ConvNet: 1D-Convolutional Neural Networks for Cardiac Arrhythmia Recognition Using ECG Signals

In healthcare, diagnostic tools of cardiac diseases are commonly known by the electrocardiogram (ECG) analysis. Atypical electrical activity can produce a cardiac arrhythmia. Various difficulties can be imposed to clinicians e.g., myocardial infarction arrhythmia via the non-stationarity and irregularity heart beat signals. Through the assistance of computer-aided diagnosis methods, timely specification of arrhythmia diseases reduces the mortality rate of affected patients. In this study, a 1 Lead QRS complex -layer deep convolutional neural network is proposed for the recognition of arrhythmia datasets. By the use of this CNN model, we planned a complete structure of the classification architecture after a pre-processing stage counting the denoising and QRS complex signals detection procedure. The chief benefit of the new proposed methodology is that the automatically training the QRS complexes without requiring all original extracted ECG signals. The proposed model was trained on the increased ECG database and separated into five classes. Experimental results display that the established CNN method has improved performance when compared to the state-of-the-art studies.

Download Full-text

ARRHYTHMIA DIAGNOSIS FROM ECG SIGNAL ANALYSIS USING STATISTICAL FEATURES AND NOVEL CLASSIFICATION METHOD

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519421500251 ◽

2021 ◽

pp. 2150025

Author(s):

SAURAV MANDAL ◽

NABANITA SINHA

Keyword(s):

Cardiac Arrhythmia ◽

Present Model ◽

Fluctuation Analysis ◽

Statistical Features ◽

Multifractal Detrended Fluctuation Analysis ◽

Ecg Signals ◽

Proposed Model ◽

Electrocardiogram Ecg ◽

Detrended Fluctuation ◽

Heart Condition

This study aims to present an efficient model for autodetection of cardiac arrhythmia by the diagnosis of self-affinity and identification of governing processes of a number of Electrocardiogram (ECG) signals taken from MIT-BIH database. In this work, the proposed model includes statistical methods to find the diagnosis pattern for detecting cardiac abnormalities which is useful for the computer aided system for arrhythmia detection. First, the Rescale Range (R/S) analysis has been employed for ECG signals to understand the scaling property of ECG signals. The value of Hurst exponent identifies the presence of abnormality in ECG signals taken for consideration with 92.58% accuracy. In this study, Higuchi method which deals with unifractality or monofractality of signals has been applied and it is found that unifractality is sufficient to detect arrhythmia with 91.61% accuracy. The Multifractal Detrended Fluctuation Analysis (MFDFA) has been used over the present signals to identify and confirm the multifractality. The nature of multifractality is different for arrhythmia patients and normal heart condition. The multifractal analysis is useful to detect abnormalities with 93.75% accuracy. Finally, the autocorrelation analysis has been used to identify the prevalent governing process in the present arrhythmic ECG signals and study confirms that all the signals are governed by stationary autoregressive methods of certain orders. In order to increase the overall efficiency, this present model deals with analyzing all the statistical features extracted from different statistical techniques for a large number of ECG signals of normal and abnormal heart condition. Finally, the result of present analysis altogether possibly indicates that the proposed model is efficient to detect cardiac arrhythmia with 99.3% accuracy.

Download Full-text

Detection of cardiac arrhythmia using deep CNN and optimized SVM

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v24.i1.pp217-225 ◽

2021 ◽

Vol 24 (1) ◽

pp. 217

Author(s):

