Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals.

An accurate detection of preterm labor and the risk of preterm delivery before 37 weeks of gestational age is crucial to increase the chance of survival rate for both mother and the infant. Thus, the uterine contractions measured using uterine electromyogram (EMG) or electro hysterogram (EHG) need to have high sensitivity in the detection of true preterm labor signs. However, visual observation and manual interpretation of EHG signals at the time of emergency situation may lead to errors.

Compact lensless digital holographic microscopy using a curved mirror for an enlarged working distance.

Digital holographic microscopy (DHM) has a wide range of applications from the analysis of microelectronic mechanical systems (MEMS) to the measurement of cells. We intend on making the system more compact to improve the portability of the device. A concave mirror has been presented to be used in a lensless DHM system to effectively enlarge the working distance and at the same time maintain the compact size of the whole system.

Local configuration pattern features for age-related macular degeneration characterization and classification.

Age-related Macular Degeneration (AMD) is an irreversible and chronic medical condition characterized by drusen, Choroidal Neovascularization (CNV) and Geographic Atrophy (GA). AMD is one of the major causes of visual loss among elderly people. It is caused by the degeneration of cells in the macula which is responsible for central vision.

Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images.

Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs.

Decision support system for age-related macular degeneration using discrete wavelet transform.

Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population.

Computer-aided diabetic retinopathy detection using trace transforms on digital fundus images.

Diabetic retinopathy (DR) is a leading cause of vision loss among diabetic patients in developed countries. Early detection of occurrence of DR can greatly help in effective treatment. Unfortunately, symptoms of DR do not show up till an advanced stage.

Computer-aided diagnosis of diabetic retinopathy: a review.

Diabetes mellitus may cause alterations in the retinal microvasculature leading to diabetic retinopathy. Unchecked, advanced diabetic retinopathy may lead to blindness. It can be tedious and time consuming to decipher subtle morphological changes in optic disk, microaneurysms, hemorrhage, blood vessels, macula, and exudates through manual inspection of fundus images.

Application of higher order spectra for accurate delineation of atrial arrhythmia.

The electrocardiogram (ECG) is being commonly used as a diagnostic tool to distinguish different types of atrial tachyarrhythmias. The inherent complexity and mechanistic and clinical inter-relationships often brings about diagnostic difficulties to treating physicians and primary health care professionals creating frequent misdiagnoses and cross classifications using visual criteria. The current paper presents a methodology for ECG based pattern analysis for detection of atrial flutter, atrial fibrillation and normal sinus rhythm beats.

Automated diagnosis of mammogram images of breast cancer using discrete wavelet transform and spherical wavelet transform features: a comparative study.

Mammograms are one of the most widely used techniques for preliminary screening of breast cancers. There is great demand for early detection and diagnosis of breast cancer using mammograms. Texture based feature extraction techniques are widely used for mammographic image analysis.

Application of higher order cumulant features for cardiac health diagnosis using ECG signals.

Electrocardiogram (ECG) is the electrical activity of the heart indicated by P, Q-R-S and T wave. The minute changes in the amplitude and duration of ECG depicts a particular type of cardiac abnormality. It is very difficult to decipher the hidden information present in this nonlinear and nonstationary signal.

Automated diagnosis of epilepsy using CWT, HOS and texture parameters.

Epilepsy is a chronic brain disorder which manifests as recurrent seizures. Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of epileptic seizures. In this work, we propose a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform (CWT), Higher Order Spectra (HOS) and textures.

Automated detection of optic disk in retinal fundus images using intuitionistic fuzzy histon segmentation.

The human eye is one of the most sophisticated organs, with perfectly interrelated retina, pupil, iris cornea, lens, and optic nerve. Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Uncontrolled diabetic retinopathy (DR) and glaucoma may lead to blindness.

Pectoral muscle segmentation: a review.

Mammograms are X-ray images of breasts which are used to detect breast cancer. The pectoral muscle is a mass of tissue on which the breast rests. During routine mammographic screenings, in medio-lateral oblique (MLO) views, the pectoral muscle turns up in the mammograms along with the breast tissues.

