An adapted double threshold protocol for spastic muscles

The onset of muscle contraction has been an important element in the understanding of human motor control system as well as in the development of medical devices. This task is problematic in the study of spasticity using surface Electromyography (sEMG). In fact, spasticity is characterized by involuntary muscle contractions that can be seen as both, a non-stationary background if they are weak or a severe non-stationary EMG signal if they are strong. In other hand, these sEMG signals present a very low signal to noise ratio, beyond the added noise that contaminates this signal. The double threshold protocol presumes non-stationary muscle activation under a stationary environment which does not accommodate non-stationary background conditions. Apart from that the Shewhart protocol which makes part of the Double Threshold Protocol (DTP) presumes an initial segment containing only noise which can't be guaranteed under spastic conditions. These are the main causes of failures of state of the art approaches when applied to sEMG spastic muscles. This paper proposes dealing with these constraints by adapting the first threshold to the noise conditions via Signal to Noise Ratio (SNR) estimation, which depends on the severity of the disease. The main idea is tuning the first threshold to low SNR conditions since it is where the DTP most degrades. This tuning is done in sEMG artificially contaminated at different SNRs where the multiple of standard deviation is heuristically determined based on experimentation. Noise is estimated in low energy segments instead of in an initial segment that can be contaminated by involuntary muscle contractions. The proposed algorithm was tested in sEMG signals from the Biceps Braquialis of 13 healthy individuals and in 9315 signals recorded in 23 subjects with spasticity. Improvements of more than 23% were obtained when compared with the classical DTP in moderate to severe spasticity.