Monitoring electrocardiogram signals are of great significance for the diagnosis of arrhythmias. In recent years, deep learning and convolutional neural networks have been widely used in the classification of cardiac arrhythmias. However, the existing neural network applied to ECG signal detection usually requires a lot of computing resources, which is not friendly to resource-constrained equipment, and it is difficult to realize real-time monitoring. In this paper, a binarized convolutional neural network suitable for ECG monitoring is proposed, which is hardware-friendly and more suitable for use in resource-constrained wearable devices. Targeting the MIT-BIH arrhythmia database, the classifier based on this network reached an accuracy of 95.67% in the five-class test. Compared with the proposed baseline full-precision network with an accuracy of 96.45%, it is only 0.78% lower. Importantly, it achieves 12.65 times the computing speedup, 24.8 times the storage compression ratio, and only requires a quarter of the memory overhead.

Arrhythmia Classifier using Binarized Convolutional Neural Network for Resource-Constrained Devices

Cardiac arrhythmia is a medical term to describe an irregular heartbeat, which occurs when electrical properties in the heart incapacitated. Although most of arrhythmias are harmless, some of them can lead to life threatening complications if not detected and monitored on time. To date, a real-time automatic arrhythmia detection is essential in the current health system. This can be seen from numerous research that has been conducted on the automatic detection and classification of various types of arrhythmias. This study seeks to find as much relevant research on the abundance of published work in the topic, to identify the state-of-the art methods and finding the gap in the research in an effort to summarize all of the available evidence of the distinguish real-time automatic arrhythmia detection. A careful quantitative research process of systematic literature review (SLR) within the Health Informatics area was applied in this research to identify and analyze the trends, datasets, and methods in real-time arrhythmia detection. Systematic searches were administered in Scopus using a defined search string, while several filters were applied based on inclusion and exclusion criteria. After removing duplicates and non-eligible articles, the data were extracted from the selected articles with the result shows that the number of studies focusing on the real-time detection of arrhythmia is still limited, but on a rising trend. Finally, the analysis of the datasets, feature extraction, classification methods and a table of algorithm comparison were presented as the result of the study, which summarized the pre-eminent methods of real-time arrhythmia classification based on its average accuracy.

Exploration of ECG-Based Real-Time Arrhythmia Detection: A Systematic Literature Review

Reasonably and effectively monitoring arrhythmias through ECG signals has significant implications for human health. With the development of deep learning, numerous ECG classification algorithms based on deep learning have emerged. However, most existing algorithms trade off high accuracy for complex models, resulting in high storage usage and power consumption. This also inevitably increases the difficulty of implementation on wearable Artificial Intelligence-of-Things (AIoT) devices with limited resources. In this study, we proposed a universally applicable ultra-lightweight binary neural network(BNN) that is capable of 5-class and 17-class arrhythmia classification based on ECG signals. Our BNN achieves 96.90% (full precision 97.09%) and 97.50% (full precision 98.00%) accuracy for 5-class and 17-class classification, respectively, with state-of-the-art storage usage (3.76 KB and 4.45 KB). Compared to other binarization works, our approach excels in supporting two multi-classification modes while achieving the smallest known storage space. Moreover, our model achieves optimal accuracy in 17-class classification and boasts an elegantly simple network architecture. The algorithm we use is optimized specifically for hardware implementation. Our research showcases the potential of lightweight deep learning models in the healthcare industry, specifically in wearable medical devices, which hold great promise for improving patient outcomes and quality of life. Code is available on: https://github.com/xpww/ECG_BNN_Net

Arrhythmia Classifier Based on Ultra-Lightweight Binary Neural Network

Monitoring electrocardiogram signals is of great significance for the diagnosis of arrhythmias. In recent years, deep learning and convolutional neural networks have been widely used in the classification of cardiac arrhythmias. However, the existing neural network applied to ECG signal detection usually requires a lot of computing resources, which is not friendlyF to resource-constrained equipment, and it is difficult to realize real-time monitoring. In this paper, a binarized convolutional neural network suitable for ECG monitoring is proposed, which is hardware-friendly and more suitable for use in resource-constrained wearable devices. Targeting the MIT-BIH arrhythmia database, the classifier based on this network reached an accuracy of 95.67% in the five-class test. Compared with the proposed baseline full-precision network with an accuracy of 96.45%, it is only 0.78% lower. Importantly, it achieves 12.65 times the computing speedup, 24.8 times the storage compression ratio, and only requires a quarter of the memory overhead. 

Cardiac arrhythmia is a medical term to describe an irregular heartbeat, which occurs when electrical properties in the heart incapacitated. Although most of arrhythmias are harmless, some of them can lead to life threatening complications if not detected and monitored on time. To date, a real-time automatic arrhythmia detection is essential in the current health system. This can be seen from numerous research that has been conducted on the automatic detection and classification of various types of arrhythmias. This study seeks to find as much relevant research on the abundance of published work in the topic, to identify the state-of-the art methods and finding the gap in the research in an effort to summarize all of the available evidence of the distinguish real-time automatic arrhythmia detection. A careful quantitative research process of systematic literature review (SLR) within the Health Informatics area was applied in this research to identify and analyze the trends, datasets, and methods in real-time arrhythmia detection. Systematic searches were administered in Scopus using a defined search string, while several filters were applied based on inclusion and exclusion criteria. After removing duplicates and non-eligible articles, the data were extracted from the selected articles with the result shows that the number of studies focusing on the real-time detection of arrhythmia is still limited, but on a rising trend. Finally, the analysis of the datasets, feature extraction, classification methods and a table of algorithm comparison were presented as the result of the study, which summarized the pre-eminent methods of real-time arrhythmia classification based on its average accuracy. 

An arrhythmia diagnosis neural network can perform real-time diagnosis through continuous monitoring, and it can warn against potential risks. Moreover, these networks can be installed in resources-constrained devices like wearable devices. However, the existing neural networks suffer from high memory consumption and power consumption, which limit their application in low-power resources-constrained devices. Here, we proposed a novel neural network classifier to classify 17 different rhythm classes using 1,000 long-duration electrocardiograms, achieving a classification accuracy of 95.72%, which is 4.32% higher than current state-of-the-art methods. Additionally, we proposed a layer-wise quantization method based on the greedy algorithm and compared it to other quantization methods. The proposed classifier achieved a 95.39% classification accuracy and reduced memory consumption by 15.5 times. Our study realizes a neural network with high performance and low resources consumption, and it demonstrates the possibility of implementing neural networks in resources-constrained devices for continuous monitoring, real-time diagnosis, and potential risk warnings.

Feng Chu

Papers

Arrhythmia Classifier Using a Layer-wise Quantized Convolutional Neural Network for Resource-Constrained Devices