Rajendra Acharya

Symptomatic vs. Asymptomatic Plaque Classification in Carotid Ultrasound

Quantitative characterization of carotid atherosclerosis and classification into symptomatic or asymptomatic type is crucial in both diagnosis and treatment planning. This paper describes a computer-aided diagnosis (CAD) system which analyzes ultrasound images and classifies them into symptomatic and asymptomatic based on the textural features. The proposed CAD system consists of three modules. The first module is preprocessing, which conditions the images for the subsequent feature extraction. The feature extraction stage uses image texture analysis to calculate Standard deviation, Entropy, Symmetry, and Run Percentage. Finally, classification is performed using AdaBoost and Support Vector Machine for automated decision making. For Adaboost, we compared the performance of five distinct configurations (Least Squares, Maximum- Likelihood, Normal Density Discriminant Function, Pocket, and Stumps) of this algorithm. For Support Vector Machine, we compared the performance using five different configurations (linear kernel, polynomial kernel configurations of different orders and radial basis function kernels). SVM with radial basis function kernel for support vector machine presented the best classification result: classification accuracy of 82.4%, sensitivity of 82.9%, and specificity of 82.1%. We feel that texture features coupled with the Support Vector Machine classifier can be used to identify the plaque tissue type. An Integrated Index, called symptomatic asymptomatic carotid index (SACI), is proposed using texture features to discriminate symptomatic and asymptomatic carotid ultrasound images using just one index or number. We hope this SACI can be used as an adjunct tool by the vascular surgeons for daily screening.

Heart rate variability analysis using correlation dimension and detrended fluctuation analysis

Abstract The ECG is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, etc., may contain useful information about the nature of disease afflicting the heart. However, the human observer can not directly monitor these subtle details. Besides, since biosignals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the heart rate variability signal parameters, extracted and analysed using computers, are highly useful in diagnostics. This paper deals with the classification of certain diseases using correlation dimension (CD) and detrended fluctuation analysis (DFA).

Breast imaging: A survey.

Breast cancer is the second leading cause of death in women. It occurs when cells in the breast start to grow out of proportion and invade neighboring tissues or spread throughout the body. Mammography is one of the most effective and popular modalities presently used for breast cancer screening and detection. Efforts have been made to improve the accuracy of breast cancer diagnosis using different imaging modalities. Ultrasound and magnetic resonance imaging have been used to detect breast cancers in high risk patients. Recently, electrical impedance imaging and nuclear medicine techniques are also being widely used for breast cancer screening and diagnosis. In this paper, we discuss the capabilities of various breast imaging modalities.

Automatic identification of epilepsy by HOS and power spectrum parameters using EEG signals: A comparative study

Epilepsy is characterized by the spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into these phenomena. The use of non-linear features motivated by the higher order spectra (HOS) had been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, the features are extracted from the power spectrum and the bispectrum. Their performance is studied by feeding them to a Gaussian mixture model (GMM) classifier. Results show that with selected HOS based features, we were able to achieve 93.11% compared to classification accuracy of 88.78% as that of features derived from PSD.

Cardiac health diagnosis using higher order spectra and support vector machine.

The Electrocardiogram (ECG) is an important bio-signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. The HRV signal can be used as a base signal to observe the hearts functioning. These signals are non-linear and non-stationary in nature. So, higher order spectral (HOS) analysis, which is more suitable for non-linear systems and is robust to noise, was used. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, we have extracted seven features from the heart rate signals using HOS and fed them to a support vector machine (SVM) for classification. Our performance evaluation protocol uses 330 subjects consisting of five different kinds of cardiac disease conditions. We demonstrate a sensitivity of 90% for the classifier with a specificity of 87.93%. Our system is ready to run on larger data sets.

Classification of Epilepsy Using High-Order Spectra Features and Principle Component Analysis

The classification of epileptic electroencephalogram (EEG) signals is challenging because of high nonlinearity, high dimensionality, and hidden states in EEG recordings. The detection of the preictal state is difficult due to its similarity to the ictal state. We present a framework for using principal components analysis (PCA) and a classification method for improving the detection rate of epileptic classes. To unearth the nonlinearity and high dimensionality in epileptic signals, we extract principal component features using PCA on the 15 high-order spectra (HOS) features extracted from the EEG data. We evaluate eight classifiers in the framework using true positive (TP) rate and area under curve (AUC) of receiver operating characteristics (ROC). We show that a simple logistic regression model achieves the highest TP rate for class “preictal” at 97.5% and the TP rate on average at 96.8% with PCA variance percentages selected at 100%, which also achieves the most AUC at 99.5%.

Identification of cataract and post-cataract surgery optical images using artificial intelligence techniques.

