Interpretation Of Electrocardiogram Signals Research Articles

Advanced Neural Network Architecture for Enhanced Multi-Lead ECG Arrhythmia Detection through Optimized Feature Extraction

Cardiovascular diseases are a pervasive global health concern, contributing significantly to morbidity and mortality rates worldwide. Among these conditions, arrhythmia, characterized by irregular heart rhythms, presents formidable diagnostic challenges. This study introduces an innovative approach utilizing deep learning techniques, specifically Convolutional Neural Networks (CNNs), to address the complexities of arrhythmia classification. Leveraging multi-lead Electrocardiogram (ECG) data, our CNN model, comprising six layers with a residual block, demonstrates promising outcomes in identifying five distinct heartbeat types: Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Premature Contraction (APC), Premature Ventricular Contraction (PVC), and Normal Beat. Through rigorous experimentation, we highlight the transformative potential of our methodology in enhancing diagnostic accuracy for cardiovascular arrhythmias. Arrhythmia diagnosis remains a critical challenge in cardiovascular care, often relying on manual interpretation of ECG signals, which can be time-consuming and prone to subjectivity. To address these limitations, we propose a novel approach that leverages deep learning algorithms to automate arrhythmia classification. By employing advanced CNN architectures and multi-lead ECG data, our methodology offers a robust solution for precise and efficient arrhythmia detection. Through comprehensive evaluation, we demonstrate the effectiveness of our approach in facilitating more accurate clinical decision-making, thereby improving patient outcomes in managing cardiovascular arrhythmias.

Deep Transfer Learning for Chronic Obstructive Pulmonary Disease Detection Utilizing Electrocardiogram Signals

The primary objective and contribution of this research is the development and design of an artificial intelligence system that will diagnose Chronic Obstructive Pulmonary Disease (COPD) utilizing only the heart signal (ECG) of the patient. In contrast to the traditional way of diagnosing COPD, which requires spirometer tests and a laborious workup in a hospital setting, the proposed system uses the classification capabilities of deep transfer learning and the patient’s heart signal, which provides COPD signs in itself and can be received from any modern smart device. The motivation of this research is that it introduces the first research on automated COPD diagnosis using deep learning and utilizes the first annotated dataset in this field. Since the disease progresses slowly and conceals itself until the final stage, hospital visits for diagnosis are uncommon. Hence, the medical goal of this research is to detect COPD using a simple heart signal before it becomes incurable. Deep transfer learning frameworks, which were previously trained on a general image data set, are transferred to carry out an automatic diagnosis of COPD by classifying patients’ electrocardiogram (ECG) signal equivalents, which are produced by signal-to-image transform techniques. Xception, VGG-19, InceptionResNetV2, DenseNet-121, and "trained-from-scratch" convolutional neural network architectures have been investigated for the detection of COPD, and it is demonstrated that they are able to obtain high performance rates in classifying nearly 33.000 instances using diverse training strategies. The highest classification rate was obtained by the Xception model at 99%. This research shows that the newly introduced COPD detection approach is effective, easily applicable, and eliminates the burden of considerable effort in a hospital. It could also be put into practice and serve as a diagnostic aid for chest disease experts by providing a deeper and faster interpretation of ECG signals. Using the knowledge gained while identifying COPD from ECG signals may aid in the early diagnosis of future diseases for which little data is currently available.

Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review

Heart disease is one of the leading causes of mortality throughout the world. Among the different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non-invasive procedure. However, the following are challenges: the scarcity of medical experts, the complexity of ECG interpretations, the manifestation similarities of heart disease in ECG signals, and heart disease comorbidity. Machine learning algorithms are viable alternatives to the traditional diagnoses of heart disease from ECG signals. However, the black box nature of complex machine learning algorithms and the difficulty in explaining a model's outcomes are obstacles for medical practitioners in having confidence in machine learning models. This observation paves the way for interpretable machine learning (IML) models as diagnostic tools that can build a physician's trust and provide evidence-based diagnoses. Therefore, in this systematic literature review, we studied and analyzed the research landscape in interpretable machine learning techniques by focusing on heart disease diagnosis from an ECG signal. In this regard, the contribution of our work is manifold; first, we present an elaborate discussion on interpretable machine learning techniques. In addition, we identify and characterize ECG signal recording datasets that are readily available for machine learning-based tasks. Furthermore, we identify the progress that has been achieved in ECG signal interpretation using IML techniques. Finally, we discuss the limitations and challenges of IML techniques in interpreting ECG signals.

