Massachusetts Eye And Ear Infirmary Research Articles

Pathological voice classification based on the features of an asymmetric fluid–structure interaction vocal cord model

Currently, the acoustic features used for classifying pathological voices do not take the physiological structure and vibration characteristics of the vocal cords into account. We proposed a feature extraction method based on the asymmetric fluid–structure interaction vocal cord model. The airflow viscous force was introduced into this model. The left and right asymmetric coefficients of the upper mass and the lower mass were designed to characterize the vibration mechanism of the diseased vocal cord. Then a hybrid simulated annealing genetic algorithm (HSAGA) optimized the vocal cord model inversion parameters to match the target voice source. The vocal cord model parameters corresponding to the actual voice signal were extracted and combined with the phonation pressure threshold and the phonation flow threshold to classify pathological voices. The experimental results (deposited in the Massachusetts Eye and Ear Infirmary (MEEI) database) showed that the weighted average relative error of the objective optimization function classifying the vibration characteristics of the vocal cords with lesions calculated by the HSAGA was 4.0%. When classifying pathological voices, the accuracy of the vocal cord model parameters was 77.18%, which is 33.18% higher than classifications using acoustic features, and effectively distinguishes different types of pathological voice.

PROSPECTIVE ARABIC LANGUAGE DATA BASE FOR IDENTIFICATION AND RECOGNITION OF VOICE DISORDER


 
 
 Speech is the underlying strong characteristic and voice is the vibration that the air creates as it's pushed out of the lungs and going into the vocal cords. Vocal cords are two folds of tissue within the larynx, sometimes called a speech box, the sound of these strings is what gives rise to speech.Anything that interferes with vocal cord movement or contact can cause a voice disorder. Speech production may often be compromised as social stressors contribute to chronic aphonia or dysphonia.Machine learning algorithm and non-invasive systems may play a major role in the early detection and in tracking and even development of efficient pathological speech diagnosis, based on a computerized acoustic analysis. A strong voice impairment dataset will help in recognition and classification ofincreasing number of voice disorders in and outside the Arab area. The database protocols for AVPD (Arabic voice pathology database) has been developed in a way to prevent previous data base deficiencies in widely used databases like MEEI (Massachusetts eye and ear infirmary) and SVD (Saarbrücken voice database).
 
 

Pathological Voice Detection Based on Phase Reconstitution and Convolutional Neural Network

The nonlinear dynamic features can effectively describe the acoustic characteristics of normal and pathological voice. In this paper, the phase space reconstruction and convolution neural network are used to classify the normal and pathological voice. The phase space information of normal and pathological voice is reconstructed using delay time and embedding dimension, the one-dimensional signal is converted to a two-dimensional matrix, and the reconstructed trajectory graph sample of the signal is generated. The trajectory graph samples are used as the input of the VGG-like convolutional neural network, and the graphical features are extracted to achieve a classification of normal and pathological voice. In order to overcome the lack of clinical data, a data enhancement scheme is used. The experiment which classifies the normal and pathological voice is carried out on three pathological databases respectively, i.e. the Massachusetts eye and ear infirmary (MEEI) database, Saarbrücken voice database (SVD) database, and a clinical database collected by the authors. Five-fold cross validation is used and the average recognition rates on the three databases are 99.42%, 97.30% and 95.88% respectively. The average recognition rates are 96.04% and 92.27% for normal, vocal fold paralysis and vocal fold non-paralysis voice in MEEI database and SVD database. The experimental results show that the method has high classification recognition rate and good robustness, and has certain universal applicability for the recognition of the normal and pathological voice.

Pathological voice detection based on gammatone short time spectral self-similarity

The acoustic detection method based on machine learning and signal processing is an important method of pathological voice detection and the extraction of voice features is one of the most important. Currently, the features widely used have disadvantage of dependence on the fundamental frequency extraction, being easily affected by noise and high computational complexity. In view of these shortcomings, a new method of pathological voice detection based on multi-band analysis and chaotic analysis is proposed. The gammatone filter bank was used to simulate the human ear auditory characteristics to analyze different frequency bands and obtain the signals in different frequency bands. According to the characteristics that turbulence noise caused by chaos in voice will worsen the spectrum convergence, we applied short time Fourier transform to each frequency band of the voice signal, then the feature gammatone short time spectral self-similarity (GSTS) was extracted, and the chaos degree of each band signal was analyzed to distinguish normal and pathological voice. The experimental results showed that combined with traditional machine learning methods, GSTS reached the accuracy of 99.50% in the pathological voice database of Massachusetts Eye and Ear Infirmary (MEEI) and had an improvement of 3.46% compared with the best existing features. Also, the time of the extraction of GSTS was far less than that of traditional nonlinear features. These results show that GSTS has higher extraction efficiency and better recognition effect than the existing features.

