Analysis and classification of heart diseases using heartbeat features and machine learning algorithms

Alarsan, Fajr Ibrahem; Younes, Mamoon

doi:10.1186/s40537-019-0244-x

Cited by 167 publications

(60 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data annotation and RR interval were recorded and pre-processed. RR interval is a time delay between the current and previous heart beats [32]. By calculating the time which elapsed between two consecutive R waves of ECG signal, RR interval was Firstly, problematic data were removed.…”

Section: Pre-processing Of the Datamentioning

confidence: 99%

Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data

2021

View full text Add to dashboard Cite

Recent developments of portable sensor devices, cloud computing, and machine learning algorithms have led to the emergence of big data analytics in healthcare. The condition of the human body, e.g. the ECG signal, can be monitored regularly by means of a portable sensor device. The use of the machine learning algorithm would then provide an overview of a patient’s current health on a regular basis compared to a medical doctor’s diagnosis that can only be made during a hospital visit. This work aimed to develop an accurate model for classifying sleep stages by features of Heart Rate Variability (HRV) extracted from Electrocardiogram (ECG). The sleep stages classification can be utilized to predict the sleep stages proportion. Where sleep stages proportion information can provide an insight of human sleep quality. The integration of Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) was utilized for selecting features and determining the number of hidden nodes. The results were compared to Support Vector Machine (SVM) and ELM methods which are lower than the integration of ELM with PSO. The results of accuracy tests for the combined ELM and PSO were 62.66%, 71.52%, 76.77%, and 82.1% respectively for 6, 4, 3, and 2 classes. To sum up, the classification accuracy can be improved by deploying PSO algorithm for feature selection.

show abstract

Section: Pre-processing Of the Datamentioning

confidence: 99%

Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data

2021

View full text Add to dashboard Cite

show abstract

“…When the updated state values v ′ (s) for any values of coefficient α remains below the action value Q(s, a), the coefficient α is determined to be 0 to maximize the updated state value, as shown in Eq. (6).…”

Section: Impulsive Wave Trend Evaluationmentioning

confidence: 99%

“…Another frequency domain analysis [4] classifies the electrocardiograms into the limited number of classifications from the least-squares support-vector machine [5]. A neural network approach [5] requires the limited number of pre-defined training models under normal heart-beats, and each type of a waveform craves its corresponding model [6]. The proposed impulsive wave trends annotate wave components of heartbeat within a single electrocardiogram, under various types of normal and pathological circumstances without prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

Myocardial infarction evaluation from stopping time decision toward interoperable algorithmic states in reinforcement learning

Park

Hwang

Shin

et al. 2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background: The Elliot wave principle commonly characterizes the impulsive and corrective wave trends for both financial market trends and electrocardiograms. The impulsive wave trends of electrocardiograms can annotate several wave components of heartbeats including pathological heartbeat waveforms. The stopping time inquires which ordinal element satisfies the assumed mathematical condition within a numerical set. The proposed work constitutes several algorithmic states in reinforcement learning from the stopping time decision, which determines the impulsive wave trends. Each proposed algorithmic state is applicable to any relevant algorithmic state in reinforcement learning with fully numerical explanations. Because commercial electrocardiographs still misinterpret myocardial infarctions from extraordinary electrocardiograms, a novel algorithm needs to be developed to evaluate myocardial infarctions. Moreover, differential diagnosis for right ventricle infarction is required to contraindicate a medication such as nitroglycerin. Methods: The proposed work implements the stopping time theory to impulsive wave trend distribution. The searching process of the stopping time theory is equivalent to the actions toward algorithmic states in reinforcement learning. The state value from each algorithmic state represents the numerically deterministic annotated results from the impulsive wave trend distribution. The shape of the impulsive waveform is evaluated from the interoperable algorithmic states via least-firstpower approximation and approximate entropy. The annotated electrocardiograms from the impulsive wave trend distribution utilize a structure of neural networks to approximate the isoelectric baseline amplitude value of the electrocardiograms, and detect the conditions of myocardial infarction. The annotated results from the impulsive wave trend distribution consist of another reinforcement learning environment for the evaluation of impulsive waveform direction. Results: The accuracy to discern myocardial infarction was found to be 99.2754% for the data from the comma-separated value format files, and 99.3579% for those containing representative beats. The clinical dataset included 276 electrocardiograms from the comma-separated value files and 623 representative beats.

show abstract

“…Some main causes of heart attacks are lack of physical activity, Smoking, elevated cholesterol, hypertension, destructive use of liquor, and undesirable diet [2]. To measure the heart's electrical activity, an electrocardiogram (ECG) procedure is used to monitor many electrical potentials of the heart [3].…”

Section: Introductionmentioning

confidence: 99%

Efficient Data-Mining Classification Approach For Ecg Data In Health Care Application

Shana¹,

Venkatachalam²

2021

Preprint

View full text Add to dashboard Cite

Data mining enables classification of Electrocardiographic (ECG) signals of the heart for diagnosing many cardiac diseases. ECG signals often consist of unwanted noises, speckles and redundant features. An unwanted noise and redundant features always degrade the quality of ECG signal and may lead to loss of accuracy in classification technique. To overcome these challenges, we introduced Optimize Discrete Kernel Vector (ODKV) classifier with an impressive pre-processing in this paper. In order to remove the noises, image processing filter namely the Adaptive Notch Filters (ANF) are initially used to remove Power Line Interference from ECG Signals. Moreover, reducing the redundant features from the ECG signal plays a vital role in diagnosing the cardiac disease. So, Optimize Discrete Kernel Vector (ODKV) classifier is used to reduce the redundant features and also to enhance the classification accuracy of the input ECG signal. Thus, Optimize Discrete Kernel Vector (ODKV) classifier identifies the Q wave, R wave and S wave in the input ECG signal. Finally, performance metrics Sensitivity, Specificity, Accuracy and Mean Square Error (MSE) are calculated and compared with the existing method such as SVM-kNN, ANN-kNN, GB-SVNN, and CNN to prove the enhancement of the classification technique.

show abstract

Analysis and classification of heart diseases using heartbeat features and machine learning algorithms

Cited by 167 publications

References 18 publications

Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data

Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data

Myocardial infarction evaluation from stopping time decision toward interoperable algorithmic states in reinforcement learning

Efficient Data-Mining Classification Approach For Ecg Data In Health Care Application

Contact Info

Product

Resources

About