Mohebbanaaz Mohebbanaaz ◽

Y. Padma Sai ◽

L. V. Rajani Kumari

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Cardiac Arrhythmia ◽

Support Vector ◽

Automated Classification ◽

Ecg Signals ◽

Huge Data ◽

Proposed Model ◽

Deep Cnn ◽

Electrocardiogram Ecg

Deep learning (DL) has become a topic of study in various applications, including healthcare. Detection of abnormalities in an electrocardiogram (ECG) plays a significant role in patient monitoring. It is noted that a deep neural network when trained on huge data, can easily detect cardiac arrhythmia. This may help cardiologists to start treatment as early as possible. This paper proposes a new deep learning model adapting the concept of transfer learning to extract deep-CNN features and facilitates automated classification of electrocardiogram (ECG) into sixteen types of ECG beats using an optimized support vector machine (SVM). The proposed strategy begins with gathering ECG datasets, removal of noise from ECG signals, and extracting beats from denoised ECG signals. Feature extraction is done using ResNet18 via concept of transfer learning. These extracted features are classified using optimized SVM. These methods are evaluated and tested on the MIT-BIH arrhythmia database. Our proposed model is effective compared to all State of Art Techniques with an accuracy of 98.70%.

Download Full-text

Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication

Applied Sciences ◽

10.3390/app11031125 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1125

Author(s):

Htet Myet Lynn ◽

Pankoo Kim ◽

Sung Bum Pan

Keyword(s):

Short Term Memory ◽

Feature Learning ◽

Biometric Authentication ◽

Qrs Complex ◽

Data Types ◽

Ecg Signals ◽

Data Independent Acquisition ◽

Deep Networks ◽

Classification Tasks ◽

Electrocardiogram Ecg

In this report, the study of non-fiducial based approaches for Electrocardiogram(ECG) biometric authentication is examined, and several excessive techniques are proposed to perform comparative experiments for evaluating the best possible approach for all the classification tasks. Non-fiducial methods are designed to extract the discriminative information of a signal without annotating fiducial points. However, this process requires peak detection to identify a heartbeat signal. Based on recent studies that usually rely on heartbeat segmentation, QRS detection is required, and the process can be complicated for ECG signals for which the QRS complex is absent. Thus, many studies only conduct biometric authentication tasks on ECG signals with QRS complexes, and are hindered by similar limitations. To overcome this issue, we proposed a data-independent acquisition method to facilitate highly generalizable signal processing and feature learning processes. This is achieved by enhancing random segmentation to avoid complicated fiducial feature extraction, along with auto-correlation to eliminate the phase difference due to random segmentation. Subsequently, a bidirectional recurrent neural network (RNN) with long short-term memory (LSTM) deep networks is utilized to automatically learn the features associated with the signal and to perform an authentication task. The experimental results suggest that the proposed data-independent approach using a BLSTM network achieves a relatively high classification accuracy for every dataset relative to the compared techniques. Moreover, it exhibited a significantly higher accuracy rate in experiments using ECG signals without the QRS complex. The results also revealed that data-dependent methods can only perform well for specified data types and amendments of data variations, whereas the presented approach can also be considered for generalization to other quasi-periodical biometric signal-based classification tasks in future studies.

Download Full-text

Deep Convolutional Neural Network Based ECG Classification System Using Information Fusion and One-Hot Encoding Techniques

Mathematical Problems in Engineering ◽

10.1155/2018/7354081 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Jia Li ◽

Yujuan Si ◽

Tao Xu ◽

Saibiao Jiang

Keyword(s):

Cardiovascular Disease ◽

Adaptive Learning ◽

Portable Devices ◽

Ecg Signals ◽

Automatic Feature Extraction ◽

Average Accuracy ◽

Proposed Model ◽

Home Health Monitoring ◽

Predictive Rate ◽

Electrocardiogram Ecg

Although convolutional neural networks (CNNs) can be used to classify electrocardiogram (ECG) beats in the diagnosis of cardiovascular disease, ECG signals are typically processed as one-dimensional signals while CNNs are better suited to multidimensional pattern or image recognition applications. In this study, the morphology and rhythm of heartbeats are fused into a two-dimensional information vector for subsequent processing by CNNs that include adaptive learning rate and biased dropout methods. The results demonstrate that the proposed CNN model is effective for detecting irregular heartbeats or arrhythmias via automatic feature extraction. When the proposed model was tested on the MIT-BIH arrhythmia database, the model achieved higher performance than other state-of-the-art methods for five and eight heartbeat categories (the average accuracy was 99.1% and 97%). In particular, the proposed system had better performance in terms of the sensitivity and positive predictive rate for V beats by more than 4.3% and 5.4%, respectively, and also for S beats by more than 22.6% and 25.9%, respectively, when compared to existing algorithms. It is anticipated that the proposed method will be suitable for implementation on portable devices for the e-home health monitoring of cardiovascular disease.

