Identification of myocardial infarction using consumer smartwatch ECG measurement

The goal of this thesis is to detect and classify acute myocardial infarctions from smartwatch ECG data. As the smartwatches have been increasing in numbers, and many of new smartwatch models have capability to detect ECG data. This study aims to answer to the question whether or not the ECG data from smartwatches can be used to detect acute myocardial infarctions. To answer to this question, and existing database has been used in tandem with smartwatch ECG data gathered from two different smartwatches. Five different machine learning models have been used to detect and classify ECG data. The best performing machine learning model was Extra Trees, which achieved accuracy of 90.84% with using Leave-One-Out Cross-Validation. These results show that ECG data from smartwatches could be used to detect infarctions. Measuring ECG with smartwatch is much easier than using clinical ECG measurement devices, meaning that ECG measuring could reach much wider audience that it has prior to this been able to reach. Further research could include gathering larger database from smartwatch ECG, and the data ownership of smartwatch, and other medical and biological data that companies collect

Application of artificial intelligence techniques for automated detection of myocardial infarction: A review

Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals around the world. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG as well as other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and other biophysical signals.Comment: 16 pages, 8 figure

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

On the detection of myocardial scar based on ECG/VCG analysis

In this paper, we address the problem of detecting the presence of myocardial scar from standard ECG/VCG recordings, giving effort to develop a screening system for the early detection of scar in the point-of-care. Based on the pathophysiological implications of scarred myocardium, which results in disordered electrical conduction, we have implemented four distinct ECG signal processing methodologies in order to obtain a set of features that can capture the presence of myocardial scar. Two of these methodologies: a.) the use of a template ECG heartbeat, from records with scar absence coupled with Wavelet coherence analysis and b.) the utilization of the VCG are novel approaches for detecting scar presence. Following, the pool of extracted features is utilized to formulate an SVM classification model through supervised learning. Feature selection is also employed to remove redundant features and maximize the classifier's performance. Classification experiments using 260 records from three different databases reveal that the proposed system achieves 89.22% accuracy when applying 10- fold cross validation, and 82.07% success rate when testing it on databases with different inherent characteristics with similar levels of sensitivity (76%) and specificity (87.5%)

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/

Detection and Localization of Myocardial Infarction Based on Multi-Scale ResNet and Attention Mechanism

PURPOSE: Myocardial infarction (MI) is one of the most common cardiovascular diseases, frequently resulting in death. Early and accurate diagnosis is therefore important, and the electrocardiogram (ECG) is a simple and effective method for achieving this. However, it requires assessment by a specialist; so many recent works have focused on the automatic assessment of ECG signals. METHODS: For the detection and localization of MI, deep learning models have been proposed, but the diagnostic accuracy of this approaches still need to be improved. Moreover, with deep learning methods the way in which a given result was achieved lacks interpretability. In this study, ECG data was obtained from the PhysioBank open access database, and was analyzed as follows. Firstly, the 12-lead ECG signal was preprocessed to identify each beat and obtain each heart interval. Secondly, a multi-scale deep learning model combined with a residual network and attention mechanism was proposed, where the input was the 12-lead ECG recording. Through the SENet model and the Grad-CAM algorithm, the weighting of each lead was calculated and visualized. Using existing knowledge of the way in which different types of MI gave characteristic patterns in specific ECG leads, the model was used to provisionally diagnose the type of MI according to the characteristics of each of the 12 ECG leads. RESULTS: Ten types of MI anterior, anterior lateral, anterior septal, inferior, inferior lateral, inferior posterior, inferior posterior lateral, lateral, posterior, and posterior lateral were diagnosed. The average accuracy, sensitivity, and specificity for MI detection of all lesion types was 99.98, 99.94, and 99.98%, respectively; and the average accuracy, sensitivity, and specificity for MI localization was 99.79, 99.88, and 99.98%, respectively. CONCLUSION: When compared to existing models based on traditional machine learning methods, convolutional neural networks and recurrent neural networks, the results showed that the proposed model had better diagnostic performance, being superior in accuracy, sensitivity, and specificity

Identification of myocardial infarction by high-frequency serial ECG measurement

The purpose of this study is to attempt to identify acute myocardial infarction with high frequency serial electrocardiogram. High-frequency ECG and serial ECG are both unique ECG analysing techniques. The idea in this study is to combine these two and see if changes between different ECGs from the same person can provide us some information, whether it being in the high-frequency or normal frequency range of the ECG. To answer the questions, an existing database which contained multiple ECGs for each person with high sampling frequency was used. 5 different machine learning models were trained and tested with this database. The results of the machine learning methods were good, producing the mean accuracy of 91.9%, while the best model was the Extra Trees machine learning model. It produced the accuracy of 97.9% when applying cross-validation to the database. After these results, high-frequency serial ECG could be stated to be relevant. However, having ECG measured regularly can be expensive and time consuming. Therefore, the possibility of using a wearable ECG device was also studied. With a device called SAFE, developed by the University of Turku, a new high-frequency serial ECG database was gathered. The already existing machine learning model trained with the previous data was applied to this database and produced a mean accuracy of 90%. The quality of the ECGs gathered with the device were also deemed to be viable. Both high-frequency ECG and serial ECG were found to be relevant methods. A wearable device could be used for AMI detection if the ECG is sufficient enough. Future studies could include increasing the dataset size of the wearable device, investigate other myocardial diseases and exploring the possibilities of high-frequency ECG further