Use of simulated data to explore the application of optical coherence tomography for classifying middle-ear pathologies.

Optical coherence tomography (OCT) vibrometry is a non-invasive tool for functional imaging of the middle ear. It provides spatially resolved vibrational responses and also anatomical images of the same ear. Our objective here was to explore the potential of OCT vibration measurements at the incus, as well as at the umbo, to distinguish among middle-ear disorders. Our approach was to build finite-element models of normal and pathological ears, generate large amounts of synthetic data, and then classify the simulated data into normal and pathological groups using a decision tree based on features extracted from simulated vibration magnitudes. We could distinguish between normal ears and ears with incudomallear joint (IMJ) disarticulation or stapes fixation, with the sensitivity and specificity both being 1.0; distinguish between stapes fixation and IMJ disarticulation with a sensitivity of 0.900 and a specificity of 0.889; and distinguish ears with ISJ disarticulation from normal ears with a sensitivity of 0.784 and a specificity of 0.872. Less extreme pathologies were also simulated. The results suggest that the vibration measurements within the middle ear that can be provided by OCT (e.g., at the incus) may be very valuable for diagnosis.

Finite-Element Modelling Based on Optical Coherence Tomography and Corresponding X-ray MicroCT Data for Three Human Middle Ears.

PURPOSE: Optical coherence tomography (OCT) is an emerging imaging modality which is non-invasive, can be employed in vivo, and can record both anatomy and vibrations. The purpose here is to explore the application of finite-element (FE) modelling to OCT data. METHODS: We recorded vibrations for three human cadaver middle ears using OCT. We also have X-ray microCT images from the same ears. Three FE models were built based on geometries obtained from the microCT images. The material properties and boundary conditions of the models were obtained from previously reported studies. RESULTS: Tympanic-membrane (TM) vibration patterns were computed for the three models and compared with the patterns measured using OCT. Frequency responses were also computed for all three models for several locations in the middle ear and compared with the OCT displacements and with the literature. The three models were compared with each other in terms of geometry and function. Parameter sensitivity analyses were done and the results were compared among the models and with the literature. The simulated TM displacement patterns are qualitatively similar to the OCT results. The simulated displacements are closer to the OCT results for 500 Hz and 1 kHz but the differences are greater at 2 kHz. CONCLUSION: This study provides an initial look at the combined use of OCT measurements and FE modelling based on subject-specific anatomy. The geometries and parameters of the existing FE models could be modified for individual patients in the future to help identify abnormalities in the middle ear.

Tracheal Sound Analysis for Automatic Detection of Respiratory Depression in Adult Patients during Cataract Surgery under Sedation.

BACKGROUND: Tracheal sound analysis is a simple way to study the abnormalities of upper airway like airway obstruction. Hence, it may be an effective method for detection of alveolar hypoventilation and respiratory depression. This study was designed to investigate the importance of tracheal sound analysis to detect respiratory depression during cataract surgery under sedation. Methods: After Institutional Ethical Committee approval and informed patients' consent, we studied thirty adults American Society of Anesthesiologists I and II patients scheduled for cataract surgery under sedation anesthesia. Recording of tracheal sounds started 1 min before administration of sedative drugs using a microphone. Recorded sounds were examined by the anesthesiologist to detect periods of respiratory depression longer than 10 s. Then, tracheal sound signals converted to spectrogram images, and image processing was done to detect respiratory depression. Finally, depression periods detected from tracheal sound analysis were compared to the depression periods detected by the anesthesiologist. RESULTS: We extracted five features from spectrogram images of tracheal sounds for the detection of respiratory depression. Then, decision tree and support vector machine (SVM) with Radial Basis Function (RBF) kernel were used to classify the data using these features, where the designed decision tree outperforms the SVM with a sensitivity of 89% and specificity of 97%. CONCLUSIONS: The results of this study show that morphological processing of spectrogram images of tracheal sound signals from a microphone placed over suprasternal notch may reliably provide an early warning of respiratory depression and the onset of airway obstruction in patients under sedation.

Effects of Minerva Orthosis on Larynx Height in Young, Healthy Volunteers.

BACKGROUND: During speech, larynx is higher in the neck for high-pitched sounds and lower for low-pitched sounds. Patients with different problems in cervical and cervicothoracic spine use cervical orthosis to limit cervical motion. This study aimed to evaluate the effects of Minerva orthosis on larynx height in young, healthy volunteers. SUBJECTS AND METHODS: This study included 18 subjects. Acoustic measurement of frequency variability has been assessed in 3 brace conditions: (1) without brace, (2) with brace, and (3) 30 minutes after wearing the brace. RESULTS: Several statistically significant differences were found in the comparison between Minerva and 30 minutes after Minerva. CONCLUSION: When planning cervical orthosis treatment, it is important to consider the reduction in larynx height that may result from bracing for those who are already at risk of developing dysphagia and dysphonia.

