Glenn Nordehn

SEX DIFFERENCES IN PAIN AND HYPOTHALAMIC-PITUITARY-ADRENOCORTICAL RESPONSES TO OPIOID BLOCKADE

Objective Sex differences in pain sensitivity and stress reactivity have been well documented. Little is known about the role of the endogenous opioid system in these differences. This study was conducted to compare adrenocortical, pain sensitivity, and blood pressure responses to opioid blockade using naltrexone in men and women. Methods Twenty-six participants completed 2 sessions during which placebo or 50 mg of naltrexone was administered, using a double-blind, counterbalanced design. Thermal pain threshold and heat tolerance were assessed. Participants also rated pain during a 90-second cold pressor test (CPT) and completed the McGill Pain Questionnaire (MPQ) after each pain challenge. Blood and saliva samples and cardiovascular and mood measures were obtained throughout the sessions. Results Plasma cortisol, adrenocorticotropin, beta endorphin, prolactin, and salivary cortisol levels increased similarly in men and women after naltrexone administration compared with placebo. Women reported more pain during both pain procedures and had lower thermal pain tolerance. In response to naltrexone, women exhibited reduced blood pressure responses and reduced MPQ pain ratings after CPT. No effects of naltrexone on these measures were found in men. Conclusions Although men and women exhibited similar hormonal responses to opioid receptor blockade, women reported less pain and showed smaller blood pressure responses during CPT. Results suggest differential effects of the endogenous opioid system on pain perception and blood pressure in men and women.

Detection of heart murmurs using wavelet analysis and artificial neural networks.

This paper presents the algorithm and technical aspects of an intelligent diagnostic system for the detection of heart murmurs. The purpose of this research is to address the lack of effectively accurate cardiac auscultation present at the primary care physician office by development of an algorithm capable of operating within the hectic environment of the primary care office. The proposed algorithm consists of three main stages. First; denoising of input data (digital recordings of heart sounds), via Wavelet Packet Analysis. Second; input vector preparation through the use of Principal Component Analysis and block processing. Third; classification of the heart sound using an Artificial Neural Network. Initial testing revealed the intelligent diagnostic system can differentiate between normal healthy heart sounds and abnormal heart sounds (e.g., murmurs), with a specificity of 70.5% and a sensitivity of 64.7%.

Detection and Classification of Cardiac Murmurs using Segmentation Techniques and Artificial Neural Networks

In this paper we present the implementation of a diagnostic system based on artificial neural networks (ANN) that can be used in the detection and classification of heart murmurs. Segmentation and alignment algorithms serve as important pre-processing steps before heart sounds are applied to the ANN structure. The system enables users to create a classifier that can be trained to detect virtually any desired target set of heart sounds. The output of the system is the classification of the sound as either normal or a type of heart murmur. The ultimate goal of this research is to implement a heart sounds diagnostic system that can be used to help physicians in the auscultation of patients and to reduce the number of unnecessary echocardiograms - those that are ordered for healthy patients. Testing has been conducted using both simulated and recorded patient heart sounds as input. Three sets of results for the tested system are included herein, corresponding to three different target sets of simulated heart sounds. The system is able to classify with up to 85 plusmn 7.4% accuracy and 95 plusmn 6.8% sensitivity. For each target set, the accuracy rate of the ANN system is compared to the accuracy rate of a group of 2nd year medical students who were asked to classify heart sounds from the same group of heart sounds classified by the ANN system. System test results are also explored using recorded patient heart sounds

Classification of Four Types of Common Murmurs using Wavelets and a Learning Vector Quantization Network

In this work we present the development of a system that can be used for the study, detection and classification of human heart sounds using digital signal processing and artificial intelligence techniques. The design and implementation of such system is broken down into two processes: digital signal processing part and artificial intelligence part. The ultimate goal of the project is to develop an intelligent system that can be used for the detection and classification of various types of human heart murmurs.

Artificial Neural Network Analysis of Heart Sounds Captured From an Acoustic Stethoscope and Emailed Using iStethoscopePro

Valvular heart disease is a significant problem. The primary care physician initially does assessment through auscultation. Accuracy in classification of sounds is suboptimal (20–40%). Technological advances have paralleled an increase in referral for Doppler echocardiography and a decrease in auscultatory skill. An increase in the referral of functionally innocent heart murmurs has contributed to the increasing cost of care. A computer-aided analysis has been shown to improve the accuracy of primary care physicians. A remote centralized computer-aided analysis could provide physicians with an additional tool in the assessment of heart murmurs, especially in settings without access to echocardiography. iStethoscopePro is an application for the iPhone and iPod Touch capable of recording and emailing sounds. We developed a device, which interfaces with iStethoscopePro and any acoustic stethoscope. We used this device to capture heart sounds from a conventional acoustic stethoscope and email them using iStethoscopePro for analysis with an artificial neural network (ANN). Hypothesis: It is possible to record heart sounds from an acoustic stethoscope, email them, and classify them with an ANN. Our device recorded heart sounds with insignificant intersample variation. After training the ANN with representations of four heart murmurs (aortic regurgitation, aortic stenosis, mitral regurgitation, and mitral stenosis) and normal, we achieved an overall accuracy of 45% with sensitivities of 50–75%. A remote centralized analysis of sound captured from an acoustic stethoscope is possible and could augment traditional auscultatory exams by offering an objective classification. Improving the accuracy and specificity of the ANN is necessary. This collection modality offers a method for the collection of a great deal of sounds for further development of artificial intelligence systems.