Download Full-text

ECG Heartbeat Classification Based on an Improved ResNet-18 Model

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/6649970 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Enbiao Jing ◽

Haiyang Zhang ◽

ZhiGang Li ◽

Yazhi Liu ◽

Zhanlin Ji ◽

...

Keyword(s):

State Of The Art ◽

Classification Performance ◽

Classification Models ◽

Heartbeat Classification ◽

Ecg Signals ◽

Residual Structure ◽

Proposed Model ◽

Model Training ◽

Electrocardiogram Ecg

Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique residual structure of the model, the utilized CNN layered structure can be deepened in order to achieve better classification performance. The results of applying the proposed model to the MIT-BIH arrhythmia database demonstrate that the model achieves higher accuracy (96.50%) compared to other state-of-the-art classification models, while specifically for the ventricular ectopic heartbeat class, its sensitivity is 93.83% and the precision is 97.44%.

Download Full-text

An ECG signal model based on a parametric description of the characteristic waves

ACTA IMEKO ◽

10.21014/acta_imeko.v9i2.760 ◽

2020 ◽

Vol 9 (2) ◽

pp. 3

Author(s):

Pavol Dolinsky ◽

Imrich Andras ◽

Linus Michaeli ◽

Jan Saliga

Keyword(s):

Model Parameters ◽

Ecg Signal ◽

Elementary Functions ◽

Signal Model ◽

Ecg Signals ◽

Model Based ◽

Proposed Model ◽

Base Functions ◽

Parametric Description ◽

Electrocardiogram Ecg

This article introduces a new electrocardiogram (ECG) signal model based on geometric signal properties. Instead of the artificial functions used in common ECG models, the proposed model is based on the modelling of real ECG signals divided into time segments. Each segment has been modelled using simple geometrical forms. The final ECG signal model is represented by the sequence of parameters of the base functions. Parameter variations allow for the generation of different waveforms for each subsequent heartbeat without mixing up the PQRST waves order. Two basic models utilize slightly modified elementary functions, which are computationally simple. A combination of both models allows for the modelling of irregularities in the consecutive heartbeats of the specific ECG waveforms. Respiratory, noise, and powerline interference can be added in order to make the generated ECG signal more realistic. The model parameters are estimated by differential evolution optimization and a comparison between the modelled ECG and the acquired signal. The proposed models are tested by the database included in the LabVIEW Biomedical Toolkit and ECG records in the MIT-BIH arrhythmia database.

Download Full-text

Automatic Detection of Arrhythmias Using a YOLO-Based Network with Long-Duration ECG Signals

Engineering Proceedings ◽

10.3390/ecsa-7-08229 ◽

2020 ◽

Vol 2 (1) ◽

pp. 84

Author(s):

Won Hee Hwang ◽

Chan Hee Jeong ◽

Dong Hyun Hwang ◽

Young Chang Jo

Keyword(s):

Embedded System ◽

High Performance ◽

Detection Algorithm ◽

Wearable Device ◽

Arrhythmia Detection ◽

Ecg Signals ◽

Long Duration ◽

Proposed Model ◽

Different Types ◽

Electrocardiogram Ecg

Early detection of arrhythmias is very important. Recently, wearable devices are being used to monitor the patient’s heartbeat to detect an arrhythmia. However, there are not satisfactory algorithms for real-time monitoring of arrhythmias in a wearable device. In this work, a novel fast and simple arrhythmia detection algorithm based on YOLO is proposed. The algorithm can detect each heartbeat on long-duration electrocardiogram (ECG) signals without R-peak detection and can classify an arrhythmia simultaneously. The model replaces the 2D Convolutional Neural networks (CNN) with a 1D CNN and the bounding box with a bounding window to utilize raw ECG signals. Results demonstrate that the proposed algorithm has high performance in speed and mean average precisionin detecting an arrhythmia. Furthermore, the bounding window can predict different window lengths on different types of arrhythmia. Therefore, the model can choose an optimal heartbeat window length for arrhythmia classification. Since the proposed model is a compact 1D CNN model based on YOLO, it can be used in a wearable device and embedded system.