Computer-aided breast cancer detection using mammograms: a review.

The American Cancer Society (ACS) recommends women aged 40 and above to have a mammogram every year and calls it a gold standard for breast cancer detection. Early detection of breast cancer can improve survival rates to a great extent. Inter-observer and intra-observer errors occur frequently in analysis of medical images, given the high variability between interpretations of different radiologists.

Compressed sampling for heart rate monitoring.

For the first time compressed sampling (CS) has been applied to heart rate (HR) measurements. The signals can be reconstructed from samples far below the Nyquist rate with negligible small errors, a sampling reduction of 8 has been demonstrated in the paper. As a result, the bitrate of the CS sampler is half when compared to a normal sampler.

Comparison of walking parameters obtained from the young, elderly and adults with support.

Data mining techniques are highly useful in the study of various medical signals and images in order to obtain useful information to better predict the diagnosis or prognosis or treatment options for the patient. Study of the human walking pattern helps us understand the variability of motion during activities such as high performance walking and normal walking. A comparison of the parameters quantifying this variability in motion in normal young and elderly subjects and the subjects who need support will aid in better understanding of the relationship among walking patterns, age and disabilities.

Automated oral cancer identification using histopathological images: a hybrid feature extraction paradigm.

Oral cancer (OC) is the sixth most common cancer in the world. In India it is the most common malignant neoplasm. Histopathological images have widely been used in the differential diagnosis of normal, oral precancerous (oral sub-mucous fibrosis (OSF)) and cancer lesions.

Computer-assisted diagnosis of tuberculosis: a first order statistical approach to chest radiograph.

Textural properties of normal and tuberculosis posterior-anterior chest radiographs were looked into in this investigation. The proposed computerized scheme segmented the lung field of interest using a user-guided snake algorithm and extracted the corresponding pixel data. For both normal and tuberculosis radiographs, the grayscale intensity distribution within the region of interest was analyzed to study their respective characteristics, and fed to classifiers for automated classification.

Analysis and automatic identification of sleep stages using higher order spectra.

Electroencephalogram (EEG) signals are widely used to study the activity of the brain, such as to determine sleep stages. These EEG signals are nonlinear and non-stationary in nature. It is difficult to perform sleep staging by visual interpretation and linear techniques.

Systems engineering principles for the design of biomedical signal processing systems.

Systems engineering aims to produce reliable systems which function according to specification. In this paper we follow a systems engineering approach to design a biomedical signal processing system. We discuss requirements capturing, specification definition, implementation and testing of a classification system.

Application of higher order statistics/spectra in biomedical signals--a review.

For many decades correlation and power spectrum have been primary tools for digital signal processing applications in the biomedical area. The information contained in the power spectrum is essentially that of the autocorrelation sequence; which is sufficient for complete statistical descriptions of Gaussian signals of known means. However, there are practical situations where one needs to look beyond autocorrelation of a signal to extract information regarding deviation from Gaussianity and the presence of phase relations.

EEG signal analysis: a survey.

The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They are highly random in nature and may contain useful information about the brain state. However, it is very difficult to get useful information from these signals directly in the time domain just by observing them.

Automatic identification of epileptic and background EEG signals using frequency domain parameters.

The analysis of electroencephalograms continues to be a problem due to our limited understanding of the signal origin. This limited understanding leads to ill-defined models, which in turn make it hard to design effective evaluation methods. Despite these shortcomings, electroencephalogram analysis is a valuable tool in the evaluation of neurological disorders and the evaluation of overall cerebral activity.

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.

Automated identification of diabetic type 2 subjects with and without neuropathy using wavelet transform on pedobarograph.

Diabetes is a disorder of metabolism-the way our bodies use digested food for growth and energy. The most common form of diabetes is Type 2 diabetes. Abnormal plantar pressures are considered to play a major role in the pathologies of neuropathic ulcers in the diabetic foot.