Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network

Cardio-vascular diseases are one of the foremost causes of mortality in today’s world. The prognosis for cardiovascular diseases is usually done by ECG signal, which is a simple 12-lead Electrocardiogram (ECG) that gives complete information about the function of the heart including the amplitude and time interval of P-QRST-U segment. This article recommends a novel approach to identify the location of thrombus in culprit artery using the Information Fuzzy Network (IFN). Information Fuzzy Network, being a supervised machine learning technique, takes known evidences based on rules to create a predicted classification model with thrombus location obtained from the vast input ECG data. These rules are well-defined procedures for selecting hypothesis that best fits a set of observations. Results illustrate that the recommended approach yields an accurateness of 92.30%. This novel approach is shown to be a viable ECG analysis approach for identifying the culprit artery and thus localizing the thrombus

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

PORTABLE HEART ATTACK WARNING SYSTEM BY MONITORING THE ST SEGMENT VIA SMARTPHONE ELECTROCARDIOGRAM PROCESSING

Cardiovascular disease (CVD) is the single leading cause of death in both developed and developing countries. The most deadly CVD is heart attack, which 7,900,000 Americans suffer each year, and 16% of cases are fatal. The Electrocardiogram (ECG) is the most widely adopted clinical tool to diagnose and assess the risk of CVD. Early diagnosis of heart attacks, by detecting abnormal ST segments within one hour of the onset of symptoms, is necessary for successful treatment. In clinical settings, resting ECGs are used to monitor patients automatically. However, given the sporadic nature of heart attacks, it is unlikely that the patient will be in a clinical setting at the onset of a heart attack. While Holter-based portable monitoring solutions offer 24 to 48-hour ECG recording, they lack the capability of providing any real-time feedback for the thousands of heart beats they record, which must be tediously analyzed offline.Processing ECG signals on a smartphone-based platform would unite the portability of Holter monitors and the real-time processing capability of state-of-the-art resting ECG machines to provide an assistive diagnosis for early heart attack warning. Furthermore, smartphones serve as an ideal platform for telemedicine and alert systems and have a portable form factor. To detect heart attacks via ECG processing, a real-time, accurate, context aware ST segment monitoring algorithm, based on principal component analysis and a support vector machine classifier is proposed and evaluated. Real-time feedback is provided by implementing a state-of-the-art, multilevel warning system ranging from audible notifications to text messages to points of contacts with the GPS location of the user. The smartphone test bed makes use of a novel, real-time verification system using a streaming database to analyze the strain of heart attack detection system under normal phone operation. Furthermore, the entire system is prototyped and fully functional, running on a smartphone to demonstrate the real-time, portable functionality of the platform. Experimental results show that a classification accuracy of 96% for ST segment elevation of individual beats can be achieved and all ST episodes were correctly detected during testing with the European ST database

Survey and classification of functional characteristics in neural network technique for the diagnosis of ischemic heart disease: A systematic review

Background: Nowadays, the prevalence of ischemic heart diseases (IHDs) leads to destructive effects such as patient death. Late diagnosis of such diseases as well as their invasive diagnostic approaches made researchers provide a decision support system based on neural network techniques, while using minimum data set for timely diagnosis. In this regard, selecting minimum useful features is significant for designing neural network structure and it paves the way to attain maximum accuracy in obtaining the results. Methods: In this systematic review, valid databases using sensitive keywords were initially searched out to find articles related to "diagnosing the ischemic heart disease using artificial neural networks" and afterwards, scientific methods were used to analyze and classify the content. Findings: Researchers applied various extractable features from demographic data, medical history, signs and symptoms, and paraclinical examinations, to design the neural network structure. Among them, the features obtained from electrocardiographic test, embedded in paraclinical examinations, had led to a remarkable increase of efficiency in neural network. Conclusion: Utilizing such diagnostic decision support systems in practical environments depends on their high confidence coefficient and physicians� acceptability. Therefore, it can be useful to improve maturity in the design of the neural network structure depending on the choice of the minimum optimal features, and to create required infrastructures to input patients� real, accurate, and flowing data in these systems. © 2018, Isfahan University of Medical Sciences(IUMS). All rights reserved

Survey and classification of functional characteristics in neural network technique for the diagnosis of ischemic heart disease: A systematic review

Background: Nowadays, the prevalence of ischemic heart diseases (IHDs) leads to destructive effects such as patient death. Late diagnosis of such diseases as well as their invasive diagnostic approaches made researchers provide a decision support system based on neural network techniques, while using minimum data set for timely diagnosis. In this regard, selecting minimum useful features is significant for designing neural network structure and it paves the way to attain maximum accuracy in obtaining the results. Methods: In this systematic review, valid databases using sensitive keywords were initially searched out to find articles related to "diagnosing the ischemic heart disease using artificial neural networks" and afterwards, scientific methods were used to analyze and classify the content. Findings: Researchers applied various extractable features from demographic data, medical history, signs and symptoms, and paraclinical examinations, to design the neural network structure. Among them, the features obtained from electrocardiographic test, embedded in paraclinical examinations, had led to a remarkable increase of efficiency in neural network. Conclusion: Utilizing such diagnostic decision support systems in practical environments depends on their high confidence coefficient and physicians� acceptability. Therefore, it can be useful to improve maturity in the design of the neural network structure depending on the choice of the minimum optimal features, and to create required infrastructures to input patients� real, accurate, and flowing data in these systems. © 2018, Isfahan University of Medical Sciences(IUMS). All rights reserved

Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion:Segmentation, prediction and insights into dynamic evolution simulation models. A critical appraisal

AbstractOver the last 15years, basic thresholding techniques in combination with standard statistical correlation-based data analysis tools have been widely used to investigate different aspects of evolution of acute or subacute to late stage ischemic stroke in both human and animal data. Yet, a wave of biology-dependent and imaging-dependent issues is still untackled pointing towards the key question: “how does an ischemic stroke evolve?” Paving the way for potential answers to this question, both magnetic resonance (MRI) and CT (computed tomography) images have been used to visualize the lesion extent, either with or without spatial distinction between dead and salvageable tissue. Combining diffusion and perfusion imaging modalities may provide the possibility of predicting further tissue recovery or eventual necrosis. Going beyond these basic thresholding techniques, in this critical appraisal, we explore different semi-automatic or fully automatic 2D/3D medical image analysis methods and mathematical models applied to human, animal (rats/rodents) and/or synthetic ischemic stroke to tackle one of the following three problems: (1) segmentation of infarcted and/or salvageable (also called penumbral) tissue, (2) prediction of final ischemic tissue fate (death or recovery) and (3) dynamic simulation of the lesion core and/or penumbra evolution. To highlight the key features in the reviewed segmentation and prediction methods, we propose a common categorization pattern. We also emphasize some key aspects of the methods such as the imaging modalities required to build and test the presented approach, the number of patients/animals or synthetic samples, the use of external user interaction and the methods of assessment (clinical or imaging-based). Furthermore, we investigate how any key difficulties, posed by the evolution of stroke such as swelling or reperfusion, were detected (or not) by each method. In the absence of any imaging-based macroscopic dynamic model applied to ischemic stroke, we have insights into relevant microscopic dynamic models simulating the evolution of brain ischemia in the hope to further promising and challenging 4D imaging-based dynamic models. By depicting the major pitfalls and the advanced aspects of the different reviewed methods, we present an overall critique of their performances and concluded our discussion by suggesting some recommendations for future research work focusing on one or more of the three addressed problems

A Framework for Remote Patient Monitoring to Diagnose the Cardiac Disorders

Electrocardiogram (ECG) is an efficient diagnostic tool to monitor the electrical activity of heart. One of the most vital benefit of using telecommunication technologies in medical field is to provide cardiac health care at a distance. Telecardiology is the most efficient way to provide faster and affordable health care for the cardiac patients located at rural areas. Early detection of cardiac disorders can minimize cardiac death rates. In real time monitoring process, ECG data from a patient usually takes large storage space in the order of gigabytes (GB). Hence, compression of bulky ECG signal is a common requirement for faster transmission of cardiac signals using wireless technologies. Several techniques such as the Fourier transform based methods, wavelet transform based methods, etc., have been reported for compression of ECG data. Though Fourier transform is suitable for analyzing the stationary signals. An improved version, the wavelet transform allows the analysis of non-stationary signal. It provides a uniform resolution for all the scales, however, wavelet transform faces difficulties like uniformly poor resolution due to limited size of the basic wavelet function and it is nonadaptive in nature. A data adaptive method to analyse non-stationary signal is based on empirical mode decomposition (EMD), where the bases are derived from the multivariate data which are nonlinear and non-stationary. A new ECG signal compression technique based on EMD is proposed, in which first EMD technique is applied to decompose the ECG signal into several intrinsic mode functions (IMFs). Next, downsampling, discrete cosine transform (DCT), window filtering and Huffman encoding processes are used sequentially to compress the ECG signal. The compressed ECG is then transmitted as short messageservice (SMS) message using a global system for mobile communications (GSM) modem. First the AT-command ‘+CMGF’ is used to set the SMS to text mode. Next, the GSM modem uses the AT-command ‘+CMGS’ to send a SMS message. The received text SMS messages are transferred to a personal computer (PC) using blue-tooth. All text SMS messages are combined in PC as per the received sequence and fed as data input to decompress the compressed ECG data. The decompression method which is used to reconstruct the original ECG signal consists of Huffman decoding, inverse discrete cosine transform (IDCT) and spline interpolation. The performance of the compression and decompression techniques are evaluated in terms of compression ratio (CR) and percent root mean square difference (PRD) respectively by using both European ST-T database and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. The average values of CR and PRD for selected ECG records of European ST-T database are found to be 23.5:1 and 1.38 respectively. All 48 ECG records of MIT-BIH arrhythmia database are used for comparison purpose and the average values of CR and PRD are found to be 23.74:1 and 1.49 respectively. The reconstructed ECG signal is then used for detection of cardiac disorders like bradycardia, tachycardia and ischemia. The preprocessing stage of the detection technique filters the normalized signal to reduce noise components and detects the QRS-complexes. Next, ECG feature extraction, ischemic beat classification and ischemic episode detection processes are applied sequentially to the filtered ECG by using rule based medical knowledge. The ST-segment and T-wave are the two features generally used for ischemic beat classification. As per the recommendation of ESC (European Society of cardiology) the ischemic episode detection procedure considers minimum 30s duration of signal. The performance of the ischemic episode detection technique is evaluated in terms of sensitivity (Se) and positive predictive accuracy (PPA) by using European ST-T database. This technique achieves an average Se and PPA of 83.08% and 92.42% respectively

