Classification of three pathological voices based on specific features groups using support vector machine

Determining and classifying pathological human sounds are still an interesting area of research in the field of speech processing. This paper explores different methods of voice features extraction, namely: Mel frequency cepstral coefficients (MFCCs), zero-crossing rate (ZCR) and discrete wavelet transform (DWT). A comparison is made between these methods in order to identify their ability in classifying any input sound as a normal or pathological voices using support vector machine (SVM). Firstly, the voice signal is processed and filtered, then vocal features are extracted using the proposed methods and finally six groups of features are used to classify the voice data as healthy, hyperkinetic dysphonia, hypokinetic dysphonia, or reflux laryngitis using separate classification processes. The classification results reach 100% accuracy using the MFCC and kurtosis feature group. While the other classification accuracies range between~60% to~97%. The Wavelet features provide very good classification results in comparison with other common voice features like MFCC and ZCR features. This paper aims to improve the diagnosis of voice disorders without the need for surgical interventions and endoscopic procedures which consumes time and burden the patients. Also, the comparison between the proposed feature extraction methods offers a good reference for further researches in the voice classification area

Convolutional neural networks for pathological voice detection

Acoustic analysis using signal processing tools can be used to extract voice features to distinguish whether a voice is pathological or healthy. The proposed work uses spectrogram of voice recordings from a voice database as the input to a Convolutional Neural Network (CNN) for automatic feature extraction and classification of disordered and normal voice. The novel classifier achieved 88.5%, 66.2% and 77.0% accuracy on training, validation and testing data set respectively on 482 normal and 482 organic dysphonia speech files. It reveals that the proposed novel algorithm on the Saarbruecken Voice Database can effectively been used for screening pathological voice recordings

Novelty, distillation, and federation in machine learning for medical imaging

The practical application of deep learning methods in the medical domain has many challenges. Pathologies are diverse and very few examples may be available for rare cases. Where data is collected it may lie in multiple institutions and cannot be pooled for practical and ethical reasons. Deep learning is powerful for image segmentation problems but ultimately its output must be interpretable at the patient level. Although clearly not an exhaustive list, these are the three problems tackled in this thesis. To address the rarity of pathology I investigate novelty detection algorithms to find outliers from normal anatomy. The problem is structured as first finding a low-dimension embedding and then detecting outliers in that embedding space. I evaluate for speed and accuracy several unsupervised embedding and outlier detection methods. Data consist of Magnetic Resonance Imaging (MRI) for interstitial lung disease for which healthy and pathological patches are available; only the healthy patches are used in model training. I then explore the clinical interpretability of a model output. I take related work by the Canon team — a model providing voxel-level detection of acute ischemic stroke signs — and deliver the Alberta Stroke Programme Early CT Score (ASPECTS, a measure of stroke severity). The data are acute head computed tomography volumes of suspected stroke patients. I convert from the voxel level to the brain region level and then to the patient level through a series of rules. Due to the real world clinical complexity of the problem, there are at each level — voxel, region and patient — multiple sources of “truth”; I evaluate my results appropriately against these truths. Finally, federated learning is used to train a model on data that are divided between multiple institutions. I introduce a novel evolution of this algorithm — dubbed “soft federated learning” — that avoids the central coordinating authority, and takes into account domain shift (covariate shift) and dataset size. I first demonstrate the key properties of these two algorithms on a series of MNIST (handwritten digits) toy problems. Then I apply the methods to the BraTS medical dataset, which contains MRI brain glioma scans from multiple institutions, to compare these algorithms in a realistic setting

An intelligent healthcare system for detection and classification to discriminate vocal fold disorders

The growing population of senior citizens around the world will appear as a big challenge in the future and they will engage a significant portion of the healthcare facilities. Therefore, it is necessary to develop intelligent healthcare systems so that they can be deployed in smart homes and cities for remote diagnosis. To overcome the problem, an intelligent healthcare system is proposed in this study. The proposed intelligent system is based on the human auditory mechanism and capable of detection and classification of various types of the vocal fold disorders. In the proposed system, critical bandwidth phenomena by using the bandpass filters spaced over Bark scale is implemented to simulate the human auditory mechanism. Therefore, the system acts like an expert clinician who can evaluate the voice of a patient by auditory perception. The experimental results show that the proposed system can detect the pathology with an accuracy of 99.72%. Moreover, the classification accuracy for vocal fold polyp, keratosis, vocal fold paralysis, vocal fold nodules, and adductor spasmodic dysphonia is 97.54%, 99.08%, 96.75%, 98.65%, 95.83%, and 95.83%, respectively. In addition, an experiment for paralysis versus all other disorders is also conducted, and an accuracy of 99.13% is achieved. The results show that the proposed system is accurate and reliable in vocal fold disorder assessment and can be deployed successfully for remote diagnosis. Moreover, the performance of the proposed system is better as compared to existing disorder assessment systems

