Efficient detection of myocardial infarction from single lead ECG signal

ECG arrhythmia detection in an inter-patient setting using Fourier decomposition and machine learning

ECG beat classification or arrhythmia detection through artificial intelligence (AI) is an active topic of research. It is vital to recognize and detect the type of arrhythmia for monitoring cardiac abnormalities. The AI-based ECG beat classification algorithms proposed in the literature suffer from two main drawbacks. Firstly, some of the works have not considered any unseen test data to validate the performance of their algorithms. Secondly, the accuracy of detecting superventricular ectopic beats (SVEB) needs to be improved. In this work, we address these issues by considering an inter-patient paradigm where the test dataset is collected from a different set of subjects than the training data. Also, the proposed methodology detects SVEB with an F1 score of 89.35%, which is better than existing algorithms. We have used the Fourier decomposition method (FDM) for multi-scale analysis of ECG signals and extracted time-domain and statistical features from the narrow-band signal components obtained using FDM. Feature selection techniques, including the Kruskal-Wallis test and minimum redundancy maximum relevance (mRMR) have been used to select only the relevant features and rank these features to remove any redundancy. Since the dataset used is highly imbalanced, Mathew's correlation coefficient (MCC) has also been used to analyze the performance of the proposed method. Support vector machine classifier with linear kernel achieves an overall 98.03% accuracy and 91.84% MCC for the MIT-BIH arrhythmia dataset.

Real-Time Myocardial Infarction Detection Approaches with a Microcontroller-Based Edge-AI Device

Myocardial Infarction (MI), commonly known as heart attack, is a cardiac condition characterized by damage to a portion of the heart, specifically the myocardium, due to the disruption of blood flow. Given its recurring and often asymptomatic nature, there is the need for continuous monitoring using wearable devices. This paper proposes a single-microcontroller-based system designed for the automatic detection of MI based on the Edge Computing paradigm. Two solutions for MI detection are evaluated, based on Machine Learning (ML) and Deep Learning (DL) techniques. The developed algorithms are based on two different approaches currently available in the literature, and they are optimized for deployment on low-resource hardware. A feasibility assessment of their implementation on a single 32-bit microcontroller with an ARM Cortex-M4 core was examined, and a comparison in terms of accuracy, inference time, and memory usage was detailed. For ML techniques, significant data processing for feature extraction, coupled with a simpler Neural Network (NN) is involved. On the other hand, the second method, based on DL, employs a Spectrogram Analysis for feature extraction and a Convolutional Neural Network (CNN) with a longer inference time and higher memory utilization. Both methods employ the same low power hardware reaching an accuracy of 89.40% and 94.76%, respectively. The final prototype is an energy-efficient system capable of real-time detection of MI without the need to connect to remote servers or the cloud. All processing is performed at the edge, enabling NN inference on the same microcontroller.

Deep learning with fetal ECG recognition

Objective.Independent component analysis (ICA) is widely used in the extraction of fetal ECG (FECG). However, the amplitude, order, and positive or negative values of the ICA results are uncertain. The main objective is to present a novel approach to FECG recognition by using a deep learning strategy.Approach.A cross-domain consistent convolutional neural network (CDC-Net) is developed for the task of FECG recognition. The output of the ICA algorithm is used as input to the CDC-Net and the CDC-Net identifies which channel's signal is the target FECG.Main results.Signals from two databases are used to test the efficiency of the proposed method. The proposed deep learning method exhibits good performance on FECG recognition. Specifically, the Precision, Recall and F1-score of the proposed method on the ADFECGDB database are 91.69%, 91.37% and 91.52%, respectively. The Precision, Recall and F1-score of the proposed method on the Daisy database are 97.85%, 97.42% and 97.63%, respectively.Significance. This study is a proof of concept that the proposed method can automatically recognize the FECG signals in multi-channel ECG data. The development of FECG recognition technology contributes to automated FECG monitoring.

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction.

A Review on the Applications of Time-Frequency Methods in ECG Analysis

The joint time-frequency analysis method represents a signal in both time and frequency. Thus, it provides more information compared to other one-dimensional methods. Several researchers recently used time-frequency methods such as the wavelet transform, short-time Fourier transform, empirical mode decomposition and reported impressive results in various electrophysiological studies. The current review provides comprehensive knowledge about different time-frequency methods and their applications in various ECG-based analyses. Typical applications include ECG signal denoising, arrhythmia detection, sleep apnea detection, biometric identification, emotion detection, and driver drowsiness detection. The paper also discusses the limitations of these methods. The review will form a reference for future researchers willing to conduct research in the same field.

