Automated QRS detection methods depend on the ECG data which is sampled at a certain frequency, irrespective of filter-based traditional methods or convolutional network (CNN) based deep learning methods. These methods require a selection of the sampling frequency at which they operate in the very first place. While working with data from two different datasets, which are sampled at different frequencies, often, data from both the datasets may need to resample at a common target frequency, which may be the frequency of either of the datasets or could be a different one. However, choosing data sampled at a certain frequency may have an impact on the model's generalisation capacity, and complexity. There exist some studies that investigate the effects of ECG sample frequencies on traditional filter-based methods, however, an extensive study of the effect of ECG sample frequency on deep learning-based models (convolutional networks), exploring their generalisability and complexity is yet to be explored. This experimental research investigates the impact of six different sample frequencies (50, 100, 250, 500, 1000, and 2000Hz) on four different convolutional network-based models' generalisability and complexity in order to form a basis to decide on an appropriate sample frequency for the QRS detection task for a particular performance requirement. Intra-database tests report an accuracy improvement no more than approximately 0.6\% from 100Hz to 250Hz and the shorter interquartile range for those two frequencies for all CNN-based models. The findings reveal that convolutional network-based deep learning models are capable of scoring higher levels of detection accuracies on ECG signals sampled at frequencies as low as 100Hz or 250Hz while maintaining lower model complexity (number of trainable parameters and training time).
This paper presents a multi-lead fusion method for the accurate and automated detection of the QRS complex location in 12 lead ECG (Electrocardiogram) signals. The proposed multi-lead fusion method accurately delineates the QRS complex by the fusion of detected QRS complexes of the individual 12 leads. The proposed algorithm consists of two major stages. Firstly, the QRS complex location of each lead is detected by the single lead QRS detection algorithm. Secondly, the multi-lead fusion algorithm combines the information of the QRS complex locations obtained in each of the 12 leads. The perfor…
Background: Accurate detection of QRS complexes during mobile, ultra-long-term ECG monitoring is challenged by instances of high heart rate, dramatic and persistent changes in signal amplitude, and intermittent deformations in signal quality that arise due to subject motion, background noise, and misplacement of the ECG electrodes. Purpose: We propose a revised QRS detection algorithm which addresses the above-mentioned challenges. Methods and Results: Our proposed algorithm is based on a state-of-the-art algorithm after applying two key modifications. The first modification is implementing lo…
The electrocardiogram (ECG) signal is the most widely used non-invasive tool for the investigation of cardiovascular diseases. Automatic delineation of ECG fiducial points, in particular the R-peak, serves as the basis for ECG processing and analysis. This study proposes a new method of ECG signal analysis by introducing a new class of graphical models based on optimal changepoint detection models, named the graph-constrained changepoint detection (GCCD) model. The GCCD model treats fiducial points delineation in the non-stationary ECG signal as a changepoint detection problem. The proposed mo…
This study presents a new viewpoint on ECG signal analysis by applying a graph-based changepoint detection model to locate R-peak positions. This model is based on a new graph learning algorithm to learn the constraint graph given the labeled ECG data. The proposed learning algorithm starts with a simple initial graph and iteratively edits the graph so that the final graph has the maximum accuracy in R-peak detection. We evaluate the performance of the algorithm on the MIT-BIH Arrhythmia Database. The evaluation results demonstrate that the proposed method can obtain comparable results to othe…
