Colles' fracture - is it a signal of osteoporosis?

Asymptomatic post-menopausal osteoporosis is common but some-times associated with pain and deformity. Symptomatic osteoporosis is usually associated with fracture. A 59 years old post-menopausal woman presented with a history of acute low-back-pain. She had menopause for 12 years. She gave history of colles' fracture at about two years back. Her mother died as consequences of femoral neck fracture. MRI of vertebral spine showed demineralization with partial collapse of D6,7,12 and L1 vertebra. Dual energy X-ray absorptiometry of vertebra showed BMD T-score of -4.5. Patient was managed with IV infusion of zoledronic acid, oral intake of vitamin D and calcium supplements and with regular non-weight-bearing exercises. Her condition improved gradually. During post-menopausal period, every women must be aware of osteoporosis and any fracture in that time must be evaluated to rule out osteoporosis.

A study on discrete wavelet-based noise removal from EEG signals.

Electroencephalogram (EEG) serves as an extremely valuable tool for clinicians and researchers to study the activity of the brain in a non-invasive manner. It has long been used for the diagnosis of various central nervous system disorders like seizures, epilepsy, and brain damage and for categorizing sleep stages in patients. The artifacts caused by various factors such as Electrooculogram (EOG), eye blink, and Electromyogram (EMG) in EEG signal increases the difficulty in analyzing them. Discrete wavelet transform has been applied in this research for removing noise from the EEG signal. The effectiveness of the noise removal is quantitatively measured using Root Mean Square (RMS) Difference. This paper reports on the effectiveness of wavelet transform applied to the EEG signal as a means of removing noise to retrieve important information related to both healthy and epileptic patients. Wavelet-based noise removal on the EEG signal of both healthy and epileptic subjects was performed using four discrete wavelet functions. With the appropriate choice of the wavelet function (WF), it is possible to remove noise effectively to analyze EEG significantly. Result of this study shows that WF Daubechies 8 (db8) provides the best noise removal from the raw EEG signal of healthy patients, while WF orthogonal Meyer does the same for epileptic patients. This algorithm is intended for FPGA implementation of portable biomedical equipments to detect different brain state in different circumstances.