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

Khan, Muhammad Umar; Aziz, Sumair; Iqtidar, Khushbakht; Zaher, Galila; AlGhamdi, Shareefa A.; Gull, Munazza

doi:10.1007/s11042-021-10805-3

Cited by 30 publications

(8 citation statements)

References 52 publications

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“…We selected the KNN 49 classifier due to its better performance and low computational complexity. KNN has been widely employed to perform PCG signal classification 50 . Algorithm 3 illustrates all steps involved in computing nearest neighbors.…”

Section: Proposed Methodologymentioning

confidence: 99%

A novel embedded system design for the detection and classification of cardiac disorders

Riaz

Aziz

Khan

et al. 2021

Computational Intelligence

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Phonocardiogram (PCG) signals hold significant prognostic and diagnostic information about cardiac health. Numerous PCG or heart sound based automated detection algorithms were previously proposed to assist the disease diagnosis process. Most of the previous studies only focused on algorithmic development. This study presents an intelligent, portable, and low‐cost embedded system for the classification of cardiac disorders associated with heart murmurs. Different stages corresponding to the developed embedded system implementation are summarized as follows: The first stage consists of the acquisition of PCG signals of both normal and patients from various hospitals with a customized and low‐cost stethoscope. The second stage describes the preprocessing, localization of S1 and S2 heart sounds, and the extraction of systole and diastole from a heart signal with an empirical mode decomposition integrated with the self‐developed algorithm. In the third stage, discriminant features are extracted to represent various cardiac classes of PCG signals in a compact manner. In the final stage of the algorithm, the k‐nearest neighbors classifier is trained and tested to distinguish between normal and four cardiac disorders. The proposed algorithm achieved 94% mean accuracy through comprehensive experimentation. The cardiac disorders classification algorithm is implemented on a RP‐based embedded system. Software application with an interactive graphical interface is also designed to assist users. The developed intelligent system is portable, low‐cost, and it enables regular patient‐monitoring. The proposed system has the potential to be employed at remote locations where the availability of doctors remains challenging.

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Section: Proposed Methodologymentioning

confidence: 99%

A novel embedded system design for the detection and classification of cardiac disorders

Riaz

Aziz

Khan

et al. 2021

Computational Intelligence

Self Cite

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“…The study was performed within 5 s of each sample on a limited number of healthy people and patients suffering from this disease. 18 In his most recent research, Khan et al 19 studied different CAD samples alongside healthy samples in the same 5 s time period. The investigated sound signals were also collected with the aid of a modified stethoscope.…”

Section: Basic Concepts and Literature Reviewmentioning

confidence: 99%

“…Thus, Empirical Mode Decomposition (EMD) was used to remove noise and extract the desired area from the raw PCG signal. 19…”

Section: Basic Concepts and Literature Reviewmentioning

confidence: 99%

“…Thus, Empirical Mode Decomposition (EMD) was used to remove noise and extract the desired area from the raw PCG signal. 19 In the present study, by using appropriate validation criteria and processing the input sound data in the shortest possible time, an attempt has been made to examine all of the input data and avoid selective investigation of them. Examination of data in all frequency ranges of the presence of heart sounds (10-800 Hz) has been done for the mentioned purpose.…”

Section: Basic Concepts and Literature Reviewmentioning

confidence: 99%

“…In some cases, the content of the data has been changed, 17 or investigations have been conducted on a small number of people under the same sampling conditions. 18,19 Such issues and the use of various noise removal filters have caused previous methods to remain dependent on a specific dataset. Thus, by changing the dataset, the need for a new filtering process was felt.…”

Section: Introductionmentioning

confidence: 99%

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Effective features extraction by analyzing heart sound for identifying cardiovascular diseases related to COVID-19: A diagnostic model

