The Change of Vocal Tract Length in People with Parkinson's Disease.

Hypokinetic dysarthria is one of the early symptoms of Parkinson's disease (PD) and has been proposed for early detection and also for monitoring of the progression of the disease. PD reduces the control of vocal tract muscles such as the tongue and lips and, therefore the length of the active vocal tract is altered. However, the change in the vocal tract length due to the disease has not been investigated. The aim of this study was to determine the difference in the apparent vocal tract length (AVTL) between people with PD and age-matched control healthy people. The phoneme, /a/ from the UCI Parkinson's Disease Classification Dataset and the Italian Parkinson's Voice and Speech Dataset were used and AVTL was calculated based on the first four formants of the sustained phoneme (F1-F4). The results show a correlation between Parkinson's disease and an increase in vocal tract length. The most sensitive feature was the AVTL calculated using the first formants of sustained phonemes (F1). The other significant finding reported in this article is that the difference is significant and only appeared in the male participants. However, the size of the database is not sufficiently large to identify the possible confounding factors such as the severity and duration of the disease, medication, age, and comorbidity factors.Clinical relevance-The outcomes of this research have the potential to improve the identification of early Parkinsonian dysarthria and monitor PD progression.

Impact of Biological Sex on Radar-Measured Heart Sound Quality.

Radar based contact-free technology has number of potential applications for monitoring the cardiopulmonary functions of patients. However, no study has evaluated the effect of gender on the quality of the recordings. This study makes an attempt to distinguish radar based recording of male and female subjects. The study analysed a publicly available dataset of radar-recorded heart sound signals from both male and female subjects. Here, we exploit the reference signal-to-noise ratio (RSNR) to quantify the signal's quality. The results indicate that there is a significant difference in the signal quality between males and females, with males having a higher RSNR value compared to females. This could be a limitation in the widespread use of the current radar based cardiopulmonary recording techniques and overcoming this should be considered for future research.Clinical relevance- This work has highlighted the gender based difference. By considering this, the radar based cardiopulmonary device has the potential for being used for patients requiring long-term monitoring.

Monitoring the Effect of Levodopa Using Sustained Phonemes in Parkinson's Disease Patients.

Parkinson's disease (PD) is a neurological disease identified by multiple symptoms, and levodopa is one of the most effective medications for treating the disease. To determine the dosage of levodopa, it is necessary to meet on a regular basis and observe motor function. The early detection and progression of the disease have been proposed using hypokinetic dysarthria. However, previous studies have not examined the effects of levodopa on speech rigorously and have provided inconsistent results. In this study, three sustained phonemes of PD patients were investigated for the effect of medication. A set of features characterizing vocal fold dynamics as well as the vocal tract coordinators were extracted from the sustained phonemes /of 28 PD patients during levodopa medication off and on states. All the features were statistically investigated and classified using a linear discriminant analysis (LDA) classifier. LDA classifier identified medication on from medication off based on the combined features from phoneme /a/, /o/ and /m/ with the accuracy=82.75% and F1-score=82.18%. Voice recording of PD patients during sustained phonemes /a/, /o/ and /m/ has the potential for identifying whether the patients are in On state or Off state of medication.Clinical Relevance- The outcomes of this study have the potential to monitor the effect and progress of levodopa on PD patients.

Voice Features of Sustained Phoneme as COVID-19 Biomarker.

BACKGROUND: The COVID-19 pandemic has resulted in enormous costs to our society. Besides finding medicines to treat those infected by the virus, it is important to find effective and efficient strategies to prevent the spreading of the disease. One key factor to prevent transmission is to identify COVID-19 biomarkers that can be used to develop an efficient, accurate, noninvasive, and self-administered screening procedure. Several COVID-19 variants cause significant respiratory symptoms, and thus a voice signal may be a potential biomarker for COVID-19 infection. AIM: This study investigated the effectiveness of different phonemes and a range of voice features in differentiating people infected by COVID-19 with respiratory tract symptoms. METHOD: This cross-sectional, longitudinal study recorded six phonemes (i.e., /a/, /e/, /i/, /o/, /u/, and /m/) from 40 COVID-19 patients and 48 healthy subjects for 22 days. The signal features were obtained for the recordings, which were statistically analyzed and classified using Support Vector Machine (SVM). RESULTS: The statistical analysis and SVM classification show that the voice features related to the vocal tract filtering (e.g., MFCC, VTL, and formants) and the stability of the respiratory muscles and lung volume (Intensity-SD) were the most sensitive to voice change due to COVID-19. The result also shows that the features extracted from the vowel /i/ during the first 3 days after admittance to the hospital were the most effective. The SVM classification accuracy with 18 ranked features extracted from /i/ was 93.5% (with F1 score of 94.3%). CONCLUSION: A measurable difference exists between the voices of people with COVID-19 and healthy people, and the phoneme /i/ shows the most pronounced difference. This supports the potential for using computerized voice analysis to detect the disease and consider it a biomarker.

