In this paper a novel method for automatic detection and classification of sleep stages using a multichannel electroencephalography (EEG) is presented. Understanding the sleep mechanism is vital for diagnosis and treatment of sleep disorders. The EEG is one of the most important tools of studying and diagnosing sleep disorders. EEG signals waveforms activity interpretation is performed by visual analysis (a very difficult procedure). This research aim is to ease the difficulties involved in the existing manual process of EEG interpretation by proposing an automatic sleep stage detection and classification system. The suggested method based on Multichannel Auto Regressive (MAR) model. The multichannel analysis approach incorporates the cross correlation information existing between different EEG signals. In the training phase, we used the vector quantization (VQ) algorithm, Linde-Buzo-Gray (LBG) and sleep stage definition, by estimation of probability mass functions (pmf) per every sleep stage using Generalized Log Likelihood Ratio (GLLR) distortion. The classification phase was performed using Kullback-Leibler (KL) divergence. The results of this research are promising with classification accuracy rate of 93.2%. The results encourage continuation of this research in the sleep field and in other biomedical signals applications.

JiBSTRACTThe problem of Speech Recognition in a noisy environment is addressed. Particularly the mismatch problem originated when training a system in a l1cleantt environment and operating it in a noisy one. When measuring the similarity between a noisy test utterance and a list of clean templates a correction process, based on a series of Wiener filters built using the hypothesized clean template, is applied to the feature vectors of the noisy word .The filtering process is optimized as a by product of the Dynamic Programing algorithm of the scoring step. Tests were conducted on two data bases, one in Hebrew and the second in Japanese, using additive white and car noise at different SNRs. The method shows a very good performance and compares well with other methods proposed in the literature. . INTRODUCTIONThe performance of speech recognition systems designed to work in noise free conditions is strongly affected by the presence of noise. If the system has to be operated in different noise environments, training the system in one environment and operating it in a different one leads to a mismatch problem responsible for a poor performance.In contrast with other methods which use a speech enhancement step in order to input to the recognition system with noise reduced utterances, the key feature of the proposed method is doing the filtering at the scoring step using the information present in the clean templatesThe proposed method performs a feature correction on the noisy tests utterances in order to eliminate noise effects. The correcting mechanism is based on optimal filtering and on Dynamic Programming. The optimal filter at each state of the Dynamic programming is based on information present in each state. This correction is performed when computing the local distance between two feature vectors, one pertaining to a clean template and the other to a noisy test word.An estimate of the background noise and different template hypothesis are used to built the correcting filters. In this way the decisions are made using all the useful information present in the recognizer. The method was implemented using the system depicted in Figure 1. The system is trained only in a quiet environment and the background noise present in the operating environment (assumed to be stationary or slowly time variant) is learned in the neighborhoods of the words to be recognized. The Hypothesized Wiener Filtering (HWF) mechanism is performed during the DTW scoring step.

No abstract

ITU-T P.862 -"Perceptual Evaluation of Speech Quality (PESQ)" is well known as an intrusive objective speech quality assessment method. Some reports have found that the PESQ time alignment mechanism fails to estimate delay where signals with high packet loss rate and dynamic time processing are present. A new time-alignment algorithm to improve the PESQ accuracy for time-scale modified voice transmission is suggested here. In the propose model, the time alignment of reference and degraded speech is estimated using Dynamic Time-Warping (DTW) in contrast to correlation and splitting methods used in the standard PESQ. Comparative results versus subjective Mean Opinion Score (MOS) show improvement in cases where dynamic time processing of signals is present.

Aims This study aimed to assess the ability of a voice analysis application to discriminate between wet and dry states in chronic heart failure (CHF) patients undergoing regular scheduled haemodialysis treatment due to volume overload as a result of their chronic renal failure. Methods and resultsIn this single-centre, observational study, five patients with CHF, peripheral oedema of ≥2, and pulmonary congestion-related dyspnoea, undergoing haemodialysis three times per week, recorded five sentences into a standard smartphone/tablet before and after haemodialysis. Recordings were provided that same noon/early evening and the next morning and evening. Patient weight was measured at the hospital before and after each haemodialysis session. Recordings were analysed by a smartphone application (app) algorithm, to compare speech measures (SMs) of utterances collected over time. On average, patients provided recordings throughout 25.8 ± 3.9 dialysis treatment cycles, resulting in a total of 472 recordings. Weight changes of 1.95 ± 0.64 kg were documented during cycles. Median baseline SM prior to dialysis was 0.87 ± 0.17, and rose to 1.07 ± 0.15 following the end of the dialysis session, at noon (P = 0.0355), and remained at a similar level until the following morning (P = 0.007). By the evening of the day following dialysis, SMs returned to baseline levels (0.88 ± 0.19). Changes in patient weight immediately after dialysis positively correlated with SM changes, with the strongest correlation measured the evening of the dialysis day [slope: À0.40 ± 0.15 (95% confidence interval: À0.71 to À0.10), P = 0.0096]. Conclusions The fluid-controlled haemodialysis model demonstrated the ability of the app algorithm to identify cyclic changes in SMs, which reflected bodily fluid levels. The voice analysis platform bears considerable potential as a harbinger of impending fluid overload in a range of clinical scenarios, which will enhance monitoring and triage efforts, ultimately optimizing remote CHF management.