Sabouri

Ghadimi

Kiani-Sarkaleh

et al. 2022

Proc Inst Mech Eng H

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Incidence and exacerbation of some of the cardiovascular diseases in the presence of the coronavirus will lead to an increase in the mortality rate among patients. Therefore, early diagnosis of such diseases is critical, especially during the COVID-19 pandemic (mild COVID-19 infection). Thus, for diagnosing the heart diseases related to the COVID-19, an automatic, non-invasive, and inexpensive method based on the heart sound processing approach is proposed. In the present study, a set of features related to the nature of heart signals is defined and extracted. The investigated features included morphological and statistical features in the heart sound frequencies. By extracting and selecting a set of effective features related to the mentioned diseases, and avoiding to use different segmentation and filtering techniques, dependence on a limited dataset and specific sampling procedures has been eliminated. Different classifiers with various kernels are applied for diagnosis in data unbalanced and balanced conditions. The results showed 93.15% accuracy and 93.72% F1-score using 60 effective features in data balanced conditions. The identification system using the extracted features from Azad dataset is able to achieve the desired results in a generalized dataset. In this way, in the shortest possible sampling time, the present system provided an effective and generalizable method and a practical model for diagnosing important cardiovascular diseases in the presence of coronavirus in the COVID-19 pandemic.

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RideNN‐OptDRN: Heart disease detection using RideNN based feature fusion and optimized deep residual network

Merugula

Padmaja

Veerubommu³

2022

Concurrency and Computation

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Heart disease detection through early-stage syndrome remains as a main confront in present world situation. If it is not detected appropriate time, then this turns out to be the major cause of death. Several existing heart disease detection techniques are developed with lower detection performance and therefore it is very significant to introduce a novel heart disease detection model that poses the potential to detect heart disease from input data. A novel detection approach named, social water cycle algorithm-based deep residual network (SWCA-based DRN) is proposed for classification of heart disease. The developed SWCA algorithm is a newly designed by the hybridization of social optimization algorithm and water cycle algorithm. Here, an input data is initially preprocessed and the feature fusion procedure is carried out RV coefficient enabled rider optimization algorithm-based neural network. With the fused feature result, heart disease classification is performed utilizing a DRN classifier where training procedure of DRN is done by proposed optimization algorithm, named SWCA. Furthermore, developed SWCA-enabled DRN technique outperformed different other present heart disease detection approaches and attained superior performance concerning the performance measures, like testing accuracy, sensitivity, and specificity with highest values of 0.941, 0.954, and 0.925. K E Y W O R D S deep residual network, heart disease, rider optimization algorithm, social optimization algorithm, water cycle algorithm INTRODUCTIONA heart is a fundamental organ which expels blood, with life-giving oxygen and supplements to various body tissues. If pumping action of heart becomes ineffective, essential organs such as kidneys as well as brain suffering, and if the heart functioning ceases completely, demise happens in a minute. Heart ailment has been regarded as one of fatal ailments across the world. Life is entirely reliant itself on an effective functioning of heart.The various indications of heart disease encompass weakness of the physical body, fatigue, swollen feet, and shortness of breath. 1,2 In addition, one of the most human life-threatening diseases around a world is heart ailments, due to variations in lifestyle and lack of physical exercise, and obesity is more general among human lives. According to World Health Organization (WHO) data, human deaths are almost 31% of overall and out of which 85% is due to stroke and heart failure. About 17 million people are losing their life due to heart disease every year. 3 Moreover, heart disease is caused based on the threat factors in lifestyle behavior, like sex, age, cholesterol, family history, obesity or inhaling cigarette smoke, improper diet, blood sugar levels, alcohol, high blood pressure, eating foods that are high in fat, and body weight. Certain threat factors are submissive. Furthermore, heart ailment can be partitioned in seven different kinds, namely, arrhythmia, cardiomyopathy, angina pectoris, coronary heart infection, congenital

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

Cited by 30 publications

References 52 publications

A novel embedded system design for the detection and classification of cardiac disorders

A novel embedded system design for the detection and classification of cardiac disorders

Effective features extraction by analyzing heart sound for identifying cardiovascular diseases related to COVID-19: A diagnostic model

RideNN‐OptDRN: Heart disease detection using RideNN based feature fusion and optimized deep residual network

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