Significance of voiced and unvoiced speech segments for the detection of common cold.

This work investigates the significance of the voiced and unvoiced region for detecting common cold from the speech signal. In literature, the entire speech signal is processed to detect the common cold and other diseases. This study uses a short-time energy-based approach to segment the voiced and unvoiced region of the speech signal. Then, frame-wise mel frequency cepstral coefficients (MFCC) features are extracted from the voiced and unvoiced segments of each speech utterance, and statistics (mean, variance, skewness, and kurtosis) are calculated to get the feature vector for each speech utterance. The support vector machine (SVM) is utilized to analyze the performance of features extracted from the voiced and unvoiced region. Result shows that the feature extracted from voiced segments, unvoiced segments, and complete active speech (CAS) gives almost similar results using the MFCC features and SVM classifier. Therefore, rather than processing the CAS, we can process the unvoiced speech segments, which have fewer frames compared to CAS and voiced regions of speech. The processing of solely unvoiced segments can reduce the time and computation complexity of a speech signal-based common cold detection system.

Time-frequency analysis of speech signal using Chirplet transform for automatic diagnosis of Parkinson's disease.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder in the world after Alzheimer's disease. Early diagnosing PD is challenging as it evolved slowly, and its symptoms eventuate gradually. Recent studies have demonstrated that changes in speech may be utilized as an excellent biomarker for the early diagnosis of PD. In this study, we have proposed a Chirplet transform (CT) based novel approach for diagnosing PD using speech signals. We employed CT to get the time-frequency matrix (TFM) of each speech recording, and we extracted time-frequency based entropy (TFE) features from the TFM. The statistical analysis demonstrates that the TFE features reflect the changes in speech that occurs in the speech due to PD, hence can be used for classifying the PD and healthy control (HC) individuals. The effectiveness of the proposed framework is validated using the vowels and words from the PC-GITA database. The genetic algorithm is utilized to select the optimum features subset, while a support vector machine (SVM), decision tree (DT), K-Nearest Neighbor (KNN), and Naïve Bayes (NB) classifiers are employed for classification. The TFE features outperform the breathiness and Mel frequency cepstral coefficients (MFCC) features. The SVM classifier is most effective compared to other machine-learning classifiers. The highest classification accuracy rates of 98% and 99% are achieved using the vowel /a/ and word /atleta/, respectively. The results reveal that the proposed CT-based entropy features effectively diagnose PD using the speech of a person.

Detection of Common Cold from Speech Signals using Deep Neural Network.

This paper presents a deep learning-based analysis and classification of cold speech observed when a person is diagnosed with the common cold. The common cold is a viral infectious disease that affects the throat and the nose. Since speech is produced by the vocal tract after linear filtering of excitation source information, during a common cold, its attributes are impacted by the throat and the nose. The proposed study attempts to develop a deep learning-based classification model that can accurately predict whether a person has a cold or not based on their speech. The common cold-related information is captured using Mel-frequency cepstral coefficients (MFCC) and linear predictive coding (LPC) from the speech signal. The data imbalance is handled using the sampling strategy, SMOTE-Tomek links. Then, utilizing MFCC and LPC features, a deep learning-based model is trained and then used to categorize cold speech. The performance of a deep learning-based method is compared to logistic regression, random forest, and gradient boosted tree classifiers. The proposed model is less complex and uses a smaller feature set while giving comparable results to other state-of-the-art methods. The proposed method gives an UAR of 67.71 % , higher than the benchmark OpenSMILE SVM result of 64 % . The study's success will yield a noninvasive method for cold detection, which can further be extended to detect other speech-affecting pathologies.

Modulation of intracellular iron levels by oxidative stress implicates a novel role for iron in signal transduction.

Reactive oxygen species (ROS) display cytotoxicity that can be exacerbated by iron. Paradoxically, HeLa cells treated with the ROS-generators menadione and 2,3-dimethoxy-1,4-naphthoquinone display increased free labile iron. HeLa cells exposed to ROS undergo apoptosis but iron chelation limits the extent of cell death suggesting the rise in intracellular iron plays a signaling role in this pathway. This idea is supported by the fact that iron chelation also alters the pattern of ROS-induced phosphorylation of stress-activated protein kinases SAPK/JNK and p38 MAPK. Thus, ROS-induced increases in cellular free iron contribute to signaling events triggered during oxidative stress response.

Multiscale Amplitude Feature and Significance of Enhanced Vocal Tract Information for Emotion Classification.

In this paper, a novel multiscale amplitude feature is proposed using multiresolution analysis (MRA) and the significance of the vocal tract is investigated for emotion classification from the speech signal. MRA decomposes the speech signal into number of sub-band signals. The proposed feature is computed by using sinusoidal model on each sub-band signal. Different emotions have different impacts on the vocal tract. As a result, vocal tract responds in a unique way for each emotion. The vocal tract information is enhanced using pre-emphasis. Therefore, emotion information manifested in the vocal tract can be well exploited. This may help in improving the performance of emotion classification. Emotion recognition is performed using German emotional EMODB database, interactive emotional dyadic motion capture database, simulated stressed speech database, and FAU AIBO database with speech signal and speech with enhanced vocal tract information (SEVTI). The performance of the proposed multiscale amplitude feature is compared with three different types of features: 1) the mel frequency cepstral coefficients; 2) the Teager energy operator (TEO)-based feature (TEO-CB-Auto-Env); and 3) the breathinesss feature. The proposed feature outperforms the other features. In terms of recognition rates, the features derived from the SEVTI signal, give better performance compared to the features derived from the speech signal. Combination of the features with SEVTI signal shows average recognition rate of 86.7% using EMODB database.

