The effect of anthropometric variations on acoustical flow estimation: proposing a novel approach for flow estimation without the need for individual calibration.

Tracheal sound average power is directly related to the breathing flow rate and recently it has attracted considerable attention for acoustical flow estimation. However, the flow-sound relationship is highly variable among people and it also changes for the same person at different flow rates. Hence, a robust model capable of estimating flow from tracheal sounds at different flow rates in a large group of individuals does not exist. In this paper, a model is proposed to estimate respiratory flow from tracheal sounds. The proposed model eliminates the dependence of the previous methods on calibrating the model for every individual and at different flow rates. To validate the model, it was applied to the respiratory sound and flow data of 93 healthy individuals. We investigated the statistical correlation between the model parameters and anthropometric features of the subjects. The results have shown that gender, height, and smoking are the most significant factors that affect the model parameters. Hence, we grouped nonsmoker subjects into four groups based on their gender and height. The average of model parameters in each group was defined as the group-calibrated model parameters. These models were applied to estimate flow from data of subjects within the same group and in the other groups. The results show that flow estimation error based on the group-calibrated model is less than 10%. The low estimation errors confirm the possibility of defining a general flow estimation model for subjects with similar anthropometric features with no need for calibrating the model parameters for every individual. This technique simplifies the acoustical flow estimation in general applications including sleep studies and patients' screening in health care facilities.

Acoustical modeling of swallowing mechanism.

In this paper, a mathematical modeling of the swallowing sound generation is presented. To evaluate the model, its application on swallowing disorder (dysphagia) diagnosis is discussed. As a starting point, a simple linear time invariant model is assumed to represent the pharyngeal wall and tissue excited by a train of impulses. The modeling is approached by two different assumptions. In one approach, it is assumed that the impulse train, representing the neural activities to trigger swallow, is the same for both groups of control and dysphagic, and it is the pharyngeal model that accounts for the difference between the two groups. On the other hand, in the second approach, it is assumed that the pharyngeal response is the same for both groups, but the neural activities to initiate the swallow are different between the two groups. The results show that the second approach complies better with the physiological characteristics of swallowing mechanism as it provides a much better discrimination between the swallowing sounds of control and dysphagic groups of this study. Though, it should be noted that our dysphagic group subjects were cerebral palsy and stroke patients. Hence, the model accounting for initiation of neural activities is reasonable to show better results.

Automatic and unsupervised snore sound extraction from respiratory sound signals.

In this paper, an automatic and unsupervised snore detection algorithm is proposed. The respiratory sound signals of 30 patients with different levels of airway obstruction were recorded by two microphones: one placed over the trachea (the tracheal microphone), and the other was a freestanding microphone (the ambient microphone). All the recordings were done simultaneously with full-night polysomnography during sleep. The sound activity episodes were identified using the vertical box (V-Box) algorithm. The 500-Hz subband energy distribution and principal component analysis were used to extract discriminative features from sound episodes. An unsupervised fuzzy C-means clustering algorithm was then deployed to label the sound episodes as either snore or no-snore class, which could be breath sound, swallowing sound, or any other noise. The algorithm was evaluated using manual annotation of the sound signals. The overall accuracy of the proposed algorithm was found to be 98.6% for tracheal sounds recordings, and 93.1% for the sounds recorded by the ambient microphone.

Estimation of 2-D center of mass movement during trunk flexion-extension movements using body accelerations.

Motions of the center of body mass (COM) and body segment acceleration signals are commonly used to indicate movement performance and stability during standing activities. The COM trajectory is usually calculated by video motion analysis, which has a time consuming setup and also is not readily available in all clinical settings. In this paper, we present a novel method to estimate the COM trajectory from the upper and lower limb accelerations, based on experimental data. We have modeled the relationships that exist between the 2-D hip and trunk acceleration data with the 2-D COM trajectory in the sagittal plane, during four trunk flexion-extension movement tasks and estimated the COM trajectory based on that model. The model accounted for between 93 +/- 9% to 97 +/- 3% of the resultant COM trajectory's variability, depending on the task. This corresponded to a range of absolute error between the true and estimated COM trajectories of 0.65 +/- 0.62 to 1.07 +/- 1.13 cm. The advantage of this model compared to our previous work on COM trajectory estimation is that it does not require any calibration and provides a reasonably accurate estimation of the COM trajectory, which can be used to study human balance performance in any clinical setting.

Ambulatory center of mass prediction using body accelerations and center of foot pressure.