The Diagnosis of Congestive Heart Failure Based on Generalized Multiscale Entropy-Wavelet Leaders.

Congestive heart failure (CHF) is a chronic heart condition associated with debilitating symptoms that can lead to mortality. The electrocardiogram (ECG) is a noninvasive and simple diagnostic method that can show detectable changes in CHF. However, manual diagnosis of ECG signals is often erroneous due to the small amplitude and duration of the ECG signals. This paper presents a CHF diagnosis method based on generalized multiscale entropy (MSE)-wavelet leaders (WL) and extreme learning machine (ELM). Firstly, ECG signals from normal sinus rhythm (NSR) and congestive heart failure (CHF) patients are pre-processed. Then, parameters such as segmentation time and scale factor are chosen, and the multifractal spectrum features and number of ELM hidden layer nodes are determined. Two different data sets (A, B) were used for training and testing. In both sets, the balanced data set (B) had the highest accuracy of 99.72%, precision, sensitivity, specificity, and F1 score of 99.46%, 100%, 99.44%, and 99.73%, respectively. The unbalanced data set (A) attained an accuracy of 99.56%, precision of 99.44%, sensitivity of 99.81%, specificity of 99.17%, and F1 score of 99.62%. Finally, increasing the number of ECG segments and different algorithms validated the probability of detection of the unbalanced data set. The results indicate that our proposed method requires a lower number of ECG segments and does not require the detection of R waves. Moreover, the method can improve the probability of detection of unbalanced data sets and provide diagnostic assistance to cardiologists by providing a more objective and faster interpretation of ECG signals.

Arrhythmia Classification Using One Dimensional Conventional Neural Network

Arrhythmia is a heart condition that occurs due to abnormalities in the heartbeat, which means that the heart's electrical signals do not work properly, resulting in an irregular heartbeat or rhythm and thus defeating the pumping of blood. Some cases of arrhythmia are not considered serious, while others are very dangerous, life-threatening, and cause death in a short period of time. In the clinical routine, cardiac arrhythmia detection is performed by electrocardiogram (ECG) signals. The ECG is a significant diagnosis tool that is used to record the electrical activity of the heart, and its signals can reveal abnormal heart activity. However, because of their small amplitude and duration, visual interpretation of ECG signals is difficult. As a result, we present a significant approach for identifying arrhythmias using ECG signals. In this study, we proposed an approach based on Deep Learning (DL) technology that is a framework of nine-layer one-dimension Conventional Neural Network (1D CNN) for classifying automatically ECG signals into four cardiac conditions named: normal (N), Atrial Premature Beat (APB), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The practical test of this work was executed with the benchmark MIT-BIH database. We achieved an average accuracy of 99%, precision of 98%, recall of 96.5%, specificity of 99.08%, and an F1-score of 95.75%. The obtained results were compared with some relevant models, and they showed that the proposed framework outperformed those models in some measures. The new approach’s performance indicates its success. Also, it has been shown that deep convolutional neural networks can be used efficiently in automated detection and, therefore, cardiovascular disease protection as well as help cardiologists in medical practice by saving time and effort. Keywords: 1-D CNN, Arrhythmia, Cardiovascular Disease, Classification, Deep learning, Electrocardiogram(ECG), MIT-BIH arrhythmia database.

