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Automated identification of normal and diabetes heart rate signals using nonlinear measures

Rajendra Acharya U, Faust O, Adib Kadri N, Suri JS, Yu W

Comput Biol Med, 2013 Oct;43(10):1523-9.
PMID: 24034744 DOI: 10.1016/j.compbiomed.2013.05.024

Diabetes mellitus (DM) affects considerable number of people in the world and the number of cases is increasing every year. Due to a strong link to the genetic basis of the disease, it is extremely difficult to cure. However, it can be controlled to prevent severe consequences, such as organ damage. Therefore, diabetes diagnosis and monitoring of its treatment is very important. In this paper, we have proposed a non-invasive diagnosis support system for DM. The system determines whether or not diabetes is present by determining the cardiac health of a patient using heart rate variability (HRV) analysis. This analysis was based on nine nonlinear features namely: Approximate Entropy (ApEn), largest Lyapunov exponet (LLE), detrended fluctuation analysis (DFA) and recurrence quantification analysis (RQA). Clinically significant measures were used as input to classification algorithms, namely AdaBoost, decision tree (DT), fuzzy Sugeno classifier (FSC), k-nearest neighbor algorithm (k-NN), probabilistic neural network (PNN) and support vector machine (SVM). Ten-fold stratified cross-validation was used to select the best classifier. AdaBoost, with least squares (LS) as weak learner, performed better than the other classifiers, yielding an average accuracy of 90%, sensitivity of 92.5% and specificity of 88.7%.
An automated technique for carotid far wall classification using grayscale features and wall thickness variability

Acharya UR, Sree SV, Molinari F, Saba L, Nicolaides A, Suri JS

J Clin Ultrasound, 2015 Jun;43(5):302-11.
PMID: 24909942 DOI: 10.1002/jcu.22183

To test a computer-aided diagnostic method for differentiating symptomatic from asymptomatic carotid B-mode ultrasonographic images.
Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images

Acharya UR, Sree SV, Muthu Rama Krishnan M, Krishnananda N, Ranjan S, Umesh P, et al.

Comput Methods Programs Biomed, 2013 Dec;112(3):624-32.
PMID: 23958645 DOI: 10.1016/j.cmpb.2013.07.012

Coronary Artery Disease (CAD), caused by the buildup of plaque on the inside of the coronary arteries, has a high mortality rate. To efficiently detect this condition from echocardiography images, with lesser inter-observer variability and visual interpretation errors, computer based data mining techniques may be exploited. We have developed and presented one such technique in this paper for the classification of normal and CAD affected cases. A multitude of grayscale features (fractal dimension, entropies based on the higher order spectra, features based on image texture and local binary patterns, and wavelet based features) were extracted from echocardiography images belonging to a huge database of 400 normal cases and 400 CAD patients. Only the features that had good discriminating capability were selected using t-test. Several combinations of the resultant significant features were used to evaluate many supervised classifiers to find the combination that presents a good accuracy. We observed that the Gaussian Mixture Model (GMM) classifier trained with a feature subset made up of nine significant features presented the highest accuracy, sensitivity, specificity, and positive predictive value of 100%. We have also developed a novel, highly discriminative HeartIndex, which is a single number that is calculated from the combination of the features, in order to objectively classify the images from either of the two classes. Such an index allows for an easier implementation of the technique for automated CAD detection in the computers in hospitals and clinics.
Linear and nonlinear analysis of normal and CAD-affected heart rate signals

Acharya UR, Faust O, Sree V, Swapna G, Martis RJ, Kadri NA, et al.

Comput Methods Programs Biomed, 2014;113(1):55-68.
PMID: 24119391 DOI: 10.1016/j.cmpb.2013.08.017

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.
Ovarian tissue characterization in ultrasound: a review

Acharya UR, Molinari F, Sree SV, Swapna G, Saba L, Guerriero S, et al.

Technol Cancer Res Treat, 2015 Jun;14(3):251-61.
PMID: 25230716 DOI: 10.1177/1533034614547445

Ovarian cancer is the most common cause of death among gynecological malignancies. We discuss different types of clinical and nonclinical features that are used to study and analyze the differences between benign and malignant ovarian tumors. Computer-aided diagnostic (CAD) systems of high accuracy are being developed as an initial test for ovarian tumor classification instead of biopsy, which is the current gold standard diagnostic test. We also discuss different aspects of developing a reliable CAD system for the automated classification of ovarian cancer into benign and malignant types. A brief description of the commonly used classifiers in ultrasound-based CAD systems is also given.
Automatic lung segmentation using control feedback system: morphology and texture paradigm

Noor NM, Than JC, Rijal OM, Kassim RM, Yunus A, Zeki AA, et al.

