. Abnormally increased intestinal iron absorption and accelerated recycling of iron lead to progressive body iron accumulation and the generation of oxidative stress in tissues. Venesection is the primary treatment of such patients. Based on the fact that two-dimensional speckle tracking echocardiography (2D STE) can evaluate left ventricle (LV) dysfunction more accurately and earlier than conventional echocardiography, the purpose of our study was to check whether it could be helpful in venesection therapy monitoring. Methods. We prospectively enrolled 20 patients with genetically confirmed HH. Classic echocardiographic and 2D STE parameters (GE VIVID E9, EchoPAC v202) were analyzed at the moment of diagnosis, and after three years of treatment with venesection; 20 healthy age- and sex-matched persons constituted the control group. Results. The HH patients had all standard echo parameters within the normal range at the first and second examinations. The rotation parameters were initially worse than in healthy people at the first visit and significantly increased after three years of treatment. The radial strain after the treatment was significantly better for the apex but not for basal LV, apart from the epicardial layer (known as the layer of iron overload in the heart). Global longitudinal strain (GLS), significantly lower than in healthy people at the first visit, did not change within the treatment (Table). Conclusions. Venesection leads to significant improvement in the rotation function of LV and increases radial strain mainly in the epicardium, which seems to help monitor the treatment and guide the intensity of venesections. GLS, which is known as a parameter for early detection of heart abnormalities in asymptomatic HH patients, did not increase within the treatment and could reveal irreversible changes in LV. Abstract Figure.

Artificial Intelligence in Primary Care: An Overview.

With the increase in computing power and the development of numerous technological devices that facilitate remote work, the involvement of artificial intelligence in medicine has seen a significant increase to help the doctor make decisions and intervene in the medical process and telemedicine. In this paper, we gave an overview of the practical involvement of artificial intelligence through different support systems used in primary medicine or telemedicine and also identified the possibilities and opportunities for the development of new support systems for family medicine. Thus, we identified systems used for primary diagnosis, diagnosis of hypertension, early detection of heart abnormalities, detection of diabetes, support in the prescription process, helping clinicians in the daily workflow by providing certain answers to questions, treatment guidance, determining patient priority for treatment for SARS-CoV-2 infection or early detection of disease, support of artificial ventilation in medical emergency centers, remote support for treatments and medication.

Design of Robust Heart Abnormality Detection System based on Wavelet Denoising Algorithm

The technology that continues to be developed by many researchers today is an automatic heart attack detection system based on an Electrocardiogram (ECG) signal. Several other studies have been carried out to build an Internet of Things (IoT) based heart abnormality detection system. Based on the analysis of related studies that have been carried out previously, several researchers have developed an ECG signal-based heart abnormality detection system using clean ECG signal data. While the reality of the concept of an IoT-based detection system, the process of recording ECG signal data on the sensor, the process of sending data to the server, and the process of retrieving data from the server are vulnerable to noise exposure. ECG signal containing noise will greatly affect the accuracy of system detection. This paper proposes the development of a noise-resistant heart condition detection system using a wavelet denoising algorithm. The process of denoising ECG signals using the Wavelet algorithm is generally able to improve the accuracy of detecting noisy ECG signals. The most significant increase in accuracy is seen in the low SNR value. The Daubechies 4 (db4) denoising algorithm is the best-performing algorithm. The ECG signal classification method uses the Artificial Neural Network (ANN) algorithm. Detection system hardware is also designed in this research using the concept based on the Internet of Things.

Automatic segmentation for neonatal phonocardiogram.

This work addresses the automatic segmentation of neonatal phonocardiogram (PCG) to be used in the artificial intelligence-assisted diagnosis of abnormal heart sounds. The proposed novel algorithm has a single free parameter - the maximum heart rate. The algorithm is compared with the baseline algorithm, which was developed for adult PCG segmentation. When evaluated on a large clinical dataset of neonatal PCG with a total duration of over 7h, an F1 score of 0.94 is achieved. The main features relevant for the segmentation of neonatal PCG are identified and discussed. The algorithm is able to increase the number of cardiac cycles by a factor of 5 compared to manual segmentation, potentially allowing to improve the performance of heart abnormality detection algorithms.

