QRS detection and classification in Holter ECG data in one inference step

Ivora, Adam; Viščor, Ivo; Nejedlý, Petr; Smíšek, Radovan; Koscova, Zuzana; Bůlková, Veronika; Halámek, Josef; Jurák, Pavel; Plešinger, Filip

doi:10.1038/s41598-022-16517-4

Cited by 12 publications

(4 citation statements)

References 28 publications

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“…This is called ensemble learning, which refers to algorithms that combine the predictions from two or more models. Ensemble learning has been applied in the medical field and shows promising results in QRS complex detection and classification, as well as arrhythmia detection [ 26 , 27 ]. In other words, a pruned CNN network is kept to extract features, and then the classification is performed based on SVM.…”

Section: Methodsmentioning

confidence: 99%

CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals

Botros

Mourad-Chehade

Laplanche

2022

Sensors

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Heart failure (HF) is a serious condition in which the heart fails to supply the body with enough oxygen and nutrients to function normally. Early and accurate detection of heart failure is critical for impeding disease progression. An electrocardiogram (ECG) is a test that records the rhythm and electrical activity of the heart and is used to detect HF. It is used to look for irregularities in the heart’s rhythm or electrical conduction, as well as a history of heart attacks, ischemia, and other conditions that may initiate HF. However, sometimes, it becomes difficult and time-consuming to interpret the ECG signal, even for a cardiac expert. This paper proposes two models to automatically detect HF from ECG signals: the first one introduces a Convolutional Neural Network (CNN), while the second one suggests an extension of it by integrating a Support Vector Machine (SVM) layer for the classification at the end of the network. The proposed models provide a more accurate automatic HF detection using 2-s ECG fragments. Both models are smaller than previously proposed models in the literature when the architecture is taken into account, reducing both training time and memory consumption. The MIT-BIH and the BIDMC databases are used for training and testing the adopted models. The experimental results demonstrate the effectiveness of the proposed framework by achieving an accuracy, sensitivity, and specificity of over 99% with blindfold cross-validation. The models proposed in this study can provide doctors with reliable references and can be used in portable devices to enable the real-time monitoring of patients.

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Section: Methodsmentioning

confidence: 99%

CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals

Botros

Mourad-Chehade

Laplanche

2022

Sensors

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“…Presented software contains two already developed deep-learning models -the first for QRS detection/classification [1] and the second model for rhythm classification. Used data originated from a private dataset (MDT, Brno, Czechia), consisting of 12,111 ECG recordings with 619,681 QRS annotations (length 45 seconds, 200Hz sampling) and 73,450 recordings accompanied by rhythm annotations (length 30 s, 200 Hz sampling).…”

Section: Datamentioning

confidence: 99%

“…Input to both models is filtered (0.5-45 Hz) and standardized. The first one detects and classifies QRS complexes into three classes [1] (Fig. 1F).…”

Section: Deep-learning Modelsmentioning

confidence: 99%

"Scalable, Multiplatform, and Autonomous ECG Processor Supported by AI for Telemedicine Center"

Plesinger¹,

Ivora²,

Vargova³

et al. 2022

Computing in Cardiology Conference (CinC)

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Background: Wearable devices play an essential role in the early diagnosis of heart diseases. However, effective management of long-term ECG measurements (1-3 weeks) by a telemedicine center (TMC) requires specifically designed software.Method: We used the multiplatform framework .NET to build the application. Deep-learning models for QRS detection, classification, and rhythm analysis were trained in the PyTorch framework; models were trained using data from Medical Data Transfer, s. r. o., Czechia (N=73,450 and 12,111) . The ONNX runtime libraries were used for model inference, including acceleration by graphic cardsResults: The pre-production benchmark (recordings of 82 patients) showed a mean accuracy of 0.97 ± 0.04 for QRS detection and classification into three classes; it also showed a mean accuracy of 0.97 ± 0.03 for rhythm classification into seven classes. Conclusion:The presented software is a fully automated, multiplatform, and scalable back-end application to process incoming ECG data in the TMC. Although it is not freely accessible, we are open to considering processing ECG data for research and strictly non-commercial purposes.

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“…Several studies analyze data ECG on the Holter test [21,22]. Authors at [21] proposed an algorithm to detect and classify arrhythmias from public ECG and their primary data. The method used is one inference step which detects and classifies arrhythmias.…”

Section: Introductionmentioning

confidence: 99%

Arrhythmia Analysis in the Long-term Electrocardiogram Monitoring System

2023

IJIES

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Cardiovascular disease (CVD) is dangerous and has a high mortality rate. One of the signs of CVD is arrhythmia. The method to find out the symptoms of arrhythmia is an electrocardiogram (ECG) long-term monitoring. The duration of ECG analysis on long-term monitoring is 6 hours, 12 hours, or 24 hours. The device used for longterm ECG is a Holter device. Arrhythmia analysis has followed: detect QRS complex and continue arrhythmia classification. Holter data is stored in local memory or on the server. Storage on local storage makes it difficult for cardiologists to perform analysis. However, using server storage causes access times to slow if patient data increases. Because of this, the cardiologist needs a powerful processing unit to analyze arrhythmia. This study proposes Arrhythmia analysis in the long-term ECG monitoring system. ECG acquisition is sent and stored in the processing unit. We use a single-board computer (SBC) Raspberry Pi as a processing unit. Besides storing data, SBC is also analyzing arrhythmias. The analysis steps are detecting R-peak using the Pan-Tompkins (PTK) algorithm, removing P and T waves using the Gaussian filter, and arrhythmia classification using the multi-layer perceptron (MLP). MLP is a low computational deep learning, which is suitable for SBC. The total storage delay consists of sending and storing data in the database. In the experiment, the propagation data to the broker is 0.023 s., and the storage time to the SQLite database is 15.16 s. The limited recording time for acquisition data is 21 hours and 36 minutes. The success rate of the device in detecting the QRS complex has a precision (+P) of 98.61% and a sensitivity (Se) of 99.8%. Our classification has good in

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QRS detection and classification in Holter ECG data in one inference step

Cited by 12 publications

References 28 publications

CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals

CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals

"Scalable, Multiplatform, and Autonomous ECG Processor Supported by AI for Telemedicine Center"

Arrhythmia Analysis in the Long-term Electrocardiogram Monitoring System

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