Khushbakht Iqtidar

Bio: Khushbakht Iqtidar is an academic researcher from College of Electrical and Mechanical Engineering. The author has contributed to research in topics: Support vector machine & Phonocardiogram. The author has an hindex of 5, co-authored 11 publications receiving 66 citations. Previous affiliations of Khushbakht Iqtidar include University of the Sciences.

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

Abstract: According to the World Health Organization, Coronary Artery Disease (CAD) is a leading cause of death globally. CAD is categorized into three types, namely Single Vessel Coronary Artery Disease (SVCAD), Double Vessel Coronary Artery Disease (DVCAD), and Triple Vessel Coronary Artery Disease (TVCAD). At present, angiography is the most popular technique to detect CAD that is quite expensive and invasive. Phonocardiogram (PCG), being economical and non-invasive, is a crucial modality towards the detection of cardiac disorders, but only trained medical professionals can interpret heart auscultations in clinical environments. This research aims to detect CAD and its types from PCG signatures through feature fusion and a two-stage classification strategy. The self-developed low-cost stethoscope was used to collect PCG data from a local hospital. The PCG signals were preprocessed through an iterative signal decomposition method known as Empirical Mode Decomposition (EMD). EMD decomposes the raw PCG signal into its constituent components called Intrinsic Mode Functions (IMFs). Preprocessed PCG signal was generated exclusively through combining those signal components that contain high discriminative characteristics and less redundancy. Next, Mel Frequency Cepstral Coefficients (MFCCs), spectral and statistical features were extracted. A two-stage classification framework was devised to identify healthy and CAD types. The first stage framework relies on the fusion of MFCC and statistical features with the K-nearest neighbor classifier to predict normal and CAD cases. The second stage is activated only when the first stage detects CAD. The fusion of spectral, statistical, and MFCC features was employed with Support Vector Machines classifier to categorize PCG signatures into DVCAD, SVCAD, and TVCAD classes in the second stage. The proposed method yields mean accuracy values of 88.0%, 89.2%, 91.1%, and 85.3% for normal, DVCAD, SVCAD, and TVCAD, respectively, through 10-fold cross-validation. Comparative analysis with existing approaches confirmed the reliability of the proposed method for categorizing CAD in general clinical environments. The proposed model enhances the diagnosis performance by providing a second opinion during the medical examination.

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

Abstract: 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.

Prediction of Acute Coronary Syndrome Using Pulse Plethysmograph

Abstract: Acute Coronary Syndrome (ACS) is one the major reason of increasing mortality rate. Despite medical advances, there isn’t any efficient method that can control this proliferating mortality rate. The major aim of this study is detection of ACS using Pulse Plethysmograph (PuPG) signal analysis. 348 samples of PuPG signal were acquired by fastening PuPG sensor to subject’s index finger. Signal data is preprocessed through Empirical Mode Decomposition (EMD) to remove any possible noise and other artifacts. Extensive experimental analysis was performed to select features having maximum intraclass distance and discriminative power to classify ACS and Normal signals through Support Vector Machines (SVM) classifier. 5-fold cross validation was used to perform training and testing of proposed model using self-collected dataset of PuPG signals. Average accuracy of 99.42%, sensitivity of 99.43% and specificity of 99.41% is obtained through SVM with cubic kernel proving proposed model as a best possible methodology in-terms of cost and efficiency as compared to existing solutions.

Pulse plethysmograph signal analysis method for classification of heart diseases using novel local spectral ternary patterns

Abstract: Cardiac disorders are one of the prime reasons for an increasing global death rate. Reliable and efficient diagnosis procedures are imperative to minimize the risk posed by heart disorders. Computer‐aided diagnosis, based on machine learning and biomedical signal analysis, has recently been adopted by researchers to accurately predict cardiac ailments. Multi‐channel Electrocardiogram signals are mostly used in scientific literature as an indicator to diagnose cardiac disorders. Recently pulse plethysmograph (PuPG) signal got attention as an evolving biosignal and promising diagnostic tool to detect heart disorders since it has a simple sensor with low cost, non‐invasive, reliable, and easy to handle technology. This article proposes a computer‐aided diagnosis system to detect Myocardial Infarction, Dilated Cardiomyopathy, and Hypertension from PuPG signals. Raw PuPG signal is first preprocessed through empirical mode decomposition (EMD) by removing the redundant and useless information content. Then, highly discriminative features are extracted from preprocessed PuPG signal through novel local spectral ternary patterns (LSTP). Extracted LSTPs are input to a variety of classification methods such as support vector machines (SVM), K‐nearest neighbours, decision tree, and so on. SVM with cubic kernel yielded the best classification performance of 98.4% accuracy, 96.7% sensitivity, and 99.6% specificity with 10‐fold cross‐validation. The proposed framework was trained and tested on a self‐collected PuPG signals database of heart disorders. A comparison with previous studies and other feature descriptors shows the superiority of the proposed system. This research provides better insights into the contributions of PuPG signals towards reliable detection of heart disorder through low‐cost and non‐invasive means.

Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features.