The center of body mass (COM), center of foot pressure (COP), and body segment acceleration signals are commonly used to indicate movement performance and stability during standing activities and walking. For balance maintenance and restoration, the human brain is capable of estimating and predicting the COM even in the absence of visual or vestibular information. Thus, we hypothesized that the COM may be acquired through the processing of proprioceptive somatosensory information, represented by body segment accelerations, and an external spatial reference, the ground support, represented by the COP. To investigate this hypothesis, we modeled the relationships that exist between the COP and accelerometer data with the 3-D COM trajectory, during walking on firm and irregular surfaces. The models accounted for 99.85 +/- 0.20% and 99.77 +/- 0.39% of the resultant COM trajectory's variability for the firm and irregular surfaces, respectively. This corresponded to a percentage error between the estimated and actual resultant COM of 16.06 +/- 11.11% for the firm surface and 21.41 +/- 12.70% for the doweling surface. In turn, this translates into an absolute error between the true and actual resultant COM of 3.62 +/- 2.69 cm and 4.74 +/- 3.01 cm for the firm and doweling surfaces, respectively. The model is novel in that it does not require any calibration and provides a reasonably accurate estimation of the COM, which can be compared to the brain's balance performance. Hence, this model could be used instead of the cumbersome method of video motion analysis for COM calculation.

Heart sound cancellation based on multiscale products and linear prediction.

This paper presents a novel method for Heart Sound (HS) cancellation from Lung Sound (LS) records. The method uses the multiscale product of the wavelet coefficients of the original signal to detect HS-included segments. Once the HS segments are identified, the method removes them from the wavelet coefficients at every level and estimates the created gaps by using a set of linear prediction filters. It is shown that if the segment to be predicted is stationary, a final record with no audible artifacts such as clicks can be reconstructed using this approach. The results were promising for HS removal from LS records and showed no hampering of the main components of the LS. The results were confirmed both qualitatively by listening to the reconstructed signal and quantitatively by spectral analysis.

A robust method for estimating respiratory flow using tracheal sounds entropy.

The relationship between respiratory sounds and flow is of great interest for researchers and physicians due to its diagnostic potentials. Due to difficulties and inaccuracy of most of the flow measurement techniques, several researchers have attempted to estimate flow from respiratory sounds. However, all of the proposed methods heavily depend on the availability of different rates of flow for calibrating the model, which makes their use limited by a large degree. In this paper, a robust and novel method for estimating flow using entropy of the band pass filtered tracheal sounds is proposed. The proposed method is novel in terms of being independent of the flow rate chosen for calibration; it requires only one breath for calibration and can estimate any flow rate even out of the range of calibration flow. After removing the effects of heart sounds (which distort the low-frequency components of tracheal sounds) on the calculated entropy of the tracheal sounds, the performance of the method at different frequency ranges were investigated. Also, the performance of the proposed method was tested using 6 different segment sizes for entropy calculation and the best segment sizes during inspiration and expiration were found. The method was tested on data of 10 healthy subjects at five different flow rates. The overall estimation error was found to be 8.3 +/- 2.8% and 9.6 +/- 2.8% for inspiration and expiration phases, respectively.

Center of mass approximation and prediction as a function of body acceleration.

In order to maintain postural stability, the central nervous system must maintain equilibrium of the total center of body mass (COM) in relation to its base of support. Thus, the trajectory of the COM provides an important measure of postural stability. Three different models were developed to estimate the COM and the results tested on 16 subjects: namely a neural network, an adaptive fuzzy interface system and a hybrid genetic algorithm sum-of-sines model. The inputs to the models were acquired via two accelerometers, one representing the trunk segment placed on T2 and the second representing the limb segment placed on the shank below the knee joint. The portability, ease of use and low cost (compared with video motion analysis systems) of the accelerometers increases the range of clinics to which the system will be available. The subjects performed a multisegmental movement task on fixed and foam surfaces, thus covering a relatively wide dynamic scope. The results are encouraging for obtaining COM estimates that have clinical applications; the genetic sum-of-sines model was found to be superior when compared to the other two models.

A robust method for heart sounds localization using lung sounds entropy.

Heart sounds are the main unavoidable interference in lung sound recording and analysis. Hence, several techniques have been developed to reduce or cancel heart sounds (HS) from lung sound records. The first step in most HS cancellation techniques is to detect the segments including HS. This paper proposes a novel method for HS localization using entropy of the lung sounds. We investigated both Shannon and Renyi entropies and the results of the method using Shannon entropy were superior. Another HS localization method based on multiresolution product of lung sounds wavelet coefficients adopted from was also implemented for comparison. The methods were tested on data from 6 healthy subjects recorded at low (7.5 ml/s/kg) and medium 115 ml/s/kg) flow rates. The error of entropy-based method using Shannon entropy was found to be 0.1 +/- 0.4% and 1.0 +/- 0.7% at low and medium flow rates, respectively, which is significantly lower than that of multiresolution product method and those of other methods reported in previous studies. The proposed method is fully automated and detects HS included segments in a completely unsupervised manner.

On modeling center of foot pressure distortion through a medium.

