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
Automatic diagnosis may help to decrease human based diagnosis error and assist physicians to focus on the correct disease and its treatment and to avoid wasting time on diagnosis. In this paper computer aided diagnos...Automatic diagnosis may help to decrease human based diagnosis error and assist physicians to focus on the correct disease and its treatment and to avoid wasting time on diagnosis. In this paper computer aided diagnosis is applied to the brain CT image processing. We compared performance of morphological operations in extracting three types of features, i.e. gray scale, symmetry and texture. Some classifiers were applied to classify normal and abnormal brain CT images. It showed that morphological operations can improve the result of accuracy. Moreover SVM classifier showed better result than other classifiers.展开更多
Phrenic nerve stimulation is a technique whereby a nerve stimulator provides electrical stimulation of the phrenic nerve to cause diaphragmatic contraction in patients with respiratory failure due to cervical spinal c...Phrenic nerve stimulation is a technique whereby a nerve stimulator provides electrical stimulation of the phrenic nerve to cause diaphragmatic contraction in patients with respiratory failure due to cervical spinal cord injury. This paper presents an eigth-channel stimulator circuit with an output stage (electrode driving circuit) that doesn’t need off-chip blocking-capacitors and is used for phrenic nerve stimulation. This stimulator circuit utilizes only 1 output stage for 8 channels. The proposed current generator circuit in this stimulator reducing to a single step the translation of the digital input bits into the stimulus current, thus minimizing silicon area and power consumption. An 8 bit implementation is utilized for this current generator circuit. The average pulse width for this eight- channel stimulator with 1 mA current, 20 Hz frequency and 8 bits resolution, is 150 - 300 μs. The average power consumption for a single-channel stimulation is 38 mW from a 1.2 V power supply. This implantable stimulator system was simulated in HSPICE using 90 nm CMOS technology.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Automatic diagnosis may help to decrease human based diagnosis error and assist physicians to focus on the correct disease and its treatment and to avoid wasting time on diagnosis. In this paper computer aided diagnosis is applied to the brain CT image processing. We compared performance of morphological operations in extracting three types of features, i.e. gray scale, symmetry and texture. Some classifiers were applied to classify normal and abnormal brain CT images. It showed that morphological operations can improve the result of accuracy. Moreover SVM classifier showed better result than other classifiers.
文摘Phrenic nerve stimulation is a technique whereby a nerve stimulator provides electrical stimulation of the phrenic nerve to cause diaphragmatic contraction in patients with respiratory failure due to cervical spinal cord injury. This paper presents an eigth-channel stimulator circuit with an output stage (electrode driving circuit) that doesn’t need off-chip blocking-capacitors and is used for phrenic nerve stimulation. This stimulator circuit utilizes only 1 output stage for 8 channels. The proposed current generator circuit in this stimulator reducing to a single step the translation of the digital input bits into the stimulus current, thus minimizing silicon area and power consumption. An 8 bit implementation is utilized for this current generator circuit. The average pulse width for this eight- channel stimulator with 1 mA current, 20 Hz frequency and 8 bits resolution, is 150 - 300 μs. The average power consumption for a single-channel stimulation is 38 mW from a 1.2 V power supply. This implantable stimulator system was simulated in HSPICE using 90 nm CMOS technology.
