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A ready — made hearing protection device (HPD) is effective on preventing noise induced hearing loss (NIHS), but workers in noisy environment often resist to wear it. Because the ready — made products are uncomfortable and are hard to understand the others. To prevent hearing loss effectively it is important that workers keep wearing a HPD. So we proposed a made — to — order HPD in which a small acoustic filter is inserted. And we researched about the actual efficiency of a new developed custom-made HPD using a small size acoustic filter. Also, we found out that workers are more satisfied with new development than a former protection device from a workers’ response.

Dynamic epicardial mapping system is a useful instrument for studying electrophysiological characteristics of atrial fibrillation (AF). It is important to develop an optimal therapy for AF. The purpose of this research is to show the electrical activity on the epicardial surface of atrium and therefore find the appropriate technique or ablation procedures to prevent AF. It emphasizes on dynamic threedimensional (3D) holograph based on whole-atrial epicardial mapping. A system was designed to display successively either sampling waveform or the pattern of depolarization distribution. 64 unipolar electrodes arranged in eight flexible patches were adopted to detect signals. They can be located respectively on the surface of posterior and anterior LA or RA, and pulmonary veins (4 patches). Each electrode patch was designed to adaptable to the corresponding epicardial surface of canine atrium. Animal tests were operated on ten dogs in which chronic AF had been induced. Each 20-second sampling data during sinus or AF rhythm were recorded and stored for analysis. Multilevel B-splines were applied to compute the whole atria surface through a set of irregularly spaced point. Thus the 3D isopotential map was drawn to reflect the conduction of depolarization wave dynamically during AF. The propagation of electrical activity was different during sinus and AF rhythm. The results demonstrate that the system could find the pathway of AF through 3D dynamic exhibition. Nevertheless, 64 sites are not enough to detect the electrical activity thoroughly. A multi-site epicardial mapping system more than 128 channels is being developed for more exact mapping.

This paper presents heart sound analysis method based on Time-Frequency Distribution (TFD) analysis and Mel Frequency Cepstrum Coefficient (MFCC). TFD represents the heart sound in term of time and frequency simultaneously which while the MFCC defines a signal in term of frequency coefficient corresponding to the Mel filter scale. There are 100 normal data and 100 data with disease obtained from the hospital which consists of various kinds of problems including mitral regurgitation and stenosis, tricuspid regurgitation and stenosis, ventricular septal defect and other structural related disease. B-Distribution is chosen from a number of time-frequency analysis methods due its capability to represent the signal in the most efficient way in term of noise and cross term reduction. The advantage of MFCC is that it is good in error reduction and able to produce a robust feature when the signal is affected by noise. SVD/PCA technique is used to extract the important features out of the B-Distribution representation. The coefficient obtained from SVD-PCA and MFCC is later used for classification Artificial Neural Network. The results show that the system is able to produce the accuracy up to 90.0% using the TFD and 80.0% using the MFCC.

In ECG signals recorded with smart clothes disturbances as intermittent loss of signal from electrodes, movement artefacts, and electromyographic interference are common. In this study a multichannel method for spatio-temporal filtering is evaluated using ECG signals from a database and with three recordings made with a T-shirt with integrated textile electrodes. The sensitivity and precision of the signals from the database were 99.6% and 98.5%, respectively, if 12 channels were used and the signal-to-noise ratio was -10 dB. The filter gave a sensitivity of 99.6% and a precision of 99.5% in the recordings from the textile electrodes. In conclusion, the results obtained indicated that multichannel spatio-temporal filtration could be a suitable method for heartbeat detection in ECG measurements with textile electrodes.

An approach based on frequency domain analysis is proposed for the development of an aging index for the cardiovascular system. The approach is non-invasive, using photoplethysmography (PPG), which is in wide clinical use. A probe is applied to the subject’s finger-tip to provide a measure of the pulse waveform. The measured waveforms are filtered, digitised and post-processed. Individual pulses are extracted and the measurements de-trended and normalised to remove slowly time-varying effects due, for example, to respiration, and reduce calibration and measurement difficulties. Frequency analysis is then performed with the amplitudes and phases of the harmonics of the pulse waveform being determined and subsequently analysed with respect to those of the fundamental. Results of measurements taken from 105 subjects are reported. The age range of the subjects was from 6 to 86 years, of whom 20% had a range of known medical conditions. In particular, the log-magnitude of the first 4 harmonics is considered. Higher harmonics are dominated by measurement noise. For each individual there would seem to be a nearly-linear relationship between the log amplitude of the harmonics and the harmonic number, with the line of best fit to this data (for the first 4 harmonics) having a slope . Apart from young children, the magnitude of the gradient of this line tends to increase with increasing age, indicating that the relative decrease of energy in the pulse waveform is greatest for the higher harmonics. This is related to the well known fact that, with ageing, the dicrotic notch in the arterial waveform becomes less pronounced. An age index, which indicates cardiovascular age, is thus proposed based on the slope of this line of best fit for a given individual.

Wheezing often accompanies pulmonary pathologies and its detection is considered of great importance for the diagnosis and management of respiratory diseases. Our aim was to develop a simple and robust algorithm for wheeze detection in respiratory sound spectra to be used for long-term monitoring and early stage assessment of asthma episode in children. The robustness of the algorithm enables wheezing detection in presence of noise and moving artifacts. Children cannot perform respiratory function tests such as peak-flow measurement and therefore we find continuous recording and processing of respiratory sounds as an alternative. The algorithm we used for wheeze detection is based on the idea of frequency domain peak detection proposed by Shabtai-Musih et al. because of its simplicity and scoring used for specifying the likelihood that the peaks in power spectra represent wheezes. In our algorithm, we have modified the way of searching peaks in the spectrogram. Before searching for peaks, wavelet denoising was used in order to remove the noise in spectrum without affecting the peaks that we were searching for. Using the scoring algorithm we were able to create a binary image of the spectrogram of the sounds - wheezes and score the length (duration) of connected components considered as wheezing. The components that did not meet length criterion were rejected and were not considered as wheezing. The algorithm was tested on respiratory sound signals from public signal databases and on our own signals recorded in a group of 26 asthmatic children. The algorithm successfully detected wheezes in all signals containing wheezing.