Automatic identification of hypernasality in normal and cleft lip and palate patients with acoustic analysis of speech.

Hypernasality is seen in cleft lip and palate patients who had undergone repair surgery as a consequence of velopharyngeal insufficiency. Hypernasality has been studied by evaluation of perturbation, noise measures, and cepstral analysis of speech. In this study, feature extraction and analysis were performed during running speech using six different sentences. Jitter, shimmer, Mel frequency cepstral coefficients, bionic wavelet transform entropy, and bionic wavelet transform energy were calculated. Support vector machines were employed for classification of data to normal or hypernasal. Finally, results of the automatic classification were compared with true labels to find accuracy, sensitivity, and specificity. Accuracy was higher when Mel frequency cepstral coefficients were combined with bionic wavelet transform energy feature. In the best case, accuracy of 85% with sensitivity of 82% and specificity of 85% was obtained. Results prove that acoustic analysis is a reliable method to find hypernasality in cleft lip and palate patients.

Computerized Analysis of Acoustic Characteristics of Patients with Internal Nasal Valve Collapse Before and After Functional Rhinoplasty.

Acoustic analysis of sounds produced during speech provides significant information about the physiology of larynx and vocal tract. The analysis of voice power spectrum is a fundamental sensitive method of acoustic assessment that provides valuable information about the voice source and characteristics of vocal tract resonance cavities. The changes in long-term average spectrum (LTAS) spectral tilt and harmony to noise ratio (HNR) were analyzed to assess the voice quality before and after functional rhinoplasty in patients with internal nasal valve collapse. Before and 3 months after functional rhinoplasty, 12 participants were evaluated and HNR and LTAS spectral tilt in /a/ and /i/ vowels were estimated. It was seen that an increase in HNR and a decrease in LTAS spectral tilt existed after surgery. Mean LTAS spectral tilt in vowel /a/ decreased from 2.37 ± 1.04 to 2.28 ± 1.17 (P = 0.388), and it was decreased from 4.16 ± 1.65 to 2.73 ± 0.69 in vowel /i/ (P = 0.008). Mean HNR in the vowel /a/ increased from 20.71 ± 3.93 to 25.06 ± 2.67 (P = 0.002), and it was increased from 21.28 ± 4.11 to 25.26 ± 3.94 in vowel /i/ (P = 0.002). Modification of the vocal tract caused the vocal cords to close sufficiently, and this showed that although rhinoplasty did not affect the larynx directly, it changes the structure of the vocal tract and consequently the resonance of voice production. The aim of this study was to investigate the changes in voice parameters after functional rhinoplasty in patients with internal nasal valve collapse by computerized analysis of acoustic characteristics.

Automated acoustic analysis in detection of spontaneous swallows in Parkinson's disease.

Acoustic monitoring of swallow frequency has become important as the frequency of spontaneous swallowing can be an index for dysphagia and related complications. In addition, it can be employed as an objective quantification of ingestive behavior. Commonly, swallowing complications are manually detected using videofluoroscopy recordings, which require expensive equipment and exposure to radiation. In this study, a noninvasive automated technique is proposed that uses breath and swallowing recordings obtained via a microphone located over the laryngopharynx. Nonlinear diffusion filters were used in which a scale-space decomposition of recorded sound at different levels extract swallows from breath sounds and artifacts. This technique was compared to manual detection of swallows using acoustic signals on a sample of 34 subjects with Parkinson's disease. A speech language pathologist identified five subjects who showed aspiration during the videofluoroscopic swallowing study. The proposed automated method identified swallows with a sensitivity of 86.67 %, a specificity of 77.50 %, and an accuracy of 82.35 %. These results indicate the validity of automated acoustic recognition of swallowing as a fast and efficient approach to objectively estimate spontaneous swallow frequency.

A new method for multiple sperm cells tracking.

Motion analysis or quality assessment of human sperm cell is great important for clinical applications of male infertility. Sperm tracking is quite complex due to cell collision, occlusion and missed detection. The aim of this study is simultaneous tracking of multiple human sperm cells. In the first step in this research, the frame difference algorithm is used for background subtraction. There are some limitations to select an appropriate threshold value since the output accuracy is strongly dependent on the selected threshold value. To eliminate this dependency, we propose an improved non-linear diffusion filtering in the time domain. Non-linear diffusion filtering is a smoothing and noise removing approach that can preserve edges in images. Many sperms that move with different speeds in different directions eventually coincide. For multiple tracking over time, an optimal matching strategy is introduced that is based on the optimization of a new cost function. A Hungarian search method is utilized to obtain the best matching for all possible candidates. The results show nearly 3.24% frame based error in dataset of videos that contain more than 1 and less than 10 sperm cells. Hence the accuracy rate was 96.76%. These results indicate the validity of the proposed algorithm to perform multiple sperms tracking.