J Med Syst, 2015 Mar;39(3):22.
PMID: 25666926 DOI: 10.1007/s10916-015-0214-6

Interstitial Lung Disease (ILD) encompasses a wide array of diseases that share some common radiologic characteristics. When diagnosing such diseases, radiologists can be affected by heavy workload and fatigue thus decreasing diagnostic accuracy. Automatic segmentation is the first step in implementing a Computer Aided Diagnosis (CAD) that will help radiologists to improve diagnostic accuracy thereby reducing manual interpretation. Automatic segmentation proposed uses an initial thresholding and morphology based segmentation coupled with feedback that detects large deviations with a corrective segmentation. This feedback is analogous to a control system which allows detection of abnormal or severe lung disease and provides a feedback to an online segmentation improving the overall performance of the system. This feedback system encompasses a texture paradigm. In this study we studied 48 males and 48 female patients consisting of 15 normal and 81 abnormal patients. A senior radiologist chose the five levels needed for ILD diagnosis. The results of segmentation were displayed by showing the comparison of the automated and ground truth boundaries (courtesy of ImgTracer™ 1.0, AtheroPoint™ LLC, Roseville, CA, USA). The left lung's performance of segmentation was 96.52% for Jaccard Index and 98.21% for Dice Similarity, 0.61 mm for Polyline Distance Metric (PDM), -1.15% for Relative Area Error and 4.09% Area Overlap Error. The right lung's performance of segmentation was 97.24% for Jaccard Index, 98.58% for Dice Similarity, 0.61 mm for PDM, -0.03% for Relative Area Error and 3.53% for Area Overlap Error. The segmentation overall has an overall similarity of 98.4%. The segmentation proposed is an accurate and fully automated system.
Inter-observer Variability Analysis of Automatic Lung Delineation in Normal and Disease Patients

Saba L, Than JC, Noor NM, Rijal OM, Kassim RM, Yunus A, et al.

J Med Syst, 2016 Jun;40(6):142.
PMID: 27114353 DOI: 10.1007/s10916-016-0504-7

Human interaction has become almost mandatory for an automated medical system wishing to be accepted by clinical regulatory agencies such as Food and Drug Administration. Since this interaction causes variability in the gathered data, the inter-observer and intra-observer variability must be analyzed in order to validate the accuracy of the system. This study focuses on the variability from different observers that interact with an automated lung delineation system that relies on human interaction in the form of delineation of the lung borders. The database consists of High Resolution Computed Tomography (HRCT): 15 normal and 81 diseased patients' images taken retrospectively at five levels per patient. Three observers manually delineated the lungs borders independently and using software called ImgTracer™ (AtheroPoint™, Roseville, CA, USA) to delineate the lung boundaries in all five levels of 3-D lung volume. The three observers consisted of Observer-1: lesser experienced novice tracer who is a resident in radiology under the guidance of radiologist, whereas Observer-2 and Observer-3 are lung image scientists trained by lung radiologist and biomedical imaging scientist and experts. The inter-observer variability can be shown by comparing each observer's tracings to the automated delineation and also by comparing each manual tracing of the observers with one another. The normality of the tracings was tested using D'Agostino-Pearson test and all observers tracings showed a normal P-value higher than 0.05. The analysis of variance (ANOVA) test between three observers and automated showed a P-value higher than 0.89 and 0.81 for the right lung (RL) and left lung (LL), respectively. The performance of the automated system was evaluated using Dice Similarity Coefficient (DSC), Jaccard Index (JI) and Hausdorff (HD) Distance measures. Although, Observer-1 has lesser experience compared to Obsever-2 and Obsever-3, the Observer Deterioration Factor (ODF) shows that Observer-1 has less than 10% difference compared to the other two, which is under acceptable range as per our analysis. To compare between observers, this study used regression plots, Bland-Altman plots, two tailed T-test, Mann-Whiney, Chi-Squared tests which showed the following P-values for RL and LL: (i) Observer-1 and Observer-3 were: 0.55, 0.48, 0.29 for RL and 0.55, 0.59, 0.29 for LL; (ii) Observer-1 and Observer-2 were: 0.57, 0.50, 0.29 for RL and 0.54, 0.59, 0.29 for LL; (iii) Observer-2 and Observer-3 were: 0.98, 0.99, 0.29 for RL and 0.99, 0.99, 0.29 for LL. Further, CC and R-squared coefficients were computed between observers which came out to be 0.9 for RL and LL. All three observers however manage to show the feature that diseased lungs are smaller than normal lungs in terms of area.
Lung disease stratification using amalgamation of Riesz and Gabor transforms in machine learning framework