Phonemes based detection of parkinson's disease for telehealth applications.

Dysarthria is an early symptom of Parkinson's disease (PD) which has been proposed for detection and monitoring of the disease with potential for telehealth. However, with inherent differences between voices of different people, computerized analysis have not demonstrated high performance that is consistent for different datasets. The aim of this study was to improve the performance in detecting PD voices and test this with different datasets. This study has investigated the effectiveness of three groups of phoneme parameters, i.e. voice intensity variation, perturbation of glottal vibration, and apparent vocal tract length (VTL) for differentiating people with PD from healthy subjects using two public databases. The parameters were extracted from five sustained phonemes; /a/, /e/, /i/, /o/, and /u/, recorded from 50 PD patients and 50 healthy subjects of PC-GITA dataset. The features were statistically investigated, and then classified using Support Vector Machine (SVM). This was repeated on Viswanathan dataset with smartphone-based recordings of /a/, /o/, and /m/ of 24 PD and 22 age-matched healthy people. VTL parameters gave the highest difference between voices of people with PD and healthy subjects; classification accuracy with the five vowels of PC-GITA dataset was 84.3% while the accuracy for other features was between 54% and 69.2%. The accuracy for Viswanathan's dataset was 96.0%. This study has demonstrated that VTL obtained from the recording of phonemes using smartphone can accurately identify people with PD. The analysis was fully computerized and automated, and this has the potential for telehealth diagnosis for PD.

Detecting Effect of Levodopa in Parkinson's Disease Patients Using Sustained Phonemes.

BACKGROUND: Parkinson's disease (PD) is a multi-symptom neurodegenerative disease generally managed with medications, of which levodopa is the most effective. Determining the dosage of levodopa requires regular meetings where motor function can be observed. Speech impairment is an early symptom in PD and has been proposed for early detection and monitoring of the disease. However, findings from previous research on the effect of levodopa on speech have not shown a consistent picture. METHOD: This study has investigated the effect of medication on PD patients for three sustained phonemes; /a/, /o/, and /m/, which were recorded from 24 PD patients during medication off and on stages, and from 22 healthy participants. The differences were statistically investigated, and the features were classified using Support Vector Machine (SVM). RESULTS: The results show that medication has a significant effect on the change of time and amplitude perturbation (jitter and shimmer) and harmonics of /m/, which was the most sensitive individual phoneme to the levodopa response. /m/ and /o/ performed at a comparable level in discriminating PD-off from control recordings. However, SVM classifications based on the combined use of the three phonemes /a/, /o/, and /m/ showed the best classifications, both for medication effect and for separating PD from control voice. The SVM classification for PD-off versus PD-on achieved an AUC of 0.81. CONCLUSION: Studies of phonation by computerized voice analysis in PD should employ recordings of multiple phonemes. Our findings are potentially relevant in research to identify early parkinsonian dysarthria, and to tele-monitoring of the levodopa response in patients with established PD.

Wavelet analysis of surface electromyography to determine muscle fatigue.

Muscle fatigue is often a result of unhealthy work practice. It has been known for some time that there is a significant change in the spectrum of the electromyography (EMG) of the muscle when it is fatigued. Due to the very complex nature of this signal however, it has been difficult to use this information to reliably automate the process of fatigue onset determination. If such a process implementation were feasible, it could be used as an indicator to reduce the chances of work-place injury. This research report on the effectiveness of the wavelet transform applied to the EMG signal as a means of identifying muscle fatigue. We report that with the appropriate choice of wavelet functions and scaling factors, it is possible to achieve reliable discrimination of the fatigue phenomenon, appropriate to an automated fatigue identification system.