Cardiovascular disease is associated with high rates of morbidity and mortality, and can be reflected by 19 abnormal features of electrocardiogram (ECG). Detecting changes in the QRS complexes in ECG 20 signals is regarded as a straightforward, noninvasive, inexpensive, and preliminary diagnosis approach 21 for evaluating the cardiac health of patients. Therefore, detecting QRS complexes in ECG signals must 22 be accurate over short times. However, the reliability of automatic QRS detection is restricted by all 23 kinds of noise and complex signal morphologies. In this study, we proposed a new…
Lobachevsky University of Nizhni Novgorod（下诺夫哥罗德罗巴切夫斯基大学）
We propose an algorithm for electrocardiogram (ECG) segmentation using a UNet-like full-convolutional neural network. The algorithm receives an arbitrary sampling rate ECG signal as an input, and gives a list of onsets and offsets of P and T waves and QRS complexes as output. Our method of segmentation differs from others in speed, a small number of parameters and a good generalization: it is adaptive to different sampling rates and it is generalized to various types of ECG monitors. The proposed approach is superior to other state-of-the-art segmentation methods in terms of quality. In partic…
Deep learning based ECG segmentation for delineation of diverse arrhythmias
Seoul National University（首尔大学）；Asan Medical Center, University of Ulsan College of Medicine（蔚山大学医学院附属峨山医院）；Pusan National University Hospital, Pusan National University School of Medicine（釜山大学医学院附属釜山国立大学医院）；AI Institute, Seoul National University（首尔大学人工智能研究院）；Seoul National University Hospital（首尔大学医院）；Medifarmsoft Co., Ltd.（Medifarmsoft有限公司）
本文提出一种类 U-Net 的心电图分割模型及后处理算法，用于在多种心律失常条件下准确 delineation P、QRS 和 T 波，并针对标准基准中代表性不足的心律失常提出改进策略。
Objectives: With the technological advancements in the field of tele-health monitoring, it is now possible to gather huge amounts of electro-physiological signals such as electrocardiogram (ECG). It is therefore necessary to develop models/algorithms that are capable of analysing these massive amounts of data in real-time. This paper proposes a deep learning model for real-time segmentation of heartbeats. Methods: The proposed algorithm, named as the DENS-ECG algorithm, combines convolutional neural network (CNN) and long short-term memory (LSTM) model to detect onset, peak, and offset of diff…
Traditional DL models are complex and resource hungry and thus, care needs to be taken in designing Internet of (medical) things (IoT, or IoMT) applications balancing efficiency-complexity trade-off. Recent IoT solutions tend to avoid using deep-learning methods due to such complexities, and rather classical filter-based methods are commonly used. We hypothesize that a shallow CNN model can offer satisfactory level of performance in combination by leveraging other essential solution-components, such as post-processing that is suitable for resource constrained environment. In an IoMT applicatio…
An electrocardiogram (ECG) feature extraction system has been developed and evaluated using Virtex-6 FPGA kit which belongs to Xilinx Ltd. In time domain, Pan-Tompkins algorithm is used for QRS detection and it is followed by a feature extractor block to extract ECG features. This whole system can be used to detect cardiac arrhythmia. The completed algorithm was implemented on Virtex-6(XC6VLX240-T) device and tested using hardware co-simulation in Modelsim and simulink environment. The software generated ECG signals are obtained from MIT-BIH arrhythmia Database [1]. The memory and time complex…
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences（波兰科学院理论与应用信息学研究所）；Warsaw University of Technology（华沙理工大学）；Cracow University of Technology（克拉科夫理工大学）；Firat University（菲拉特大学）；Singapore University of Social Sciences（新加坡社会科学大学）；University of Southern Queensland（南昆士兰大学）；Ngee Ann Polytechnic（义安理工学院）；Asia University（亚洲大学）；Kumamoto University（熊本大学）；National Heart Centre Singapore（新加坡国家心脏中心）