Chaos Theory and ARTFA: Emerging Tools for Interpreting ECG Signals to Diagnose Cardiac Arrhythmias

Timely detection of cardiac abnormalities from an Electrocardiogram (ECG) signal is very essential. This requires its appropriate and efficient processing. In the literature, most of the researchers focussed on linear techniques that were applied on filtered ECG datasets leaving an ample scope for exploring the use of non-linear techniques in the presence and absence of natural noise-processes. Therefore, there is a need of supplementing the existing research on ECG signal interpretation by using non-linear techniques on noisy ECG data. Non-linear techniques are expected to yield supplementary clues about the non-linearities in the underlying cardiovascular system. One such promising non-linear technique, known as chaos theory (analysis), has been considered here to estimate reliable and robust thresholds for R-peak detection using fractal dimension, Approximate Entropy, Sample Entropy, correlation dimension, and Lyapunov exponent based on time-delay dimension (embedding). Also, time–frequency analysis techniques have shown their effectiveness for analyzing such types of non-linear and non-stationary signals due to simultaneous interpretation of the signal in both time and frequency domain. Among existing TFA techniques such as wavelet transform, short time Fourier transform, Hilbert transform, Auto-regressive Time Frequency Analysis (ARTFA) offers good time–frequency resolution. Therefore, Chaos theory and ARTFA have been considered in this paper. First, raw ECG signal was filtered using Savitzky Golay Digital Filtering (SGDF) because it retains all important signal features after filtering. Second, a novel optimal trajectory detection step was proposed on the basis of phase space reconstruction (attractors) in chaos theory. Third, ARTFA has been used to find the spectral components of the extracted features using chaos theory. Here, ARTFA has been used for finding the autoregressive coefficients in the first step and time–frequency description in the second step. Burg method has been considered for Auto-regressive modeling to fit an ARTFA model for analyzing ECG signal by minimizing (least squares) the forward and backward prediction errors. MIT-BIH arrhythmia database has been considered for validating the present research effort. Some real time signals were also tested to explore direct usage of the proposed technique in practical applications. The obtained results show that the proposed technique enhances sensitivity, positive predictive value and accuracy thereby improving the detection of arrhythmias. Computational cost of the proposed technique is reduced to a great extent by using the chaos theory (analysis) yielding efficient detection performance. All results have been obtained in MATLAB environment R2011a. Improved values viz. 99.96% SE, 99.97% PPV, and 99.93% ACC are obtained.

Fusion based Feature Extraction Analysis of ECG Signal Interpretation - A Systematic Approach

Our human heart is classified into four sections called the left side and right side of the atrium and ventricle accordingly. Monitoring and taking care of the heart of every human is the very essential part. Therefore, the early prediction is essential to save and give awareness to humans about diet plan, lifestyle schedule. Also, this is used to improve the clinical diagnosis and treatment of any patients. To predict or identifying any cardiovascular problems, Electro Cardio Gram (ECG) is used to record the electrical signal of the heart from the body surface of humans. The algorithm learns the dataset from before cluster is called supervised; The algorithm learns to train the data from the set of a dataset is called unsupervised. Then the classification of more amount of heartbeat for different category of normal, abnormal, irregular heartbeats to detect cardiovascular diseases. In this research article, a comparison of various methods to classify the dataset with a fusion-based feature extraction method. Besides, our research work consists of a de-noising filter to reconstruct the raw data from the original input. Our proposed framework performing preprocessing that consists of a filtering approach to remove noises from the raw data set. The signal is affected by thermal noise and instrumentation noise, calibration noise due to power line fluctuation. This interference is high in many handheld devices which can be eliminated by de-noising filters. The output of the de-noising filter is input for fusion-based feature extraction and prediction model construction. This workflow progress has given good results of classifier effectiveness and imbalance arrangement conditions. We achieved good accuracy 96.5% and minimum computation time for classification of ECG signal.