Gammatone spectral latitude features extraction for pathological voice detection and classification

To improve the performance of pathological voice detection and classification, gammatone spectral latitude (GTSL) features were proposed. GTSL features are inspired by the nonlinear phenomena produced from the human phonation, presenting explicit physiological meaning. The features combine with human auditory perception characteristics. GTSL features quantify the turbulent noise by the nonlinear compression of peak value and dynamic range of the spectrums in each frequency channel. For pathological voice detection, gammatone spectral latitude (GTSL) features fitted better with traditional machine learning algorithms than traditional nonlinear features and gammatone ceptral coefficients (GTCCs). In the classification between healthy, neuromuscular and structural voices, the proposed features achieved average accuracy of 99.6% in the Massachusetts Eye and Ear Infirmary (MEEI) database, which is 35.6% higher than other gammatone features. The accuracies in other database, Saarbruecken Voice Database (SVD) and Hospital Universitario Prłncipe de Asturias (HUPA), were 89.9% and 97.4% respectively. The experimental results indicate that, GTSL features can provide objective evaluation of voice diseases with low computational complexity and database dependency.

Impact of noise on the performance of automatic systems for vocal fold lesions detection

Automatic voice condition analysis systems have been developed to automatically discriminate pathological voices from healthy ones in the context of two disorders related to exudative lesions of Reinke’s space: nodules and Reinke’s edema. The systems are based on acoustic features, extracted from sustained vowel recordings. Reduced subsets of features have been obtained from a larger set by a feature selection algorithm based on Whale Optimization in combination with Support Vector Machine classification. Robustness of the proposed systems is assessed by adding noise of two different types (synthetic white noise and actual noise recorded in a clinical environment) to corrupt the speech signals. Two speech databases were used for this investigation: the Massachusetts Eye and Ear Infirmary (MEEI) database and a second one specifically collected in Hospital San Pedro de Alcántara (Cáceres, Spain) for the scope of this work (UEX-Voice database). The results show that the prediction performance of the detection systems appreciably decrease when moving from MEEI to a database recorded in more realistic conditions. For both pathologies, the prediction performance declines under noisy conditions, being the effect of white noise more pronounced than the effect of noise recorded in the clinical environment.

Class-Imbalanced Voice Pathology Detection and Classification Using Fuzzy Cluster Oversampling Method

The Massachusetts Eye and Ear Infirmary (MEEI) database is an international-standard training database for voice pathology detection (VPD) systems. However, there is a class-imbalanced distribution in normal and pathological voice samples and different types of pathological voice samples in the MEEI database. This study aimed to develop a VPD system that uses the fuzzy clustering synthetic minority oversampling technique algorithm (FC-SMOTE) to automatically detect and classify four types of pathological voices in a multi-class imbalanced database. The proposed FC-SMOTE algorithm processes the initial class-imbalanced dataset. A set of machine learning models was evaluated and validated using the resulting class-balanced dataset as an input. The effectiveness of the VPD system with FC-SMOTE was further verified by an external validation set and another pathological voice database (Saarbruecken Voice Database (SVD)). The experimental results show that, in the multi-classification of pathological voice for the class-imbalanced dataset, the method we propose can significantly improve the diagnostic accuracy. Meanwhile, FC-SMOTE outperforms the traditional imbalanced data oversampling algorithms, and it is preferred for imbalanced voice diagnosis in practical applications.

Recognition of Pathological Voices by Human Factor Cepstral Coefficients (HFCC)

Human speech is a means of communication that is very important in our daily lives. It is characterized by its great ability to transmit our ideas, our emotions, our personality etc. So, any alteration of the voice can prevent the person from exercising his professional and daily life naturally. It is for these reasons that it is very necessary to implement systems for detecting and classifying vocal pathologies. These automatic systems can help clinicians customize and detect the existence of any vocal pathology. In this context, several tools have been introduced to achieve early detection of voice disorders. Among these tools are the Human Factor Cepstral Coefficients (HFCC) combined with prosodic parameters, the Noise-Harmonic Ratio (NHR), the Harmonic-Noise Ratio (HNR), analysis of trend Fluctuations (DFA) and Fundamental frequency (F0). These parameters are introduced and calculated in every frame. In this study, we used a variation of HFCC called Equivalent Rectangular Bandwidth (ERB) to study the effects of HFCC on the classification of pathological voices. Using the HTK classifiers, the classification is carried out on two pathological databases, Massachusetts Eye and Ear Infirmary (MEEI) and Saarbruecken Voice Database (SVD). To assess the performance of the system, we used sensitivity and specificity.

