Early detection of sudden cardiac death may be used for surviving the life of cardiac patients. In this paper we have investigated an algorithm to detect and predict sudden cardiac death, by processing of heart rate v...Early detection of sudden cardiac death may be used for surviving the life of cardiac patients. In this paper we have investigated an algorithm to detect and predict sudden cardiac death, by processing of heart rate variability signal through the classical and time-frequency methods. At first, one minute of ECG signals, just before the cardiac death event are extracted and used to compute heart rate variability (HRV) signal. Five features in time domain and four features in frequency domain are extracted from the HRV signal and used as classical linear features. Then the Wigner Ville transform is applied to the HRV signal, and 11 extra features in the time-frequency (TF) domain are obtained. In order to improve the performance of classification, the principal component analysis (PCA) is applied to the obtained features vector. Finally a neural network classifier is applied to the reduced features. The obtained results show that the TF method can classify normal and SCD subjects, more efficiently than the classical methods. A MIT-BIH ECG database was used to evaluate the proposed method. The proposed method was implemented using MLP classifier and had 74.36% and 99.16% correct detection rate (accuracy) for classical features and TF method, respectively. Also, the accuracy of the KNN classifier were 73.87% and 96.04%.展开更多
在MIMO-OFDM水声通信系统中,由于信道间的相互干扰和水声信道严重时延扩展产生的频率选择性衰落,系统的通信误码率较高。针对这一问题,研究了空频编码的MIMO-OFDM通信,提出空频迭代信道估计与均衡(Spatial Frequency Iterative Channel ...在MIMO-OFDM水声通信系统中,由于信道间的相互干扰和水声信道严重时延扩展产生的频率选择性衰落,系统的通信误码率较高。针对这一问题,研究了空频编码的MIMO-OFDM通信,提出空频迭代信道估计与均衡(Spatial Frequency Iterative Channel Estimation and Equalization,SFICEE)方法。该方法通过载波间的空频正交性进行各收发阵元对的信道估计,并通过空频均衡获得符号初始估计,迭代更新信道估计,而后通过符号后验软信息反馈进行迭代空频软均衡。仿真结果表明,当误码率为10^(-3)时,文中所提出的SFICEE方法经过二次迭代与STBC方法相比具有4.8 d B的性能增益,相对于SFBC方法有2.8 d B的性能提升。当输入信噪比相同时,文中所提出方法的星座图更加收敛,可以更好地降低水下通信系统的误码率。展开更多
Ayurvedic and other alternative medical practi-tioners throughout the world have been using pulse diagnosis to detect disease and the organ at distress by feeling the palpations at three close yet precise positions of...Ayurvedic and other alternative medical practi-tioners throughout the world have been using pulse diagnosis to detect disease and the organ at distress by feeling the palpations at three close yet precise positions of the radial artery. This paper presents a robust electro-mechanical system, ‘Nadi Yantra’ which uses piezoelectric based pressure sensors to capture the signals from the radial artery. Morphology of the waveforms obtained from our system concurs with standard physiological arterial signals. Reproducibility and stability of the system has been verified. Signal processing techniques were applied to obtain features such as amplitude, power spectral density, bandpower and spectral centroid to reflect variations in signals from the three channels. Further, wavelet based techniques were used to process the pressure signals and percussion peaks were identified. The interval between the percussion peaks was used to calculate Heart Rate Varibility (HRV), a useful tool for assessing the status of the autonomic nervous system of the human body non-invasively. Time domain indices were calculated from direct measurement of peak-peak (PP) intervals and from differences between the PP intervals. Frequency domain indices such as very low frequency (VLF) power, low frequency (LF) power, high frequency (HF) power, LF/HF ratio were also calculated. Thereafter, nonlinear Poincare analysis was carried out. A map of consecutive PP intervals was fitted to an ellipse using least squares method. Results from 7 datasets are depicted in this paper. A novel pressure pulse recording instrument is deve loped for the objective assessment of the ancient sci-ence of pulse diagnosis. The features calculated using multi resolution wavelet analysis show potential in the evaluation of the autonomic nervous system of the human body.展开更多
文摘Early detection of sudden cardiac death may be used for surviving the life of cardiac patients. In this paper we have investigated an algorithm to detect and predict sudden cardiac death, by processing of heart rate variability signal through the classical and time-frequency methods. At first, one minute of ECG signals, just before the cardiac death event are extracted and used to compute heart rate variability (HRV) signal. Five features in time domain and four features in frequency domain are extracted from the HRV signal and used as classical linear features. Then the Wigner Ville transform is applied to the HRV signal, and 11 extra features in the time-frequency (TF) domain are obtained. In order to improve the performance of classification, the principal component analysis (PCA) is applied to the obtained features vector. Finally a neural network classifier is applied to the reduced features. The obtained results show that the TF method can classify normal and SCD subjects, more efficiently than the classical methods. A MIT-BIH ECG database was used to evaluate the proposed method. The proposed method was implemented using MLP classifier and had 74.36% and 99.16% correct detection rate (accuracy) for classical features and TF method, respectively. Also, the accuracy of the KNN classifier were 73.87% and 96.04%.