Chaos Theory: An Emerging Tool for Arrhythmia Detection

The heart is an important muscular organ of the human body which pumps blood throughout the body. It is essential for human life. Timely and accurate assessment of the functioning of the heart has great relevance for reducing the death rate due to cardiac diseases around the world. If the heart is not able to pump blood smoothly, then heart diseases are likely to appear. These heart diseases are known as arrhythmia. Electrocardiogram (ECG) is a diagnostic tool for assessing the functioning of heart non-invasively. It not only detects cardiovascular diseases, but also examines breathing pattern and mental stress. ECG appears in the form of an electrical signal that comprises of P-QRS-T waves and is captured by pasting electrodes on the surface of the skin in a conductive medium. Features of these wave components, such as clinical frequencies, heart rate (HR) measurement, RR interval measurement, spectral components, non-linearity, trajectory identification, and amplitudes, help doctors to diagnose cardiac arrhythmias accurately. This paper presents a computer aided diagnosis (CAD) system to extract non-linearity and trajectory patterns using the theory of chaos analysis to aid cardiologists diagnose arrhythmia accurately. ECG signal is non-stationary and non-linear in nature due to which it contains multiple time-varying frequencies. So a more reliable and accurate technique like time–frequency transform such as short-time Fourier transform (STFT) etc. is needed. In this paper, STFT is used, which is an efficient technique to observe frequency contents of small non-linear segments in time domain. It is used to determine the sinusoidal frequency and phase content of the local sections of a signal. R-peak is very crucial for classifying cardiac arrhythmia. Therefore, STFT is used for detecting R-peaks and their frequency contents. Due to limited time–frequency resolution, STFT usually misses on some information. Therefore, there is a need of supplementing the existing research on the ECG signal interpretation by using non-linear techniques. These gaps have motivated us to use chaos theory (analysis) for ECG signal analysis. The non-linear techniques are expected to yield supplementary clues about the non-linearities in the considered segment. In chaos analysis, the sketched trajectories represent the flow of the system where each trajectory involves a subregion of the phase space known as an attractor. In phase space, set of points depicts complete status of the cardiac cycles through which the system migrates over time. An attractor showcases the best preview according to the initial conditions and time delay dimension. The shape of an attractor may be oval, egg-shaped, circular and in some cases corn-type. It increases the decision capability of the proposed system by identifying correct arrhythmia type. For validating this research work, physioNet database [Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia database (M-BArr DB), Ventricular Tachyarrhythmia database (VT DB)] and real time database (R T DB) have been used. The proposed technique has been evaluated on the basis of sensitivity (Se) and positive predictive value (PPV). Se of 99.92% and PPV of 99.93% are obtained for the considered databases (M-BArr DB, VT DB and RT DB). Chaos theory together with STFT has proved itself as a good approach that reduces the occurrence of spurious outcomes and has demonstrated the properties that are typical outlook of deterministic chaotic systems.

Arrhythmia recognition and classification through deep learning-based approach

Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, which can be life-threatening. Electrocardiogram (ECG) is the principal diagnostic tool used to detect arrhythmias or heart abnormalities. It contains information about the different types of arrhythmias. However, due to the complexity and nonlinearity of ECG signals, such as the presence of noise, the time dependence of ECG signals and the irregularity of the heartbeat, it is troublesome to analyse ECG signals manually. Moreover, the interpretation of ECG signals is subjective and might vary among experts in the field. Therefore, an automatic, high-precision ECG recognition method is important to arrhythmia detection. For such, a method is proposed in this paper for arrhythmia classification, which is based on deep learning-based approach long short-term memory (LSTM), where five classes of arrhythmias as recommended by the Association for Advancement of Medical Instrumentation (AAMI) are analysed. The method has been tested on the MIT-BIH arrhythmia database with a number of useful performance evaluation measures, showing that is a promising and better performance than other artificial intelligence methods used.

Computer-aided diagnosis of atrial fibrillation based on ECG Signals: A review

Arrhythmia is a type of disorder that affects the pattern and rate of the heartbeat. Among the various arrhythmia conditions, atrial fibrillation (AF) is the most prevalent. AF is associated with a chaotic, and frequently fast, heartbeat. Moreover, AF increases the risk of cardioembolic stroke and other heart-related problems such as heart failure. Thus, it is necessary to screen for AF and receive proper treatment before the condition progresses. To date, electrocardiogram (ECG) feature analysis is the gold standard for the diagnosis of AF. However, because it is time-varying, AF ECG signals are difficult to interpret. The ECG signals are often contaminated with noise. Further, manual interpretation of ECG signals may be subjective, time-consuming, and susceptible to inter-observer variabilities. Various computer-aided diagnosis (CADx) methods have been proposed to remedy these shortcomings. In this paper, different CADx systems developed by researchers are discussed. Also, the potentials of the CADx system are highlighted.

Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals

Congestive heart failure (CHF) is a chronic heart condition associated with debilitating symptoms that result in increased mortality, morbidity, healthcare expenditure and decreased quality of life. Electrocardiogram (ECG) is a noninvasive and simple diagnostic method that may demonstrate detectable changes in CHF. However, manual diagnosis of ECG signal is often subject to errors due to the small amplitude and duration of the ECG signals, and in isolation, is neither sensitive nor specific for CHF diagnosis. An automated computer-aided system may enhance the diagnostic objectivity and reliability of ECG signals in CHF. We present an 11-layer deep convolutional neural network (CNN) model for CHF diagnosis herein. This proposed CNN model requires minimum pre-processing of ECG signals, and no engineered features or classification are required. Four different sets of data (A, B, C and D) were used to train and test the proposed CNN model. Out of the four sets, Set B attained the highest accuracy of 98.97%, specificity and sensitivity of 99.01% and 98.87% respectively. The proposed CNN model can be put into practice and serve as a diagnostic aid for cardiologists by providing more objective and faster interpretation of ECG signals.

KNN and PCA classifier with Autoregressive modelling during different ECG signal interpretation

Electrocardiogram (ECG) conveys the information of the heart. Autoregressive (AR) coefficients extract features of the ECG signal. Three different conditions of subjects, i.e. Atrial Tachycardia, Premature Atrial Contractions and Sinus Arrhythmia were examined. For classification K-Nearest Neighbor (KNN) classifier and Principal Component analysis (PCA) classifier has carried out. Autoregressive modelling is done with an ECG signal with baseline wander noise and removed baseline wander noise signal. Among KNN-classifier and PCA classifier with autoregressive modeling methods those are Yule-Walker (YW) and Burg method; KNN classifier with Burg method shows good result at model order 9 and PCA classifier with YW method shows good result at model order 8. Signal to Noise ratio (SNR) is also calculated and checked SNRs were found to be 9.81dB, 11.23dB and 8.35dB for subjects 1, 2 and 3 respectively.

A robust QRS complex detection using regular grammar and deterministic automata

Abstract A novel approach is proposed for medical analysis and clinical decision support of the Electrocardiogram (ECG) signals based on the deterministic finite automata (DFA) with the addition of some requirements. This paper proves regular grammar is effective in the extraction of QRS complex and interpretation of ECG signals. The DFA will be used to represent a normalized QRS complex as a sequence of negative and positive peaks. A QRS is considered as a set of adjacent peaks that satisfy certain criteria of standard deviation and duration. The proposed method is applied on several kinds of ECG signals collected from the standard MIT-BIH arrhythmia database. Several metrics are calculated including QRS durations, RR distances and peak amplitudes. Furthermore, σRR and σQRS metrics were added to quantify RR distances regularity and QRS durations, respectively. Regular grammar with the addition of some requirements and deterministic automata proved functional for both biomedical signals and ECG signal diagnosis. The suggested method provided a sensitivity rate of 99.74% and the positive predictivity rate of 99.86%. The algorithm was compared to other works in the literature and the quality performance detection was compared with several algorithms tested and validated on the MIT-BIH database. A head-to-head comparison in terms of sensitivity and CPU runtime was provided with the wavelet method.

Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network

Our cardiovascular system weakens and is more prone to arrhythmia as we age. An arrhythmia is an abnormal heartbeat rhythm which can be life-threatening. Atrial fibrillation (Afib), atrial flutter (Afl), and ventricular fibrillation (Vfib) are the recurring life-threatening arrhythmias that affect the elderly population. An electrocardiogram (ECG) is the principal diagnostic tool employed to record and interpret ECG signals. These signals contain information about the different types of arrhythmias. However, due to the complexity and non-linearity of ECG signals, it is difficult to manually analyze these signals. Moreover, the interpretation of ECG signals is subjective and might vary between the experts. Hence, a computer-aided diagnosis (CAD) system is proposed. The CAD system will ensure that the assessment of ECG signals is objective and accurate. In this work, we present a convolutional neural network (CNN) technique to automatically detect the different ECG segments. Our algorithm consists of an eleven-layer deep CNN with the output layer of four neurons, each representing the normal (Nsr), Afib, Afl, and Vfib ECG class. In this work, we have used ECG signals of two seconds and five seconds’ durations without QRS detection. We achieved an accuracy, sensitivity, and specificity of 92.50%, 98.09%, and 93.13% respectively for two seconds of ECG segments. We obtained an accuracy of 94.90%, the sensitivity of 99.13%, and specificity of 81.44% for five seconds of ECG duration. This proposed algorithm can serve as an adjunct tool to assist clinicians in confirming their diagnosis.

Arrhythmia recognition and classification through deep learning based approach

Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, which can be life-threatening. Electrocardiogram (ECG) is the principal diagnostic tool used to detect arrhythmias or heart abnormalities. It contains information about the different types of arrhythmias. However, due to the complexity and nonlinearity of ECG signals, such as the presence of noise, the time dependence of ECG signals and the irregularity of the heartbeat, it is troublesome to analyse ECG signals manually. Moreover, the interpretation of ECG signals is subjective and might vary among experts in the field. Therefore, an automatic, high-precision ECG recognition method is important to arrhythmia detection. For such, a method is proposed in this paper for arrhythmia classification, which is based on deep learning-based approach long short-term memory (LSTM), where five classes of arrhythmias as recommended by the Association for Advancement of Medical Instrumentation (AAMI) are analysed. The method has been tested on the MIT-BIH arrhythmia database with a number of useful performance evaluation measures, showing that is a promising and better performance than other artificial intelligence methods used.

A REVIEW OF ECG-BASED DIAGNOSIS SUPPORT SYSTEMS FOR OBSTRUCTIVE SLEEP APNEA

Humans need sleep. It is important for physical and psychological recreation. During sleep our consciousness is suspended or least altered. Hence, our ability to avoid or react to disturbances is reduced. These disturbances can come from external sources or from disorders within the body. Obstructive Sleep Apnea (OSA) is such a disorder. It is caused by obstruction of the upper airways which causes periods where the breathing ceases. In many cases, periods of reduced breathing, known as hypopnea, precede OSA events. The medical background of OSA is well understood, but the traditional diagnosis is expensive, as it requires sophisticated measurements and human interpretation of potentially large amounts of physiological data. Electrocardiogram (ECG) measurements have the potential to reduce the cost of OSA diagnosis by simplifying the measurement process. On the down side, detecting OSA events based on ECG data is a complex task which requires highly skilled practitioners. Computer algorithms can help to detect the subtle signal changes which indicate the presence of a disorder. That approach has the following advantages: computers never tire, processing resources are economical and progress, in the form of better algorithms, can be easily disseminated as updates over the internet. Furthermore, Computer-Aided Diagnosis (CAD) reduces intra- and inter-observer variability. In this review, we adopt and support the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy.

ECG Signal Denoising and Ischemic Event Feature Extraction using Daubechies Wavelets

The ability of an intelligent system to correctly classify and recognize patterns makes them particularly suitable for use in an expert system that aids in the interpretation of ECG signals. The features extracted from ECG are highly useful in diagnosis. Wavelet based methods present a best performance as irregularity measures and makes them suitable for ECG data analysis. In this paper, we propose an algorithm for detection of myocardial Ischemic episodes from Electrocardiogram (ECG) signal using Daubechies Wavelet transform technique. The ECG signal was denoised by removing the corresponding wavelet coefficients at higher scale. Analysis is carried out using MATLAB software. The algorithm was evaluated using two cases of data, the first case is with healthy subjects, and second case is with subjects affected by myocardial Ischemia. ECGs are obtained from MIT-BIH Arrhythmia Database which is manually annotated and developed for validation. From the results, it is concluded that Daubechies wavelets are best suitable for small datasets and are able to clearly demark the healthy and disease subjects such as myocardial ischemia subjects.