Mycotic aneurysm remains a lethal pathologic entity, especially when rupture occurs. It may result from primary aortitis, be induced by septic emboli, or be secondary to an adjacent infection, such as pancreatitis or a psoas muscle abscess. Surgical intervention is the only way to treat such disease. Even when successful repair is achieved by insertion of an interposition in situ graft or by performance of an extra-anatomic bypass, the prognosis is poor. The aim of this study was to present our experience of managing mycotic aortic aneurysms during the past 10 years. From January 1994 to June 2004, a total of 734 patients with aortic aneurysms underwent surgical repair at our institution. Among these cases, 17 (2.3%) were shown to be mycotic aneurysms of the ascending aorta (n = 1), aortic arch (2), thoracic and thoracoabdominal aorta (3), or abdominal aorta (11); 14 patients (mean age, 58.8 years) were male. Preoperative imaging studies were performed in all patients. Mycotic aortic aneurysms were suspected in 12 of the 17 patients (70.6%) preoperatively, and 4 of these 12 patients were found to have ruptures on imaging. At the time of surgery, 9 of the 17 aneurysms (52.9%) were ruptured. Fifteen patients had an interposition graft inserted after meticulous debridement, 1 underwent an aorto-aortic bypass, and 1 underwent an extra-anatomic (axillo-femoral) bypass. An omentum patch was applied to wrap the graft in 8 of 11 mycotic aortic aneurysms of the abdominal aorta. The most common pathogens were Salmonella spp. (n = 7) and Staphylococcus spp. (4). All patients received antibiotic therapy, according to the culture report, for about 4-6 weeks postoperatively. In-hospital mortality was 11.8% (n = 2). Another patient died from massive upper gastrointestinal bleeding 6 months after operation because of complications involving an aorto-duodenal fistula, and another died from stomach cancer 6 years after surgery. Long-term follow-up (mean, 37 months; range, 3-111 months) revealed that, at the time of writing, the remaining 13 patients were alive and well, without any recurrence of aneurysm. Mycotic aneurysm of the aorta is a life-threatening disease, especially when rupture occurs. The high mortality rate is due not only to the high rupture rate, but also to sepsis. When mycotic aortic aneurysm is diagnosed, early surgical intervention is mandatory.

This paper studies the amplitude-frequency characteristic of frontal steady-state visual evoked potential (SSVEP) and its feasibility as a control signal for brain computer interface (BCI). SSVEPs induced by different stimulation frequencies, from 13 ~ 31 Hz in 2 Hz steps, were measured in eight young subjects, eight elders and seven ALS patients. Each subject was requested to participate in a calibration study and an application study. The calibration study was designed to find the amplitude-frequency characteristics of SSVEPs recorded from Oz and Fpz positions, while the application study was designed to test the feasibility of using frontal SSVEP to control a two-command SSVEP-based BCI. The SSVEP amplitude was detected by an epoch-average process which enables artifact-contaminated epochs can be removed. The seven ALS patients were severely impaired, and four patients, who were incapable of completing our BCI task, were excluded from calculation of BCI performance. The averaged accuracies, command transfer intervals and information transfer rates in operating frontal SSVEP-based BCI were 96.1%, 3.43 s/command, and 14.42 bits/min in young subjects; 91.8%, 6.22 s/command, and 6.16 bits/min in elders; 81.2%, 12.14 s/command, and 1.51 bits/min in ALS patients, respectively. The frontal SSVEP could be an alternative choice to design SSVEP-based BCI.

The motor imagery (MI)-based brain computer interface (BCI) is an intuitive interface that enables users to communicate with external environments through their minds. However, current MI-BCI systems ask naïve subjects to perform unfamiliar MI tasks with simple textual instruction or a visual/auditory cue. The unclear instruction for MI execution not only results in large inter-subject variability in the measured EEG patterns but also causes the difficulty of grouping cross-subject data for big-data training. In this study, we designed an BCI training method in a virtual reality (VR) environment. Subjects wore a head-mounted device (HMD) and executed action observation (AO) concurrently with MI (i.e., AO + MI) in VR environments. EEG signals recorded in AO + MI task were used to train an initial model, and the initial model was continually improved by the provision of EEG data in the following BCI training sessions. We recruited five healthy subjects, and each subject was requested to participate in three kinds of tasks, including an AO + MI task, an MI task, and the task of MI with visual feedback (MI-FB) three times. This study adopted a transformer- based spatial-temporal network (TSTN) to decode the user’s MI intentions. In contrast to other convolutional neural network (CNN) or recurrent neural network (RNN) approaches, the TSTN extracts spatial and temporal features, and applies attention mechanisms along spatial and temporal dimensions to perceive the global dependencies. The mean detection accuracies of TSTN were 0.63, 0.68, 0.75, and 0.77 in the MI, first MI-FB, second MI-FB, and third MI-FB sessions, respectively. This study demonstrated the AO + MI gave an easier way for subjects to conform their imagery actions, and the BCI performance was improved with the continual learning of the MI-FB training process.

Respiration is a crucial vital sign to indicate acidotic state of human body. In this paper, we have developed a Holo-Hilbert spectral analysis (HHSA)-based approach to detect subject’s respiration frequency from wrist photoplethysmogram (PPG) signals. The HHSA is a two-layer EMD architecture which first decomposes the original signal into intrinsic mode functions and then tries to find additive and multiplicative interactions among the participating components. The HHSA enables frequency-modulated (FM) and amplitude-modulated (AM) features of a signal can be comprehensively presented on a Holo-Hilbert spectrum (HHS). With the help of HHSA, subject’s respiration frequency can be identified on HHS by finding the AM frequency with the most prominent amplitude around the FM frequency of heart rate. The efficacy of the proposed method has been demonstrated in two designed experiments. In the first experiment, 75 subjects with ages ranged from 20–80 years old, and the difference between the detected results of proposed method and the readouts of transthoracic impedance plethysmography is 0.04 ± 0.96 breathes-per-minute (brm). In the second experiment, six subjects were requested to breathe at 6, 12, 18, 24 brm, following the pacing rhythms of a metronome. The difference between detected respiration rates and expected breathing rates is 0.01 ± 0.70 brm. The HHSA-based approach has manifested its capability to extract respiration-induced multiplicative component in PPG signal. It avoids the ambiguity of frequency representation in signal multiplication when using traditional additive decomposition methods.