Interpretation of electrocardiography (ECG) signals is required for diagnosing cardiac arrhythmia. Recently, machine learning techniques have been applied for automated computer-aided diagnosis. Machine learning tasks can be divided into regression and classification. Regression can be used for noise and artifacts removal as well as resolve issues of missing data from low sampling frequency. Classification task concerns the prediction of output diagnostic classes according to expert-labeled input classes. In this work, we propose a deep neural network model capable of solving regression and cl…
Electrocardiogram (ECG) signal is the most commonly used non-invasive tool in the assessment of cardiovascular diseases. Segmentation of the ECG signal to locate its constitutive waves, in particular the R-peaks, is a key step in ECG processing and analysis. Over the years, several segmentation and QRS complex detection algorithms have been proposed with different features; however, their performance highly depends on applying preprocessing steps which makes them unreliable in real-time data analysis of ambulatory care settings and remote monitoring systems, where the collected data is highly …
Interpretable machine learning for identifying individual-specific cardiogram signatures
University of Belgrade – School of Electrical Engineering（贝尔格莱德大学电气工程学院）；Institute for Artificial Intelligence Research and Development of Serbia（塞尔维亚人工智能研究与开发研究所）；Institute of Psychology and LIRA lab, Faculty of Philosophy, University of Belgrade（贝尔格莱德大学哲学院心理学研究所及LIRA实验室）；Department of Psychology and LIRA lab, Faculty of Philosophy, University of Belgrade（贝尔格莱德大学哲学院心理学系及LIRA实验室）；University of Belgrade – Faculty of Special Education and Rehabilitation（贝尔格莱德大学特殊教育与康复学院）；Institute for Biological Research ”Siniša Stanković”, National Institute of the Republic of Serbia, University of Belgrade（塞尔维亚共和国国家研究所西尼沙·斯坦科维奇生物学研究所，贝尔格莱德大学）；Trinity College Dublin - School of Psychology（都柏林三一学院心理学院）；Faculty of Electrical Engineering, University of Ljubljana（卢布尔雅那大学电气工程学院）
Universitat Pompeu Fabra（庞培法布拉大学）；Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)（生物工程、生物材料与纳米医学生物医学研究网络中心（CIBER-BBN））；Aragón Institute of Engineering Research（阿拉贡工程研究所）；University of Zaragoza（萨拉戈萨大学）
Electrocardiogram (ECG) detection and delineation are key steps for numerous tasks in clinical practice, as ECG is the most performed non-invasive test for assessing cardiac condition. State-of-the-art algorithms employ digital signal processing (DSP), which require laborious rule adaptation to new morphologies. In contrast, deep learning (DL) algorithms, especially for classification, are gaining weight in academic and industrial settings. However, the lack of model explainability and small databases hinder their applicability. We demonstrate DL can be successfully applied to low interpretati…
Technion - Israel Institute of Technology（以色列理工学院）；The Hospital for Sick Children（病童医院）；University of Toronto（多伦多大学）；KU Leuven（鲁汶大学）；Alberta Children’s Hospital（阿尔伯塔儿童医院）；University of Calgary（卡尔加里大学）；Li Ka Shing Knowledge Institute of St. Michael’s Hospital（圣迈克尔医院李嘉诚知识研究院）
Objective: Epileptic seizures are relatively common in critically-ill children admitted to the pediatric intensive care unit (PICU) and thus serve as an important target for identification and treatment. Most of these seizures have no discernible clinical manifestation but still have a significant impact on morbidity and mortality. Children that are deemed at risk for seizures within the PICU are monitored using continuous-electroencephalogram (cEEG). cEEG monitoring cost is considerable and as the number of available machines is always limited, clinicians need to resort to triaging patients a…
ArrhythmiaVision: Resource-Conscious Deep Learning Models with Visual Explanations for ECG Arrhythmia Classification
Dipartimento di Informatica, Università di Verona, Verona, Italy（信息学院，威尼斯大学，威尼斯，意大利）；Department of Computer Science, School of Mathematics and Computer Science, Institute of Business Administration, Karachi, Pakistan（计算机科学系，数学与计算机科学学院，商学院，卡拉奇，巴基斯坦）；Faculty of Information Technology, Salim Habib University, Karachi, Pakistan（信息技术学院，萨利姆·哈比大学，卡拉奇，巴基斯坦）