Domain Wall Motion-Based Dual-Threshold Activation Unit for Low-Power Classification of Non-Linearly Separable Functions.

Recently, a great deal of scientific endeavour has been devoted to developing spin-based neuromorphic platforms owing to the ultra-low-power benefits offered by spin devices and the inherent correspondence between spintronic phenomena and the desired neuronal, synaptic behavior. While domain wall motion-based threshold activation unit has previously been demonstrated for neuromorphic circuits, it remains well known that neurons with threshold activation cannot completely learn nonlinearly separable functions. This paper addresses this fundamental limitation by proposing a novel domain wall motion-based dual-threshold activation unit with additional nonlinearity in its function. Furthermore, a new learning algorithm is formulated for a neuron with this activation function. We perform 100 trials of tenfold training and testing of our neural networks on real-world datasets taken from the UCI machine learning repository. On an average, the proposed algorithm achieves [Formula: see text] lower misclassification rate (MCR) than the traditional perceptron learning algorithm. In a circuit-level simulation, the neural networks with the proposed activation unit are observed to outperform the perceptron networks by as much as [Formula: see text] MCR. The energy consumption of a neuron having the proposed domain wall motion-based activation unit averages to [Formula: see text] approximately.

Analysis of physiological signals using state space correlation entropy.

In this letter, the authors propose a new entropy measure for analysis of time series. This measure is termed as the state space correlation entropy (SSCE). The state space reconstruction is used to evaluate the embedding vectors of a time series. The SSCE is computed from the probability of the correlations of the embedding vectors. The performance of SSCE measure is evaluated using both synthetic and real valued signals. The experimental results reveal that, the proposed SSCE measure along with SVM classifier have sensitivity value of 91.60%, which is higher than the performance of both sample entropy and permutation entropy features for detection of shockable ventricular arrhythmia.

Application of radio frequency based digital thermometer for real-time monitoring of dairy cattle rectal temperature.

AIM: Dairy cattle health monitoring program becomes vital for detecting the febrile conditions to prevent the outbreak of the animal diseases as well as ensuring the fitness of the animals that are directly affecting the health of the consumers. The aim of this study was to validate real-time rectal temperature (RT) data of radio frequency based digital (RFD) thermometer with RT data of mercury bulb (MB) thermometer in dairy cattle. MATERIALS AND METHODS: Two experiments were conducted. In experiment I, six female Jersey crossbred cattle with a mean (±standard error of the mean) body weight of 534.83±13.90 kg at the age of 12±0.52 years were used to record RT for 2 h on empty stomach and 2 h after feeding at 0, 30, 60, 90, and 120 min using a RFD thermometer as well as a MB thermometer. In experiment II, six female Jersey crossbred cattle were further used to record RT for 2 h before exercise and 2 h after exercise at 0, 30, 60, 90, and 120 min. Two-way repeated measures analysis of variance with post hoc comparisons by Bonferroni test was done. RESULTS: Real-time RT data recorded by RFD thermometer as well as MB thermometer did not differ (p>0.05) before and after feeding/exercise. An increase (p<0.05) in RT after feeding/exercise in experimental crossbred cattle was recorded by both RFD thermometer and MB thermometer. CONCLUSION: The results obtained in the present study suggest that the body temperature recordings from RFD thermometer would be acceptable and thus RFD thermometer could work well for monitoring real-time RT in cattle.

Beta-amyloid induces the production of active, matrix-degrading proteases in cultured rat astrocytes.

The senile and neuritic plaque neuropathology of Alzheimer's disease (AD) is accompanied by an inflammatory response that includes activated astrocytes and microglia. Activated mononuclear phagocytes and reactive astrocytes, in response to inflammatory cytokines, secrete a set of extracellular matrix (ECM)-degrading enzymes that include the matrix metalloproteinases (MMPs). The major peptide component of senile plaques of AD, beta-amyloid (Abeta), stimulates the production of several MMPs from cultured rat astrocytes and microglia. The purpose of this study was two-fold: (1) to compare the pattern of MMP induction in rat astrocytes on treatment with 'soluble' and 'fibrillar' Abeta(1-40) and Abeta(1-42), and (2) to examine whether treatment of astrocytes with Abeta results in degraded fragments of ECM. Abeta aggregation differentially affected the production of MMP-2 and MMP-9 in astrocyte cultures. Activation experiments with amino phenyl mercuric acetate suggested that the 52-54 kDa gelatin-degrading activity was an activated form of MMP-2. In addition, Abeta peptide induced both MMP-3 and plasminogen activator-like activity from astrocytes. When medium from Abeta-treated, astrocyte cultures was immunoblotted for fibronectin, several immunopositive, lower molecular weight bands were observed as compared to untreated conditioned medium, suggestive of the presence of an active fibronectin-degrading protease. Thus, Abeta induces the secretion of several matrix-degrading proteases and stimulates matrix degradation in rat astrocytes. Since matrix-degrading proteases are elevated in AD brain, these proteases may influence the stability of ECM or other MMP substrates and thus may play a role in the neurotrophic/neurotoxic events associated with AD.

Women's empowerment and regional variation of contraceptive norms in Bangladesh.

Using data derived from the 2007 Bangladesh Demographic and Health Survey (BDHS), this study investigates the regional variation of contraceptive norms according to the empowerment status of women in Bangladesh. The result suggests that contraceptive norms vary from region to region. Logistic regression analysis suggests that there exists a positive relationship between women's empowerment and use of contraceptive methods in all regions except Barisal and Chittagong. The result also indicates that women's empowerment has a significant positive effect on contraceptive norms in the Dhaka, Khulna, and Rajshahi regions.