Human eyes are most sophisticated organ, with perfect and interrelated subsystems such as retina, pupil, iris, cornea, lens and optic nerve. The eye disorder such as cataract is a major health problem in the old age. Cataract is formed by clouding of lens, which is painless and developed slowly over a long period. Cataract will slowly diminish the vision leading to the blindness. At an average age of 65, it is most common and one third of the people of this age in world have cataract in one or both the eyes. A system for detection of the cataract and to test for the efficacy of the post-cataract surgery using optical images is proposed using artificial intelligence techniques. Images processing and Fuzzy K-means clustering algorithm is applied on the raw optical images to detect the features specific to three classes to be classified. Then the backpropagation algorithm (BPA) was used for the classification. In this work, we have used 140 optical image belonging to the three classes. The ANN classifier showed an average rate of 93.3% in detecting normal, cataract and post cataract optical images. The system proposed exhibited 98% sensitivity and 100% specificity, which indicates that the results are clinically significant. This system can also be used to test the efficacy of the cataract operation by testing the post-cataract surgery optical images.

Clinical Trial of Thermal Pulsation (LipiFlow) in Meibomian Gland Dysfunction With Preteatment Meibography.

Objectives: Thermal pulsation (LipiFlow) has been advocated for meibomian gland dysfunction (MGD) treatment and was found useful. We aimed to evaluate the efficacy and safety of thermal pulsation in Asian patients with different grades of meibomian gland loss. Methods: A hospital-based interventional study comparing thermal pulsation to warm compresses for MGD treatment. Fifty patients were recruited from the dry eye clinic of a Singapore tertiary eye hospital. The ocular surface and symptom were evaluated before treatment, and one and three months after treatment. Twenty-five patients underwent thermal pulsation (single session), whereas 25 patients underwent warm compresses (twice daily) for 3 months. Meibomian gland loss was graded using infrared meibography, whereas function was graded using the number of glands with liquid secretion. Results: The mean age (SD) of participants was 56.4 (11.4) years in the warm compress group and 55.6 (12.7) years in the thermal pulsation group. Seventy-six percent of the participants were female. Irritation symptom significantly improved over 3 months in both groups (P<0.01), whereas tear breakup time (TBUT) was modestly improved at 1 month in only the thermal pulsation group (P=0.048), without significant difference between both groups over the 3 months (P=0.88). There was also no significant difference in irritation symptom, TBUT, Schirmer test, and gland secretion variables between patients with different grades of gland loss or function at follow-ups. Conclusions: A single session of thermal pulsation was similar in its efficacy and safety profile to 3 months of twice daily warm compresses in Asians. Treatment efficacy was not affected by pretreatment gland loss.

Comparing normal walking and compensated walking: their stability and perturbation resistance. A simulation study.

Abstract People usually develop different kinds of compensated gait in response to local function deficits, such as muscle weakness, spasticity in specific muscle groups, or joint stiffness, in order to overcome the falling risk factors. Compensated walking has been analysed empirically in the impaired gait analysis area. However, the compensation could be identified spatially and temporally. The stability and perturbation resistance of compensated walking have not been analysed quantitatively. In this research, a biomimetic human walking simulator was employed to model one individual paraplegic subject with plantarflexor spasticity. The pes equinus was expressed by biasing the outputs of plantarflexor neurons corresponding to the spastic muscles. Then, the compensatory mechanism was explored by adjusting the outputs of the other muscles. It was shown that this approach can be used for quantitative analysis of the spastic gait and compensated walking. Thus, this research can improve the understanding of the behaviour of compensated walking, bringing insights not only for building useful walking assist systems with high safety but also for designing effective rehabilitation interventions.

Acupuncture and herbal formulation compared with artificial tears alone: evaluation of dry eye symptoms and associated tests in randomised clinical trial

Objective Dry eye is a common disease with great health burden and no satisfactory treatment. Traditional Chinese medicine, an increasingly popular form of complementary medicine, has been used to treat dry eye but studies have been inconclusive. To address this issue, we conducted a randomised investigator-masked study which included the robust assessment of disease mechanisms. Methods and analysis Eligible participants (total 150) were treated with artificial tear (AT) alone, with added eight sessions of acupuncture (AC) or additional daily oral herb (HB) over a month. Results Participants treated with AC were more likely to respond symptomatically than those on AT (88% vs 72%, p=0.039) with a difference of 16% (95% CI: 0.18 to 31.1). The number-to-treat with AC to achieve response in one person was 7 (3 to 157). Participants in the AC group also had reduced conjunctival redness (automatic grading with Oculus keratograph) compared with AT (p=0.043) and reduced tear T helper cell (Th1)-cytokine tumour necrosis factor α (p=0.027) and Th2-cytokine interleukin 4 concentrations (p=0.038). AC was not significantly superior to AT in other outcomes such as tear osmolarity, tear evaporation rates, corneal staining and tear break-up times. No significant adverse effects were encountered. HB was not significantly different in the primary outcome from AT (80% vs 72%, p=0.26). Conclusions AC is safe and provides additional benefit in mild to moderate dry eye up to 1 month, compared with ATs alone. Treatment is associated with demonstrable molecular evidence of reduced inflammation. Provided that suitably qualified practitioners are available to implement standardised treatment, AC may be recommended as adjunctive therapy to AT. Trial registration number ClinicalTrials.gov (NCT02219204)registered on 14 August 2014.