scholarly journals Machine Algorithm for Heartbeat Monitoring and Arrhythmia Detection Based on ECG Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ahmed I. Taloba ◽  
Rayan Alanazi ◽  
Osama R. Shahin ◽  
Ahmed Elhadad ◽  
Amr Abozeid ◽  
...  
Keyword(s):  
Fractional Fourier Transform ◽  
Training Dataset ◽  
Ecg Signals ◽  
Current State ◽  
Signal Features ◽  
Machine Learning Approach ◽  
Life Threatening ◽  
A New Technique ◽  
Electrical Impulses ◽  
Fft Algorithms

Cardiac arrhythmia is an illness in which a heartbeat is erratic, either too slow or too rapid. It happens as a result of faulty electrical impulses that coordinate the heartbeats. Sudden cardiac death can occur as a result of certain serious arrhythmia disorders. As a result, the primary goal of electrocardiogram (ECG) investigation is to reliably perceive arrhythmias as life-threatening to provide a suitable therapy and save lives. ECG signals are waveforms that denote the electrical movement of the human heart (P, QRS, and T). The duration, structure, and distances between various peaks of each waveform are utilized to identify heart problems. The signals’ autoregressive (AR) analysis is then used to obtain a specific selection of signal features, the parameters of the AR signal model. Groups of retrieved AR characteristics for three various ECG kinds are cleanly separated in the training dataset, providing high connection classification and heart problem diagnosis to each ECG signal within the training dataset. A new technique based on two-event-related moving averages (TERMAs) and fractional Fourier transform (FFT) algorithms is suggested to better evaluate ECG signals. This study could help researchers examine the current state-of-the-art approaches employed in the detection of arrhythmia situations. The characteristic of our suggested machine learning approach is cross-database training and testing with improved characteristics.

Automated Recognition of Arrhythmia Using Deep Neural Networks for 12-Lead Electrocardiograms with Fractional Time–Frequency Domain Extension

2020 ◽  
Vol 10 (11) ◽  
pp. 2764-2767
Author(s):  
Chuanbin Ge ◽  
Di Liu ◽  
Juan Liu ◽  
Bingshuai Liu ◽  
Yi Xin
Keyword(s):  
Neural Networks ◽  
Frequency Domain ◽  
Deep Neural Networks ◽  
Fractional Fourier Transform ◽  
Training Dataset ◽  
Clinical Tool ◽  
Time Frequency ◽  
Ecg Signals ◽  
Physiological Signal ◽  
Ecg Data

Arrhythmia is a group of conditions in which the heartbeat is irregular. There are many types of arrhythmia. Some can be life-threatening. Electrocardiogram (ECG) is an effective clinical tool used to diagnosis arrhythmia. Automatic recognition of different arrhythmia types in ECG signals has become an important and challenging issue. In this article, we proposed an algorithm to detect arrhythmia in 12-lead ECG signals and classify signals into 9 categories. Two 19-layer deep neural networks combining convolutional neural network and gated recurrent unit were proposed to realize this work. The first one was trained directly with the raw 12-lead ECG data while the other one was trained with an 18-"lead" ECG data, where the six extra leads containing morphology information in fractional time–frequency domain were generated utilizing fractional Fourier transform (FRFT). Overall detection results were obtained by fusing the output of these two networks and the final classification results on the testing dataset reports our proposed algorithm obtained a F1 score of 0.855. Furthermore, with our proposed algorithm, a better F1 score 0.81 was attained using training dataset provided by the China Physiological Signal Challenge held in 2018.

scholarly journals ECG-based machine-learning algorithms for heartbeat classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Saira Aziz ◽  
Sajid Ahmed ◽  
Mohamed-Slim Alouini
Keyword(s):  
Machine Learning ◽  
Fractional Fourier Transform ◽  
Heart Diseases ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Heartbeat Classification ◽  
Time Frequency ◽  
Ecg Signals ◽  
Signal Features ◽  
Machine Learning Model

AbstractElectrocardiogram (ECG) signals represent the electrical activity of the human hearts and consist of several waveforms (P, QRS, and T). The duration and shape of each waveform and the distances between different peaks are used to diagnose heart diseases. In this work, to better analyze ECG signals, a new algorithm that exploits two-event related moving-averages (TERMA) and fractional-Fourier-transform (FrFT) algorithms is proposed. The TERMA algorithm specifies certain areas of interest to locate desired peak, while the FrFT rotates ECG signals in the time-frequency plane to manifest the locations of various peaks. The proposed algorithm’s performance outperforms state-of-the-art algorithms. Moreover, to automatically classify heart disease, estimated peaks, durations between different peaks, and other ECG signal features were used to train a machine-learning model. Most of the available studies uses the MIT-BIH database (only 48 patients). However, in this work, the recently reported Shaoxing People’s Hospital (SPH) database, which consists of more than 10,000 patients, was used to train the proposed machine-learning model, which is more realistic for classification. The cross-database training and testing with promising results is the uniqueness of our proposed machine-learning model.