Deep learning based classification of unsegmented phonocardiogram spectrograms leveraging transfer learning

Objective. Cardiovascular diseases (CVDs) are a main cause of deaths all over the world. This research focuses on computer-aided analysis of phonocardiogram (PCG) signals based on deep learning that can enable improved and timely detection of heart abnormalities. The two widely used publicly available PCG datasets are from the PhysioNet/CinC (2016) and PASCAL (2011) challenges. The datasets are significantly different in terms of the tools used for data acquisition, clinical protocols, digital storages and signal qualities, making it challenging to process and analyze. Approach. In this work, we have used short-time Fourier transform-based spectrograms to learn the representative patterns of the normal and abnormal PCG signals. Spectrograms generated from both the datasets are utilized to perform four different studies: (i) train, validate and test different variants of convolutional neural network (CNN) models with PhysioNet dataset, (ii) train, validate and test the best performing CNN structure on the PASCAL dataset, as well as (iii) on the combined PhysioNet-PASCAL dataset and (iv) finally, the transfer learning technique is employed to train the best performing pre-trained network from the first study with PASCAL dataset. Main results. The first study achieves an accuracy, sensitivity, specificity, precision and F1 scores of 95.75%, 96.3%, 94.1%, 97.52%, and 96.93%, respectively, while the second study shows accuracy, sensitivity, specificity, precision and F1 scores of 75.25%, 74.2%, 76.4%, 76.73%, and 75.42%, respectively. The third study shows accuracy, sensitivity, specificity, precision and F1 scores of 92.7%, 94.98%, 89.95%, 95.3% and 94.6%, respectively. Finally, the fourth study shows a precision of 96.98% on the noisy PASCAL dataset with transfer learning approach. Significance. The proposed approach employs a less complex and relatively light custom CNN model that outperforms most of the recent competing studies by achieving comparatively high classification accuracy and precision, making it suitable for screening CVDs using PCG signals.

Use of Wavelets in Electrocardiogram Research: a Literature Review

Currently the introduction and detection of heart abnormalities using electrocardiogram (ECG) is very much. ECG conducted many research approaches in various methods, one of which is wavelet. This article aims to explain the trends of ECG research using wavelet approach in the last ten years. We reviewed journals with the keyword title "ecg wavelet" and published from 2011 to 2020. Articles classified by the most frequently discussed topics include: datasets, case studies, pre-processing, feature extraction and classification/identification methods. The increase in the number of ECG-related articles in recent years is still growing in new ways and methods. This study is very interesting because only a few researchers focus on researching about it. Several approaches from many researchers are used to obtain the best results, both by using machine learning and deep learning. This article will provide further explanation of the most widely used algorithms against ECG research with wavelet approaches. At the end of this article it is also shown that the critical aspect of ECG research can be done in the future is the use of datasets, as well as the extraction of characteristics and classifications by looking at the level of accuracy.

RETRACTED ARTICLE: Accurate prediction of heart disease based on bio system using regressive learning based neural network classifier

Heart disease diagnosis is a very hard task in the medical field, so the mortality rate is increased every day. Also, the diagnosing process is implemented in recent times to predict heart disease. The method of diagnosing a disease in the medical field can be regarded not only as a new unknown situation to obtain clinical data and data collected from clinical experience, but also as a decision-making process as well as a doctor's diagnosis. The detection of heart abnormalities mainly depends on the examination of the ECG signal at the appropriate sampling period. The data is trained and tested must include more data to get the data as features. These properties are an accurate measure of the diagnosis of heart disease. The conventional system is having some problems like processing time is high, and it gives low accuracy, so the proposed Regressive Learning-Based Neural Network Classifier (RLNNC) system is implemented. The proposed system RLNNC presents a fully automated algorithm for the classification of heart disease, based on the Regressive Learning-Based Neural Network Classifier (RLNNC) and automated initial seed detection. With the advancement of machine learning and information technology, the development of an automated system. This can be predicted the same on this basis for patients with heart disease, and the drug occurs for the benefit of detecting and analyzing the heart disease. Analysis has shown that the proposed Regressive Learning-Based Neural Network Classifier (RLNNC) based techniques promote greater efficiency and higher accuracy than traditional methods.

Detection of Heart Abnormalities Using Signal Processing

Detection of Heart Abnormalities Using Signal Processing

Normal values of cardiac axis (CA) measurements in healthy fetuses during the first trimester screening ultrasound.

To report the normal fetal cardiac axis (CA) values at the time of the first trimester screening ultrasound. Standardized images and measurement of the CA were obtained from 100 healthy fetuses between 11+0and 13+6weeks of gestation along with the nucal thichkness (NT), Crown-rump length (CRL) and other measurements. We excluded cases with abnormal NT, later diagnosis of abnormalities, and suspected fetal cardiopathy during the pregnancy follow-up. Data analysis was performed after all the patients delivered and cardiopathy was excluded. CA was measurable in all the cases. Higher CRL was associated with a decrease in the CA. The mean±SD embryonic/fetal CA was 48±5,2°, ranging from 39 to 60°, The 2.5 percentile was defined at 40° and the 97.5 percentile at 59°. The Pearson test resulted in a significant correlation between CA and CRL with a coefficient R of 70% and p-value <0.01. CA tends to decrease at the 11 to 13+6 gestational ages. We defined 2.5 and 97.5% curves for the normal values of CA in our Middle Eastern population. Alarger study will be required to differentiate normal andabnormal values for the early detection of heart abnormalities.

Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features

Objective: Heart abnormality detection using heart sound signals (phonocardiogram (PCG)) has been an active research area for the last few decades. In this paper, automatic heart sound classification using segmented and unsegmented PCG signals is presented. Approach: In this paper: (i) we perform an in-depth analysis of various time and frequency domain features, followed by experimental determination of effective feature subsets for improved classification performance; (ii) both segmented and unsegmented PCG signals are studied and important results concerning the respective feature subsets and their classification performances are reported; and (iii) different classification algorithms, including the support vector machine, kth nearest neighbor, decision tree, ensemble classifier, artificial neural network and long short-term memory network (LSTMs), are employed to evaluate the performance of the proposed feature subsets and their comparison with other established features and methods is presented. Main results: It is observed that LSTM performs better on mel-frequency cepstral coefficient (MFCC) features extracted from unsegmented PCG data, with an area under curve (AUC) score of 91.39%, however, the MFCC features do not show a consistent performance with other classifiers (the second highest AUC score is 62.08% with the decision tree classifier). In contrast, in the case of time-frequency features from segmented data, the performance of all the classifiers is appreciable with AUC scores over 70%. In particular, the conventional machine learning techniques shows consistency in achieving over 80% in AUC scores. Significance: The results of this study highlight the importance of time and frequency domain features. Thus it is necessary to employ both the time and frequency features of segmented PCG signals to achieve improved classification.

P932 What is new in the assessment of patients with hereditary hemochromatosis - can 2D speckle tracking echocardiography be helpful in the early detection of heart abnormalities?

Abstract Background Hereditary hemochromatosis (HH) is a very common genetic disease. Abnormally increased intestinal iron absorption and accelerated recycling of iron lead to progressive body iron accumulation and the generation of oxidative stress in tissues. In the late stages iron overload of the heart can lead to the left ventricular (LV) dysfunction. It is believed, that two dimensional speckle tracking echocardiography (2D STE) can evaluate LV dysfunction more accurately and earlier than conventional echocardiography. Evaluation of such assessment was the purpose of this paper. Methods We prospectively enrolled 58 patients with genetically confirmed HH; 29 healthy age- and sex-matched volunteers constituted the control group. Classic echocardiographic and 2D STE parameters (GE VIVID E9, EchoPAC v201) were compared between the groups, additionally correlations between echo and iron turnover parameters were performed. Results The HH patients had all standard echo parameters within the normal range. All 2D STE parameters were significantly worse in HH than in control group (Table 1). We did not find any correlation between echo and iron turnover parameters, whilst significant correlation with the time from diagnosis and the numbers of venesections was noticed. Conclusions Novel 2D STE analyzes seem to be helpful in early detection of heart abnormalities in HH patients. The correlations between the echo and iron indices are weak, allowing to suggest the lack of a "direct" relationship between the iron turnover and myocardial function and that myocardial iron overload is not the only mechanism involved in development of HH cardiomyopathy. HH All n = 58 Controls n = 29 p LAVI (ml/BSA) 31 (23-37) 22 (19-27) &amp;lt;0.001 RWT 0.42 (0.38-0.47) 0.38 (0.34-0.43) &amp;lt;0.003 LVMI (g/BSA) 78 (58-96) 66 (53-72) &amp;lt;0.006 E/Em 7.0 (5.6-8.3) 6.7 (5.0-7.5) 0.071 LVEF (%) 60 (54-62) 63 (61-65) &amp;lt;0.006 LV twist (º) 17.2 (13.1-22.2) 24.1 (19.9-32.1) &amp;lt;0.001 LV torsion (º/cm) 2.2 (1.6-2.9) 3.3 (2.7-4.3) &amp;lt;0.001 LV peak rotation velocity (º/s) 118.0 (88.3-146.3) 140.0 (112.7-168.9) &amp;lt;0.015 LV peak untwisting velocity (º/s) -132.0 (-163.5–93.0) -156.0 (-197.0–122.6) &amp;lt;0.039 Peak systolic longitudinal strain (%) -18.3 (-20.0–16.9) -21.0 (-22.0–19.3) &amp;lt;0.001

