Detecting Parkinson's disease from sustained phonation and speech signals.

This study investigates signals from sustained phonation and text-dependent speech modalities for Parkinson's disease screening. Phonation corresponds to the vowel /a/ voicing task and speech to the pronunciation of a short sentence in Lithuanian language. Signals were recorded through two channels simultaneously, namely, acoustic cardioid (AC) and smart phone (SP) microphones. Additional modalities were obtained by splitting speech recording into voiced and unvoiced parts. Information in each modality is summarized by 18 well-known audio feature sets. Random forest (RF) is used as a machine learning algorithm, both for individual feature sets and for decision-level fusion. Detection performance is measured by the out-of-bag equal error rate (EER) and the cost of log-likelihood-ratio. Essentia audio feature set was the best using the AC speech modality and YAAFE audio feature set was the best using the SP unvoiced modality, achieving EER of 20.30% and 25.57%, respectively. Fusion of all feature sets and modalities resulted in EER of 19.27% for the AC and 23.00% for the SP channel. Non-linear projection of a RF-based proximity matrix into the 2D space enriched medical decision support by visualization.

Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness.

This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG) signal stream, during the golf swing using a 7-iron club and exploits information extracted from EMG dynamics to predict the success of the resulting shot. Muscles of the arm and shoulder on both the left and right sides, namely flexor carpi radialis, extensor digitorum communis, rhomboideus and trapezius, are considered for 15 golf players (∼5 shots each). The method using Gaussian filtering is outlined for EMG onset time estimation in each channel and activation sequence profiling. Shots of each player revealed a persistent pattern of muscle activation. Profiles were plotted and insights with respect to player effectiveness were provided. Inspection of EMG dynamics revealed a pair of highest peaks in each channel as the hallmark of golf swing, and a custom application of peak detection for automatic extraction of swing segment was introduced. Various EMG features, encompassing 22 feature sets, were constructed. Feature sets were used individually and also in decision-level fusion for the prediction of shot effectiveness. The prediction of the target attribute, such as club head speed or ball carry distance, was investigated using random forest as the learner in detection and regression tasks. Detection evaluates the personal effectiveness of a shot with respect to the player-specific average, whereas regression estimates the value of target attribute, using EMG features as predictors. Fusion after decision optimization provided the best results: the equal error rate in detection was 24.3% for the speed and 31.7% for the distance; the mean absolute percentage error in regression was 3.2% for the speed and 6.4% for the distance. Proposed EMG feature sets were found to be useful, especially when used in combination. Rankings of feature sets indicated statistics for muscle activity in both the left and right body sides, correlation-based analysis of EMG dynamics and features derived from the properties of two highest peaks as important predictors of personal shot effectiveness. Activation sequence profiles helped in analyzing muscle orchestration during golf shot, exposing a specific avalanche pattern, but data from more players are needed for stronger conclusions. Results demonstrate that information arising from an EMG signal stream is useful for predicting golf shot success, in terms of club head speed and ball carry distance, with acceptable accuracy. Surface EMG data, collected with a goal to automatically evaluate golf player's performance, enables wearable computing in the field of ambient intelligence and has potential to enhance exercising of a long carry distance drive.

Exploring the feasibility of smart phone microphone for measurement of acoustic voice parameters and voice pathology screening.

The objective of this study is to evaluate the reliability of acoustic voice parameters obtained using smart phone (SP) microphones and investigate the utility of use of SP voice recordings for voice screening. Voice samples of sustained vowel/a/obtained from 118 subjects (34 normal and 84 pathological voices) were recorded simultaneously through two microphones: oral AKG Perception 220 microphone and SP Samsung Galaxy Note3 microphone. Acoustic voice signal data were measured for fundamental frequency, jitter and shimmer, normalized noise energy (NNE), signal to noise ratio and harmonic to noise ratio using Dr. Speech software. Discriminant analysis-based Correct Classification Rate (CCR) and Random Forest Classifier (RFC) based Equal Error Rate (EER) were used to evaluate the feasibility of acoustic voice parameters classifying normal and pathological voice classes. Lithuanian version of Glottal Function Index (LT_GFI) questionnaire was utilized for self-assessment of the severity of voice disorder. The correlations of acoustic voice parameters obtained with two types of microphones were statistically significant and strong (r = 0.73-1.0) for the entire measurements. When classifying into normal/pathological voice classes, the Oral-NNE revealed the CCR of 73.7% and the pair of SP-NNE and SP-shimmer parameters revealed CCR of 79.5%. However, fusion of the results obtained from SP voice recordings and GFI data provided the CCR of 84.60% and RFC revealed the EER of 7.9%, respectively. In conclusion, measurements of acoustic voice parameters using SP microphone were shown to be reliable in clinical settings demonstrating high CCR and low EER when distinguishing normal and pathological voice classes, and validated the suitability of the SP microphone signal for the task of automatic voice analysis and screening.

Categorizing normal and pathological voices: automated and perceptual categorization.

OBJECTIVES: The aims of the present study were to evaluate the accuracy of an elaborated automated voice categorization system that classified voice signal samples into healthy and pathological classes and to compare it with classification accuracy that was attained by human experts. MATERIAL AND METHODS: We investigated the effectiveness of 10 different feature sets in the classification of voice recordings of the sustained phonation of the vowel sound /a/ into the healthy and two pathological voice classes, and proposed a new approach to building a sequential committee of support vector machines (SVMs) for the classification. By applying "genetic search" (a search technique used to find solutions to optimization problems), we determined the optimal values of hyper-parameters of the committee and the feature sets that provided the best performance. Four experienced clinical voice specialists who evaluated the same voice recordings served as experts. The "gold standard" for classification was clinically and histologically proven diagnosis. RESULTS: A considerable improvement in the classification accuracy was obtained from the committee when compared with the single feature type-based classifiers. In the experimental investigations that were performed using 444 voice recordings coming from 148 subjects, three recordings from each subject, we obtained the correct classification rate (CCR) of over 92% when classifying into the healthy-pathological voice classes, and over 90% when classifying into three classes (healthy voice and two nodular or diffuse lesion voice classes). The CCR obtained from human experts was about 74% and 60%, respectively. CONCLUSION: When operating under the same experimental conditions, the automated voice discrimination technique based on sequential committee of SVM was considerably more effective than the human experts.