Optical System Design of Laser-Based Stethoscope

University of Minnesota M.S. thesis. June 2012. Major: Clinical Research. Advisor: Professor Jing Bai. 1 computer file (PDF); ix, 59 pages.

Heart murmur detection/classification using Cochlea-Like Pre-Processing and Artificial Intelligence

In this research paper, we used a novel approach to pre-process the heart sound signals by altering the electrical signal in a similar way as is done by human cochlea before they go to Artificial Intelligence (AI) for murmur detection/classification. Cochlea-like pre-processing changes the spectral contents of the heart sounds to enhance the murmur information which can then be detected/classified more accurately by AI circuitry. We designed a heart murmur detection/classification system based upon this approach and tested this system using simulated sounds of various murmur types. Our test results show that this approach significantly improves heart murmur detection/classification accuracy.

Development of an Animal Model to Test an Active Noise Cancellation System for Infant Incubators

Medical, therapeutic and technological advancements, including the use of neonatal incubators and the development of neonatal intensive care units (NICUs), have significantly increased the survival of premature and ill infants. However, high levels of noise in the NICU result in numerous adverse health effects, including hearing loss, sleep disturbances and other forms of stress. Even normal levels of ambient noise may be of considerable risk for the most premature infants. It is well documented that the mammalian auditory system is most vulnerable to environmental influences immediately after the time that it first begins to function. In humans, the critical period spans approximately weeks 24–30 of gestation, which corresponds to the age when the most extremely premature infants are now able to survive ex utero. Premature infants are, therefore, at high risk for environmentally-induced hearing loss. Development of techniques that increase the amount of protection against noise-induced hearing loss (NIHL) could significantly improve quality of life, both while neonates are in the NICU, and long term. The long-term goal of our research is to develop a version of an existing active noise cancellation (ANC) system that can be used to reduce sound levels in NICU incubators, in a manner that does not require considerable space. The core component of the ANC system is a carbon nanotube-based transparent actuator, which is controlled by an adaptive controller so that an exact out-of-phase anti-noise can be produced from the actuator (Yu et al, 2005, 2007). The basic principle of the ANC system is to cancel the unwanted primary noise through the introduction of a destructive anti-noise sound. Experimental results showed that a reduction of greater than 15 dB in the primary noise can be achieved by the ANC system (Yu et al. 2007). Ultimately, this transparent actuator could be built into the side of an infant incubator, providing noise protection without adding equipment to the already crowded NICU environment. Before human trials can begin, animal studies must be completed to demonstrate that the ANC system can prevent the hearing loss that results from exposure to incubator noise during the critical period. One complication in animal testing is that individual species respond to different frequency ranges. For example, the human cochlea is most sensitive to sound frequencies between 2 and 5 kHz, while mice respond best between 8 and 16 kHz. It was hypothesized that a frequency translation based on the cochlear frequency/place relationship could be used to convert incubator noise into an appropriate stimulus for testing of the ANC in mice. Neonatal mice were exposed to untranslated incubator noise (IN) or frequency-shifted incubator noise (FSIN) during the critical period, and hearing sensitivity was measured following the noise exposure. IN had no effect on acoustic thresholds, but FSIN caused a moderately severe (60–70 dB) high frequency hearing loss in all mice tested. Based on these data, the FSIN stimulus represents the first accurate model of neonatal noise-induced hearing loss. Future experiments will use this model to test the ability of the ANC system to protect against NIHL.

Heart Murmur Detection∕Classification System Using Cochlea-Like Pre-Processing

University of Minnesota M.S. thesis. January 2010. Major: Electrical and Computer Engineering. Advisor: Prof. M. Imran Hayee. i computer file (PDF); vi, 48 pages. Ill. (some col.)

Retention Of Cardiac Auscultation Skill Requires Paired Visual And Audio Information In New Learners

Introduction: Cardiac auscultation accuracy is poor: 20% to 40%. Audio-only of 500 heart sounds cycles over a short time period significantly improved auscultation scores. Hypothesis: adding visual information to an audio-only format, significantly (p<.05) improves short and long term accuracy. Methods: Pre-test: Twenty-two 1st and 2nd year medical student participants took an audio-only pre-test. Seven students comprising our audio-only training cohort heard audio-only, of 500 heart sound repetitions. 15 students comprising our paired visual with audio cohort heard and simultaneously watched video spectrograms of the heart sounds. Immediately after trainings, both cohorts took audio-only post-tests; the visual with audio cohort also took a visual with audio post-test, a test providing audio with simultaneous video spectrograms. All tests were repeated in six months. Results: All tests given immediately after trainings showed significant improvement with no significant difference between the cohorts. Six months later neither cohorts maintained significant improvement on audio-only post-tests. Six months later the visual with audio cohort maintained significant improvement (p<.05) on the visual with audio post-test. Conclusions: Audio retention of heart sound recognition is not maintained if: trained using audio-only; or, trained using visual with audio. Providing visual with audio in training and testing allows retention of auscultation accuracy. Devices providing visual information during auscultation could prove beneficial.