文摘依据FFT→优化窗→IFFT思路,突破线性时频变换的窗函数积分性能桎梏,实现高性能优化窗函数的线性时频变换应用,建立新型时频变换算法——K-S变换.对信号x(t)的FFT频谱向量进行频移处理后,与该频移点下Kaiser优化窗的频谱向量进行Hadamard乘积,再将乘积结果进行FFT逆变换(IFFT),构造出K-S变换复时频矩阵,由此获得x(t)的时间-频率-幅值、时间-频率-相位三维信息;给出逆变换的数学推导与局部性质、线性性质和变分辨率特性;0~150 kHz电网的稳态与时变超谐波信号仿真实验表明,K-S变换的时域、频域分辨能力均优于流行的短时傅里叶变换、S变换,具有优良的变分辨率性能;0~40 kHz超谐波信号的实测证明,基于K-S变换的超谐波电压幅值测量绝对误差均小于0.032 3 V.
文摘在MIMO-OFDM水声通信系统中,由于信道间的相互干扰和水声信道严重时延扩展产生的频率选择性衰落,系统的通信误码率较高。针对这一问题,研究了空频编码的MIMO-OFDM通信,提出空频迭代信道估计与均衡(Spatial Frequency Iterative Channel Estimation and Equalization,SFICEE)方法。该方法通过载波间的空频正交性进行各收发阵元对的信道估计,并通过空频均衡获得符号初始估计,迭代更新信道估计,而后通过符号后验软信息反馈进行迭代空频软均衡。仿真结果表明,当误码率为10^(-3)时,文中所提出的SFICEE方法经过二次迭代与STBC方法相比具有4.8 d B的性能增益,相对于SFBC方法有2.8 d B的性能提升。当输入信噪比相同时,文中所提出方法的星座图更加收敛,可以更好地降低水下通信系统的误码率。
文摘Ayurvedic and other alternative medical practi-tioners throughout the world have been using pulse diagnosis to detect disease and the organ at distress by feeling the palpations at three close yet precise positions of the radial artery. This paper presents a robust electro-mechanical system, ‘Nadi Yantra’ which uses piezoelectric based pressure sensors to capture the signals from the radial artery. Morphology of the waveforms obtained from our system concurs with standard physiological arterial signals. Reproducibility and stability of the system has been verified. Signal processing techniques were applied to obtain features such as amplitude, power spectral density, bandpower and spectral centroid to reflect variations in signals from the three channels. Further, wavelet based techniques were used to process the pressure signals and percussion peaks were identified. The interval between the percussion peaks was used to calculate Heart Rate Varibility (HRV), a useful tool for assessing the status of the autonomic nervous system of the human body non-invasively. Time domain indices were calculated from direct measurement of peak-peak (PP) intervals and from differences between the PP intervals. Frequency domain indices such as very low frequency (VLF) power, low frequency (LF) power, high frequency (HF) power, LF/HF ratio were also calculated. Thereafter, nonlinear Poincare analysis was carried out. A map of consecutive PP intervals was fitted to an ellipse using least squares method. Results from 7 datasets are depicted in this paper. A novel pressure pulse recording instrument is deve loped for the objective assessment of the ancient sci-ence of pulse diagnosis. The features calculated using multi resolution wavelet analysis show potential in the evaluation of the autonomic nervous system of the human body.
