Linear and nonlinear analysis of normal and CAD-affected heart rate signals

Coronary Artery Disease (CAD) is one of the dangerous cardiac disease, often may lead to sudden cardiac death. It is difficult to diagnose CAD by manual inspection of electrocardiogram (ECG) signals. To automate this detection task, in this study, we extracted the Heart Rate (HR) from the ECG signals and used them as base signal for further analysis. We then analyzed the HR signals of both normal and CAD subjects using (i) time domain, (ii) frequency domain and (iii) nonlinear techniques. The following are the nonlinear methods that were used in this work: Poincare plots, Recurrence Quantification Analysis (RQA) parameters, Shannon entropy, Approximate Entropy (ApEn), Sample Entropy (SampEn), Higher Order Spectra (HOS) methods, Detrended Fluctuation Analysis (DFA), Empirical Mode Decomposition (EMD), Cumulants, and Correlation Dimension. As a result of the analysis, we present unique recurrence, Poincare and HOS plots for normal and CAD subjects. We have also observed significant variations in the range of these features with respect to normal and CAD classes, and have presented the same in this paper. We found that the RQA parameters were higher for CAD subjects indicating more rhythm. Since the activity of CAD subjects is less, similar signal patterns repeat more frequently compared to the normal subjects. The entropy based parameters, ApEn and SampEn, are lower for CAD subjects indicating lower entropy (less activity due to impairment) for CAD. Almost all HOS parameters showed higher values for the CAD group, indicating the presence of higher frequency content in the CAD signals. Thus, our study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD

RR Interval-based atrial fibrillation detection using traditional and ensemble machine learning algorithms

Atrial fibrillation (AF) is a life threatening disease and can cause stroke, heart failure, and sometimes death. To reduce the rate of mortality and morbidity due to increased prevalence of AF, early detection of the same becomes a prior concern. Traditional machine learning (TML) algorithms and ensemble machine learning (EML) algorithms are proposed to detect AF in this article. The performances of both these methods are compared in this study. Methodology involves computation of RR interval features extracted from electrocardiogram and its classification into: normal, AF, and other rhythms. TML techniques such as Classification and Regression Tree, K Nearest Neighbor, C4.5, Iterative Dichotomiser 3, Support Vector Machine and EML classifier such as Random Forest (RF), and Rotation Forest are used for classification. The proposed method is evaluated using PhysioNet challenge 2017. During the tenfold cross validation, it is observed that RF classifier provided good classification accuracy of 99.10% with area under the curve of 0.998. Apart from contributing a new methodology, the proposed study also experimentally proves higher performance with ensemble learning method, RF. The methodology has many applications in health care management systems including defibrillators, cardiac pacemakers, etc

A Novel Fusion Approach For Early Lung Cancer Detection Using Computer Aided Diagnosis Techniques

Computer Aided Diagnosis (CAD) plays an effective and important role in radiology. It provides second opinion to the radiologists during patient image assessment. In this work, early detection of lung cancer is chosen for the study. Computed tomography is one of the normally preferred modality to record the interior body parts, particularly lungs. Recent advances in radiology also supports to record the two dimensional (2D) and three dimensional (3D) images of lungs which is associated with the abnormality, such as lesion/tumor. The main clinical challenge is to develop a suitable CAD system and Content Supported Medical Image Retrieval (CSMIR) system to extract and analyze the lesion/tumor from 2D and 3D radiology images. Hence, it is essential to develop an automated system with the following capability: detection, categorization and quantification of the lung lesion/tumor. In the proposed work, lung abnormality is segmented using the wavelet approach and the segmented Region of Interest (ROI) is then classified using a novel classifier unit. The experimental result confirms that, proposed approach offers enhanced accuracy and specificity compared with the other methods considered in this study

A Novel Fusion Approach for Early Lung Cancer Detection Using Computer Aided Diagnosis Techniques

Computer Aided Diagnosis (CAD) plays an effective and important role in radiology. It provides second opinion to the radiologists during patient image assessment. In this work, early detection of lung cancer is chosen for the study. Computed tomography is one of the normally preferred modality to record the interior body parts, particularly lungs. Recent advances in radiology also supports to record the two dimensional (2D) and three dimensional (3D) images of lungs which is associated with the abnormality, such as lesion/tumor. The main clinical challenge is to develop a suitable CAD system and Content Supported Medical Image Retrieval (CSMIR) system to extract and analyze the lesion/tumor from 2D and 3D radiology images. Hence, it is essential to develop an automated system with the following capability: detection, categorization and quantification of the lung lesion/tumor. In the proposed work, lung abnormality is segmented using the wavelet approach and the segmented Region of Interest (ROI) is then classified using a novel classifier unit. The experimental result confirms that, proposed approach offers enhanced accuracy and specificity compared with the other methods considered in this study

