A case of multiple abnormalities of the azygos venous system: a praeaortic interazygos vein.

The posterior thoracic wall, an area drained by the azygos venous system, is a common site for surgical intervention. Since the venous part of the cardiovascular system is subject to most common variation, abnormalities in the azygos venous system are often reported. Some of the anatomical variants have significant clinical implications for computed tomography image assessment and mediastinal surgery. During dissection of the posterior mediastinum in a 76 year-old Caucasian male cadaver we found a rare variation in the azygos venous system. The hemiazygos vein drained the left 9th to 11th left posterior intercostal veins. While passing ventrally to the aorta at the level of the body of the eighth thoracic vertebra it was joined by two separate vessels found to be the continuations of the 7th and 8th left posterior intercostal veins. The resultant dilated vessel, termed the "interazygos vein", then opened into the azygos vein on the right side of the vertebral column. Variation in the azygos venous system has often been reported, but the abnormality observed by us appears to be extremely rare. The interazygos vein passing ventrally to the aorta may mimic enlarged lymph nodes and cause misinterpretation of a computed tomography image or, if accidentally damaged during mediastinal surgery, may lead to intraoperative haemorrhage. To the best of our knowledge this report provides new data of potential clinical significance.

Surface and intracranial EEG spike detection based on discrete wavelet decomposition and random forest classification.

Epilepsy is a neurological disorder for which the electroencephalogram (EEG) is the most important diagnostic tool. In particular, this diagnosis heavily depends on the detection of interictal (between seizures) paroxysmal epileptic discharges (IPED) in the EEG. This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist visual inspections of human readers. We present a new method, which allows automatic detection of IPED based on discrete wavelet decomposition and a random forest classifier. The algorithm was trained and cross validated using 17 subjects with scalp EEG and 10 subjects with intracranial EEG. The performance of this method reached 62% recall and 26% precision for surface EEG subjects and 63% recall and 53% precision for intracranial EEG subjects. Thus, the method hereby proposed has great potential for diagnosis support in clinical environments.