Tainsong Chen

Adaptive wavelet network for multiple cardiac arrhythmias recognition

This paper proposes a method for electrocardiogram (ECG) heartbeat detection and recognition using adaptive wavelet network (AWN). The ECG beat recognition can be divided into a sequence of stages, starting with feature extraction from QRS complexes, and then according to characteristic features to identify the cardiac arrhythmias including the supraventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. The method of ECG beats is a two-subnetwork architecture, Morlet wavelets are used to enhance the features from each heartbeat, and probabilistic neural network (PNN) performs the recognition tasks. The AWN method is used for application in a dynamic environment, with add-in and delete-off features using automatic target adjustment and parameter tuning. The experimental results used from the MIT-BIH arrhythmia database demonstrate the efficiency of the proposed non-invasive method. Compared with conventional multi-layer neural networks, the test results also show accurate discrimination, fast learning, good adaptability, and faster processing time for detection.

Portable hand motion classifier for multi-channel surface electromyography recognition using grey relational analysis

This paper proposes the portable hand motion classifier (HMC) for multi-channel surface electromyography (SEMG) recognition using grey relational analysis (GRA). SEMG provides information on motion detection of flexion and extension of fingers, wrist, forearm, and arm. A portable HMC is developed to identify hand motion from the SEMG signals with an electrode configuration system (ECS) and GRA-based classifier. The ECS consists of seven active electrodes place around the forearm to acquire the multi-channel SEMG signals of corresponding muscle groups. Six parameters are extracted from each electrode channel and various 42 (7 Channels by 6 Parameters) parameters could be constructed as specific patterns. Sequentially, these patterns are sent to the GRA-based classifier to recognize 11 hand motions. Twelve subjects including eight males and four females participate in this study. Compared with the multi-layer neural networks (MLNNs) based classifier, GRA demonstrates the processing time, computational efficiency, and accurate recognition for recognizing SEMG signals. It takes about 0.05s CPU time to identify each hand motion which is close to a real-time process. Therefore, the GRA-based classifier could be further recommend to implement in prosthesis control, robotic manipulator or hand motion classification applications.

Quantitative assessment of divergence eye movements

PURPOSE This study sought to quantify divergence eye movements and differences between divergence and convergence to smoothly moving ramp, step, and disappearing step stimuli. METHODS Eight visually normal, adult subjects participated in three experiments investigating the dynamics of responses using an infrared limbal eye tracker. RESULTS There were four primary findings: (1) a smooth tracking behavior was observed for slow ramps while the fast ramps elicited smooth tracking combined with a high-velocity, step-like behavior; (2) the high-velocity components observed in the faster ramps had a similar main sequence as divergence steps; (3) divergence dynamics to disappearing steps starting at the subjects near dissociated phoria level were similar to corresponding step responses; and (4) the high-velocity components from divergence ramps were dependent on initial vergence position, whereas the high-velocity components from convergence ramps were not. CONCLUSION The results suggest a preprogrammed component is present in divergence similar to convergence; however, unlike convergence, the high-velocity components from divergence ramp responses are dependent on initial vergence position.

A Rule-Based Decision-Making Diagnosis System to Evaluate Arteriovenous Shunt Stenosis for Hemodialysis Treatment of Patients Using Fuzzy Petri Nets

This paper proposes a rule-based decision-making diagnosis system to evaluate arteriovenous shunt (AVS) stenosis for long-term hemodialysis treatment of patients using fuzzy petri nets (FPNs). AVS stenoses are often associated with blood sounds, resulting from turbulent flow over the narrowed blood vessel. Phonoangiography provides a noninvasive technique to monitor the sounds of the AVS. Since the power spectra changes in frequency and amplitude with the degree of AVS stenosis, it is difficult to make a human-made decision to judge the degree using a combination of those variances. The Burg autoregressive (AR) method is used to estimate the frequency spectra of a phonoangiographic signal and identify the characteristic frequencies. A rule-based decision-making method, FPNs, is designed as a decision-making system to evaluate the degree of stenosis (DOS) in routine examinations. For 42 long-term follow-up patients, the examination results show the proposed diagnosis system has greater efficiency in evaluating AVS stenosis.

The measurements of ultrasound parameters on calcaneus by two-sided interrogation techniques

Recently, ultrasound techniques have become an important alternative in the assessment of osteoporosis. The speed of sound (SOS) and broadband ultrasound attenuation (BUA) on calcaneus are commonly used in an ultrasound densitometer for osteoporosis evaluation. However, the quantitative ultrasound (QUS) parameters provided by a densitometer using most commercial ultrasound instruments are based on the assumption of a fixed bone thickness. Information on bone thickness is a critical factor for accurate estimation of SOS through conventional approaches; yet, the thickness of bone tissue is not available through in vivo measurements and it is almost impossible to obtain the thickness of bone tissue via conventional approaches. Therefore, the SOS measurements will be incorrect. The purpose of this work is to develop a two-sided interrogation technique for the SOS measurements that is less susceptible to bone thickness. The results show that this proposed technique can obtain a better SOS estimation on bone tissue. Using bone phantoms that mimic actual tissue, the validity of the approach is confirmed with measurements showing high accuracy (>99%) and low standard deviation (<0.5%). Finally, the measurements of 14 healthy subjects are also reported. The results show that this technique can provide the bone thickness information to reduce the SOS estimation errors compared with the fixed bone thickness assumption.