Detection of Heart Abnormalities Using Fuzzy Decision Making and Wireless Transmission of Disease Information

Abstract This research work aims to implement the automatic detection of heart abnormality type without doctor’s assessment using electrocardiogram (ECG) features. First, the ECG signal is acquired via Ag-AgCl electrodes, preprocessed using the adaptive Wiener filter to remove the noises and classified with the help of feature extraction techniques and fuzzy decision making (FDM) algorithm. The FDM also finds the type of heart abnormality based on ECG features, such as RRp interval, R peak amplitude detection, and QRS complex interval, and then sends the classified type to the doctor wirelessly via a ZigBee module. The virtual instrument software has been used to validate the proposed concept, and results of both software and hardware parts have been presented to show the effectiveness of the work.

QRS Complex Detection Using STFT, Chaos Analysis, and PCA in Standard and Real-Time ECG Databases

The early detection of heart abnormalities through electrocardiography (ECG) is essential for reducing the prevalence of cardiac arrest worldwide. Often, subjects are unaware of the condition of their hearts until detected at the last stage. In this study, various records in real-time and PhysioNet databases were examined using chaos analysis. Chaos analysis was implemented by plotting different attractors against various time-delay dimensions. The main advantages of chaos analysis approach include: (1) a preprocessing stage is not demanded to the recorded ECG signal, and (2) it helps to estimate the reliable and robust thresholds for QRS detection using time-delay dimension (embedding), correlation dimension, Lyapunov exponent, and entropy. ECG may be a useful candidate to classify heart diseases; however, visualization through ECG may not be sufficient because of the minute differences that exist in the ECG recordings. Therefore, the effective automatic detection of ECG signals is essential. Further, ECG datasets should be analyzed using time–frequency representations for getting frequency contents of the signal at each time point. ECG signals are nonstationary in nature; the assumption of stationarity is valid on a short-time basis. For this purpose, a short-time spectrum is computed using the short-time Fourier transform (STFT) as a feature extraction tool in this paper. Noise and baseline wander are filtered before the STFT operation to ensure correct frequency components of the QRS complex. For filtering, a digital band-pass filter has been used since its filtering characteristics are invariant with drift and temperature. The automatic detection of QRS complex has been proposed which is useful in early diagnosis of cardiac diseases. The essential feature of detection stage is to build feature selection approach for having a minimal feature set which includes ample information about data for the planned application. In this paper, the QRS complex is detected by applying principal component analysis (PCA) on the fused results of individual features extracted using chaos analysis and STFT. Using PCA, the estimated principal components show the degree of morphological beat-to-beat variability. The detection performance is evaluated in terms of sensitivity (Se), positive predictivity (PP), detection error rate (DER), and accuracy (Acc). The proposed technique yields encouraging performance parameter values such as 99.93% Se, 99.97% PP, 0.0895% DER, and 99.91% Acc in the analysis of data from the PhysioNet database and 99.93% Se, 99.96% PP, 0.097% DER, and 99.90% Acc in the analysis of data from the real-time database. Suitable comparisons have been presented with the existing techniques.

Heart abnormality detection by using artificial neural network

The detection of heart abnormality activity of a patient is important when an abnormal symptom occurred. This project is aimed to detect the disease by using Hybrid Multilayer Perceptron (HMLP) network. Several data from the ECG signal is extracted to be set as input parameters. In order to get the best result, the proposed training algorithm of HMLP network is by using Modified Recursive Prediction Error (MRPE). The result obtained is then compared with other training algorithm method to determine the best performance. The HMLP network is expected to give better performance compared with other network classifiers.

Organizational Data Classification Based on the Importance Concept of Complex Networks.

Data classification is a common task, which can be performed by both computers and human beings. However, a fundamental difference between them can be observed: computer-based classification considers only physical features (e.g., similarity, distance, or distribution) of input data; by contrast, brain-based classification takes into account not only physical features, but also the organizational structure of data. In this paper, we figure out the data organizational structure for classification using complex networks constructed from training data. Specifically, an unlabeled instance is classified by the importance concept characterized by Google's PageRank measure of the underlying data networks. Before a test data instance is classified, a network is constructed from vector-based data set and the test instance is inserted into the network in a proper manner. To this end, we also propose a measure, called spatio-structural differential efficiency, to combine the physical and topological features of the input data. Such a method allows for the classification technique to capture a variety of data patterns using the unique importance measure. Extensive experiments demonstrate that the proposed technique has promising predictive performance on the detection of heart abnormalities.