scholarly journals Moment-to-Moment Continuous Attention Fluctuation Monitoring through Consumer-Grade EEG Device

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3419
Author(s):  
Shan Zhang ◽  
Zihan Yan ◽  
Shardul Sapkota ◽  
Shengdong Zhao ◽  
Wei Tsang Ooi
Keyword(s):  
Ground Truth ◽  
Detection Accuracy ◽  
Continuous Performance Task ◽  
Eeg Signals ◽  
Gradual Onset ◽  
Machine Learning Approach ◽  
A New Technique ◽  
Thought Probes ◽  
Labeling Method ◽  
Fluctuation Measurement

While numerous studies have explored using various sensing techniques to measure attention states, moment-to-moment attention fluctuation measurement is unavailable. To bridge this gap, we applied a novel paradigm in psychology, the gradual-onset continuous performance task (gradCPT), to collect the ground truth of attention states. GradCPT allows for the precise labeling of attention fluctuation on an 800 ms time scale. We then developed a new technique for measuring continuous attention fluctuation, based on a machine learning approach that uses the spectral properties of EEG signals as the main features. We demonstrated that, even using a consumer grade EEG device, the detection accuracy of moment-to-moment attention fluctuations was 73.49%. Next, we empirically validated our technique in a video learning scenario and found that our technique match with the classification obtained through thought probes, with an average F1 score of 0.77. Our results suggest the effectiveness of using gradCPT as a ground truth labeling method and the feasibility of using consumer-grade EEG devices for continuous attention fluctuation detection.

Ensemble Learning Approach with LASSO for Predicting Catalytic Reaction Rates

Synlett ◽  
2020 ◽  
Author(s):  
Akira Yada ◽  
Kazuhiko Sato ◽  
Tarojiro Matsumura ◽  
Yasunobu Ando ◽  
Kenji Nagata ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Reaction Rates ◽  
Initial Reaction Rate ◽  
Training Dataset ◽  
Initial Reaction ◽  
Learning Approach ◽  
Learning Framework ◽  
Machine Learning Approach ◽  
Reasonable Prediction ◽  
Epoxidation Of Alkenes

AbstractThe prediction of the initial reaction rate in the tungsten-catalyzed epoxidation of alkenes by using a machine learning approach is demonstrated. The ensemble learning framework used in this study consists of random sampling with replacement from the training dataset, the construction of several predictive models (weak learners), and the combination of their outputs. This approach enables us to obtain a reasonable prediction model that avoids the problem of overfitting, even when analyzing a small dataset.

Machine Learning Approach to Detect Cardiac Arrhythmias in ECG Signals: A Survey

IRBM ◽  
2020 ◽  
Vol 41 (4) ◽  
pp. 185-194 ◽  
Author(s):  
S. Sahoo ◽  
M. Dash ◽  
S. Behera ◽  
S. Sabut
Keyword(s):  
Machine Learning ◽  
Cardiac Arrhythmias ◽  
Learning Approach ◽  
Ecg Signals ◽  
Machine Learning Approach

Cardiac Arrhythmia Detection using Dual Tree Wavelet Transform and Convolutional Neural Network

2021 ◽  
Author(s):  
K Reddy Madhavi ◽  
Padmavathi kora ◽  
L Venkateswara Reddy ◽  
J Avanija ◽  
KLS Soujanya ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Cardiac Cells ◽  
P Wave ◽  
Computer Assisted ◽  
Ecg Signals ◽  
Coronary Diseases ◽  
Life Saving ◽  
Life Threatening ◽  
Assisted Diagnosis

Abstract The non-stationary ECG signals are used as a key tools in screening coronary diseases. ECG recording is collected from millions of cardiac cells’ and depolarization and re-polarization conducted in a synchronized manner as: The P-wave occurs first, followed by the QRScomplex and the T-wave, which will repeat in each beat. The signal is altered in a cardiac beat period for different heart conditions. This change can be observed in order to diagnose the patient’s heart status. There are life-threatening (critical) and non-life - threatening (noncritical) arrhythmia (abnormal Heart). Critical arrhythmia gives little time for surgery, whereas non-critical needs additional life-saving care. Simple naked eye diagnosis can mislead the detection. At that point, Computer Assisted Diagnosis (CAD) is therefore required. In this paper Dual Tree Wavelet Transform (DTWT) used as a feature extraction technique along with Convolution Neural Network (CNN) to detect abnormal Heart. The findings of this research and associated studies are without any cumbersome artificial environments. The CAD method proposed has high generalizability; it can help doctors efficiently identify diseases and decrease misdiagnosis.