Kullback–Leibler divergence and sample skewness for pathological voice quality assessment

This paper proposes new features aiming to improve the performance of an automatic voice pathology detection system. The features are designed precisely in terms of voice pathologies effects upon the speech signal. The system is intended to deliver high accuracy with a low number of parameters. Kullback–Leibler divergence (KLD) applied to consecutive frames of the speech signal provides a measure of voice instability. In this work, the KLD is applied to frame’s histogram and a modified form of its spectrum named higher amplitude suppression spectrum (HASS). The H-KLD (histogram KLD) and the HASS-KLD are two of the three features presently approached. An additional feature that provides the level of damping of the voice pitch period waveform is proposed, the short-term sample skewness of the signal. The H-KLD, the HASS-KLD, and the sample skewness are features employed along with mel-frequency cepstral coefficients (MFCC) in a voice pathology detection system. The system is composed of a Gaussian mixture models (GMM) classifier and two generalized extreme value (GEV) distribution classifiers. They are fused by means of a Gaussian naïve Bayes classifier. A standard subset of the Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database is adopted for evaluating the system. The obtained global success rate of 99.55% shows that the proposed features are suitable for pathological voice quality assessment.

Voice Pathology Analysis using DT-CWPT and ReliefF Algorithm

Voice pathology analysis has been one of the useful tools in the diagnosis of the pathological voice. This method is non-invasive, inexpensive and reduces time required for analysis. This paper investigates the feature extraction based on the Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) with entropies and energy measures tested with two classifiers, k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). Feature selection using ReliefF algorithm is applied to reduce redundancy features set and obtain the optimum features for classification. Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and Saarbruecken Voice Database (SVD) are used. This research was done on multiclass and by specific pathology. The experimental results automates the process of voice analysis hence produce promising results of the presence of diseases in vocal folds.

Pathological Voice Source Analysis System Using a Flow Waveform-Matched Biomechanical Model.

Voice production occurs through vocal cord and vibration coupled to glottal airflow. Vocal cord lesions affect the vocal system and lead to voice disorders. In this paper, a pathological voice source analysis system is designed. This study integrates nonlinear dynamics with an optimized asymmetric two-mass model to explore nonlinear characteristics of vocal cord vibration, and changes in acoustic parameters, such as fundamental frequency, caused by distinct subglottal pressure and varying degrees of vocal cord paralysis are analyzed. Various samples of sustained vowel /a/ of normal and pathological voices were extracted from MEEI (Massachusetts Eye and Ear Infirmary) database. A fitting procedure combining genetic particle swarm optimization and a quasi-Newton method was developed to optimize the biomechanical model parameters and match the targeted voice source. Experimental results validate the applicability of the proposed model to reproduce vocal cord vibration with high accuracy, and show that paralyzed vocal cord increases the model coupling stiffness.

Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach

Computerized detection of voice disorders has attracted considerable academic and clinical interest in the hope of providing an effective screening method for voice diseases before endoscopic confirmation. This study proposes a deep-learning-based approach to detect pathological voice and examines its performance and utility compared with other automatic classification algorithms. This study retrospectively collected 60 normal voice samples and 402 pathological voice samples of 8 common clinical voice disorders in a voice clinic of a tertiary teaching hospital. We extracted Mel frequency cepstral coefficients from 3-second samples of a sustained vowel. The performances of three machine learning algorithms, namely, deep neural network (DNN), support vector machine, and Gaussian mixture model, were evaluated based on a fivefold cross-validation. Collective cases from the voice disorder database of MEEI (Massachusetts Eye and Ear Infirmary) were used to verify the performance of the classification mechanisms. The experimental results demonstrated that DNN outperforms Gaussian mixture model and support vector machine. Its accuracy in detecting voice pathologies reached 94.26% and 90.52% in male and female subjects, based on three representative Mel frequency cepstral coefficient features. When applied to the MEEI database for validation, the DNN also achieved a higher accuracy (99.32%) than the other two classification algorithms. By stacking several layers of neurons with optimized weights, the proposed DNN algorithm can fully utilize the acoustic features and efficiently differentiate between normal and pathological voice samples. Based on this pilot study, future research may proceed to explore more application of DNN from laboratory and clinical perspectives.