Abstract: Congenital heart disease (CHD) is a heart disorder associated with the devastating indications that result in increased mortality, increased morbidity, increased healthcare expenditure, and decreased quality of life. Ventricular Septal Defects (VSDs) and Arterial Septal Defects (ASDs) are the most common types of CHD. CHDs can be controlled before reaching a serious phase with an early diagnosis. The phonocardiogram (PCG) or heart sound auscultation is a simple and non-invasive technique that may reveal obvious variations of different CHDs. Diagnosis based on heart sounds is difficult and requires a high level of medical training and skills due to human hearing limitations and the non-stationary nature of PCGs. An automated computer-aided system may boost the diagnostic objectivity and consistency of PCG signals in the detection of CHDs. The objective of this research was to assess the effects of various pattern recognition modalities for the design of an automated system that effectively differentiates normal, ASD, and VSD categories using short term PCG time series. The proposed model in this study adopts three-stage processing: pre-processing, feature extraction, and classification. Empirical mode decomposition (EMD) was used to denoise the raw PCG signals acquired from subjects. One-dimensional local ternary patterns (1D-LTPs) and Mel-frequency cepstral coefficients (MFCCs) were extracted from the denoised PCG signal for precise representation of data from different classes. In the final stage, the fused feature vector of 1D-LTPs and MFCCs was fed to the support vector machine (SVM) classifier using 10-fold cross-validation. The PCG signals were acquired from the subjects admitted to local hospitals and classified by applying various experiments. The proposed methodology achieves a mean accuracy of 95.24% in classifying ASD, VSD, and normal subjects. The proposed model can be put into practice and serve as a second opinion for cardiologists by providing more objective and faster interpretations of PCG signals.

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

Abstract: According to the World Health Organization, Coronary Artery Disease (CAD) is a leading cause of death globally. CAD is categorized into three types, namely Single Vessel Coronary Artery Disease (SVCAD), Double Vessel Coronary Artery Disease (DVCAD), and Triple Vessel Coronary Artery Disease (TVCAD). At present, angiography is the most popular technique to detect CAD that is quite expensive and invasive. Phonocardiogram (PCG), being economical and non-invasive, is a crucial modality towards the detection of cardiac disorders, but only trained medical professionals can interpret heart auscultations in clinical environments. This research aims to detect CAD and its types from PCG signatures through feature fusion and a two-stage classification strategy. The self-developed low-cost stethoscope was used to collect PCG data from a local hospital. The PCG signals were preprocessed through an iterative signal decomposition method known as Empirical Mode Decomposition (EMD). EMD decomposes the raw PCG signal into its constituent components called Intrinsic Mode Functions (IMFs). Preprocessed PCG signal was generated exclusively through combining those signal components that contain high discriminative characteristics and less redundancy. Next, Mel Frequency Cepstral Coefficients (MFCCs), spectral and statistical features were extracted. A two-stage classification framework was devised to identify healthy and CAD types. The first stage framework relies on the fusion of MFCC and statistical features with the K-nearest neighbor classifier to predict normal and CAD cases. The second stage is activated only when the first stage detects CAD. The fusion of spectral, statistical, and MFCC features was employed with Support Vector Machines classifier to categorize PCG signatures into DVCAD, SVCAD, and TVCAD classes in the second stage. The proposed method yields mean accuracy values of 88.0%, 89.2%, 91.1%, and 85.3% for normal, DVCAD, SVCAD, and TVCAD, respectively, through 10-fold cross-validation. Comparative analysis with existing approaches confirmed the reliability of the proposed method for categorizing CAD in general clinical environments. The proposed model enhances the diagnosis performance by providing a second opinion during the medical examination.

Pulse plethysmograph signal analysis method for classification of heart diseases using novel local spectral ternary patterns

Abstract: Cardiac disorders are one of the prime reasons for an increasing global death rate. Reliable and efficient diagnosis procedures are imperative to minimize the risk posed by heart disorders. Computer‐aided diagnosis, based on machine learning and biomedical signal analysis, has recently been adopted by researchers to accurately predict cardiac ailments. Multi‐channel Electrocardiogram signals are mostly used in scientific literature as an indicator to diagnose cardiac disorders. Recently pulse plethysmograph (PuPG) signal got attention as an evolving biosignal and promising diagnostic tool to detect heart disorders since it has a simple sensor with low cost, non‐invasive, reliable, and easy to handle technology. This article proposes a computer‐aided diagnosis system to detect Myocardial Infarction, Dilated Cardiomyopathy, and Hypertension from PuPG signals. Raw PuPG signal is first preprocessed through empirical mode decomposition (EMD) by removing the redundant and useless information content. Then, highly discriminative features are extracted from preprocessed PuPG signal through novel local spectral ternary patterns (LSTP). Extracted LSTPs are input to a variety of classification methods such as support vector machines (SVM), K‐nearest neighbours, decision tree, and so on. SVM with cubic kernel yielded the best classification performance of 98.4% accuracy, 96.7% sensitivity, and 99.6% specificity with 10‐fold cross‐validation. The proposed framework was trained and tested on a self‐collected PuPG signals database of heart disorders. A comparison with previous studies and other feature descriptors shows the superiority of the proposed system. This research provides better insights into the contributions of PuPG signals towards reliable detection of heart disorder through low‐cost and non‐invasive means.