PVC Arrhythmia Detection Using Neural Networks

2007 ◽  
Author(s):  
Ali Gharaviri ◽  
Mohammad Teshnehlab ◽  
Hamid Abrishami Moghaddam
Keyword(s):  
Neural Networks ◽  
Arrhythmia Detection

scholarly journals Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1579
Author(s):  
Dongqi Wang ◽  
Qinghua Meng ◽  
Dongming Chen ◽  
Hupo Zhang ◽  
Lisheng Xu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Channel Model ◽  
Expert Knowledge ◽  
Automatic Detection ◽  
Data Representation ◽  
Learning Technology ◽  
Arrhythmia Detection ◽  
Automatic Feature Extraction

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings

2019 ◽  
Vol 40 (5) ◽  
pp. 054009 ◽  
Author(s):  
Shenda Hong ◽  
Yuxi Zhou ◽  
Meng Wu ◽  
Junyuan Shang ◽  
Qingyun Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cardiac Arrhythmia ◽  
Deep Neural Networks ◽  
Arrhythmia Detection

scholarly journals Real‐Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks

2021 ◽  
Author(s):  
Sandeep Chandra Bollepalli ◽  
Rahul K. Sevakula ◽  
Wan‐Tai M. Au‐Yeung ◽  
Mohamad B. Kassab ◽  
Faisal M. Merchant ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
High Rate ◽  
Superior Performance ◽  
False Alarms ◽  
Arrhythmia Detection ◽  
Arterial Blood ◽  
Life Threatening

Background Accurate detection of arrhythmic events in the intensive care units (ICU) is of paramount significance in providing timely care. However, traditional ICU monitors generate a high rate of false alarms causing alarm fatigue. In this work, we develop an algorithm to improve life threatening arrhythmia detection in the ICUs using a deep learning approach. Methods and Results This study involves a total of 953 independent life‐threatening arrhythmia alarms generated from the ICU bedside monitors of 410 patients. Specifically, we used the ECG (4 channels), arterial blood pressure, and photoplethysmograph signals to accurately detect the onset and offset of various arrhythmias, without prior knowledge of the alarm type. We used a hybrid convolutional neural network based classifier that fuses traditional handcrafted features with features automatically learned using convolutional neural networks. Further, the proposed architecture remains flexible to be adapted to various arrhythmic conditions as well as multiple physiological signals. Our hybrid‐ convolutional neural network approach achieved superior performance compared with methods which only used convolutional neural network. We evaluated our algorithm using 5‐fold cross‐validation for 5 times and obtained an accuracy of 87.5%±0.5%, and a score of 81%±0.9%. Independent evaluation of our algorithm on the publicly available PhysioNet 2015 Challenge database resulted in overall classification accuracy and score of 93.9% and 84.3%, respectively, indicating its efficacy and generalizability. Conclusions Our method accurately detects multiple arrhythmic conditions. Suitable translation of our algorithm may significantly improve the quality of care in ICUs by reducing the burden of false alarms.

scholarly journals Energy efficient convolutional neural networks for arrhythmia detection

Array ◽  
2022 ◽  
pp. 100127
Author(s):  
Nikoletta Katsaouni ◽  
Florian Aul ◽  
Lukas Krischker ◽  
Sascha Schmalhofer ◽  
Lars Hedrich ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Energy Efficient ◽  
Arrhythmia Detection

scholarly journals A Spectrogram Based Novel Approach for Arrhythmia Detection with Convolutional Neural Networks

2021 ◽  
Author(s):  
Sneha Rao ◽  
Vishwa Mohan Singh ◽  
Siddhivinayak Kulkarni ◽  
Vibhor Saran
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Medical Diagnosis ◽  
Light Weight ◽  
Arrhythmia Detection ◽  
Ecg Signals ◽  
Novel Approach ◽  
Convolution Model

Abstract ECG is one of the most important medical scans which is used for diagnosis of various heart related conditions and diseases. One of the most common of these is arrhythmia, which is caused by the irregularity of the heart beats. Artificial Intelligence has had a major impact in the field of vital monitoring and autonomous medical diagnosis. Therefore, a lot of work has demonstrated its effectiveness in arrhythmia detection. In this paper, we propose a method that tries to improve upon the accuracy of such models with the help of a light weight deep learning architecture that utilized 2D Separable CNN with a group of graphical representations of the ECG signals like the STFT, CWT and MFCC. Our model has achieved an accuracy of 97.41 and an F1 score of 88.20 on a processed version of the MIT-BIH dataset and takes on an average 7.93 times less calculations compared to a simple 2D Convolution model.

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