Magnetocardiography-Based Ischemic Heart Disease Detection and Localization Using Machine Learning Methods

Tao, Rong; Zhang, Shulin; Huang, Xiao; Tao, Minfang; Jian, Ma; Ma, Shixin; Zhang, Chaoxiang; Zhang, Tongxin; Tang, Fakuan; Lu, Jianping; Shen, Chenxing; Xie, Xiaoming

doi:10.1109/tbme.2018.2877649

Cited by 81 publications

(32 citation statements)

References 26 publications

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“…Heart failure prediction has been modeled by relying on machine learning techniques applied to EHR data and reached a high AUC score of 77% using Logistic regression with model selection based on Bayesian information criterion [39]. Moreover, machine learning techniques have been proven to efficiently classify different types of medical data such as magneto-cardiograph recordings using k-nearest neighbor and XGBoost techniques [40], or clustering multi-label documents in order to help finding co-occurrence of heart disease with other diseases [41,42].…”

Section: Related Workmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.

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Section: Related Workmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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“…By contrast, MCG provides much more localized fields and detailed 3D imaging over the heart so that the exact location of coronary stenosis can be detected. Research also shows that other MCG features, such as magnetic pole characteristics, are also associated with location identification [110].…”

Section: A Magnetocardiograpy (Mcg)mentioning

confidence: 86%

A Review of Magnetic Field Emissions From the Human Body: Sources, Sensors, and Uses

Zhu

Kiourti

2022

IEEE Open J. Antennas Propag.

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It has long been common practice to capture the electric fields emanated by the human body as a means of detecting and/or monitoring diverse health conditions. However, these electric fields are strongly impacted by the complex permittivity of biological tissues which deteriorates their waveforms and limits their diagnostic capabilities. As an alternative, recent progress has been made in the measurement of bio-magnetic fields occur from the natural currents flowing through the body. The advantage in this case is, since tissues are non-magnetic, magnetic fields propagate in an uninterrupted manner towards the skin surface where they are eventually collected. This unveils game-changing opportunities for future medical diagnostics. Nevertheless, a major challenge associated with sensing these naturally emanated magnetic fields is that they are extremely weak, and in fact orders of magnitude smaller than those generated by the Earth. To this end, extensive efforts have been pursued to realize sensing technology that is sensitive enough to collect biomagnetic fields. Example fields of use include magnetomyography (MMG), magnetocardiography (MCG), magnetoencephalography (MEG), and Magnetoneurography (MNG) (including magnetospinography (MSG)). This review will provide an overview of technologies used to sense bio-magnetic fields, list their merits and limits in a critical manner, and discuss clinical applications.INDEX TERMS Bioelectromagnetics, magnetocardiography (MCG), magnetoencephalography (MEG), magnetomyography (MMG), Magnetoneurography (MNG), magnetospinography (MSG).

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“…Generally speaking, the traditional machine learning method has achieved good results in the field of image processing due to its long development time and effective theoretical system [29]. Nevertheless, these methods are usually composed of several independent steps, so they need a lot of storage space to store the intermediate results [30].…”

Section: B End-to-end Learning Systemmentioning

confidence: 99%

A Novel Multi-Branch Channel Expansion Network for Garbage Image Classification

2020

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Due to the lack of data available for training, deep learning hardly performed well in the field of garbage image classification. We choose the TrashNet data set which is widely used in the field of garbage image classification, and try to overcome data deficiencies in this field by optimizing the network structure. In this paper, it is found that the deeper network and short-circuit connection, which are generally accepted in the field of deep learning, will not work well on the TrashNet data set. By analyzing and modifying the network structure, we propose an effective method to improve the network performance on TrashNet data set. This method widens the network by expanding branches, and then uses add layers to realize the fusion of feature information. It can make full use of feature information at slight additional computational cost. Using this method to replace the core structure of the Xception network, the performance of the improved network has been improved greatly. Finally, the M-b Xception network proposed by us achieves 94.34% classification accuracy on the TrashNet data set, and has certain advantages over some state-of-theart methods on multiple indicators. The python code can be download from https://github.com/scp19801980/Trash-classify-M_b-Xception.

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Magnetocardiography-Based Ischemic Heart Disease Detection and Localization Using Machine Learning Methods

Cited by 81 publications

References 26 publications

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

A Review of Magnetic Field Emissions From the Human Body: Sources, Sensors, and Uses

A Novel Multi-Branch Channel Expansion Network for Garbage Image Classification

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