Muhammad Umar Khan

Bio: Muhammad Umar Khan is an academic researcher from University of Engineering and Technology. The author has contributed to research in topics: Support vector machine & Phonocardiogram. The author has an hindex of 13, co-authored 64 publications receiving 424 citations. Previous affiliations of Muhammad Umar Khan include University of Engineering and Technology, Lahore.

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.

ECG-based Biometric Authentication using Empirical Mode Decomposition and Support Vector Machines

Abstract: Electrocardiogram (ECG) is an electric signal of cardiac activity posing highly discriminative properties related to human recognition. ECG based authentication has gained much success in recent times however discriminant feature extraction and efficient pattern classification still encounter numerous challenges. This paper proposed a novel methodology for ECG based biometric authentication system. Proposed method first denoise single lead raw ECG signal through empirical mode decomposition (EMD). Region of interest from ECG signals having maximum characteristic information related to subject’s recognition is also extracted through EMD. Next, feature extraction is performed by combination of five features from statistical, time and frequency domains. Finally, selected features were categorized with range of different classification methods such as Support Vector Machines (SVM), K-nearest neighbor (KNN) and Decision Tree (DT). 10-fold cross validation based classification evaluation reveals that SVM with cubic kernel achieves best accuracy of 98.7%, sensitivity of 100% and 98.8% specificity for successful classification of 14 subjects.

Classification of EMG Signals for Assessment of Neuromuscular Disorder using Empirical Mode Decomposition and Logistic Regression

Abstract: The electromyographic (EMG) signal generated in muscle fibers has been the topic under substantial research in immediate past years as it provides fairly large amount of information for assessment of neuromuscular diseases particularly amyotrophic lateral sclerosis (ALS). Besides this, the design of an accurate and computationally efficient diagnostic system remains a challenge due to variation of EMG signals taken from different muscles with different level of needle insertion. This study offers a complete framework for accurate classification of EMG signals which includes denoising by empirical mode decomposition (EMD), feature extraction from both the time and frequency domains and classification by logistic regression (LR) and support vector machine (SVM). The presented work efficiently discriminates between EMG signal of healthy subject and patient with ALS disease independent of which muscle is used for EMG signal acquisition and what insertion level of needle is. Performance evaluation measures such as sensitivity, specificity, F-measure, total classification accuracy and area under ROC curve (AVC) are used to validate the performance of both classifiers. LR classification technique shows superlative performance with a classification accuracy of 95.1%. These results shows the competence of proposed diagnostic system for classification of EMG signals. Moreover, the proposed method can be used in clinical applications for diagnoses of neuromuscular diseases.

Characterization of Term and Preterm Deliveries using Electrohysterograms Signatures

Abstract: Preterm birth is the leading cause defining the infant mortality and morbidity globally. Non-invasive surface uterine electromyogram (sEMG) also known as Electrohysterogram (EHG) is the most promising biophysical signature for the study of uterine contractions. Therefore, it can prove to be a marker for the detection of preterm birth, which might enable us to diagnose the preterm birth before the labor. In this study, we proposed a signal processing approach to predict Preterm birth using raw EHG signals with shorter time recording (1 min). The raw EHG recording is first preprocessed and segmented using Empirical Mode Decomposition by selecting only first intrinsic mode function. Only four features namely Shannon Energy, Log Energy, Median Frequency and Lyapunov Exponent, extracted from segmented EHG record are fed to Support Vector Machine classifier. The system achieves 95.5% accuracy on publicly available Term-Preterm EHG Database. Such an accurate system will help medical professionals to make effective decisions about the treatment. Hence the expectant mothers undergo minimal or no complications of preterm labor. On the other hand, it also helps to avoid unnecessary hospitalization and treatment for women who are having a false labor pain.