Than JCM, Saba L, Noor NM, Rijal OM, Kassim RM, Yunus A, et al.

Comput Biol Med, 2017 10 01;89:197-211.
PMID: 28825994 DOI: 10.1016/j.compbiomed.2017.08.014

Lung disease risk stratification is important for both diagnosis and treatment planning, particularly in biopsies and radiation therapy. Manual lung disease risk stratification is challenging because of: (a) large lung data sizes, (b) inter- and intra-observer variability of the lung delineation and (c) lack of feature amalgamation during machine learning paradigm. This paper presents a two stage CADx cascaded system consisting of: (a) semi-automated lung delineation subsystem (LDS) for lung region extraction in CT slices followed by (b) morphology-based lung tissue characterization, thereby addressing the above shortcomings. LDS primarily uses entropy-based region extraction while ML-based lung characterization is mainly based on an amalgamation of directional transforms such as Riesz and Gabor along with texture-based features comprising of 100 greyscale features using the K-fold cross-validation protocol (K = 2, 3, 5 and 10). The lung database consisted of 96 patients: 15 normal and 81 diseased. We use five high resolution Computed Tomography (HRCT) levels representing different anatomy landmarks where disease is commonly seen. We demonstrate the amalgamated ML stratification accuracy of 99.53%, an increase of 2% against the conventional non-amalgamation ML system that uses alone Riesz-based feature embedded with feature selection based on feature strength. The robustness of the system was determined based on the reliability and stability that showed a reliability index of 0.99 and the deviation in risk stratification accuracies less than 5%. Our CADx system shows 10% better performance when compared against the mean of five other prominent studies available in the current literature covering over one decade.
Extracranial internal carotid artery calcium volume measurement using computer tomography

Baradaran H, Ng CR, Gupta A, Noor NM, Al-Dasuqi KW, Mtui EE, et al.

Int Angiol, 2017 Oct;36(5):445-461.
PMID: 28541017 DOI: 10.23736/S0392-9590.17.03811-1

BACKGROUND: The extent of calcium volume in the carotid arteries of contrast-based computer tomography (CT) is a valuable indicator of stroke risk. This study presents an automated, simple and fast calcium volume computation system. Since the high contrast agent can sometimes obscure the presence of calcium in the CT slices, it is therefore necessary to identify these slices before the corrected volume can be estimated.
METHODS: The system typically consists of segmenting the calcium region from the CT scan into slices based on Hounsfield Unit-based threshold, and subsequently computing the summation of the calcium areas in each slice. However, when the carotid volume has intermittently higher concentration of contrast agent, a dependable approach is adapted to correct the calcium region using the neighboring slices, thereby estimating the correct volume. Furthermore, mitigation is provided following the regulatory constraints by changing the system to semi-automated criteria as a fall back solution. We evaluate the automated and semi-automated techniques using completely manual calcium volumes computed based on the manual tracings by the neuroradiologist.
RESULTS: A total of 64 patients with calcified plaque in the internal carotid artery were analyzed. Using the above algorithm, our automated and semi-automated system yields correlation coefficients (CC) of 0.89 and 0.96 against first manual readings and 0.90 and 0.96 against second manual readings, respectively. Using the t-test, there was no significant difference between the automated and semi-automated methods against manual. The intra-observer reliability was excellent with CC 0.98.
CONCLUSIONS: Compared to automated method, the semi-automated method for calcium volume is acceptable and closer to manual strategy for calcium volume. Further work evaluating and confirming the performance of our semi-automated protocol is now warranted.