MCA-net: A multi-task channel attention network for Myocardial infarction detection and location using 12-lead ECGs

PROBLEM

Myocardial infarction (MI) is a classic cardiovascular disease (CVD) that requires prompt diagnosis. However, due to the complexity of its pathology, it is difficult for cardiologists to make an accurate diagnosis in a short period.

AIM

In the clinical, MI can be detected and located by the morphological changes on a 12-lead electrocardiogram (ECG). Therefore, we need to develop an automatic, high-performance, and easily scalable algorithm for MI detection and location using 12-lead ECGs to effectively reduce the burden on cardiologists.

METHODS

This paper proposes a multi-task channel attention network (MCA-net) for MI detection and location using 12-lead ECGs. It employs a channel attention network based on a residual structure to efficiently capture and integrate features from different leads. On top of this, a multi-task framework is used to additionally introduce the shared and complementary information between MI detection and location tasks to further enhance the model performance.

RESULTS

Our method is evaluated on two datasets (The PTB and PTBXL datasets). It achieved more than 90% accuracy for MI detection task on both datasets. For MI location tasks, we achieved 68.90% and 49.18% accuracy on the PTB dataset, respectively. And on the PTBXL dataset, we achieved more than 80% accuracy.

CONCLUSION

Numerous comparison experiments demonstrate that MCA-net outperforms the state-of-the-art methods and has a better generalization. Therefore, it can effectively assist cardiologists to detect and locate MI and has important implications for the early diagnosis of MI and patient prognosis.

Automated Detection of Myocardial Infarction and Heart Conduction Disorders Based on Feature Selection and a Deep Learning Model

An electrocardiogram (ECG) is an essential piece of medical equipment that helps diagnose various heart-related conditions in patients. An automated diagnostic tool is required to detect significant episodes in long-term ECG records. It is a very challenging task for cardiologists to analyze long-term ECG records in a short time. Therefore, a computer-based diagnosis tool is required to identify crucial episodes. Myocardial infarction (MI) and conduction disorders (CDs), sometimes known as heart blocks, are medical diseases that occur when a coronary artery becomes fully or suddenly stopped or when blood flow in these arteries slows dramatically. As a result, several researchers have utilized deep learning methods for MI and CD detection. However, there are one or more of the following challenges when using deep learning algorithms: (i) struggles with real-life data, (ii) the time after the training phase also requires high processing power, (iii) they are very computationally expensive, requiring large amounts of memory and computational resources, and it is not easy to transfer them to other problems, (iv) they are hard to describe and are not completely understood (black box), and (v) most of the literature is based on the MIT-BIH or PTB databases, which do not cover most of the crucial arrhythmias. This paper proposes a new deep learning approach based on machine learning for detecting MI and CDs using large PTB-XL ECG data. First, all challenging issues of these heart signals have been considered, as the signal data are from different datasets and the data are filtered. After that, the MI and CD signals are fed to the deep learning model to extract the deep features. In addition, a new custom activation function is proposed, which has fast convergence to the regular activation functions. Later, these features are fed to an external classifier, such as a support vector machine (SVM), for detection. The efficiency of the proposed method is demonstrated by the experimental findings, which show that it improves satisfactorily with an overall accuracy of 99.20% when using a CNN for extracting the features with an SVM classifier.

A Robustness Evaluation of Machine Learning Algorithms for ECG Myocardial Infarction Detection

An automatic electrocardiogram (ECG) myocardial infarction detection system needs to satisfy several requirements to be efficient in real-world practice. These requirements, such as reliability, less complexity, and high performance in decision-making, remain very important in a realistic clinical environment. In this study, we investigated an automatic ECG myocardial infarction detection system and presented a new approach to evaluate its robustness and durability performance in classifying the myocardial infarction (with no feature extraction) under different noise types. We employed three well-known supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF), and tested the performance and robustness of these techniques in classifying normal (NOR) and myocardial infarction (MI) using real ECG records from the PTB database after normalization and segmentation of the data, with a suggested inter-patient paradigm separation as well as noise from the MIT-BIH noise stress test database (NSTDB). Finally, we measured four metrics: accuracy, precision, recall, and F1-score. The simulation revealed that all of the models performed well, with values of over 0.50 at lower SNR levels, in terms of all the metrics investigated against different types of noise, indicating that they are encouraging and acceptable under extreme noise situations are are thus considered sustainable and robust models for specific forms of noise. All of the methods tested could be used as ECG myocardial infarction detection tools in real-world practice under challenging circumstances.