Deep learning tools for outcome prediction in a trial fibrilation from cardiac MRI

Tese de mestrado integrado em Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), Universidade de Lisboa, Faculdade de Ciências, 2021Atrial fibrillation (AF), is the most frequent sustained cardiac arrhythmia, described by an irregular and rapid contraction of the two upper chambers of the heart (the atria). AF development is promoted and predisposed by atrial dilation, which is a consequence of atria adaptation to AF. However, it is not clear whether atrial dilation appears similarly over the cardiac cycle and how it affects ventricular volumes. Catheter ablation is arguably the AF gold standard treatment. In their current form, ablations are capable of directly terminating AF in selected patients but are only first-time effective in approximately 50% of the cases. In the first part of this work, volumetric functional markers of the left atrium (LA) and left ventricle (LV) of AF patients were studied. More precisely, a customised convolutional neural network (CNN) was proposed to segment, across the cardiac cycle, the LA from short axis CINE MRI images acquired with full cardiac coverage in AF patients. Using the proposed automatic LA segmentation, volumetric time curves were plotted and ejection fractions (EF) were automatically calculated for both chambers. The second part of the project was dedicated to developing classification models based on cardiac MR images. The EMIDEC STACOM 2020 challenge was used as an initial project and basis to create binary classifiers based on fully automatic classification neural networks (NNs), since it presented a relatively simple binary classification task (presence/absence of disease) and a large dataset. For the challenge, a deep learning NN was proposed to automatically classify myocardial disease from delayed enhancement cardiac MR (DE-CMR) and patient clinical information. The highest classification accuracy (100%) was achieved with Clinic-NET+, a NN that used information from images, segmentations and clinical annotations. For the final goal of this project, the previously referred NNs were re-trained to predict AF recurrence after catheter ablation (CA) in AF patients using pre-ablation LA short axis in CINE MRI images. In this task, the best overall performance was achieved by Clinic-NET+ with a test accuracy of 88%. This work shown the potential of NNs to interpret and extract clinical information from cardiac MRI. If more data is available, in the future, these methods can potentially be used to help and guide clinical AF prognosis and diagnosis

Artificial neural network for atrial fibrillation identification in portable devices

none6siAtrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the “AF Classification from a Short Single Lead ECG Recording” database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1%–93.0%), 90.2% (CI: 86.2%–94.3%) and 90.8% (CI: 88.1%–93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices.openMarinucci D.; Sbrollini A.; Marcantoni I.; Morettini M.; Swenne C.A.; Burattini L.Marinucci, D.; Sbrollini, A.; Marcantoni, I.; Morettini, M.; Swenne, C. A.; Burattini, L

Automatic identification of ischemia using lightweight attention network in PET cardiac perfusion imaging

Ischemic disease, caused by inadequate blood supply to organs or tissues, poses a significant global health challenge. Early detection of ischemia is crucial for timely intervention and improved patient outcomes. Myocardial perfusion imaging with positron-emission tomography (PET) is a non-invasive technique used to identify ischemia. However, accurately interpreting PET images can be challenging, necessitating the development of reliable classification methods. In this study, we propose a novel approach using MS-DenseNet, a lightweight attention network, for the detection and classification of ischemia from myocardial polar maps. Our model incorporates the squeeze and excitation modules to emphasize relevant feature channels and suppress unnecessary ones. By effectively utilizing channel interdependencies, we achieve optimum reuse of interchannel interactions, enhancing the model's performance. To evaluate the efficacy and accuracy of our proposed model, we compare it with transfer learning models commonly used in medical image analysis. We conducted experiments using a dataset of 138 polar maps (JPEG) obtained from 15O_H2O stress perfusion studies, comprising patients with ischemic and non-ischemic condition. Our results demonstrate that MS-DenseNet outperforms the transfer learning models, highlighting its potential for accurate ischemia detection and classification. This research contributes to the field of ischemia diagnosis by introducing a lightweight attention network that effectively captures the relevant features from myocardial polar maps. The integration of the squeeze and excitation modules further enhances the model's discriminative capabilities. The proposed MS-DenseNet offers a promising solution for accurate and efficient ischemia detection, potentially improving the speed and accuracy of diagnosis and leading to better patient outcomes