Obstructive sleep apnea syndrome (OSAS) is a common sleep disorder. It has been reported that approximately 40% of patients with moderate or severe OSAS die within the first eight years of disease. In hospitals, OSAS ...Obstructive sleep apnea syndrome (OSAS) is a common sleep disorder. It has been reported that approximately 40% of patients with moderate or severe OSAS die within the first eight years of disease. In hospitals, OSAS is inspected using polysomnography, which uses a number of sensors. Because of the cumbersome nature of this polysomnography, an initial OSAS screening is usually conducted. In recent years, OSAS screening techniques using Holter electrocardiogram (ECG) have been reported. However, the techniques so far reported cannot perform an OSAS severity assessment. The present study presents a new method to distinguish the obstructive sleep apnea (OSA) and non-OSA epochs at one-second intervals based on the Apnea Hypopnea Index assessment, defined as the duration of continuous apnea. In the proposed method, the time-frequency components of the heart rate variability and three ECG-derived respiration signals calculated by the complex Morlet wavelet transformation are adopted as features. A support vector machine is employed for classification. The proposed method is evaluated using three eight-hour ECG recordings containing OSA episodes from three subjects. As a result, the sensitivity and specificity of classification are found to reach approximately 90%, a level suitable for OSAS screening in clinical settings.展开更多
It is difficult for the existing Automated External Defibrillator (AED) on-board microprocessors to accurately classify electrocardiographic signals (ECGs) mixed with Cardiopulmonary Resuscitation artifacts in real-ti...It is difficult for the existing Automated External Defibrillator (AED) on-board microprocessors to accurately classify electrocardiographic signals (ECGs) mixed with Cardiopulmonary Resuscitation artifacts in real-time. In order to improve recognition speed and accuracy of electrocardiographic signals containing Cardiopulmonary Resuscitation artifacts, a new special coprocessor system-on-chip (SoC) for defibrillators was designed. In this study, a microprocessor was designed based on the RISC-V architecture to achieve hardware acceleration for ECGs classification;Besides, an Approximate Entropy (ApEn) and Convolutional neural networks (CNNs) integrated algorithm capable of running on it was designed. The algorithm differs from traditional electrocardiographic (ECG) classification algorithms. It can be used to perform ECG classification while chest compressions are applied. The proposed co-processor can be used to accelerate computation rate of ApEn by 34 times compared with pure software computation. It can also be used to accelerate the speed of CNNs ECG recognition by 33 times. The combined algorithm was used to classify ECGs with CPR artifacts. It achieved a precision of 96%, which was significantly superior to that of simple CNNs. The coprocessor can be used to significantly improve the recognition efficiency and accuracy of ECGs containing CPR artifacts. It is suitable for automatic external defibrillator and other medical devices in which one-dimensional physiological signals.展开更多
文摘Obstructive sleep apnea syndrome (OSAS) is a common sleep disorder. It has been reported that approximately 40% of patients with moderate or severe OSAS die within the first eight years of disease. In hospitals, OSAS is inspected using polysomnography, which uses a number of sensors. Because of the cumbersome nature of this polysomnography, an initial OSAS screening is usually conducted. In recent years, OSAS screening techniques using Holter electrocardiogram (ECG) have been reported. However, the techniques so far reported cannot perform an OSAS severity assessment. The present study presents a new method to distinguish the obstructive sleep apnea (OSA) and non-OSA epochs at one-second intervals based on the Apnea Hypopnea Index assessment, defined as the duration of continuous apnea. In the proposed method, the time-frequency components of the heart rate variability and three ECG-derived respiration signals calculated by the complex Morlet wavelet transformation are adopted as features. A support vector machine is employed for classification. The proposed method is evaluated using three eight-hour ECG recordings containing OSA episodes from three subjects. As a result, the sensitivity and specificity of classification are found to reach approximately 90%, a level suitable for OSAS screening in clinical settings.
文摘It is difficult for the existing Automated External Defibrillator (AED) on-board microprocessors to accurately classify electrocardiographic signals (ECGs) mixed with Cardiopulmonary Resuscitation artifacts in real-time. In order to improve recognition speed and accuracy of electrocardiographic signals containing Cardiopulmonary Resuscitation artifacts, a new special coprocessor system-on-chip (SoC) for defibrillators was designed. In this study, a microprocessor was designed based on the RISC-V architecture to achieve hardware acceleration for ECGs classification;Besides, an Approximate Entropy (ApEn) and Convolutional neural networks (CNNs) integrated algorithm capable of running on it was designed. The algorithm differs from traditional electrocardiographic (ECG) classification algorithms. It can be used to perform ECG classification while chest compressions are applied. The proposed co-processor can be used to accelerate computation rate of ApEn by 34 times compared with pure software computation. It can also be used to accelerate the speed of CNNs ECG recognition by 33 times. The combined algorithm was used to classify ECGs with CPR artifacts. It achieved a precision of 96%, which was significantly superior to that of simple CNNs. The coprocessor can be used to significantly improve the recognition efficiency and accuracy of ECGs containing CPR artifacts. It is suitable for automatic external defibrillator and other medical devices in which one-dimensional physiological signals.
