An integrated intelligent computing method for the detection and interpretation of ECG based cardiac diseases

Pandey, Babita; Mishra, R. B.

doi:10.1504/ijkesdp.2010.034682

Cited by 10 publications

(3 citation statements)

References 31 publications

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“…In [20], Pandey and Mishra describe a method for detecting cardiac diseases from ECG. In the present study, the authors use QRS complex as a reference to analyse the ECG.…”

Section: State Of the Artmentioning

confidence: 99%

Algorithms Based on CWT and Classifiers to Control Cardiac Alterations and Stress Using an ECG and a SCR

Villarejo

Zapirain

Zorrilla

2013

Sensors

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This paper presents the results of using a commercial pulsimeter as an electrocardiogram (ECG) for wireless detection of cardiac alterations and stress levels for home control. For these purposes, signal processing techniques (Continuous Wavelet Transform (CWT) and J48) have been used, respectively. The designed algorithm analyses the ECG signal and is able to detect the heart rate (99.42%), arrhythmia (93.48%) and extrasystoles (99.29%). The detection of stress level is complemented with Skin Conductance Response (SCR), whose success is 94.02%. The heart rate variability does not show added value to the stress detection in this case. With this pulsimeter, it is possible to prevent and detect anomalies for a non-intrusive way associated to a telemedicine system. It is also possible to use it during physical activity due to the fact the CWT minimizes the motion artifacts.

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“…In [20], Pandey and Mishra describe a method for detecting cardiac diseases from ECG. In the present study, the authors use QRS complex as a reference to analyse the ECG.…”

Section: State Of the Artmentioning

confidence: 99%

Algorithms Based on CWT and Classifiers to Control Cardiac Alterations and Stress Using an ECG and a SCR

Villarejo

Zapirain

Zorrilla

2013

Sensors

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“…ECG indicates an electrical activity of heart which provides cardiologists to extract the useful information about the functioning of the heart [1], [2], [3]. Any disorder in the heart, leads towards the Heart Disease often detected by an ECG signal [4], [5], [6], [7]. Some of the common heart diseases are Coronary, Arrhythmia, and Myocardial Infarction.…”

Section: Introductionmentioning

confidence: 99%

Semiautomatic detection of cardiac diseases employing dual tree complex wavelet transform

Prashar¹,

Sood

Jain

2018

PEN

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Electrocardiogram (ECG) contains lot of information which can be utilized for a mechanism to detect cardiac abnormalities. The ECG signal is too sensitive to various types of noises as it is of low frequency and has weak amplitude, these noises reduce the diagnostic accuracy and may lead to the incorrect decision of the clinician. So, denoising of ECG signal is an essential requirement for an accurate detection of Heart disease. In this paper, a Dual-Tree Complex Wavelet Transform technique (DTCWT) is presented to denoise the noisy ECG signal and to extract the Principal features followed by implementation of Peak Detection Algorithm. The performance is evaluated on the basis of performance metrics and an increase in SNR is achieved using the technique. With the proposed technique, calculated heart rate is in consensus with the gold standard of the various bench mark databases used and accurate heart disease was determined.

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“…Likely, the physio-psycho factors and parameters based on EEG play an important role to diagnose sleep disorders. Pandey and Mishra [1], [2] originated an intelligent model to diagnose the diseases based on ECG and EMG. Some analysts have described intelligent techniques for the diagnosis of sleep disorders [1], [2] and [3], but there is a lag to diagnose EEG based disease using physio-psycho factors including RBR and CBR.…”

Section: Introductionmentioning

confidence: 99%

Knowledge based System for the Diagnosis of Sleep Disorders

KumarGarg¹,

Bansal²

2015

IJCA

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Intelligent computing systems (ICS) and knowledge-based systems (KBS) have shown an imperative aspect in the detection and interpretation of electroencephalography (EEG) based sleep disorders that are associated by psychological and physiological factors like Sleep Apnea, Insomnia, Parasomnia and Snoring. Heuristic detection methods based on EEG parameters for sleep disorders have also been described in the paper, and a small attempt has been made to integrate rulebased reasoning (RBR) and case-based reasoning belongs to KBS. Integrated methods of RBR and CBR enhance the computation and logical competence of thought process involved to solve a problem. In this paper, an integrated model is developed in which RBR and CBR used for developing cases which are further used for detection and interpretation of sleep disorders. All these sleep disorders are framed into physio-psycho (muscular, cognitive and psychological) and EEG based parameters. The prime aim of this paper is to develop a combined model based on RBR and CBR in which RBR interacts with the sign and symptoms of the disorders, CBR is used for recognize the sleep disorders.

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An integrated intelligent computing method for the detection and interpretation of ECG based cardiac diseases

Cited by 10 publications

References 31 publications

Algorithms Based on CWT and Classifiers to Control Cardiac Alterations and Stress Using an ECG and a SCR

Algorithms Based on CWT and Classifiers to Control Cardiac Alterations and Stress Using an ECG and a SCR

Semiautomatic detection of cardiac diseases employing dual tree complex wavelet transform

Knowledge based System for the Diagnosis of Sleep Disorders

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