scholarly journals Detection & Classification of Cardiac Arrhythmia

2017 ◽  
Vol 6 (1) ◽  
pp. 31
Author(s):  
Chetan M. Jadhav ◽  
V. K. Bairagi
Keyword(s):  
Cardiac Arrhythmia ◽  
Early Stage ◽  
Low Cost ◽  
Simple Method ◽  
Arrhythmia Detection ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Electrical Impulses ◽  
Very High

<p>The term Arrhythmia refers to any change from the normal sequence in the electrical impulses. It is also treated as abnormal heart rhythms or irregular heartbeats. The rate of growth of Cardiac Arrhythmia disease is very high &amp; its effects can be observed in any age group in society. Arrhythmia detection can be done in many ways but effective &amp; simple method for detection &amp; diagnosis of  Cardiac Arrhythmia is by doing analysis of Electrocardiogram signals from ECG sensors. ECG signal can give us the detail information of heart activities, so we can use ECG signals to detect the rhythm &amp; behaviour of heart beats resulting into detection &amp; diagnosis of Cardiac Arrhythmia. In this paper new &amp; improved methodology for early Detection &amp; Classification of Cardiac Arrhythmia has been proposed. In this paper ECG signals are captured using ECG sensors &amp; this ECG signals are used &amp; processed to get the required data regarding heart beats of the human being &amp; then proposed methodology applies for Detection &amp; Classification of Cardiac Arrhythmia. Detection of Cardiac Arrhythmia using ECG signals allows us for easy &amp; reliable way with low cost solution to diagnose Arrhythmia in its prior early stage.</p>

scholarly journals Discovery of Highly Polymorphic Organic Materials: A New Machine Learning Approach

2019 ◽  
Author(s):  
Zied Hosni ◽  
Annalisa Riccardi ◽  
Stephanie Yerdelen ◽  
Alan R. G. Martin ◽  
Deborah Bowering ◽  
...  
Keyword(s):  
Machine Learning ◽  
Structure Prediction ◽  
External Validation ◽  
New Drugs ◽  
Training Dataset ◽  
Validation Dataset ◽  
Machine Learning Classification ◽  
Novel Approach ◽  
Physical Form ◽  
Machine Learning Approach

<div><div><p>Polymorphism is the capacity of a molecule to adopt different conformations or molecular packing arrangements in the solid state. This is a key property to control during pharmaceutical manufacturing because it can impact a range of properties including stability and solubility. In this study, a novel approach based on machine learning classification methods is used to predict the likelihood for an organic compound to crystallise in multiple forms. A training dataset of drug-like molecules was curated from the Cambridge Structural Database (CSD) and filtered according to entries in the Drug Bank database. The number of separate forms in the CSD for each molecule was recorded. A metaclassifier was trained using this dataset to predict the expected number of crystalline forms from the compound descriptors. This approach was used to estimate the number of crystallographic forms for an external validation dataset. These results suggest this novel methodology can be used to predict the extent of polymorphism of new drugs or not-yet experimentally screened molecules. This promising method complements expensive ab initio methods for crystal structure prediction and as integral to experimental physical form screening, may identify systems that with unexplored potential.</p> </div> </div>

scholarly journals Biomarkers in Posttraumatic Stress Disorder: Overview and Implications for Future Research

2013 ◽  
Vol 35 ◽  
pp. 43-54 ◽  
Author(s):  
Ulrike Schmidt ◽  
Sebastian F. Kaltwasser ◽  
Carsten T. Wotjak
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Sexual Abuse ◽  
Anxiety Disorder ◽  
Posttraumatic Stress ◽  
Stress Disorder ◽  
Future Research ◽  
Clinical Use ◽  
Current State ◽  
Life Threatening ◽  
State Of Research

PTSD can develop in the aftermath of traumatic incidents like combat, sexual abuse, or life threatening accidents. Unfortunately, there are still no biomarkers for this debilitating anxiety disorder in clinical use. Anyhow, there are numerous studies describing potential PTSD biomarkers, some of which might progress to the point of practical use in the future. Here, we outline and comment on some of the most prominent findings on potential imaging, psychological, endocrine, and molecular PTSD biomarkers and classify them into risk, disease, and therapy markers. Since for most of these potential PTSD markers a causal role in PTSD has been demonstrated or at least postulated, this review also gives an overview on the current state of research on PTSD pathobiology.

Sign in / Sign up

Export Citation Format

Share Document