Comparison of auditory evoked potential parameters for predicting clinically anaesthetized state

Frequency and Time Domain Analysis of EEG Based Auditory Evoked Potentials to Detect Binaural Hearing in Noise

Hearing loss is a prevalent health issue that affects individuals worldwide. Binaural hearing refers to the ability to integrate information received simultaneously from both ears, allowing individuals to identify, locate, and separate sound sources. Auditory evoked potentials (AEPs) refer to the electrical responses that are generated within any part of the auditory system in response to auditory stimuli presented externally. Electroencephalography (EEG) is a non-invasive technology used for the monitoring of AEPs. This research aims to investigate the use of audiometric EEGs as an objective method to detect specific features of binaural hearing with frequency and time domain analysis techniques. Thirty-five subjects with normal hearing and a mean age of 27.35 participated in the research. The stimuli used in the current study were designed to investigate the impact of binaural phase shifts of the auditory stimuli in the presence of noise. The frequency domain and time domain analyses provided statistically significant and promising novel findings. The study utilized Blackman windowed 18 ms and 48 ms pure tones as stimuli, embedded in noise maskers, of frequencies 125 Hz, 250 Hz, 500 Hz, 750 Hz, 1000 Hz in homophasic (the same phase in both ears) and antiphasic (180-degree phase difference between the two ears) conditions. The study focuses on the effect of phase reversal of auditory stimuli in noise of the middle latency response (MLR) and late latency response (LLR) regions of the AEPs. The frequency domain analysis revealed a significant difference in the frequency bands of 20 to 25 Hz and 25 to 30 Hz when elicited by antiphasic and homophasic stimuli of 500 Hz for MLRs and 500 Hz and 250 Hz for LLRs. The time domain analysis identified the Na peak of the MLR for 500 Hz, the N1 peak of the LLR for 500 Hz stimuli and the P300 peak of the LLR for 250 Hz as significant potential markers in detecting binaural processing in the brain.

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part II: Brain Signals

As it was mentioned in the previous part of this work (Part I)-the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work-various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.

Processed electroencephalogram in depth of anesthesia monitoring

PURPOSE OF REVIEW

We critically review the principles underlying processed electroencephalogram (EEG) monitors and recent studies validating their use in monitoring anesthetic depth.

RECENT FINDINGS

Depth of anesthesia is a theoretical construct to conceptualize anesthetic effects on the central nervous system as discrete or continuous phases or states. Clinical signs for assessing anesthetic depth are currently being supplemented by brain monitors. Their use may help to prevent insufficient anesthesia, which can lead to intraoperative awareness with recall, as well as anesthetic overdose, which may be associated with adverse events. Commercial and open-source brain monitoring indices are computed from frequency, entropy, or information theoretic analysis of the spontaneous or evoked EEG. These techniques are undergoing refinement to determine the best method for titrating anesthetics. Future depth-of-anesthesia monitors will benefit from current work aimed at correlating anesthetic effects to alterations in specific neural circuits.

SUMMARY

Current processed EEG monitors are limited by their calibration range and the interpatient variability in their dose-response curves. The next generation of depth-of-anesthesia monitors will require a greater understanding of the transformations of cortical and subcortical activity into EEG signals, the effects of anesthetics at a systems level, and the neural correlates of consciousness.