scholarly journals Detection of Obstructive Sleep Apnoea Using Features Extracted from Segmented Time-Series ECG Signals Using a One Dimensional Convolutional Neural Network

2020 ◽  
Author(s):  
Steven Thompson ◽  
Paul Fergus ◽  
Carl Chalmers ◽  
Denis Reilly
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Obstructive Sleep Apnoea ◽  
Sleep Apnoea ◽  
One Dimensional ◽  
Ecg Signals ◽  
Obstructive Sleep

scholarly journals Automatic Arrhythmia Detection Using One-Dimensional Convolutional Neural Network

2020 ◽  
Author(s):  
Zhong Liu ◽  
Xin’an Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Cardiac Activity ◽  
The Elderly ◽  
Accurate Method ◽  
Detection Algorithm ◽  
Dynamic Monitoring ◽  
Ecg Signal ◽  
One Dimensional ◽  
Ecg Signals

Abstract Background: Cardiovascular diseases (CVDs) are common diseases that pose significant threats to human health. Statistics have demonstrated that a large number of individuals die unexpectedly from sudden CVDs. Therefore, real-time monitoring and diagnosis of abnormal changes in cardiac activity are critical, as they can help the elderly and patients handle emergencies in a timely manner. To this end, a round-the-clock electrocardiogram (ECG) monitoring system can be developed with the quick detection of an ECG signal, segmentation of the detected ECG signal, and rapid diagnosis of a single segmented ECG beat. In this paper, to achieve the automatic detection and diagnosis of an ECG signal, five common types of ECG signals are used for recognition. For pre-processing the original ECG signal, the dual-slope detection algorithm is proposed and developed. Then, with the pre-processed ECG data, a five-layer one-dimensional convolutional neural network is constructed to classify five categories of heartbeats, namely, a normal heartbeat and four types of abnormal heartbeats. Results: To be able to compare the results of the experiment, the experimental data used in this study are obtained from the open-source MIT-BIH arrhythmia database. This database is authoritative, as each ECG signal cycle is annotated by at least two cardiologists, and abnormal ECG signals are classified into different categories. By comparing the detection and recognition results in this study with the results annotated in the MIT-BIH arrhythmia database, an overall accuracy of 96.20% is achieved in the classification of normal ECG signals and four categories of abnormal ECG signals.Conclusions: This paper provides an accurate method with low computational complexity for 24-hour dynamic monitoring and automated diagnosis of heartbeat conditions. With wearable devices, this method can be used at home for the initial screening of CVDs. In addition, it can perform diagnosis and warning for postoperative patients or patients with chronic CVDs.

scholarly journals Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2558 ◽  
Author(s):  
Yinsheng Ji ◽  
Sen Zhang ◽  
Wendong Xiao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
System Development ◽  
Support Vector ◽  
One Dimensional ◽  
Ecg Signals ◽  
Two Dimensional Image ◽  
Average Accuracy ◽  
Effective System ◽  
The One

The classification of electrocardiograms (ECG) plays an important role in the clinical diagnosis of heart disease. This paper proposes an effective system development and implementation for ECG classification based on faster regions with a convolutional neural network (Faster R-CNN) algorithm. The original one-dimensional ECG signals contain the preprocessed patient ECG signals and some ECG recordings from the MIT-BIH database in this experiment. Each ECG beat of one-dimensional ECG signals was transformed into a two-dimensional image for experimental training sets and test sets. As a result, we classified the ECG beats into five categories with an average accuracy of 99.21%. In addition, we did a comparative experiment using the one versus rest support vector machine (OVR SVM) algorithm, and the classification accuracy of the proposed Faster R-CNN was shown to be 2.59% higher.

NON-STATIONARY TIME SERIES PREDICTION USING ONE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK MODELS

2020 ◽  
Vol 3 (1) ◽  
pp. 362-372
Author(s):  
Svitlana Antoshchuk ◽  
Oksana Babilunha ◽  
Thanh Tran Kim ◽  
Anatolii Nikolenko ◽  
Tien Nguyen Thi Khanh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Time Series Prediction ◽  
Network Models ◽  
Stationary Time Series ◽  
Neural Network Models ◽  
One Dimensional ◽  
Stationary Time

scholarly journals Contribution of Different Subbands of ECG in Sleep Apnea Detection Evaluated Using Filter Bank Decomposition and a Convolutional Neural Network

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 510
Author(s):  
Cheng-Yu Yeh ◽  
Hung-Yu Chang ◽  
Jiy-Yao Hu ◽  
Chun-Cheng Lin
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Filter Bank ◽  
Ecg Signals ◽  
Obstructive Sleep ◽  
Apnea Detection ◽  
The Difference ◽  
Electrocardiogram Ecg ◽  
The Given

A variety of feature extraction and classification approaches have been proposed using electrocardiogram (ECG) and ECG-derived signals for improving the performance of detecting apnea events and diagnosing patients with obstructive sleep apnea (OSA). The purpose of this study is to further evaluate whether the reduction of lower frequency P and T waves can increase the accuracy of the detection of apnea events. This study proposed filter bank decomposition to decompose the ECG signal into 15 subband signals, and a one-dimensional (1D) convolutional neural network (CNN) model independently cooperating with each subband to extract and classify the features of the given subband signal. One-minute ECG signals obtained from the MIT PhysioNet Apnea-ECG database were used to train the CNN models and test the accuracy of detecting apnea events for different subbands. The results show that the use of the newly selected subject-independent datasets can avoid the overestimation of the accuracy of the apnea event detection and can test the difference in the accuracy of different subbands. The frequency band of 31.25–37.5 Hz can achieve 100% per-recording accuracy with 85.8% per-minute accuracy using the newly selected subject-independent datasets and is recommended as a promising subband of ECG signals that can cooperate with the proposed 1D CNN model for the diagnosis of OSA.

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