Long-term effects of baroreflex function after stenting in patients with carotid artery stenosis

Baroreflex sensitivity is recognized for its prognostic relevance to cardio-vascular and cerebro-vascular risks. However, little is known about the long-term outcome of baroreflex function in patients with carotid stenosis undergoing carotid stenting. Heart rate variability and cardio-vascular autonomic function, including baroreflex sensitivity, were examined using non-invasive methods in 22 adult patients who underwent carotid stenting. They were compared with the normal control group with 22 sex- and age-matched normal volunteers and the risk control group with 10 adult patients with severe stenosis or even total occlusion of the carotid artery without stenting. The groups of patients with stenting and risk controls had significantly reduced valsalva ratio and baroreflex sensitivity measured by the valsalva method compared to normal controls. However, there was no significant difference between patients with stenting and risk controls. There was significant decrease in heart rate response to deep breathing and to head-up tilt in patients with carotid stenting compared to normal controls. Other parameters of cardio-vascular autonomic function showed no difference among the three groups. Reduced baroreceptor function in patients with carotid stenting may be due to underlying diseases rather than the stenting itself. There was no short-term parasympathetic hyperactivity after the stenting, suggesting that the effect is transient rather than permanent.

Clinical Verification of A Clinical Decision Support System for Ventilator Weaning

BackgroundWeaning is typically regarded as a process of discontinuing mechanical ventilation in the daily practice of an intensive care unit (ICU). Among the ICU patients, 39%-40% need mechanical ventilator for sustaining their lives. The predictive rate of successful weaning achieved only 35-60% for decisions made by physicians. Clinical decision support systems (CDSSs) are promising in enhancing diagnostic performance and improve healthcare quality in clinical setting. To our knowledge, a prospective study has never been conducted to verify the effectiveness of the CDSS in ventilator weaning before. In this study, the CDSS capable of predicting weaning outcome and reducing duration of ventilator support for patients has been verified.MethodsA total of 380 patients admitted to the respiratory care center of the hospital were randomly assigned to either control or study group. In the control group, patients were weaned with traditional weaning method, while in the study group, patients were weaned with CDSS monitored by physicians. After excluding the patients who transferred to other hospitals, refused further treatments, or expired the admission period, data of 168 and 144 patients in the study and control groups, respectively, were used for analysis.ResultsThe results show that a sensitivity of 87.7% has been achieved, which is significantly higher (p<0.01) than the weaning determined by physicians (sensitivity: 61.4%). Furthermore, the days using mechanical ventilator for the study group (38.41 ± 3.35) is significantly (p<0.001) shorter than the control group (43.69 ± 14.89), with a decrease of 5.2 days in average, resulting in a saving of healthcare cost of NT

Synchronizing chaotification with support vector machine and wolf pack search algorithm for estimation of peripheral vascular occlusion in diabetes mellitus

45,000 (US

Implementation of a Burn Scar Assessment System by Ultrasound Techniques

1,500) per patient in the current Taiwanese National Health Insurance setting.ConclusionsThe CDSS is demonstrated to be effective in identifying the earliest time of ventilator weaning for patients to resume and sustain spontaneous breathing, thereby avoiding unnecessary prolonged ventilator use and decreasing healthcare cost.

Multiple Cardiac Arrhythmia Recognition Using Adaptive Wavelet Network

Abstract This study proposes a method for the estimation of peripheral vascular occlusion (PVO) in diabetic foot using a support vector machine (SVM) classifier with the wolf pack search (WPS) algorithm. The long-term presence of elevated blood sugar levels commonly results in peripheral neuropathy, peripheral vascular disease, nephropathy, and retinopathy in patients with Type 2 diabetes mellitus. Patients with PVO disease have decreased walking capability and life quality in diabetes mellitus and poor peripheral circulation of PVO causes morbidity like infection and amputation of the legs or feet of diabetics. This progressively vascular occlusion is often ignored by the patients and primary care physicians in early stage. Therefore, a reliable method of diagnostic assistance is crucial for early diagnosis and monitoring of PVO and prevention of amputation. Photoplethysmography (PPG) is a non-invasive technique for detecting blood volume changes in peripheral vascular bed. Literature indicates that the pulse transit time increases and waveform shape changes increase in PPG of the vascular occlusion. PPG pulses of feet gradually become asynchronous due to the different speed of deteriorating patency and collateral circulation in the peripheral arteries. We utilized synchronizing chaotification to compare the bilateral similarity and asymmetry of PPG signals, and applied SVM to estimate three degrees of PVO. Among 33 subjects tested, this classification technique could recognize various butterfly motion patterns representing severities successfully including normal condition, lower-degree disease, and higher-degree disease. The proposed method has potential for providing diagnostic assistance for PVO of diabetics and other high-risk populations, with efficiency and higher accuracy.