The center of foot pressure (COP) is a commonly used output measure of the postural control system as it is indicative of the systems stability. A dense piece of foam, i.e., a sponge, can be used to emulate random environmental conditions that distort the ground reaction forces received and interpreted by the cutaneous sensors in the feet; thus introducing uncertainty into the control system. In this paper, the density and size of the sponge was selected such that a subject's weight did not cause full compression. In general, the COP is measured from the bottom of the sponge. As the sponge is used to distort ground reaction forces, it is reasonable then to assume that the COP signal would also be distorted. The use of other sensory information to identify state of balance, and compute necessary balance adjustments, is therefore required. In addition to a sponge, many different types of specialized footwear and inserts are used for people with peripheral neuropathy, such as diabetics. However, it is difficult to design diabetic footwear without a better understanding of the mechanical and physiological effects that different surfaces typical of outdoor terrains, such as a sponge, which cannot be predicted without the sense of the foot, have on balance. Therefore, the goal of this study was to investigate the change of the COP signal from the top and bottom of the sponge. Portable force sensing mats from Vista Medical were used to obtain the COP from the top and bottom of the sponge. The COP measured on the bottom of the sponge is not the same as the COP measured on the top, particularly in the medial-lateral direction. Several linear and nonlinear models were used to identify the unknown plant; i.e., the sponge. Overall, the nonlinear neural network method had superior performance when compared with the linear models. Thus, the results indicate that the signals from the top and bottom of the sponge are in fact different, and furthermore, they are nonlinearly related. A nonlinear mathematical model is proposed which describes COP distortion through a medium such as a sponge. Although the values for the model parameters determined were for a particular sponge, this study suggests that a neural network plant identification model may be applied to any medium other than the sponge; the information can then be used to determine how the balance control model is affected given the sensory information received.

Classification of normal and dysphagic swallows by acoustical means.

This paper proposes a noninvasive, acoustic-based method to differentiate between individuals with and without dysphagia or swallowing dysfunction. Swallowing sound signals, both normal and abnormal (i.e., at risk of some degree of dysphagia) were recorded with accelerometers over the trachea. Segmentation based on waveform dimension trajectory (a distance-based technique) was developed to segment the nonstationary swallowing sound signals. Two characteristic sections emerged, Opening and Transmission, and 24 characteristic features were extracted and subsequently reduced via discriminant analysis. A discriminant algorithm was also employed for classification, with the system trained and tested using the leave-one-out approach. Overall, 350 signals were used from three bolus consistencies (semisolid, thick and thin liquids). A final screening algorithm correctly classified 13 of 15 control subjects and 11 of 11 subjects with some degree of dysphagia and/or neurological impairments. The proposed method has great potential to reduce the need for videofluoroscopic swallowing studies (the current gold standard method for swallowing assessment, which is invasive and nonportable) and to assist in the overall clinical assessment of swallowing sound signals.

The role of kilovoltage irradiation in the treatment of keloids.

This is a retrospective analysis of the results of kilovoltage irradiation given to prevent the regrowth of 203 keloids excised at the University of Iowa Hospitals and Clinics, Iowa City, Iowa, Lutheran Hospital in Moline, Illinois, and Mercy Hospital in Cedar Rapids, Iowa. We found that a minimum follow-up of 1 year is needed to evaluate the results of post-excisional kilovoltage x-ray therapy. A dose versus response effect was also observed. Although it is desirable to use the lowest possible dose of radiation that is likely to be effective, the likelihood of failure is too great to justify the routine use of doses of less than 900 cGy regardless of how they are fractionated or when they are given. It appears that the total dose of irradiation that is given to prevent the regrowth of an excised keloid is more important than when irradiation is started, the size of the largest fraction given, whether the irradiation is completed in 1 week or 3, or where the keloid has grown. When a small number of keloids were irradiated less than 1 year after they first appeared greater than or equal to 1500 cGy were sufficient to control 90% of them without re-excision.

Primary malignant lymphoma of the mandible.

From 1945 through 1985, 32 cases of primary lymphoma of bone were treated at the University of Iowa Hospitals and Clinics. Sixteen cases (50%) demonstrated the lesion in the long tubular bones with a predilection for the lower and upper extremities. The frequently involved flat bones (six cases) were the bones of the pelvis. There were only three cases (9%) where the mandible was the primary site. In this report, the literature is reviewed and three cases with primary lymphoma of the mandible are presented.

Bacteriological flora of meat and meat products.

The bacterial flora of meat and meat products consumed by Egyptian people was estimated. The highest counts, either for total viable bacteria and coliforms were found in sausage and in raw kofta and kabab, but they markedly decreased after grilling. Heavy loads of bacteria were also observed in the fresh beef samples, frozen imported beef, minced meat, and liver. The luncheon meat samples gave the lowest total bacterial counts and seemed to be free of coliforms. Gram-negative rods constituted most of the total isolates of the different types of meat. In the heat-treated meat products, the most predominant bacteria were Gram-positive cocci. The predominating organisms in fresh beef were Pseudomonas, Bacillus and Micrococcus spp. as well as E. coli and Lactobacillus plantarum. On adopting the total viable and coliform standard counts suggested in the U.S.A., it was found that some of the meat products (sausage, raw kabab and kofta) contained higher total and coliform counts than that of the standard, whereas the total counts of the other meat products were lower.