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.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Standards of Medical Care in Diabetes

Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Digestive disease week 2000. American Association for the Study of Liver Diseases

Abstract: The latest advances in hepatology were presented in oral and poster presentations. In order to cover the varying subspecialties, the sessions were divided into various sections including 'Acute Liver Failure and Artificial Liver Support', 'Biliary Tract and Immunologic Liver Diseases', 'Cellular and Molecular Biology', 'Clinical and Experimental Hepatobiliary Surgery', 'Hepatotoxicity and Cell Death', 'Transport and Biliary Physiology', 'Viral Hepatitis', 'Evaluation and Treatment of Biliary Disease', 'Necrosis/Apoptosis', 'Portal Hypertension', 'Blood Flow and Vascular Disorders of Cirrhosis', 'Liver Transplantation', 'Fibrogenesis', 'Hepatocellular Carcinoma', 'Metabolism and Genetic Disease', and 'Public Policy, Epidemiology and Decision Analysis'. Drug therapy focused on treatments for viral hepatitis, autoimmune hepatitis, primary biliary cirrhosis and primary sclerosing cholangitis, and recurrent viral disease following liver transplant. High dose interferon therapy or various combinations of interferon/ribavirin (ICN Pharmaceuticals Inc) therapy seem to offer the best current therapy for chronic HCV. PEGylated interferon (F Hoffmann-La Roche Ltd) offers hope for treatment and histologic improvement in patients with chronic HCV. Following liver transplantation, combination interferon/ribavirin therapy may also find success, but caution with new potent immunosuppressant monoclonal antibodies is advised. For HBV, intramuscular H-BIG (NABI) appears to be effective and less costly than iv H-BIG administration following liver transplantation. Percutaneous radiofrequency ablation may hold promise over conventional ethanol injection therapy for small hepatocellular carcinoma. Autoimmune hepatitis may respond to tacrolimus therapy whereas budesonide therapy did not provide any advantage to prednisone therapy. For primary biliary cirrhosis, eicosapentate and ursodeoxycholic acid may provide benefit to some patients while silymarin from milk thistle did not provide any additional benefit. In primary sclerosing cholangitis, high dose ursodeoxycholic acid may provide benefit. Ursodeoxycholic acid may also provide benefit for mothers with intrahepatic cholestasis of pregnancy by decreasing pruritus, lowering laboratory values and allowing deliveries to occur closer to term.

Davidson's Principles and Practice of Medicine

Abstract: I first read Davidson 30 years ago: at that time it was already in its 5th edition. A brief comparison shows that it then contained about 440,000 words in 1,100 pages: the current edition has compressed 625,000 words into 800 pages. I think I bought it as a student because it was cheap: but also because it seemed to be comprehensive and straightforward, and I have used it as a basis for my medical knowledge ever since. So I miss some of the old pictures of the acute skin rashes such as scarlet fever: in fact infectious disease has been transferred to the back and genetic factors take the first chapters. But careful linguistic comparison will still uncover the old phrases which some of us know by heart-there are minor changes such as 'alarming reactions to intravenous iron are uncommon, but have occasionally been noted', which becomes 'alarming systemic anaphyllactic reactions can occur'. Sir Stanley Davidson made 'no attempt to describe every rare disease or syndrome, but devoted most of the space available to those disorders most commonly encountered in practice'. I have grown up with successive editions, and have gradually come to appreciate the problems of the authors in the compression of knowledge. Having got to know many of them personally as real people rather than as names I can still recommend the book. It is the essential starting point for the study of internal medicine and for many doctors will remain their base reference work. The present editors have kept up-to-date and been prepared to prune the dead wood. There are many competitors in the market, and the publishers must take care with layout and illustration, although Davidson is still the best value for money. I will continue to recommend it to my clinical students: they will need to read it and know it to pass final MB. Postgraduates will need to remember the facts, but also to be able to place them in a broader perspective. The older consultant will still happily read it, and to get to know the authors themselves is really to complete your medical education. Dr John Macleod and his team have successfully kept alive the primary objective 'to provide a rational and easily comprehensible basis for the practice of medicine'. The book is economical in price and compact in size, but still contains the essential truths for the practice of good medicine.