OBJECTIVE: In intraoperative analysis of electromygraphic signals (EMG) for monitoring purposes, baseline artefacts frequently pose considerable problems. Since artefact sources in the operating room can only be reduced to a limited degree, signal-processing methods are needed to correct the registered data online without major changes to the relevant data itself. We describe a method for baseline correction based on "discrete wavelet transform" (DWT) and evaluate its performance compared to commonly used digital filters. METHODS: EMG data from 10 patients who underwent removal of acoustic neuromas were processed. Effectiveness, preservation of relevant EMG patterns and processing speed of a DWT based correction method was assessed and compared to a range of commonly used Butterworth, Resistor-Capacitor and Gaussian filters. RESULTS: Butterworth and DWT filters showed better performance regarding artefact correction and pattern preservation compared to Resistor-Capacitor and Gaussian filters. Assuming equal weighting of both characteristics, DWT outperformed the other methods: While Butterworth, Resistor-Capacitor and Gaussian provided good pattern preservation, the effectiveness was low and vice versa, while DWT baseline correction at level 6 performed well in both characteristics. CONCLUSIONS: The DWT method allows reliable and efficient intraoperative baseline correction in real-time. It is superior to commonly used methods and may be crucial for intraoperative analysis of EMG data, for example for intraoperative assessment of facial nerve function.
OBJECTIVE: In intraoperative analysis of electromygraphic signals (EMG) for monitoring purposes, baseline artefacts frequently pose considerable problems. Since artefact sources in the operating room can only be reduced to a limited degree, signal-processing methods are needed to correct the registered data online without major changes to the relevant data itself. We describe a method for baseline correction based on "discrete wavelet transform" (DWT) and evaluate its performance compared to commonly used digital filters. METHODS: EMG data from 10 patients who underwent removal of acoustic neuromas were processed. Effectiveness, preservation of relevant EMG patterns and processing speed of a DWT based correction method was assessed and compared to a range of commonly used Butterworth, Resistor-Capacitor and Gaussian filters. RESULTS: Butterworth and DWT filters showed better performance regarding artefact correction and pattern preservation compared to Resistor-Capacitor and Gaussian filters. Assuming equal weighting of both characteristics, DWT outperformed the other methods: While Butterworth, Resistor-Capacitor and Gaussian provided good pattern preservation, the effectiveness was low and vice versa, while DWT baseline correction at level 6 performed well in both characteristics. CONCLUSIONS: The DWT method allows reliable and efficient intraoperative baseline correction in real-time. It is superior to commonly used methods and may be crucial for intraoperative analysis of EMG data, for example for intraoperative assessment of facial nerve function.