Voice Pathology Detection and Classification Using Auto-Correlation and Entropy Features in Different Frequency Regions

Automatic voice pathology detection and classification systems effectively contribute to the assessment of voice disorders, enabling the early detection of voice pathologies and the diagnosis of the type of pathology from which patients suffer. This paper concentrates on developing an accurate and robust feature extraction for detecting and classifying voice pathologies by investigating different frequency bands using autocorrelation and entropy. We extracted maximum peak values and their corresponding lag values from each frame of a voiced signal by using autocorrelation as features to detect and classify pathological samples. We also extracted the entropy for each frame of the voice signal after we normalized its values to be used as the features. These features were investigated in distinct frequency bands to assess the contribution of each band to the detection and classification processes. Various samples of the sustained vowel /a/ for both normal and pathological voices were extracted from three different databases in English, German, and Arabic. A support vector machine was used as a classifier. We also performed u-tests to investigate if there is a significant difference between the means of the normal and pathological samples. The best achieved accuracies in both detection and classification varied depending on the used band, method, and database. The most contributive bands in both detection and classification were between 1000 and 8000 Hz. The highest obtained accuracies in the case of detection were 99.69%, 92.79%, and 99.79% for Massachusetts eye and ear infirmary (MEEI), Saarbrucken voice database (SVD), and Arabic voice pathology database (AVPD), respectively. However, the highest achieved accuracies for classification were 99.54%, 99.53%, and 96.02% for MEEI, SVD, and AVPD, correspondingly, using the combined feature.

Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms.

A voice disorder database is an essential element in doing research on automatic voice disorder detection and classification. Ethnicity affects the voice characteristics of a person, and so it is necessary to develop a database by collecting the voice samples of the targeted ethnic group. This will enhance the chances of arriving at a global solution for the accurate and reliable diagnosis of voice disorders by understanding the characteristics of a local group. Motivated by such idea, an Arabic voice pathology database (AVPD) is designed and developed in this study by recording three vowels, running speech, and isolated words. For each recorded samples, the perceptual severity is also provided which is a unique aspect of the AVPD. During the development of the AVPD, the shortcomings of different voice disorder databases were identified so that they could be avoided in the AVPD. In addition, the AVPD is evaluated by using six different types of speech features and four types of machine learning algorithms. The results of detection and classification of voice disorders obtained with the sustained vowel and the running speech are also compared with the results of an English-language disorder database, the Massachusetts Eye and Ear Infirmary (MEEI) database.

Post Hybrid Cochlear Implant Hearing Loss and Endolymphatic Hydrops.

To evaluate for potential causes of delayed loss of residual hearing that variably occurs with hybrid cochlear implants. Histopathological evaluation of 29 human temporal bone (HTB) with cochlear implant (CI). The Neurotology and House HTB Laboratory of UCLA (House-UCLA). HTB from CI patients from the House-UCLA HTB Laboratory (n = 28) and one courtesy of Massachusetts Eye and Ear Infirmary (MEEI). Histopathological analysis to identify the location of cochleostomy, fibrosis, and bone formation in the scala vestibuli and tympani, and endolymphatic hydrops. Spiral ganglion neuron counts were obtained. Statistical analysis compared presence of cochleostomy and location with the histopathological findings. Seventeen of 29 bones with fibrosis in the scala vestibule (SV) and tympani had evidence of a cochleostomy involving the SV containing the ductus reunions, all of which had hydrops. Ten of 11 bones had no SV fibrosis, and a cochleostomy limited to the scala tympani, of which all had no hydrops. One HTB had moderate SV fibrosis not involving the ductus reuniens, and was without hydrops. One HTB had a SV cochleostomy but the electrode ruptured Reissner's membrane, and was without hydrops. Cochleostomy was significantly associated with SV fibrosis and hydrops (p < 0.01), those without hydrops had no SV atrophy (p < 0.01). Round window insertion was associated with no fibrosis and no hydrops. We hypothesize that cochleostomies involving scala vestibuli incite fibrosis, compromising the ductus reuniens, causing hydrops which may cause the delayed loss of residual low frequency hearing in CI.

