COMPUTER AIDED DIAGNOSIS OF VENTRICULAR ARRHYTHMIAS FROM ELECTROCARDIOGRAM LEAD II SIGNALS

In this work, we use computer aided diagnosis (CADx) to extract features from ECG signals and detect different types of cardiac ventricular arrhythmias including Ventricular Tachycardia (VT),Ventricular Fibrillation (VF), Ventricular Couplet (VC), and Ventricular Bigeminy (VB).Our methodology is unique in computing features of lower and higher order statistical parameters from six different data domains: time domain, Fourier domain, and four Wavelet domains (Daubechies, Coiflet, Symlet, and Meyer). These features proved to give superior classification performance, in general, regardless of the type of classifier used as compared with previous studies. However, Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers got better performance than other classifiers tried including KNN and Naïve Bayes classifiers. Our unique features enabled classifiers to perform better in comparison with previous studies: for VT, 100% accuracy while best previous work got 95.8%, for VF, 100% accuracy while best previous work got 97.5%, for VC, 100% sensitivity while best previous work got 71.8%, and for VB, 100% sensitivity while best previous work got 84.6%

Combining Ultrasound and Photoacoustic Imaging for Improving the Diagnosis of Cancer

Combining ultrasound and optical imaging modalities has shown promising results for improving the detection of cancer. Ultrasound maps the anatomical structure while optical imaging modalities can provide contrast related to the vasculature density or tumor angiogenesis. In this work, we developed a new technology that allows live tissue characterization with real-time co-registered ultrasound (US) and photoacoustic (PA) imaging. A field programmable gate array (FPGA) based reconfigurable processor is specifically designed to allow real-time switching between the two modalities by adjusting its structure for optimum performance of each. Furthermore, we investigated various image processing and recognition techniques to improve the probability of detecting malignant ovaries from the co-registered US and PA images. This work also includes theoretical contribution to diffuse optical tomography by introducing a novel idea of estimating closed-form solutions of optical fluence inside a turbid medium using the gradient descent optimization, which can be applied to any boundary shape for any given source location. This numerical approach provides new means of faster imaging reconstruction. The applications include accurate tumor characterization and better tracking of tumor chemotherapy response

Design of optimal light delivery system for co-registered transvaginal ultrasound and photoacoustic imaging of ovarian tissue

A hand-held transvaginal probe suitable for co-registered photoacoustic and ultrasound imaging of ovarian tissue was designed and evaluated. The imaging probe consists of an ultrasound transducer and four 1-mm-core multi-mode optical fibers both housed in a custom-made sheath. The probe was optimized for the highest light delivery output and best beam uniformity on tissue surface, by simulating the light fluence and power output for different design parameters. The laser fluence profiles were experimentally measured through chicken breast tissue and calibrated intralipid solution at various imaging depths. Polyethylene tubing filled with rat blood mimicking a blood vessel was successfully imaged up to ∼30 mm depth through porcine vaginal tissue at 750 nm. This imaging depth was achieved with a laser fluence on the tissue surface of 20 mJ/cm2, which is below the maximum permissible exposure (MPE) of 25 mJ/cm2 recommended by the American National Standards Institute (ANSI). Furthermore, the probe imaging capability was verified with ex vivo imaging of benign and malignant human ovaries. The co-registered images clearly showed different vasculature distributions on the surface of the benign cyst and the malignant ovary. These results suggest that our imaging system has the clinical potential for in vivo imaging and characterization of ovarian tissues