Biomedical subjects

Kevin D Donohue

Publications and source records attributed to Kevin D Donohue.

4 recordsLinked to original sources

PubMed2006

Blood pressure regulation in neurally intact human vs. acutely injured paraplegic and tetraplegic patients during passive tilt.

We investigated autonomic control of cardiovascular function in able-bodied (AB), paraplegic (PARA), and tetraplegic (TETRA) subjects in response to head-up tilt following spinal cord injury. We evaluated spectral power of blood pressure (BP), baroreflex sensitivity (BRS), baroreflex effectiveness index (BEI), occurrence of systolic blood pressure (SBP) ramps, baroreflex sequences, and cross-correlation of SBP with heart rate (HR) in low (0.04-0.15 Hz)- and high (0.15-0.4 Hz)-frequency regions. During tilt, AB and PARA effectively regulated BP and HR, but TETRA did not. The numbers of SBP ramps and percentages of heartbeats involved in SBP ramps and baroreflex sequences increased in AB, were unchanged in PARA, and declined in TETRA. BRS was lowest in PARA and declined with tilt in all groups. BEI was greatest in AB and declined with tilt in all groups. Low-frequency power of BP and the peak of the SBP/HR cross-correlation magnitude were greatest in AB, increased during tilt in AB, remained unchanged in PARA, and declined in TETRA. The peak cross-correlation magnitude in HF decreased with tilt in all groups. Our data indicate that spinal cord injury results in decreased stimulation of arterial baroreceptors and less engagement of feedback control as demonstrated by lower 1) spectral power of BP, 2) number (and percentages) of SBP ramps and barosequences, 3) cross-correlation magnitude of SBP/HR, 4) BEI, and 5) changes in delay between SBP/HR. Diminished vasomotion and impaired baroreflex regulation may be major contributors to decreased orthostatic tolerance following injury.

Adult↗

PubMed2006

Detection of breast lesion regions in ultrasound images using wavelets and order statistics.

Accurate detection and segmentation of suspicious regions within the complex and irregular tissues of the breast, as depicted with ultrasonic B scans, typically require human analysis and decision making. Tissue characterization methods for classifying suspicious regions often depend on identifying and then accurately segmenting these regions. Motivated by an ultimate goal to automate this critical identification and segmentation step for tissue characterization problems, this work examines ultrasonic signal characteristics between various regions of breast tissue broadly classified as normal tissue and breast lesions. This paper introduces a nonparametric model based on order statistics (OS) estimated from multiresolution (MR) decompositions of energy-normalized subregions. Experimental results demonstrate the classification performance of the OS-based features extracted from the tumor and normal tissue regions in multiple scans from 84 patients, which resulted in a total of 204 tumor regions (from 43 malignant and 161 benign) and 816 normal tissue regions. Performance results indicate that OS-based features achieved an area under the receiver-operator characteristic curve of 91% in the discrimination between breast lesions and surrounding normal tissues.

Algorithms↗

PubMed2003

ROC analysis of ultrasound tissue characterization classifiers for breast cancer diagnosis.

Breast cancer diagnosis through ultrasound tissue characterization was studied using receiver operating characteristic (ROC) analysis of combinations of acoustic features, patient age, and radiological findings. A feature fusion method was devised that operates even if only partial diagnostic data are available. The ROC methodology uses ordinal dominance theory and bootstrap resampling to evaluate A(z) and confidence intervals in simple as well as paired data analyses. The combined diagnostic feature had an A(z) of 0.96 with a confidence interval of at a significance level of 0.05. The combined features show statistically significant improvement over prebiopsy radiological findings. These results indicate that ultrasound tissue characterization, in combination with patient record and clinical findings, may greatly reduce the need to perform biopsies of benign breast lesions.

Age Factors↗

PubMed2003

Malignant and benign breast tissue classification performance using a scatterer structure preclassifier.

Benign and malignant breast tissue classification is examined for generalized-spectrum parameters computed from RF ultrasound data when a preclassification of subregions based on general scattering properties is performed. Results using a clinical database of 84 patients show statistically significant improvements (over 10% in receiver operation characteristic (ROC) areas) when only coherent scatterer subregions are used as compared to using all subregions within the region of interest.

Algorithms↗