Application Value of Remote ECG Monitoring in Early Diagnosis of PCI for Acute Myocardial Infarction

The blockage of blood in the vessels results in heart attacks and cardiac arrests which are referred to as myocardial infarction. Early detection of such infarction is feasible through percutaneous coronary intervention (PCI) based on electrocardiogram (ECG) monitoring. The variations in blood flow and clot are precisely observed through periodic ECG monitoring and previous correlations. This article introduces a concentrated value assessment model (CVAM) for determining PCI levels in treating myocardial infarction. The ECG observations from the previous observation sessions are accumulated and organized for validating the infarction rate. This requires the accompanying concentrated data like a heartbeat, blood pressure, and flow rate observed in different sessions. Based on the session observation and normal data correlation, the PCI level is recommended for the patient. In this analysis process, the value shift due to blocks and high and low blood pressure is accounted for through the deep learning paradigm. This paradigm correlates the above factors with the ECG values for precisely determining PCI from the last known concentration. The learning paradigm is trained based on session and normal observation data through different intervals. This model is validated using the metrics precision, analysis rate, diagnosis recommendation, and complexity.

A multi-modal assessment of sleep stages using adaptive Fourier decomposition and machine learning

Healthy sleep is essential for the rejuvenation of the body and helps in maintaining good health. Many people suffer from sleep disorders that are characterized by abnormal sleep patterns. Automated assessment of such disorders using biomedical signals has been an active subject of research. Electroencephalogram (EEG) is a popular diagnostic used in this regard. We consider a widely-used publicly available database and process the signals using the Fourier decomposition method (FDM) to obtain narrowband signal components. Statistical features extracted from these components are passed on to machine learning classifiers to identify different stages of sleep. A novel feature measuring the non-stationarity of the signal is also used to capture salient information. It is shown that classification results can be improved by using multi-channel EEG instead of single-channel EEG data. Simultaneous utilization of multiple modalities, such as Electromyogram (EMG), Electrooculogram (EOG) along with EEG data leads to further enhancement in the obtained results. The proposed method can be efficiently implemented in real-time using fast Fourier transform (FFT), and it provides better classification results than the other algorithms existing in the literature. It can assist in the development of low-cost sensor-based setups for continuous patient monitoring and feedback.

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology.

MFB-LANN: A lightweight and updatable myocardial infarction diagnosis system based on convolutional neural networks and active learning

BACKGROUND AND OBJECTIVES

12 leads electrocardiogram (ECG) are widely used to diagnose myocardial infarction (MI). Generally, the symptoms of MI can be reflected by waveforms in the heartbeat, and the contribution of different ECG leads to different types of MI is different. Therefore, it is significant to use the heartbeat waveform features and the lead relationship features for multi-category MI diagnosis. Moreover, the challenge of individual differences and lightweight algorithms also need to be further resolved and explored in the ECG automatic diagnosis system.

METHODS

This paper presents a lightweight MI diagnosis system named multi-feature-branch lead attention neural network (MFB-LANN) via 12 leads ECG signals. It is designed based on the characteristics of the ECG lead. Specifically, 12 independent feature branches correspond to different leads, and each branch contains different convolutional layers to extract features in the heartbeat, then a novel attention module is developed named lead attention mechanism (LAM) to assign different weights to each feature branch. Finally all the weighted feature branches are fused for classification. Furthermore, to overcome individual differences, patient-specific scheme and active learning (AL) are used to train and update the model iteratively.

RESULTS

Experimental results based on Physikalisch-Technische Bundesanstalt (PTB) database shows that the MFB-LANN achieved satisfactory results with accuracy of 99.63% based on 5-fold cross validation under the intra-patient scheme. The patient-specific experiment yielded an average accuracy of 96.99% compared to the state-of-the-art. By contrast, the model achieved acceptable results on the hybrid database (PTB and PTB-XL), especially achieving 94.19% accuracy after the update. Moreover, the system can complete the update process and real-time diagnosis on the ARM Cortex-A72 platform.

CONCLUSIONS

Experiments show that the proposed method for MI diagnosis has more obvious advantages compared to other recent methods, and it has great potential to be applied to the mobile medical field.

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part I: Cardiac Signals

Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today's clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.