Combining image, voice, and the patient's questionnaire data to categorize laryngeal disorders.

OBJECTIVE: This paper is concerned with soft computing techniques for categorizing laryngeal disorders based on information extracted from an image of patient's vocal folds, a voice signal, and questionnaire data. METHODS: Multiple feature sets are exploited to characterize images and voice signals. To characterize colour, texture, and geometry of biological structures seen in colour images of vocal folds, eight feature sets are used. Twelve feature sets are used to obtain a comprehensive characterization of a voice signal (the sustained phonation of the vowel sound /a/). Answers to 14 questions constitute the questionnaire feature set. A committee of support vector machines is designed for categorizing the image, voice, and query data represented by the multiple feature sets into the healthy, nodular and diffuse classes. Five alternatives to aggregate separate SVMs into a committee are explored. Feature selection and classifier design are combined into the same learning process based on genetic search. RESULTS: Data of all the three modalities were available from 240 patients. Among those, 151 patients belong to the nodular class, 64 to the diffuse class and 25 to the healthy class. When using a single feature set to characterize each modality, the test set data classification accuracy of 75.0%, 72.1%, and 85.0% was obtained for the image, voice and questionnaire data, respectively. The use of multiple feature sets allowed to increase the accuracy to 89.5% and 87.7% for the image and voice data, respectively. The test set data classification accuracy of over 98.0% was obtained from a committee exploiting multiple feature sets from all the three modalities. The highest classification accuracy was achieved when using the SVM-based aggregation with hyper parameters of the SVM determined by genetic search. Bearing in mind the difficulty of the task, the obtained classification accuracy is rather encouraging. CONCLUSIONS: Combination of both multiple feature sets characterizing a single modality and the three modalities allowed to substantially improve the classification accuracy if compared to the highest accuracy obtained from a single feature set and a single modality. In spite of the unbalanced data sets used, the error rates obtained for the three classes were rather similar.

Advances in laryngeal imaging.

Imaging and image analysis became an important issue in laryngeal diagnostics. Various techniques, such as videostroboscopy, videokymography, digital kymography, or ultrasonography are available and are used in research and clinical practice. This paper reviews recent advances in imaging for laryngeal diagnostics.

Quantitative microlaryngoscopic measurements of vocal fold polyps, glottal gap and their relation to vocal function.

OBJECTIVES: The purpose of this study was to quantify the size of vocal fold polyps and to investigate the relationship between the glottal gap and parameters of acoustic voice analysis and phonetography. MATERIAL AND METHODS: Eighty-one microlaryngoscopic images and digital recordings of voices (acoustic analysis and phonetogram) acquired from the patients with vocal fold polyps (VFPs) were employed in this study. Vocal fold (VF) images were collected during routine direct microlaryngoscopy using Moller-Wedel Universa 300 surgical microscope, 3-CCD Elmo 768 x 576-pixel color video camera and a 300 W Xenon light source. Acoustic voice analysis and phonetography were established using Dr. Speech (Tiger Electronics Inc.) software. Microlaryngoscopic images were processed by original software created by ELINTA and displayed on a monitor. The relative lengths and widths of vocal fold polyps as well as percentage area of VFP were calculated. The Pearson's correlation was applied to reveal the correlation between VFP dimensions and acoustic voice parameters. RESULTS: There were no statistically significant differences between the dimensions of left and right vocal folds and VFPs. Statistically significant slight to mild correlations between measured dimensions of VFP acoustic and phonetogram parameters were revealed, with HNR and phonetogram area showing the strongest correlation to the size of VFPs. CONCLUSION: The results of our study confirm that quantitative microlaryngoscopic measurements of vocal fold polyp and glottal gap dimensions may be a useful tool for objective assessment of glottic incompetence and voice impairment.

Development of teleconsultations systems for e-health.

Two prototype telemedicine systems have been developed: 1) a wireless system for status assessment of cardiology patients (WSCP), 2) a system for medical image management and teleconsultations (IMTS). The former system enables the patient to record an ECG on a personal digital assistant (PDA), view it and send it via a wireless connection. The doctor on duty is then able to view the received ECG and make appropriate decisions, also to apply for consultation by sending the received ECG to the PDA of a cardiology expert. The system logs all performed operations. The hardware used in the system consists of personal computers (PCs), PDAs, analog-digital converters, ECG sensors and GPRS modems. Software consists of programs for patients, doctors on duty, cardiology experts and administration, along with a central database. The second system is intended to be used by professional doctors for management of collected images and for teleconsultations via videoconferencing in order to obtain a second opinion. The system provides an integrated environment eliminating the need to jump between many applications. By using the system, doctors are able to acquire images from analog and digital cameras, process and enhance them, as well as upload them to local or remote databases. Doctors are also able to design custom database forms. The teleconsultation part of the system supports video and audio over ISDN and TCP-IP, using both a hardware codec (Zydacron Z360) and a software codec (based on MS Netmeeting). Images are sent from one client to another using the standard protocol T.120. Images become synchronized immediately upon reception by another client.