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

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

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

Toward Automated Feature Extraction for Deep Learning Classification of Electrocardiogram Signals

Many recent studies have focused on the automatic classification of electrocardiogram (ECG) signals using deep learning (DL) methods. Most rely on existing complex DL methods, such as transfer learning or providing the models with carefully designed extracted features based on domain knowledge. A common assumption is that the deeper and more complex the DL model is, the better it learns. In this study, we propose two different DL models for automatic feature extraction from ECG signals for classification tasks: A CNN-LSTM hybrid model and an attention/transformer-based model with wavelet transform for the dimensional embedding. Both of the models extract the features from time series at the initial layers of the neural networks and can obtain performance at least equal to, if not greater than, many contemporary deep neural networks. To validate our hypothesis, we used three publicly available data-sets to evaluate the proposed models. Our model achieved a benchmark accuracy of 99.92% for fall detection and 99.93% for the PTB database for myocardial infarction versus normal heartbeat classification

Development of a Real-Time Single-Lead Single-Beat Frequency-Independent Myocardial Infarction Detector

The central aim of this research is the development and deployment of a novel multilayer machine learning design with unique application for the diagnosis of myocardial infarctions (MIs) from individual heartbeats of single-lead electrocardiograms (EKGs) irrespective of their sampling frequencies over a given range. To the best of our knowledge, this design is the first to attempt inter-patient myocardial infarction detection from individual heartbeats of single-lead (lead II) electrocardiograms that achieves high accuracy and near real-time diagnosis. The processing time of 300 milliseconds to a diagnosis is just at the time range in between extremely fast heartbeats of around 300 milliseconds, or 200 beats per minute. The design achieves stable performance metrics over the frequency range of 202Hz to 2.8kHz with an accuracy of 77.12%, positive predictive value (PPV) of 75.85%, and a negative predictive value (NPV) of 83.02% over the entire PTB database; 85.07%, 81.54%, 87.31% over the PTB-XL (the largest EKG database available for research) validation set, and 84.17%, 78.37%, 87.55% over the PTB-XL test set. Major design contributions and findings of this work reveal (1) a method for the realtime detection of ventricular depolarization events in the PQRST complex from 12-lead electrocardiograms using Independent Component Analysis (ICA), with a slightly different use of ICA proposed for electrocardiogram analysis and R-peak detection/localization; (2) a multilayer Long-Short Term Memory (LSTM) neural network design that identifies infarcted patients from a single heartbeat of a single-lead (lead II) electrocardiogram; (3) and integrated LSTM neural network with an algorithm that detects the R-peaks in real time for instantaneous detection of myocardial infarctions and for effective monitoring of patients under cardiac stress and/or at risk of myocardial infarction; (4) a fully integrated 12-lead real-time classifier with even higher detection metrics and a deeper neural architecture, which could serve as a near real-time monitoring tool that could gauge disease progression and evaluate benefits gained from early intervention and treatment planning; (5) a real-time frequency-independent design based on a single-lead single-beat MI detector, which is of pivotal importance to deployment as there is no standard sampling frequency for EKGs, making them span a wider frequency spectrum. vi