Download Full-text

ALGORITHM FOR AUTOMATIC DETECTION OF ECG WAVES

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519410003848 ◽

2011 ◽

Vol 11 (01) ◽

pp. 15-29 ◽

Cited By ~ 4

Author(s):

DIB. NABIL ◽

F. BEREKSI-REGUIG

Keyword(s):

Detection Accuracy ◽

Ecg Signal ◽

Qrs Complex ◽

Qrs Detection ◽

Accurate Identification ◽

Ecg Signals ◽

Good Ability ◽

Noisy Conditions ◽

Electrocardiogram Ecg ◽

T Waves

An accurate measurement of the different electrocardiogram (ECG) intervals is dependent on the accurate identification of the beginning and the end of the P, QRS, and T waves. Available commercial systems provide a good QRS detection accuracy. However, the detection of the P and T waves remains a serious challenge due to their widely differing morphologies in normal and abnormal beats. In this paper, a new algorithm for the detection of the QRS complex as well as for P and T waves identification is provided. The proposed algorithm is based on different approaches and methods such as derivations, thresholding, and surface indicator. The proposed algorithm is tested and evaluated on ECG signals from the universal MIT-BIH database. It shows a good ability to detect P, QRS, and T waves for different cases of ECG signal even in very noisy conditions. The obtained QRS, sensitivity and positive predictivity are respectively 95.39% and 98.19%. The developed algorithm is also able to separate the overlapping P and T waves.

Download Full-text

Symbolic Recurrence Analysis of RR Interval to Detect Atrial Fibrillation

Journal of Clinical Medicine ◽

10.3390/jcm8111840 ◽

2019 ◽

Vol 8 (11) ◽

pp. 1840 ◽

Cited By ~ 2

Author(s):

Jesús Pérez-Valero ◽

M. Victoria Caballero Pintado ◽

Francisco Melgarejo ◽

Antonio-Javier García-Sánchez ◽

Joan Garcia-Haro ◽

...

Keyword(s):

Atrial Fibrillation ◽

Ambulatory Monitoring ◽

Ecg Signals ◽

Predictive Algorithm ◽

Predictive Algorithms ◽

Proposed Model ◽

Quantification Analysis ◽

Fitting In ◽

Electrocardiogram Ecg ◽

Fold Cross Validation

Atrial fibrillation (AF) is a sustained cardiac arrhythmia associated with stroke, heart failure, and related health conditions. Though easily diagnosed upon presentation in a clinical setting, the transient and/or intermittent emergence of AF episodes present diagnostic and clinical monitoring challenges that would ideally be met with automated ambulatory monitoring and detection. Current approaches to address these needs, commonly available both in smartphone applications and dedicated technologies, combine electrocardiogram (ECG) sensors with predictive algorithms to detect AF. These methods typically require extensive preprocessing, preliminary signal analysis, and the integration of a wide and complex array of features for the detection of AF events, and are consequently vulnerable to over-fitting. In this paper, we introduce the application of symbolic recurrence quantification analysis (SRQA) for the study of ECG signals and detection of AF events, which requires minimal pre-processing and allows the construction of highly accurate predictive algorithms from relatively few features. In addition, this approach is robust against commonly-encountered signal processing challenges that are expected in ambulatory monitoring contexts, including noisy and non-stationary data. We demonstrate the application of this method to yield a highly accurate predictive algorithm, which at optimal threshold values is 97.9% sensitive, 97.6% specific, and 97.7% accurate in classifying AF signals. To confirm the robust generalizability of this approach, we further evaluated its performance in the implementation of a 10-fold cross-validation paradigm, yielding 97.4% accuracy. In sum, these findings emphasize the robust utility of SRQA for the analysis of ECG signals and detection of AF. To the best of our knowledge, the proposed model is the first to incorporate symbolic analysis for AF beat detection.

