Detection of Dilated Cardiomyopathy using Pulse Plethysmographic Signal Analysis

Khan, Muhammad Umar; Aziz, Sumair; Amjad, Fatima; Mohsin, Muhammad; Gull-E-Saher,

doi:10.1109/inmic48123.2019.9022734

Cited by 21 publications

(12 citation statements)

References 3 publications

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“…Human blood vessels and the microcirculation system experience transformations with the rise in blood pressure (BP); these changes are exceptionally obvious for patients with severe hypertension. PuPG signals carry a wealth of information about the cardiac health [ 25 , 26 , 27 ]. The PuPG signal reflects physical changes in blood volume pressure in blood vessels during the cardiac cycle.…”

Section: Discussionmentioning

confidence: 99%

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Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme

Khan

Aziz

Akram

et al. 2021

Sensors

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Hypertension is an antecedent to cardiac disorders. According to the World Health Organization (WHO), the number of people affected with hypertension will reach around 1.56 billion by 2025. Early detection of hypertension is imperative to prevent the complications caused by cardiac abnormalities. Hypertension usually possesses no apparent detectable symptoms; hence, the control rate is significantly low. Computer-aided diagnosis based on machine learning and signal analysis has recently been applied to identify biomarkers for the accurate prediction of hypertension. This research proposes a new expert hypertension detection system (EHDS) from pulse plethysmograph (PuPG) signals for the categorization of normal and hypertension. The PuPG signal data set, including rich information of cardiac activity, was acquired from healthy and hypertensive subjects. The raw PuPG signals were preprocessed through empirical mode decomposition (EMD) by decomposing a signal into its constituent components. A combination of multi-domain features was extracted from the preprocessed PuPG signal. The features exhibiting high discriminative characteristics were selected and reduced through a proposed hybrid feature selection and reduction (HFSR) scheme. Selected features were subjected to various classification methods in a comparative fashion in which the best performance of 99.4% accuracy, 99.6% sensitivity, and 99.2% specificity was achieved through weighted k-nearest neighbor (KNN-W). The performance of the proposed EHDS was thoroughly assessed by tenfold cross-validation. The proposed EHDS achieved better detection performance in comparison to other electrocardiogram (ECG) and photoplethysmograph (PPG)-based methods.

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Section: Discussionmentioning

confidence: 99%

“…In this research, novel PuPG signals were used to design the hypertension detection system. The PuPG signal includes considerable cardiac health characteristic information [ 25 , 26 , 27 ]. The PuPG signals are recently being used for emotions classification [ 28 ] and biometric systems [ 29 ] as well.…”

Section: Introductionmentioning

confidence: 99%

Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme

Khan

Aziz

Akram

et al. 2021

Sensors

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show abstract

“…Previously, only three studies [20][21][22] utilized the same PuPG signatures to identify MI, DCM, and ACS with 98.5%, 99.7%, and 99.42% accuracies respectively. All of those works were based on a two-class problem.…”

Section: Discussionmentioning

confidence: 99%

“…But this study proposed a multi-class approach to detect four classes and on a comparatively larger dataset as compared to that used in [20][21][22]. Table 6 shows the mean processing times of each step involved in detection and 4-class systems.…”

Section: Discussionmentioning

confidence: 99%

“…A method to identify MI from PuPG signal analysis [20] achieved 98.5% accuracy using three features and an SVM classifier. DCM was detected [21] from PuPG time-series with an accuracy of 99.7% using Discrete Wavelet Transform (DWT) and SVM. Diagnosis of Acute Coronary Syndrome (ACS) from PuPG signal analysis by employing Empirical Mode Decomposition (EMD) and SVM is presented in [22].…”

Section: Related Workmentioning

confidence: 99%

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A novel pulse plethysmograph signal analysis method for identification of myocardial infarction, dilated cardiomyopathy, and hypertension

Khan¹,

Aziz²

2021

Turk J Elec Eng & Comp Sci

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Cardiac diseases (CDs) are one of the leading causes of the growing global mortality rate. Early detection of CDs is necessary to avoid a high increase in the mortality rate. Machine learning-based computer-aided diagnosis of CDs using various physiological signals has recently been used by researchers. Since Pulse Plethysmograph (PuPG) signal contains a wealth of information about cardiac pathologies, therefore, this paper presents an expert system design for the automatic diagnosis of cardiac disorders like Hypertension, Dilated Cardiomyopathy and Myocardial Infarction using a novel fingertip PuPG signal analysis. The proposed system first performs signal de-noising of raw PuPG sensor data using Discrete Wavelet Transform (DWT). After signal segmentation, it extracts discriminant and simplest timedomain features, which are used to perform the detection of normal and abnormal subjects through a support vector machine (SVM) classifier. The proposed detection and classification systems are tested using 10-fold cross-validation which yielded an average accuracy of 98.90%, sensitivity of 100.00%, and specificity of 98.02% for detection (normal vs abnormal) experiments with only four features and an average accuracy of 97.57% for the multiclass problem using five computationally inexpensive features. Comparative analysis with existing methods based on Electrocardiogram, Photoplethysmograph, and Phonocardiogram revealed that the proposed system has high efficiency in terms of CD detection with very low computational complexity. The findings of this work provide insights into the contribution of PuPG signal analysis towards accurate detection of cardiac disorders through innovative, low cost, and non-invasive methods. Such a system with mobile cardiac health monitoring could be used as a counterpart or second opinion with clinical diagnoses and provide patients with additional but subtle indicators of varying heart dynamics.

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A two-stage classification model integrating feature fusion for coronary artery disease detection and classification

Khan

Aziz

Iqtidar

et al. 2021

Multimed Tools Appl

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Detection of Dilated Cardiomyopathy using Pulse Plethysmographic Signal Analysis

Cited by 21 publications

References 3 publications

Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme

Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme

A novel pulse plethysmograph signal analysis method for identification of myocardial infarction, dilated cardiomyopathy, and hypertension

A two-stage classification model integrating feature fusion for coronary artery disease detection and classification

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