A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length

Kamaleswaran, Rishikesan; Mahajan, Ruhi; Akbilgiç, Oğuz

doi:10.1088/1361-6579/aaaa9d

Cited by 95 publications

(49 citation statements)

References 23 publications

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“…We briefly introduced the most recent automatic detecting schemes such as convolutional neural networks (CNN), recurrent neural networks (RNN) [9,22,34,39], and its variation of longshort-term memory (LSTM) model [18,22], which aims on analyzing different types of cardiac arrhythmias. We presented an investigation of practical examples and applications of deep learning on automatic ECG diagnosis [5,7,16,27,31,36], which consists of a deep learning-based lightweight classifier on ECG data identification, deep belief network (DBN) [1,8,28] on diagnosing cardiac arrhythmia via wearable ECG monitoring devices, and a health cloud platform on automatic ECG detection, data mining and classification. We combined the theoretical concepts of artificial intelligence (AI)-oriented topics such as deep learning, big data health cloud platform to real medical applications, i.e., minishape ECG monitoring devices [9,41], domestic cardiac arrhythmia analyzer [40], automatic ECG diagnosis on © 2019 The Author(s).…”

Section: Discussionmentioning

confidence: 99%

“…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%

“…Traditional machine learning schemes are greatly affected by data, which demands complex preprocessing such as noise removal and data normalization. Besides, it is also vulnerable to over-extract unnecessary features, requiring filter design and sorting out redundant features, and then finally input another algorithm for classification [10,16,19,21,[25][26][27][28]. While good effects on recognition got achieved, the overall procedure is more complicated than those of the deep learning schemes [1,6,9,14,17,21,25,27].…”

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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Section: Discussionmentioning

confidence: 99%

Section: Automatic Ecg Diagnosis Via Deep Learning: Lightweight Classmentioning

confidence: 99%

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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“…The results showed that deep learning is more efficient and competitive in terms of sensitivity and specificity which were 98.55% and 99.52% respectively. In this paper (Kamaleswaran et al, 2018) CNN implemented to early detect normal sinus rhythm, AF, other abnormal rhythms, and noise. CNN was trained on 8,528 single lead short ECG signals and 3,658 recordings for testing set.…”

Section: Application Of Cnn For Ecg Analysismentioning

confidence: 99%

Convolutional Neural Network Application in Biomedical Signals

Alaskar¹

2018

JCSIT

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Recent improvements in big data and machine learning have enhanced the importance of biomedical signal and image-processing research. One part of machine learning evolution is deep learning networks. Deep learning networks are designed for the task of exploiting compositional structure in data. The golden age of the deep learning network in particular convolutional neural networks (CNNs) began in 2012. CNNs have rapidly become a methodology of optimal choice for analysing biomedical signals. CNNs have been successful in detecting and diagnosing an abnormality in biomedical signals. This paper has three distinct aims. The key primary aim is to provide state of the art knowledge about how deep learning evolved and revolutionized machine learning in the past few years. Second, to critically review the application of deep learning for different biomedical signals analysis and provide a holistic overview of current works of literature. Finally, to discuss the research opportunities with deep learning algorithms in the field of study that can serve as a starting point for new researchers to identify the future research direction in a concise manner.

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“…For instance, these algorithms have been used to predict at risk patients or patient outcomes, and to reduce alarm fatigue [9–11]. Similarly, machine learning algorithms have been successfully implemented in various medical image analyses to assist diagnosis and therapy in neurology, cardiology, and the detection of various cancers [12–19]. Machine learning algorithms have also shown promise in detecting/predicting sepsis [20, 21].…”

Section: Introductionmentioning

confidence: 99%

Physiomarkers in Real-Time Physiological Data Streams Predict Adult Sepsis Onset Earlier Than Clinical Practice

Wyk¹,

Khojandi²,

Davis

et al. 2018

Preprint

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Rationale: Sepsis is a life-threatening condition with high mortality rates and expensive treatment costs. To improve short- and long-term outcomes, it is critical to detect at-risk sepsis patients at an early stage.Objective: Our primary goal was to develop machine learning models capable of predicting sepsis using streaming physiological data in real-time.Methods: A dataset consisting of high-frequency physiological data from 1,161 critically ill patients admitted to the intensive care unit (ICU) was analyzed in this IRB-approved retrospective observational cohort study. Of that total, 634 patients were identified to have developed sepsis. In this paper, we define sepsis as meeting the Systemic Inflammatory Response Syndrome (SIRS) criteria in the presence of the suspicion of infection. In addition to the physiological data, we include white blood cell count (WBC) to develop a model that can signal the future occurrence of sepsis. A random forest classifier was trained to discriminate between sepsis and non-sepsis patients using a total of 108 features extracted from 2-hour moving time-windows. The models were trained on 80% of the patients and were tested on the remaining 20% of the patients, for two observational periods of lengths 3 and 6 hours.Results: The models, respectively, resulted in F1 scores of 75% and 69% half-hour before sepsis onset and 79% and 76% ten minutes before sepsis onset. On average, the models were able to predict sepsis 210 minutes (3.5 hours) before the onset.Conclusions: The use of robust machine learning algorithms, continuous streams of physiological data, and WBC, allows for early identification of at-risk patients in real-time with high accuracy.

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A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length

Abstract: We have presented a robust deep learning-based architecture that can identify abnormal cardiac rhythms using short single-lead ECG recordings. The proposed architecture is computationally fast and can also be used in real-time cardiac arrhythmia detection applications.

Cited by 95 publications

References 23 publications

Diagnosing Abnormal Electrocardiogram (ECG) via Deep Learning

Diagnosing Abnormal Electrocardiogram (ECG) via Deep Learning

Convolutional Neural Network Application in Biomedical Signals

Physiomarkers in Real-Time Physiological Data Streams Predict Adult Sepsis Onset Earlier Than Clinical Practice

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