An Automatic Cardiac Arrhythmia Classification System With Wearable Electrocardiogram

Xia, Yufa; Zhang, Huailing; Xu, Lin; Gao, Zhifan; Liu, Huafeng; Li, Shuo

doi:10.1109/access.2018.2807700

Cited by 114 publications

(53 citation statements)

References 33 publications

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“…Hence, the classification algorithm is needed for forming dataset and interpreting the large volume data in the modern remote healthcare system. In Xia et al, 10 they explained the wearable device for IoT application. Then, they explained about automatic cardiac arrhythmia classification based in ECG signal.…”

Section: Review Of Related Work In Ecg Classificationmentioning

confidence: 99%

RETRACTED: Atrial fibrillation classification using deep learning algorithm in Internet of Things–based smart healthcare system

Pitchaimani¹,

Rajan²

2019

Health Informatics J

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Detecting the electrocardiogram pattern in Internet of Things–based healthcare system and notifying this to the user is a challenging task. Using advance computing methods for classification of electrocardiogram signal is a notable research topic. In this research work, an intelligent electrocardiogram signal classification, employing deep learning algorithm, developed and tested in Internet of Things–based smart healthcare system was proposed. For classification of acquired electrocardiogram signal, a partitioned deep convolutional neural network was proposed. The electrocardiogram feature continuously in the Internet of Things–based monitoring system was learnt. To make use of learned features in the continuous time series data, it forms a higher order space in the server. We have made quantifiable comparative analysis with other classification algorithm with the same time series data collected from different atrial fibrillation samples in the Internet of Things–based e-health system. Our proposed algorithm learned features were tested in atrial fibrillation classified signal with other conventional classifiers with various performance indices. We obtained an accuracy of 96.3 percent with 93.5-percent sensitivity and 97.5-percent precision. From the obtained result, processing with proposed deep convolutional neural network provides reliable timely assist and accurate classification of electrocardiogram signal in Internet of Things–based smart healthcare system.

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Section: Review Of Related Work In Ecg Classificationmentioning

confidence: 99%

RETRACTED: Atrial fibrillation classification using deep learning algorithm in Internet of Things–based smart healthcare system

Pitchaimani¹,

Rajan²

2019

Health Informatics J

View full text Add to dashboard Cite

show abstract

“…Note that after passing the deep data features through a pooled layer max-pooling, those features will be sequentially loaded into two fully connected layers named as dense, in which purification is performed on the hierarchical data features; in the next step, the classifier function takes responsibility for feature classification on the output of purified data features. Compared with other similar schemes on automatic ECG diagnosis [5,7,16,27,31,36], the invented lightweight algorithm has been released from requiring large set of calculation parameters but still ensures constant accuracy on recognition effects, which is able to realize parallel processing of ECG data despite of limited network resources or running memory on GPU [40].…”

Section: Automatic Ecg Diagnosis Via Deep Learning: Lightweight Classmentioning

confidence: 99%

“…Previously reported medications and medical procedures such as pacemaker insertion and surgery offer well-established treatments for most arrhythmias; meanwhile, a large quantity of signal and image processing algorithms as well as sensor devices provided useful tools on electrocardiogram-assisted diagnosis [8,18,20,26,31,32]. Recently, many researchers have been devoting themselves on computer-aided ECG analysis, where the technical developments are enriched from the booming growth on machine learning and deep learning algorithms [6, 9, 11, 13-17, 21, 24, 25, 27, 30, 33, 35-37].…”

Section: Introductionmentioning

confidence: 99%

“…Common training modes of algorithmic ECG diagnosis usually compose single lead and multilead [9]. Multilead data combined with multichannel neural network algorithms (MCNN) are capable of achieving considerably good results; however, their shortcomings display on the relatively larger training parameters of models and much longer training time, which increases the difficulties to realize real-time monitoring associated with the existing mobile devices [9,10,31,32]. Comparing to single-lead ECG data processing, the performance of recognition by multilead can be achieved with satisfactory, i.e., using the AlexNet structure, while the weakness lies on that these methods were regarded as relatively out of date [9].…”

Section: Introductionmentioning

confidence: 99%

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Diagnosing Abnormal Electrocardiogram (ECG) via Deep Learning

Gao¹

2020

Practical Applications of Electrocardiogram

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In this chapter, we investigate the most recent automatic detecting algorithms on abnormal electrocardiogram (ECG) in a variety of cardiac arrhythmias. We present typical examples of a medical case study and technical applications related to diagnosing ECG, which include (i) a recently patented data classifier on the basis of deep learning model, (ii) a deep neural network scheme to diagnose variable types of arrhythmia through wearable ECG monitoring devices, and (iii) implementation of the health cloud platform, which consists of automatic detection, data mining, and classifying via the Android terminal module. Our work establishes a cross-area study, which relates artificial intelligence (AI), deep learning, cloud computing on huge amount of data to minishape ECG monitoring devices, and portable interaction platforms. Experimental results display the technical advantages such as saving cost, better reliability, and higher accuracy of deep learning-based models in contrast to conventional schemes on cardiac diagnosis.

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“…In [20] The article of Xia et al [22], features an automatic wearable ECG classification and monitoring with a stack denoization autoencoder system. Using a wireless sensor device to retrieve ECG data and send that data to a Bluetooth 4.2 computer, where softmax regression is used to rate the ECG beats.…”

Section: Related Workmentioning

confidence: 99%

Mobile Device ECG Classification using quantized Neural Networks

Santos

Almeida

Costa

et al. 2020

Preprint

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In this paper, a novel method for classifying electrocardiogram signals in mobile devices is proposed, which classifies different arrhythmias according to the Association for the Advancement of Medical Instrumentation standard EC57. A convolutional neural network has been constructed, trained and validated with the MIT-BIH Arrhythmia Dataset, which has 5 different classes: normal beat, supraventricular premature beat, premature ventricular contraction, fusion of ventricular and normal beat, unclassifiable beat. Once trained and validated, the model is subjected to a post-training quantization stage using the TensorFlow Lite conversion method. The obtained results were satisfactory, before and after the quantization, the convolutional neural network obtained an accuracy of 98.5%. With the quantization technique it was possible to obtain a significant reduction in model size, thus enabling the development of the mobile application, this reduction was approximately 90% compared to the original model size.

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An Automatic Cardiac Arrhythmia Classification System With Wearable Electrocardiogram

Cited by 114 publications

References 33 publications

RETRACTED: Atrial fibrillation classification using deep learning algorithm in Internet of Things–based smart healthcare system

RETRACTED: Atrial fibrillation classification using deep learning algorithm in Internet of Things–based smart healthcare system

Diagnosing Abnormal Electrocardiogram (ECG) via Deep Learning

Mobile Device ECG Classification using quantized Neural Networks

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