Successful intraoperative electrophysiologic monitoring of the recurrent laryngeal nerve, a multidisciplinary approach: The Massachusetts Eye and Ear Infirmary monitoring collaborative protocol with experience in over 3000 cases.

Although intraoperative nerve monitoring (IONM) is utilized increasingly, the information on the related anesthesia technique is limited. This study presents an up-to-date clinical algorithm, including setup and troubleshooting of an IONM system, endotracheal tube placement, and anesthetic parameters. To our knowledge, this is the first interdisciplinary collaborative protocol for monitored neck surgery based on the published evidence and clinical experience. The Departments of Otolaryngology Head and Neck Surgery, Anesthesiology, and Audiology collaboratively developed a protocol for IONM of the recurrent laryngeal nerve (RLN) based on published evidence and our experience with 3000 patients over a 16-year period. No complications related to monitoring or endotracheal tube placement were noted when the IONM protocol was implemented at Massachusetts Eye and Ear Infirmary (MEEI). The IONM protocol has proven to be vital in standardizing care and in avoiding intraoperative errors. An IONM system entails an anesthesiologist who understands the challenges posed by this technique; muscle relaxation must be minimized/eliminated to optimize IONM. © 2016 Wiley Periodicals, Inc. Head Neck 38: First-1494, 2016.

Automatic voice pathology detection and classification using vocal tract area irregularity

In this paper, an automatic voice pathology detection (VPD) system based on voice production theory is developed. More specifically, features are extracted from vocal tract area, which is connected to the glottis. Voice pathology is related to a vocal fold problem, and hence the vocal tract area which is connected to vocal folds or glottis should exhibit irregular patterns over frames in case of a sustained vowel for a pathological voice. This irregular pattern is quantified in the form of different moments across the frames to distinguish between normal and pathological voices. The proposed VPD system is evaluated on the Massachusetts Eye and Ear Infirmary (MEEI) database and Saarbrucken Voice Database (SVD) with sustained vowel samples. Vocal tract irregularity features and support vector machine classifier are used in the proposed system. The proposed system achieves 99.22%±0.01 accuracy on the MEEI database and 94.7%±0.21 accuracy on the SVD. The results indicate that vocal tract irregularity measures can be used effectively in automatic voice pathology detection.

The role of serological titres in the diagnosis of ocular toxoplasmosis.

The role of serological titres in the diagnosis of ocular toxoplasmosis.

Herpes zoster ophthalmicus: declining age at presentation

ObjectiveTo investigate changes in the age of occurrence of herpes zoster ophthalmicus (HZO) in patients presenting to the Massachusetts Eye and Ear Infirmary (MEEI) from 2007 through 2013.DesignRetrospective chart review.SettingAcademic...

Robust and complex approach of pathological speech signal analysis

This paper presents a study of the approaches in the state-of-the-art in the field of pathological speech signal analysis with a special focus on parametrization techniques. It provides a description of 92 speech features where some of them are already widely used in this field of science and some of them have not been tried yet (they come from different areas of speech signal processing like speech recognition or coding). As an original contribution, this work introduces 36 completely new pathological voice measures based on modulation spectra, inferior colliculus coefficients, bicepstrum, sample and approximate entropy and empirical mode decomposition. The significance of these features was tested on 3 (English, Spanish and Czech) pathological voice databases with respect to classification accuracy, sensitivity and specificity. To our best knowledge the introduced approach based on complex feature extraction and robust testing outperformed all works that have been published already in this field. The results (accuracy, sensitivity and specificity equal to 100.0±0.0%) are discussable in the case of Massachusetts Eye and Ear Infirmary (MEEI) database because of its limitation related to a length of sustained vowels, however in the case of Príncipe de Asturias (PdA) Hospital in Alcalá de Henares of Madrid database we made improvements in classification accuracy (82.1±3.3%) and specificity (83.8±5.1%) when considering a single-classifier approach. Hopefully, large improvements may be achieved in the case of Czech Parkinsonian Speech Database (PARCZ), which are discussed in this work as well. All the features introduced in this work were identified by Mann–Whitney U test as significant (p<0.05) when processing at least one of the mentioned databases. The largest discriminative power from these proposed features has a cepstral peak prominence extracted from the first intrinsic mode function (p=6.9443×10−32) which means, that among all newly designed features those that quantify especially hoarseness or breathiness are good candidates for pathological speech identification. The paper also mentions some ideas for the future work in the field of pathological speech signal analysis that can be valuable especially under the clinical point of view.