A multi-stage automatic arrhythmia recognition and classification system

Kutlu, Yakup; Kuntalp, Damla

doi:10.1016/j.compbiomed.2010.11.003

Cited by 97 publications

(31 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In signal processing field, it is customary to combine several stages of classification when number of classes increase or differet types of patterns are to be recognized [11],[12]. We have employed a two-stage classification algorithm which with near absolute accuracy can distinguish between 7 different control commands of Left, Right, Up, Down, Left-Select, Right-Select, and Resting (Neutral) inside the oral cavity (Fig.…”

Section: Data Processingmentioning

confidence: 99%

Command detection and classification in tongue drive assistive technology

Sadeghian

Huo

Ghovanloo

2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

Tongue Drive System (TDS) is a new assistive technology that enables individuals with severe disabilities such as those with spinal cord injury (SCI) to regain environmental control using their tongue motion. We have developed a new sensor signal processing (SSP) algorithm which uses four 3-axial magneto-resistive sensor outputs to accurately detect and classify between seven different user-control commands in stationary as well as mobile conditions. The new algorithm employs a two-stage classification method with a combination of 9 classifiers to discriminate between 4 commands on the left or right side of the oral cavity (one neutral command shared on both sides). Evaluation of the new SSP algorithm on five able-bodied subjects resulted in true positive rates in the range of 70–99% with corresponding false positive rates in the range of 5–7%, showing a notable improvement in the resulted true-false (TF) differences when compared to the previous algorithm.

show abstract

Section: Data Processingmentioning

confidence: 99%

Command detection and classification in tongue drive assistive technology

Sadeghian

Huo

Ghovanloo

2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

show abstract

“…By using HOS-based techniques, ECG data is less affected by the morphological variations. In [21], an arrhythmia diagnosis method using a mixture of varied features containing morphological features, HOS, higher order statistics of the wavelet coefficients and Fourier transform coefficients, was presented. Das and Ari in [22] mixed the wavelet transform and S-transform (ST) features to select the more effective features by combining these features.…”

Section: Introductionmentioning

confidence: 99%

An efficient and automatic ECG arrhythmia diagnosis system using DWT and HOS features and entropy- based feature selection procedure

Chashmi

Amirani

2019

Journal of Electrical Bioimpedance

View full text Add to dashboard Cite

Primary recognition of heart diseases by exploiting computer aided diagnosis (CAD) machines, decreases the vast rate of fatality among cardiac patients. Recognition of heart abnormalities is a staggering task because the low changes in ECG signals may not be exactly specified with eyesight. In this paper, an efficient approach for ECG arrhythmia diagnosis is proposed based on a combination of discrete wavelet transform and higher order statistics feature extraction and entropy based feature selection methods. Using the neural network and support vector machine, five classes of heartbeat categories are classified. Applying the neural network and support vector machine method, our proposed system is able to classify the arrhythmia classes with high accuracy (99.83%) and (99.03%), respectively. The advantage of the presented procedure has been experimentally demonstrated compared to the other recently presented methods in terms of accuracy.

show abstract

“…ECG is an inexpensive and non-invasive diagnostic tool that is extensively utilized in numerous applications. ECG recognizes the changes in electrical activity of the heart and also it extracts necessary physiological information, which is utilized for analysing the heart function [1][2][3]. The ECG is a periodic signal that is composed of a wave sequence (P, Q, R, S, and T waves), which repeat periodically in time [4,5].…”

Section: Introductionmentioning

confidence: 99%

Infinite Feature Selection Based Arrhythmia Classification Using Semantic Features and Random Forest

Rangappa¹,

Prasad²,

Agarwal³

2019

IJIES

View full text Add to dashboard Cite

In this research study, arrhythmia classification was assessed by using Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. After signal acquisition, semantic feature extraction (combination of statistical, entropy, linear and non-linear features) was used for extracting the feature values from acquired electrocardiogram (ECG) signals. Here, twenty-six features were combined to perform feature extraction on acquired ECG signals. Then, the infinite feature selection was used for rejecting the irrelevant feature vectors or selecting the optimal feature subsets. After selecting the optimal feature vectors, a binary classifier: random forest was used to classify the eight arrhythmia heartbeat conditions: Normal sinus rhythm (N), Sinoauricular Heart Block (SHB), Premature Ventricular Contraction (PVC), Auricular Fibrillation (AF), Left Bundle Branch Block (LBBB), Premature Atrial Contraction (PAC), Supra-Ventricular-Tachycardia (SVT) and Right Bundle Branch Block (RBBB). This research work includes numerous advantages; earlier detection of arrhythmia diseases, assists clinicians during surgery, and cost efficient compared to the existing machine learning methodologies. The experimental outcome shows that the proposed methodology effectively distinguishes the heartbeat conditions by means of sensitivity, specificity, Negative Predictive Value (NPV), Positive Predictive Value (PPV) and accuracy. The proposed methodology enhances the classification accuracy up to 1.4-1.5% compared to the existing methodologies.

show abstract

A multi-stage automatic arrhythmia recognition and classification system

Cited by 97 publications

References 41 publications

Command detection and classification in tongue drive assistive technology

Command detection and classification in tongue drive assistive technology

An efficient and automatic ECG arrhythmia diagnosis system using DWT and HOS features and entropy- based feature selection procedure

Infinite Feature Selection Based Arrhythmia Classification Using Semantic Features and Random Forest

Contact Info

Product

Resources

About