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference

Desenvolvimento de protótipo de sistema de suporte ao diagnóstico de patologias da voz

Mestrado em IPB-ESTGRESUMOA voz é uma ferramenta de comunicação primordial nas relações inter-humanas, por meio de inflexões, pausas, variações de ritmo e de intensidade. É considerada a integridade da nossa identidade, pois através dela somos reconhecidos e a sua qualidade permite-nos expressar eficazmente. As patologias vocais encontram-se presentes na nossa sociedade, com profundo impacto na qualidade de vida das pessoas. A origem deve-se a várias causas e apresentam diferentes graus de gravidade. A patologia pode progredir de forma benigna ou maligna, por isso é de extrema importância ter atenção aos sinais de alteração. Um diagnóstico precoce é muito relevante para o tratamento. Porém, as formas de avaliação existentes nesta área são invasivas e desagradáveis, sendo incomodativas para o paciente. Estes aspetos motivaram o desenvolvimento de métodos não invasivos, que possam fazer uma avaliação exata e possam ser utilizados como um método de ajuda ao diagnóstico eficaz. Neste trabalho desenvolveu-se um sistema de suporte à decisão médica no diagnóstico de patologias vocais. Para o desenvolvimento deste sistema foi necessário o estudo de um conjunto de parâmetros acústicos, bem como de classificadores, como rede neuronal artificial (RNA), com o objetivo de fazer a classificação final do paciente entre saudável e patológico. Os parâmetros utilizados neste trabalho são: Jitter Absoluto (Jitta), Jitter Relativo (Jitter), Shimmer Absoluto (ShdB), Shimmer Relativo (Shim) , Harmonic to Noise Ratio (HNR), e a Autocorrelacão. E como classificador o modelo da rede neuronal Multi Layer Perceptron (MLP). O sistema interface gráfica desenvolvido neste trabalho servirá como um método complementar no pré-diagnóstico de patologias da voz. O modelo MLP utilizada obteve uma taxa de exatidão de 98.86% que se encontra entre os melhores valores tendo em conta estado a arte, no entanto a possibilidade da inserção deste sistema em clínicas e hospitais contribuirá para o seu aperfeiçoamento por meio de familiarização com profissionais de saúde.Voice is a powerful tool that make possible the communication between humans being, by way of inflections, pauses, variations in rhythm and intensity. As the integrity of our identity, it is considered to allow us to express ourselves effectively to be recognized. Vocal pathology become a reality in modern society, having a deep impact by affecting people life quality. it is extremely important to be aware of signs of alterations that can be derived from different causes, with different degrees of severity, which evolve in a benign or malignant. Early diagnostic intervention revealed to be extremely crucial to treatment. However, the already existed examination procedures in this field show to be invasive and unpleasant, which can be uncomfortable to the patient. Therefore, the need to develop a non-invasive method emerged in order to delivery an accurate and effective diagnosis. In this work, a medical decision support system in the diagnosis of vocal pathologies was developed. For the development of this system, it was necessary to study a set of acoustic parameters, as well as classifiers, of artificial neural networks (ANN), with the objective of making the final classification of the patient between healthy and pathological. The parameters used in this work are Absolute Jitter (Jitta), Relative Jitter (Jitt), Absolute Shimmer (ShdB), Relative Shimmer (Shim), Harmonic to Noise Ratio (HNR), and Autocorrelation. And as a classifier, the Multi-Layer Perception (MLP) neural network model. The graphic interface system developed in this work will serve as a complementary method in the pre-diagnosis of voice pathologies. The MLP model used obtained a success rate of 98.86%, which is among the best values taking into account the state of the art, however the possibility of inserting this system in clinics and hospitals will contribute to its improvement through familiarization with health professionals

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions