Study of Brain Radio Signals Using Bispectral Analysis

Using a special sensor (probe antenna-applicator), radio signals emitted by the human brain are recorded. The changes of different parameters are investigated while studying different physical and emotional states of people. The studies are carried out using spectral and bispectral analysis in the LabVIEW environment. It is shown that in a stressful state the bispectrum of the brain’s radio signal is significantly enriched, and many phase coupled spectral components appear. They undergo changes in their amplitude and frequency. The results of these studies may be useful for understanding the nature and identification of fast processes in the human brain.

Erratum: Martín-Montero et al. Bispectral Analysis of Heart Rate Variability to Characterize and Help Diagnose Pediatric Sleep Apnea. Entropy 2021, 23, 1016

The author wishes to make the following correction to this paper [...]

Monitoring of Anesthesia by Bispectral Analysis of EEG Signals

Background. In intensive care, monitoring the depth of anesthesia during surgical procedures is a key element in the success of the medical operation and postoperative recovery. However, despite the development of anesthesia thanks to technological and pharmacological advances, its side effects such as underdose or overdose of hypnotics remain a major problem. Observation and monitoring must combine clinical observations (loss of consciousness and reactivity) with tools for real-time measurement of changes in the depth of anesthesia. Methodology. In this work, we will develop a noninvasive method for calculating, monitoring, and controlling the depth of general anesthesia during surgery. The objective is to reduce the effects of pharmacological usage of hypnotics and to ensure better quality recovery. Thanks to the overall activity of sets of neurons in the brain, we have developed a BIS technique based on bispectral analysis of the electroencephalographic signal EEG. Discussion. By collecting the electrical voltages from the brain, we distinguish light sleep from deep sleep according to the values of the BIS indicator (ranging from 0 : sleep to 100 : wake) and also control it by acting on the dosage of propofol and sevoflurane. We showed that the BIS value must be maintained during the operation and the anesthesia at a value greater than 60. Conclusion. This study showed that the BIS technology led to an optimization of the anesthetic management, the adequacy of the hypnotic dosage, and a better postoperative recovery.

Bispectral Analysis of Heart Rate Variability to Characterize and Help Diagnose Pediatric Sleep Apnea

Pediatric obstructive sleep apnea (OSA) is a breathing disorder that alters heart rate variability (HRV) dynamics during sleep. HRV in children is commonly assessed through conventional spectral analysis. However, bispectral analysis provides both linearity and stationarity information and has not been applied to the assessment of HRV in pediatric OSA. Here, this work aimed to assess HRV using bispectral analysis in children with OSA for signal characterization and diagnostic purposes in two large pediatric databases (0–13 years). The first database (training set) was composed of 981 overnight ECG recordings obtained during polysomnography. The second database (test set) was a subset of the Childhood Adenotonsillectomy Trial database (757 children). We characterized three bispectral regions based on the classic HRV frequency ranges (very low frequency: 0–0.04 Hz; low frequency: 0.04–0.15 Hz; and high frequency: 0.15–0.40 Hz), as well as three OSA-specific frequency ranges obtained in recent studies (BW1: 0.001–0.005 Hz; BW2: 0.028–0.074 Hz; BWRes: a subject-adaptive respiratory region). In each region, up to 14 bispectral features were computed. The fast correlation-based filter was applied to the features obtained from the classic and OSA-specific regions, showing complementary information regarding OSA alterations in HRV. This information was then used to train multi-layer perceptron (MLP) neural networks aimed at automatically detecting pediatric OSA using three clinically defined severity classifiers. Both classic and OSA-specific MLP models showed high and similar accuracy (Acc) and areas under the receiver operating characteristic curve (AUCs) for moderate (classic regions: Acc = 81.0%, AUC = 0.774; OSA-specific regions: Acc = 81.0%, AUC = 0.791) and severe (classic regions: Acc = 91.7%, AUC = 0.847; OSA-specific regions: Acc = 89.3%, AUC = 0.841) OSA levels. Thus, the current findings highlight the usefulness of bispectral analysis on HRV to characterize and diagnose pediatric OSA.

Study of the impact of clicks and murmurs on cardiac sounds S1 and S2 through bispectral analysis

This paper presents a study of the impact of clicks, and murmurs on cardiac sound S1, and S2, and the measure of severity degree through synchronization degree between frequencies, using bispectral analysis. The algorithm is applied on three groups of Phonocardiogram (PCG) signal: group A represents PCG signals having a morphology similar to that of the normal PCG signal without click or murmur, group B represents PCG signals with a click (reduced murmur), and group C represent PCG signals with murmurs. The proposed algorithm permits us to evaluate and quantify the relationship between the two sounds S1 and S2 on one hand and between the two sounds, click and murmur on the other hand. The obtained results show that the clicks and murmurs can affect both the heart sounds, and vice versa. This study shows that the heart works in perfect harmony and that the frequencies of sounds S1, S2, clicks, and murmurs are not accidentally generated; but they are generated by the same generator system. It might also suggest that one of the obtained frequencies causes the others. The proposed algorithm permits us also to determine the synchronization degree. It shows high values in group C; indicating high severity degrees, low values for group B, and zero in group A. The algorithm is compared to Short-Time Fourier Transform (STFT) and continuous wavelet transform (CWT) analysis. Although the STFT can provide correctly the time, it can’t distinguish between the internal components of sounds S1 and S2, which are successfully determined by CWT, which, in turn, cannot find the relationship between them. The algorithm was also evaluated and compared to the energetic ratio. the obtained results show very satisfactory results and very good discrimination between the three groups. We can conclude that the three algorithms (STFT, CWT, and bispectral analysis) are complementary to facilitate a good approach and to better understand the cardiac sounds

Laboratory study of non-linear wave–wave interactions of extreme focused waves in the nearshore zone

Extreme waves play a crucial role in marine inundation hazards and coastal erosion. Prediction of non-linear wave–wave interactions is crucial in assessing the propagation of shallow water extreme waves in coastal regions. In this article, we experimentally study non-linear wave–wave interactions of large-amplitude focused wave groups propagating in a two-dimensional wave flume over a mild slope (β=1:25). The influence of the frequency spectrum and the steepness on the non-linear interactions of focused waves are examined. The generated wave trains correspond to Pierson–Moskowitz and JONSWAP (γ=3.3 or γ=7) spectra. Subsequently, we experimentally approach this problem by the use of a bispectral analysis applied on short time series, via the wavelet-based bicoherence parameter, which identifies and quantifies the phase coupling resulting from non-resonant or bound triad interactions with the peak frequency. The bispectral analysis shows that the phase coupling increases gradually and approaches 1 just prior to breaking, accordingly with the spectrum broadening and the energy increase in high-frequency components. Downstream breaking, the values of phase coupling between the peak frequency and its higher harmonics decrease drastically, and the bicoherence spectrum becomes less structured.