An electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVDs). ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient–doctor dialogs. Notwithstanding its proven utility, deciphering large data sets to determine appropriate information remains a challenge in ECG-based CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction protocol. This is followed by using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. Comparison of the performance recorded for the proposed technique alongside state-of-the-art methods reported the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The outcomes suggest that the proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected.

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A Multitier Deep Learning Model for Arrhythmia Detection

In recent years, the development of artificial intelligence (AI) and the gradual beginning of AI's research in the medical field have allowed people to see the excellent prospects of the integration of AI and healthcare. Among them, the hot deep learning field has shown greater potential in applications such as disease prediction and drug response prediction. From the initial logistic regression model to the machine learning model, and then to the deep learning model today, the accuracy of medical disease prediction has been continuously improved, and the performance in all aspects has also been significantly improved. This article introduces some basic deep learning frameworks and some common diseases, and summarizes the deep learning prediction methods corresponding to different diseases. Point out a series of problems in the current disease prediction, and make a prospect for the future development. It aims to clarify the effectiveness of deep learning in disease prediction, and demonstrates the high correlation between deep learning and the medical field in future development. The unique feature extraction methods of deep learning methods can still play an important role in future medical research. © 2021 Tech Science Press. All rights reserved.

https://www.techscience.com/CMES/v128n2/43921/pdf

Multi-disease prediction based on deep learning: A survey

The Internet of Things (IoT) is playing a vital role in the rapid automation of the healthcare sector. The branch of IoT dedicated towards medical science is at times termed as Healthcare Internet of Things (H-IoT). The key elements of all H-IoT applications are data gathering and processing. Due to the large amount of data involved in healthcare, and the enormous value that accurate predictions hold, the integration of machine learning (ML) algorithms into H-IoT is imperative. This paper aims to serve both as a compilation as well as a review of the various state of the art applications of ML algorithms currently being integrated with H-IoT. Some of the most widely used ML algorithms have been briefly introduced and their use in various H-IoT applications has been analyzed in terms of their advantages, scope, and possible improvements. Applications have been divided into the domains of diagnosis, prognosis and spread control, assistive systems, monitoring, and logistics. In healthcare, practical use of a model requires it to be highly accurate and to have ample measures against security attacks. The applications of ML algorithms in H-IoT discussed in this paper have shown experimental evidence of accuracy and practical usability. The constraints and drawbacks of each of these applications have also been described.

A Review on the Role of Machine Learning in Enabling IoT Based Healthcare Applications

Actual bearing compound fault diagnosis based on active learning and decoupling attentional residual network

Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network

A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification.

A high-precision arrhythmia classification method based on dual fully connected neural network

The arrhythmia is an important group of cardiovascular disease. Electrocardiogram (ECG) is commonly used for detecting arrhythmias. Computer-aided diagnosis system can diagnose ECG automatically without the limitations of visual inspection. In order to improve the performance of ECG heartbeat classification, this paper proposes a novel automatic classification system. Based on convolutional neural network (CNN) and long short-term memory (LSTM) network, a deep structure with multiple input layers is proposed. Four input layers are constructed based on different regions of a heartbeat and RR interval features. The first three inputs are convolved using different strides. The three outputs of CNN are then concatenated and go through an LSTM network. Two fully-connected layers follow and the output is concatenated with the fourth input. Eventually, the last fully-connected layer outputs the predicted label. The proposed system was evaluated by two division schemes of the MIT-BIH arrhythmia database. Class-oriented scheme achieved an overall accuracy of 99.26% and subject-oriented scheme obtained an accuracy of 94.20%. The comparison with previous works showed the excellent performance of the novel network. The combination of automatic features and handcraft features was demonstrated to be helpful in heartbeat classification. Hence, the system can be used for clinical application.

Automated heartbeat classification based on deep neural network with multiple input layers

With age, our blood vessels are prone to aging, which induces cardiovascular disease. As an important basis for diagnosing heart disease and evaluating heart function, the electrocardiogram (ECG) records cardiac physiological electrical activity. Abnormalities in cardiac physiological activity are directly reflected in the ECG. Thus, ECG research is conducive to heart disease diagnosis. Considering the complexity of arrhythmia detection, we present an improved convolutional neural network (CNN) model for accurate classification. Compared with the traditional machine learning methods, CNN requires no additional feature extraction steps due to the automatic feature processing layers. In this paper, an improved CNN is proposed to automatically classify the heartbeat of arrhythmia. Firstly, all the heartbeats are divided from the original signals. After segmentation, the ECG heartbeats can be inputted into the first convolutional layers. In the proposed structure, kernels with different sizes are used in each convolution layer, which takes full advantage of the features in different scales. Then a max-pooling layer followed. The outputs of the last pooling layer are merged and as the input to fully-connected layers. Our experiment is in accordance with the AAMI inter-patient standard, which included normal beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F), and unknown beats (Q). For verification, the MIT arrhythmia database is introduced to confirm the accuracy of the proposed method, then, comparative experiments are conducted. The experiment demonstrates that our proposed method has high performance for arrhythmia detection, the accuracy is 99.06%. When properly trained, the proposed improved CNN model can be employed as a tool to automatically detect different kinds of arrhythmia from ECG.

Liqun Zhao

Papers

A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification.

A high-precision arrhythmia classification method based on dual fully connected neural network

Automated heartbeat classification based on deep neural network with multiple input layers

An Improved Convolutional Neural Network Based Approach for Automated Heartbeat Classification

An incremental learning system for atrial fibrillation detection based on transfer learning and active learning.