We propose a method that combines independent component analysis (ICA) and probabilistic neural network to classify electrocardiogram (ECG) signals. In this study, ICA is used to extract important features from ECG signals. A probabilistic neural network follows to classify the input ECG beats into one of eight beat types. The independent components are estimated from the training ECG beats and serve as the bases of the system. The ECG beat samples are then projected on the bases to construct the ICA features for different beat types. The features based on ICA and the time interval between successive ECG beats are then built into a feature vector and employed as inputs to the probabilistic neural network. In the study, 9800 QRS samples, including eight different ECG types, were sampled from the MIT-BIH arrhythmia database. Half of the samples were used in the training phase and the other half in the testing phase. The experiments showed an impressive classification accuracy of 98.71% under the condition that 33 independent components were used. The results show the proposed method is deserved to be an excellent tool in the computer-aided diagnosis system.

Computer-aided bedside patient monitoring requires real-time vital function analysis. On-line Holter monitors need reliable and quick algorithms to perform all the necessary signal processing tasks. This paper presents all the methods that were conceptualized and implemented at the development of such a monitoring system at Medical Clinic No. 4 of Târgu Mureš. The system performs the following ECG signal processing steps: (1) Decomposition of the ECG signals using multi-resolution wavelet transformation, which also eliminates most of the high and low frequency noises. These components will serve as input for wave classification algorithms. (2) Identification of QRS complexes, P and T waves using two different algorithms: a sequential classification and a neural-network-based clustering algorithm. This latter also distinguishes normal R waves from abnormal cases. (3) Localization of several kinds of arrhythmia using a spectral method. An autoregressive (AR) model is applied to estimate the series of R-R intervals. The coefficients of the AR model are predicted using the Kalman filter, and these coefficients will determine a local spectrum for each QRS complex. By analyzing this spectrum, different arrhythmia cases (bigeminy, trigeminy, ventricular flutter, etc.) are identified. The algorithms were tested using the MIT-BIH signal database and own multi-channel ECG registrations. The QRS complex detection ratio is over 99.6%. Using the output of the above mentioned methods, the on-line monitor system performs heart rate variability (HRV) and heart rate turbulence (HRT) analysis, which can help the diagnosis and can predict dangerous states of the patient.

A number of Digital Signal Processing (DSP) techniques are being applied to Surface Electromyography (SEMG) for classification using signal feature extraction. This research is aimed at using Continuous Wavelet Transform (CWT) analysis of SEMG signals to develop and adopt a sound methodology for classification of the signals at different force levels. Traditional analysis methods such as Fast Fourier Transform (FFT) could not be used alone because muscle diagnosis requires time-based information. Therefore CWT was selected for this research as it includes time-based information as well as scales that can be converted into frequencies, making muscle diagnosis easier. CWT produces a scalogram plot along with its corresponding time-based frequency spectrum plot. Using both of these plots, extracted features of the dominant frequencies and the related scales can be used to train and validate a signal classifier based on an Artificial Neural Network (ANN). SEMG signals were obtained for a 10 second period sampled at 2048 Hz and digitally filtered using a Butterworth bandpass filter (5 to 500 Hz, 4th order). Signals were collected from the vastus medialis muscle of both legs of 45 healthy male subjects at 25%, 50% and 75% of their Maximum Voluntary Isometric Contraction (MVIC) force of the quadriceps. The extracted features selected for the two second period of the signal were the mean and median frequencies of the average power spectrum, and the RMS values at scale 8, 16, 32, 64 and 128 of the scalogram. The signals were analysed using CWT in LabVIEW with its Signal Processing Toolset. In this paper, the results are presented to show that the selected extracted features are suitable for the classification of SEMG signals at different force levels.

In Magnetic resonance imaging(MRI), imaging a moving organ such as the heart requires a trigger so that successive scans can be synchronized. In the case of cardiac imaging, the QRS complex of ECG is used as a trigger signal for MRI scan. But, gradient and RF(radio frequency) artifacts which are caused to static and dynamic field in MRI scanner cause interference in the ECG. Also, the signal shape of theses artifacts can be similar to the QRS-complex, causing possible misinterpretation during patient monitoring and false gating of the MRI. In case of using general FIR or IIR band-pass filters for minimizing the artifacts, artifact-reduction-ratio is not excellent. So, an adaptive real-time digital filter is proposed for reduction of noise by gradient and RF(radio frequency) artifacts. The proposed filter for MRI-Gating is based on the noise-canceller with NLMS(Normalized Least Mean Square) algorithm. The reference signals of the adaptive noise canceller are a combination of the noisy three channel ECG signals. Various tests were done on normal volunteers in various scenarios[SE(spin echo), FSE(fast spin echo), TR(transition time), TX-Gain, RX-gain, Spin-echo-degree, soon]. The noise canceller’s performance was measured offline, simulating real-time processing by point-by-point operations. In conclusions, the proposed method showed the acceptable quality of ECG signal with sufficient SNR for gating the MRI and possibility of real time implementation