Download Full-text

Characteristics of the reindeer electrocardiogram

Rangifer ◽

10.7557/2.2.2.412 ◽

1982 ◽

Vol 2 (2) ◽

pp. 36

Author(s):

Jouni Timisjärvi ◽

Mauri Nieminen ◽

Sven Nikander

Keyword(s):

Heart Rate ◽

Indirect Evidence ◽

Frontal Plane ◽

Heart Beat ◽

P Wave ◽

Qrs Complex ◽

Single Plane ◽

T Wave ◽

Wide Range ◽

Electrocardiogram Ecg

The electrocardiogram (ECG) provides reliable information about heart rate, initiation of heart beat and also, to some degree, indirect evidence on the functional state of the heart muscle. A wide range of such information is readily obtainable from conventional scalar leads, even if the records are limited to a single plane. The present investigation deals with the normal reindeer ECG in the frontal plane. The technique used is the scalar recording technique based on the Einthovenian postulates. The P wave was positive in leads II, III and aVF, negative in lead aVL and variable in leads I and aVR. The direction of the P vector was 60 to 120°. The QRS complex was variable. The most common forms of QRS complex were R and rS in leads I and aVR; R, Rs and rS in lead aVL and Qr or qR in other leads. The most common direction of the QRS vector was 240 to 300°. The T wave was variable. The duration of various intervals and deflection depended on heart rate.Elektrokardiogram på ren.Abstract in Swedish / Sammandrag: Elektrokardiogramet (EKG) ger tillförlitliga uppgifter om hjärtfrekvens, retledning och, indirekt, delvis även om hjärtmuskelns funktionell tillstånd. Största delen av denna information fås med normal skalar koppling även om registrering sker i ett plan. I detta arbete har renens normala EKG i frontalplanet undersökts. Kopplingarna har baserats på Einthovs postulat. P-vågen var riktad uppåt i koppling II, III och aVF, nedåt i koppling aVL och den varierade i koppling I och aVR. P-vektorns riktning var 60 - 120°. QRS-komplexet varierade. De vanligaste formerna var R och rS i koppling I och aVR; R, Rs och rS i koppling aVL och Qr eller qR i andra kopplingar. Vanligen var QRS-vektorns riktning 240 - 300°. T-vågen varierade. Awikelserna och intervallernas längd var beroende av hiärtfrekvenssen.Poron sydänsähkökäyrän ominaisuuksia.Abstract in Finnish / Yhteenveto: Sydänsähkökäyrästä saadaan luotettavaa tietoa sydämen syketiheydestä, sähköisestä johtumisesta ja välillisesti jossain määrin myös sydänlihaksen toiminnallisesta tilasta. Suurin osa tämänkaltaista tietoa voidaan saada tavanomaisia skalaarisia kytkentöjäkäyttäen, ja usein yhdessä tasossa tapahtuva rekisteröinti on riittävä. Tässä työssä on tutkittu porojen normaalia sydänsähkökäyrää ja sen eri poikkeamien suuntautumista frontaalitasossa, kun rekisteröinnissä on käytetty Einthovenin postulaattien mukaisia raajakytkentöjä. P aalto suuntautui ylöspäin kythkennöissä II, III ja aVF, alaspäin kytkennässä aVL ja vaihteli kytkennöissä I ja aVR. P vektorin suunta oli 60 - 120°. QRS kompleksi vaihteli. Tavallisimmat muodot olivat R ja rS kytkennöissä I ja aVR; R, Rs ja rS kytkennässä aVL ja Qr tai qR muissa kytkennöissä. Tavallisin QRS vektorin suunta oli 240 - 300°. T aalto vaihteli. Poikkeaminen ja intervallien kesto riippui sydämen syketiheydestä.

Download Full-text