scholarly journals Adaptive Filtering Improved Apnea Detection Performance Using Tracheal Sounds in Noisy Environment: A Simulation Study

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yanan Wu ◽  
Jing Liu ◽  
Baolin He ◽  
Xiaotong Zhang ◽  
Lu Yu
Keyword(s):  
Adaptive Filtering ◽  
Predictive Value ◽  
Poor Performance ◽  
Detection Algorithm ◽  
Kappa Coefficient ◽  
Detection Performance ◽  
Least Mean Square ◽  
Noisy Environment ◽  
Apnea Detection ◽  
Tracheal Sounds

Objective. Tracheal sounds were used to detect apnea on various occasions. However, ambient noises can contaminate tracheal sounds which result in poor performance of apnea detection. The objective of this paper was to apply the adaptive filtering (AF) algorithm to improve the quality of tracheal sounds and examine the accuracy of the apnea detection algorithm using tracheal sounds after AF. Method. Tracheal sounds were acquired using a primary microphone encased in a plastic bell, and the ambient noises were collected using a reference microphone resting outside the plastic bell in quiet and noisy environments, respectively. Simultaneously, the flow pressure signals and thoracic and abdominal movement were obtained as the standard signals to determine apnea events. Then, the normalized least mean square (NLMS) AF algorithm was applied to the tracheal sounds mixed with noises. Finally, the algorithm of apnea detection was used to the tracheal sounds with AF and the tracheal sounds without AF. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and Cohen’s kappa coefficient of apnea detection were calculated. Results. Forty-six healthy subjects, aged 18-35 years and with BMI<21.4, were included in the study. The apnea detection performance using tracheal sounds was as follows: in the quiet environment, the tracheal sounds without AF detected apnea with 97.2% sensitivity, 99.9% specificity, 99.8% PPV, 99.4% NPV, 99.5% accuracy, and 0.982 kappa coefficient. The tracheal sounds with AF detected apnea with 98.2% sensitivity, 99.9% specificity, 99.4% PPV, 99.6% NPV, 99.6% accuracy, and 0.985 kappa coefficient. While in the noisy environment, the tracheal sounds without AF detected apnea with 81.1% sensitivity, 96.9% specificity, 85.1% PPV, 96% NPV, 94.2% accuracy, and 0.795 kappa coefficient and the tracheal sounds with AF detected apnea with 91.5% sensitivity, 97.4% specificity, 88.4% PPV, 98.2% NPV, 96.4% accuracy, and 0.877 kappa coefficient. Conclusion. The performance of apnea detection using tracheal sounds with the NLMS AF algorithm in the noisy environment proved to be accurate and reliable. The AF technology could be applied to the respiratory monitoring using tracheal sounds.

Realization of Enhanced LMS Filter Using Carry Skip Adder And Quantum Dot Cellular Automation In VLSI For Removing Impulse Noises In Medical Image

2019 ◽  
Vol 14 ◽  
Author(s):  
K.R. Shankarkumar ◽  
Gokul Kumar
Keyword(s):  
Adaptive Filtering ◽  
Adaptive Filters ◽  
Pass Band ◽  
Least Mean Square ◽  
Low Pass ◽  
Parallel Prefix ◽  
Loop Feedback ◽  
Band Pass ◽  
The Difference ◽  
Prefix Adder

: Filtering is an important step in the field of image processing to suppress the required parts or to remove any artifacts present in it. There are different types of filters like low pass, high pass, Band pass, IIR, FIR and adaptive filtering etc.., in these filters adaptive filters is an important filter because it is used to remove the noisy signal and images. Least Mean Square filter is a type of an adaptive filtering which is used to remove the noises present in the medical images. The working of LMS is based on the minimization of the difference between the error images using a closed loop feedback. Therefore presented technique called as Q-CSKA. Here the CSKA performs its operation in stages which is based on the nucleus stage. In the traditional CSKA the nucleus stage is depend on the parallel prefix adder in this work it is replaced by the QCA adder. The QCA adder utilizes the less area compared to PPA and it can be realized in Nanometer range also. For multiplexers, And OR Invert, OR and Invert logic is used to reduce the area and delay. Due to these advantages of the QCA, AOI-OAI logic the proposed method outperformed the LMS implementation in area, power, and accuracy and delay, this based five type image noise of medical pictures related to the best technique is out comes. It helps to medicinal practitioner to resolve the symptoms of patient with ease.

Study of BLAST Signal Detection Algorithm in LTE System

2013 ◽  
Vol 756-759 ◽  
pp. 3183-3188
Author(s):  
Tao Lei ◽  
Deng Ping He ◽  
Fang Tang Chen
Keyword(s):  
Signal Detection ◽  
High Speed ◽  
Data Communication ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Detection Algorithms ◽  
High Speed Data ◽  
Simulation Results ◽  
Better Than

BLAST can achieve high speed data communication. Its signal detection directly affects performance of BLAST receiver. This paper introduced several signal detection algorithmsZF algorithm, MMSE algorithm, ZF-SIC algorithm and MMSE-SIC algorithm. The simulation results show that the traditional ZF algorithm has the worst performance, the traditional MMSE algorithm and the ZF-SIC algorithm is similar, but with the increase of the SNR, the performance of ZF-SIC algorithm is better than MMSE algorithm. MMSE-SIC algorithm has the best detection performance in these detection algorithms.

