Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform

Giri, Donna; Acharya, U. Rajendra; Martis, Roshan Joy; Sree, S. Vinitha; Lim, Teik‐Cheng; Ahamed, VI Thajudin; Suri, Jasjit S.

doi:10.1016/j.knosys.2012.08.011

Cited by 202 publications

(110 citation statements)

References 36 publications

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“…However, even with state of the art processing facilities, such a brute force approach is very time consuming, because running classification algorithms for all feature permutations is computationally complex. The final method relies on dimension reduction through algorithms, such as Principal Component Analysis (PCA), Kernel PCA, Neighborhood Preserving Embedding (NPE), Locality Sensitive Discriminant Analysis (LSDA), Independent Component Analysis (ICA) [35,56,57]. The idea, behind that method, is to establish an ordered sequence of parameters from a feature set.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

A Review of Ecg-Based Diagnosis Support Systems for Obstructive Sleep Apnea

Faust

Acharya

et al. 2016

J. Mech. Med. Biol.

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Humans need sleep. It is important for physical and psychological recreation. During sleep our consciousness is suspended or least altered. Hence, our ability to avoid or react to disturbances is reduced. These disturbances can come from external sources or from disorders within the body. Obstructive Sleep Apnea (OSA) is such a disorder. It is caused by obstruction of the upper airways which causes periods where the breathing ceases. In many cases, periods of reduced breathing, known as hypopnea, precede OSA events. The medical background of OSA is well understood, but the traditional diagnosis is expensive, as it requires sophisticated measurements and human interpretation of potentially large amounts of physiological data. Electrocardiogram (ECG) measurements have the potential to reduce the cost of OSA diagnosis by simplifying the measurement process. On the down side, detecting OSA events based on ECG data is a complex task which requires highly skilled practitioners. Computer algorithms can help to detect the subtle signal changes which indicate the presence of a disorder. That approach has the following advantages: computers never tire, processing resources are economical and progress, in the form of better algorithms, can be easily disseminated as updates over the internet. Furthermore, Computer-Aided Diagnosis (CAD) reduces intra- and inter-observer variability. In this review, we adopt and support the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy.

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Section: Feature Extraction and Selectionmentioning

confidence: 99%

A Review of Ecg-Based Diagnosis Support Systems for Obstructive Sleep Apnea

Faust

Acharya

et al. 2016

J. Mech. Med. Biol.

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“…For example, the hear rate variability is such a quantity of interest [25] • First and second order statistics -Mean and variance [26] • Discrete Wavelet Transform (DWT) -This feature extraction technique is closely related to spectrum techniques. Spectrum techniques provide only frequency restitution, whereas DWT provides both time and frequency resolution [27,28,27,29] • Independent Component Analysis (ICA) -The technique separates multivariate signals into their additive subcomponents [27,15,28] • Principal Component Analysis (PCA) -The statistical procedure is based on orthogonal transformation which produces linearly uncorrelated parameters known as the principal components [30,29,28,31,32] • Linear Discriminant Analysis (LDA) -Yields features that characterizes two or more signal classes [28] • Discrete Cosine Transform (DCT) -Spectrum technique based on cosine waves [15,33] Nonlinear methods are based on the more recent concepts of chaos and fuzzy logic [34,35,36]. The novelty of these methods is reflected in the fact that only a few recent CAD systems employ them, as indicated in the following list.…”

Section: Feature Extractionmentioning

confidence: 99%

Computer Aided Diagnosis for Cardiovascular Diseases Based on Ecg Signals: A Survey

Faust

2016

J. Mech. Med. Biol.

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The interpretation of Electroencephalography (ECG) signals is difficult, because even subtle changes in the waveform can indicate a serious heart disease. Furthermore, these waveform changes might not be present all the time. As a consequence, it takes years of training for a medical practitioner to become an expert in ECG-based cardiovascular disease diagnosis. That training is a major investment in a specific skill. Even with expert ability, the signal interpretation takes time. In addition, human interpretation of ECG signals causes interoperator and intraoperator variability. ECG-based Computer-Aided Diagnosis (CAD) holds the promise of improving the diagnosis accuracy and reducing the cost. The same ECG signal will result in the same diagnosis support regardless of time and place. This paper introduces both the techniques used to realize the CAD functionality and the methods used to assess the established functionality. This survey aims to instill trust in CAD of cardiovascular diseases using ECG signals by introducing both a conceptional overview of the system and the necessary assessment methods.

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“…The Accuracy is the ratio of the number of correctly classified samples to the total number of samples, [21],The Coverage of an association rule is the ratio of cases in the data that have the attribute values or items specified on the left hand side of the rule. [22] .…”

Section: Comparing Of Rule Algorithmsmentioning

confidence: 99%

Design Advance Database Supported with some of Data Mining Ensemble for Early Deduction of Osteoporosis

Al-Deen¹,

Fahady²,

Al-jarah³

2015

IJCA

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A computerize Osteoporosis system has been designed, it is based on a database with factors that represent the cause of Osteoporosis, in this database the Visual Basic.net language were used for designing system forms ,as well as TSQL language and SQL Server. Data Mining is considered to be the most important tool used to extract information from data and uncover its hidden paten. The Utility of its tools enable researchers to use it in many applications and to data irrelative of its size. In this paper the Naïve Bayes Classifier and Rule Induction, were used as a data mining tools, to classify person with, or without, Osteoporosis, and to detect the main causes that leads to Osteoporosis. Data was collected from a sample of patient, from Al-Salam and Al-Jamhori Hospitals in Mosul City. Direct interview to those patient were used to collect information about the history and the stage of the cases. Based on this data a Model for the Osteoporosis Stages was derived and software was designed, to be used by specialist in the diagnosis and treatment of the disease. The study reached some conclusion and suggestion

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Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform

Cited by 202 publications

References 36 publications

A Review of Ecg-Based Diagnosis Support Systems for Obstructive Sleep Apnea

A Review of Ecg-Based Diagnosis Support Systems for Obstructive Sleep Apnea

Computer Aided Diagnosis for Cardiovascular Diseases Based on Ecg Signals: A Survey

Design Advance Database Supported with some of Data Mining Ensemble for Early Deduction of Osteoporosis

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