Recent Advances In Big Data Analytics, Internet Of Things And Machine Learning

Big data analytics, Internet of Things, and machine learning are some of the rising areas of science and technology forming the next generation of artificial intelligence-based computing systems. It is also important to note that this aforementioned emerging field is diverse and in some strange ways both transformative and transdisciplinary in nature. This transformative and transdisciplinary nature of this field enables it to grow both in terms of its theoretical foundations and applications. In this special issue we focus on some advanced research projects in these areas that are transformative and transdisciplinary in nature. The projects and experiments discussed in this special issue constitute the advancement in synthesis of decision support systems that aid further advancement of healthcare delivery, diagnosing diseases, and analysis of behavioral science

ECG signal generation and heart rate variability signal extraction : signal processing, features detection, and their correlation with cardiac diseases

The sum total of millions of cardiac cell depolarization potentials can be represented by an electrocardiogram (ECG). Inspection of the P–QRS–T wave allows for the identification of the cardiac bioelectrical health and disorders of a subject. In order to extract the important features of the ECG signal, the detection of the P wave, QRS complex, and ST segment is essential. Therefore, abnormalities of these ECG parameters are associated with cardiac disorders. In this work, an introduction to the genesis of the ECG is given, followed by a depiction of some abnormal ECG patterns and rhythms (associated with P–QRS–T wave parameters), which have come to be empirically correlated with cardiac disorders (such as sinus bradycardia, premature ventricular contraction, bundle-branch block, atrial flutter, and atrial fibrillation). We employed algorithms for ECG pattern analysis, for the accurate detection of the P wave, QRS complex, and ST segment of the ECG signal. We then catagorited and tabulated these cardiac disorders in terms of heart rate, PR interval, QRS width, and P wave amplitude. Finally, we discussed the characteristics and different methods (and their measures) of analyting the heart rate variability (HRV) signal, derived from the ECG waveform. The HRV signals are characterised in terms of these measures, then fed into classifiers for grouping into categories (for normal subjects and for disorders such as cardiac disorders and diabetes) for carrying out diagnosis

Empirical mode decomposition analysis of near-infrared spectroscopy muscular signals to assess the effect of physical activity in type 2 diabetic patients

Type 2 diabetes is a metabolic disorder that may cause major problems to several physiological systems. Exercise has proven to be very effective in the prevention, management and improvement of this pathology in patients. Muscle metabolism is often studied with near-infrared spectroscopy (NIRS), a noninvasive technique that can measure changes in the concentration of oxygenated (O2Hb) and reduced hemoglobin (HHb) of tissues. These NIRS signals are highly non-stationary, non-Gaussian and nonlinear in nature. The empirical mode decomposition (EMD) is used as a nonlinear adaptive model to extract information present in the NIRS signals. NIRS signals acquired from the tibialis anterior muscle of controls and type 2 diabetic patients are processed by EMD to yield three intrinsic mode functions (IMF). The sample entropy (SE), fractal dimension (FD), and Hurst exponent (HE) are computed from these IMFs. Subjects are monitored at the beginning of the study and after one year of a physical training programme. Following the exercise programme, we observed an increase in the SE and FD and a decrease in the HE in all diabetic subjects. Our results show the influence of physical exercise program in improving muscle performance and muscle drive by the central nervous system in the patients. A multivariate analysis of variance performed at the end of the training programme also indicated that the NIRS metabolic patterns of controls and diabetic subjects are more similar than at the beginning of the study. Hence, the proposed EMD technique applied to NIRS signals may be very useful to gain a non-invasive understanding of the neuromuscular and vascular impairment in diabetic subjects

Application of intrinsic time-scale decomposition (ITD) to EEG signals for automated seizure prediction

Intrinsic time-scale decomposition (ITD) is a new nonlinear method of time-frequency representation which can decipher the minute changes in the nonlinear EEG signals. In this work, we have automatically classified normal, interictal and ictal EEG signals using the features derived from the ITD representation. The energy, fractal dimension and sample entropy features computed on ITD representation coupled with decision tree classifier has yielded an average classification accuracy of 95.67%, sensitivity and specificity of 99% and 99.5%, respectively using 10-fold cross validation scheme. With application of the nonlinear ITD representation, along with conceptual advancement and improvement of the accuracy, the developed system is clinically ready for mass screening in resource constrained and emerging economy scenarios