Exploring ECG Signal Analysis Techniques for Arrhythmia Detection: A Review

The heart holds paramount importance in the human body as it serves the crucial function of supplying blood and nutrients to various organs. Thus, maintaining its health is imperative. Arrhythmia, a heart disorder, arises when the heart's rhythm becomes irregular. Electrocardiogram (ECG) signals are commonly utilized for analyzing arrhythmia due to their simplicity and cost-effectiveness. The peaks observed in ECG graphs, particularly the R peak, are indicative of heart conditions, facilitating arrhythmia diagnosis. Arrhythmia is broadly categorized into Tachycardia and Bradycardia for identification purposes. This paper explores diverse techniques such as Deep Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Support Vector Machines (SVM), Neural Network (NN) classifiers, as well as Wavelet and Time–Frequency Transform (TQWT), which have been employed over the past decade for arrhythmia detection using various datasets. The study delves into the analysis of arrhythmia classification on ECG datasets, highlighting the effectiveness of data preprocessing, feature extraction, and classification techniques in achieving superior performance in classifying ECG signals for arrhythmia detection

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.N/

HEART RHYTHM CLASSIFICATION FROM STATIC AND ECG TIME-SERIES DATA USING HYBRID MULTIMODAL DEEP LEARNING

Cardiovascular arrhythmia diseases are considered as the most common diseases that cause death around the world. Abnormal arrhythmia diseases can be identified by analyzing heart rhythm using an electrocardiogram (ECG). However, this analysis is done manually by cardiologists, which may be subjective and susceptible to different cardiologist observations and experiences, as well as to noise and irregularities in those signals. This can lead to misdiagnosis. Motivated by this challenge, an automated heart rhythm diagnosis approach from ECG signals using Deep Learning has been proposed. In order to achieve this goal, three research problems have been addressed. First, recognize the role of each single-lead of a 12-lead ECG to classify heart rhythms. Second, understanding the importance of static data (e.g., demographics and clinical profile) in classifying heart rhythms. Third, realizing whether the static data can be combined with the ECG time series data for better classification performance. In this thesis, different deep learning models have been proposed to address these problems and satisfactory results are achieved. Therefore, using this knowledge, an effective hybrid deep learning model to classify heart rhythms has been proposed. As per knowledge obtained from relevant literature, this is the first work to identify the importance of individual lead and combined lead as well as the importance of combining static data with ECG time series data in classifying heart rhythms. Extensive experiments have been performed to evaluate this algorithm on a 12-lead ECG database that contains data from more than 10,000 individual subjects and obtained a high average of accuracy (up to 98.7%) and F1-measure (up to 98.7%). Moreover, in this thesis, the distribution of heart rhythms from the database based on heart rhythm type, gender, and age group have been analyzed, which will be valuable for further improvement of classification performance. This study will provide valuable insights and will prove to be an effective tool in automated heart rhythm classification and will assist cardiologists in effectively and accurately diagnosing heart disease

Enhanced IoT-Based Electrocardiogram Monitoring System with Deep Learning

Due to the rapid development of computing and sensing technologies, Internet of Things (IoT)-based cardiac monitoring plays a crucial role in providing patients with cost-efficient solutions for long-term, continuous, and pervasive electrocardiogram (ECG) monitoring outside a hospital setting. In a typical IoT-based ECG monitoring system, ECG signals are picked up by sensors located on the edge, and then uploaded to the remote cloud servers. ECG interpretation is performed for the collected ECGs in the cloud servers and the analysis results can be made instantly available to the patients as well as their healthcare providers.In this dissertation, we first examine the ECG classification models in the cloud. Although deep learning technologies have shown their great use in extracting ECG signal features and recognizing useful patterns for diagnosis, the existing methods are found to have unacceptable levels of performance (with an accuracy capped at only 60%) in identifying certain abnormal rhythms that can cause life-threating cardiac events. In combating this deficiency, we have developed three methods that help produce, preserve, and sharpen the abnormality-relevant features needed to improve the detection of the abnormalities. These three methods are then integrated into a DNN framework for the detection of the ECG rhythms of interest. The experiment results on a publicly available data set demonstrate the effectiveness of the proposed method with the best accuracy result ever published. On the edge end of the IoT-based ECG monitoring system, both extremely noisy and almost noise-free ECGs could be locked in by the device worn by a mobile patient. However, transmitting an indiscriminate collection of noisy and noise-free ECG cycles to the cloud for the categorization of cardiac abnormalities typically leads to significant false alarm rates. Alternatively, merely relying on a single denoising or quality assessing process on the edge to cope with all the recorded ECG signals can also be problematic, as the former can catastrophically distort those noise-free sections of the ECG signal, while the latter tends to cause notable loss of meaningful clinical information by discarding the signal sections that stand a good chance to be recovered by a denoising process. In this dissertation, we present a series of machine learning based models in support of edge-level stratification and preprocessing, for selecting the ECG signals that either have clear morphologies or retain their morphologies after necessary denoising to upload to the cloud. On the other hand, signals that are useless for diagnosis will be deleted early in the signal chain to lessen the load that would otherwise be imposed on the communication network and the cloud. In specific, the severity of the noise presence in the collected ECG signals is first evaluated right on the edge, after which the ECG signals get stratified into three levels and processed accordingly: (1) Signals that are assessed to have clear morphologies are admitted to the cloud for classification; (2) Signals with significantly corrupted morphologies—caused by baseline wandering, electrode motion, and muscle artifacts—are judged to be useless for classification on the cloud and are therefore dropped right away on the edge; (3) Signals that fall between the previous two extremes with partially corrupted morphologies are warranted to go through a denoising process. This very last type of signals after denoising will be assessed again by a dedicated quality assurance algorithm, and only the denoised signal that carries recognizable diagnostic information will be sent to the cloud for classification. The performances of the proposed method are evaluated using five publicly available datasets, and the results have confirmed a saving of the network traffic and a noticeable load reduction at the cloud, which is critical to an edge-cloud computing environment. Since selective denoising, indicated above, becomes an integral part of ECG processing flow of the proposed (IoT)-based cardiac monitoring system, we have developed a set of machine-learning based denoising models that take into account of the limited power capabilities of the edge. Instead of resting on sophisticated and power-hungry denoising methods to indiscriminately cleanse ECG signals across the whole spectrum of noise conditions, our focus is placed on denoising ECG signals that have moderate noise levels and thus being able to recover useful ECG morphologies for ECG signal stratification purposes described above. Specially, we propose a series of machine-learning based denoising models that allows us (1) to select signals’ spectrums that are most relevant to diagnostically useful morphologies in the frequency domain, and subsequently, (2) recover recognizable diagnostic information from them. The experiment results on five publicly available datasets confirm the effectiveness of the proposed method