Implementation of Adaptive Noise Canceller System for Audio-Related Applications

2013 ◽  
Vol 3 (4) ◽  
pp. 51-67
Author(s):  
Swati S. Godbole ◽  
Sanjay B. Pokle
Keyword(s):  
Adaptive Filtering ◽  
Noise Cancellation ◽  
Mean Square ◽  
Least Mean Square ◽  
Adaptive Noise Cancellation ◽  
Noise Component ◽  
Wide Range ◽  
Adaptive Noise ◽  
Adaptive Noise Canceller ◽  
Filtering Techniques

This paper describes the performance of Adaptive Noise Cancellation system. Basic concept of adaptive noise canceller is to process signals from two input sources and to reduce the level of undesired noise with adaptive filtering techniques. Adaptive filtering techniques play vital role in wide range of applications. An implementation of adaptive noise cancellation system is used to remove undesired noise from a received signal for various audio related applications that has been developed and implemented by MATLAB. The dual channel adaptive noise cancellation system uses an adaptive filter with least mean square algorithm to cancel noise component from primary signal picked up by primary sensor. Various parameters such as convergence behavior, tracking ability of the algorithm, signal to noise ratio, mean square error etc. of ANC system are studied, analyzed for various applications of adaptive noise cancellation and the same are discussed in this paper.

A complementary least-mean-square algorithm of adaptive filtering for SQUID based magnetocardiography

2005 ◽  
Vol 14 (6) ◽  
pp. 1095-1100 ◽  
Author(s):  
Li Zhuo ◽  
Chen Geng-Hua ◽  
Zhang Li-Hua ◽  
Yang Qian-Sheng ◽  
Feng Ji
Keyword(s):  
Adaptive Filtering ◽  
Mean Square ◽  
Least Mean Square ◽  
Least Mean Square Algorithm

Depth-First QRD-M Detection Algorithm for MIMO Systems

2013 ◽  
Vol 347-350 ◽  
pp. 3478-3481
Author(s):  
Li Liu ◽  
Jin Kuan Wang ◽  
Xin Song ◽  
Yin Hua Han ◽  
Yu Huan Wang
Keyword(s):  
Ordered Set ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Detection Algorithm ◽  
Qr Decomposition ◽  
Detection Performance ◽  
Detection Scheme ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Ml Detection

Maximum likelihood (ML) detection algorithm for multiple input multiple output (MIMO) systems provided the best bit error rate (BER) performance with heavy calculating complexity. The use of QR decomposition with M-algorithm (QRD-M) had been proposed to provide near-ML detection performance and lower calculating complexity. However, its complexity still grew exponentially with increasing dimension of the transmitted signal. To reduce the problem, an improved detection scheme was proposed here. After constructing the tree detecting model of MIMO systems, the ML search of one layer was done, the branch metrics were calculated and sorted, which gave an ordered set of the layer, then depth-first search were used to search the left layers with termination methods. The proposed algorithm provides near QRD-M detection performance.

scholarly journals Development of a Japanese version of the SARC-F for diabetic patients: an examination of reliability and validity

2016 ◽  
Vol 29 (5) ◽  
pp. 935-942 ◽  
Author(s):  
Satoshi Ida ◽  
Kazuya Murata ◽  
Daiki Nakadachi ◽  
Yuki Ishihara ◽  
Kanako Imataka ◽  
...  
Keyword(s):  
Predictive Value ◽  
Reliability And Validity ◽  
Japanese Version ◽  
Cross Sectional Study ◽  
Kappa Coefficient ◽  
Diabetic Patients ◽  
Cross Sectional ◽  
Predictive Values ◽  
Retest Reliability ◽  
European Working

Abstract Background SARC-F is a 5-item, self-administered questionnaire developed to screen sarcopenia. To date, no Japanese version of the SARC-F has been developed. Aims To create a Japanese version of the SARC-F (SARC-F-J), a questionnaire for diabetic patients, and to investigate its reliability and validity. Methods This was a cross-sectional study. A Japanese translation of the SARC-F was created and revised, and the authors of the original version of the SARC-F verified the back-translation. The questionnaire was tested in diabetic outpatients aged ≥65 years who had received treatment at our hospital. After 14 weeks, the kappa coefficient was used to evaluate the retest reliability. Using the diagnostic criteria for sarcopenia based on the European Working Group on Sarcopenia in Older People as the reference standard, we calculated the sensitivity, specificity, positive predictive value, and negative predictive value of the SARC-F-J. Results The study comprised 207 patients (men, 60.8%; women, 39.2%). The kappa coefficient was 0.66. For men and women, the sensitivities were 14.6 and 33.3%, specificities were 85.8 and 72.4%, positive predictive values were 33.3 and 17.3%, and negative predictive values were 65.7 and 86.2%, respectively. Discussion The probability of identifying the condition is considered high when patients are diagnosed with sarcopenia using SARC-F-J. Conclusions The retest reliability of SARC-F-J was regarded to be good. When EWGSOP was assumed as a reference, the specificity of SARC-F-J was high. Because the sensitivity was low, patients with sarcopenia could not be screened, and hence, attention is needed.

Sign in / Sign up

Export Citation Format

Share Document