MEAs-Filter: a novel filter framework utilizing evolutionary algorithms for cardiovascular diseases diagnosis

Cardiovascular disease management often involves adjusting medication dosage based on changes in electrocardiogram (ECG) signals' waveform and rhythm. However, the diagnostic utility of ECG signals is often hindered by various types of noise interference. In this work, we propose a novel filter based on a multi-engine evolution framework named MEAs-Filter to address this issue. Our approach eliminates the need for predefined dimensions and allows adaptation to diverse ECG morphologies. By leveraging state-of-the-art optimization algorithms as evolution engine and incorporating prior information inputs from classical filters, MEAs-Filter achieves superior performance while minimizing order. We evaluate the effectiveness of MEAs-Filter on a real ECG database and compare it against commonly used filters such as the Butterworth, Chebyshev filters, and evolution algorithm-based (EA-based) filters. The experimental results indicate that MEAs-Filter outperforms other filters by achieving a reduction of approximately 30% to 60% in terms of the loss function compared to the other algorithms. In denoising experiments conducted on ECG waveforms across various scenarios, MEAs-Filter demonstrates an improvement of approximately 20% in signal-to-noise (SNR) ratio and a 9% improvement in correlation. Moreover, it does not exhibit higher losses of the R-wave compared to other filters. These findings highlight the potential of MEAs-Filter as a valuable tool for high-fidelity extraction of ECG signals, enabling accurate diagnosis in the field of cardiovascular diseases.

Predictors of Low Risk for Delirium during Anesthesia Emergence

BACKGROUND

Processed electroencephalography (EEG) is used to monitor the level of anesthesia, and it has shown the potential to predict the occurrence of delirium. While emergence trajectories of relative EEG band power identified post hoc show promising results in predicting a risk for a delirium, they are not easily transferable into an online predictive application. This article describes a low-resource and easily applicable method to differentiate between patients at high risk and low risk for delirium, with patients at low risk expected to show decreasing EEG power during emergence.

METHODS

This study includes data from 169 patients (median age, 61 yr [49, 73]) who underwent surgery with general anesthesia maintained with propofol, sevoflurane, or desflurane. The data were derived from a previously published study. The investigators chose a single frontal channel, calculated the total and spectral band power from the EEG and calculated a linear regression model to observe the parameters' change during anesthesia emergence, described as slope. The slope of total power and single band power was correlated with the occurrence of delirium.

RESULTS

Of 169 patients, 32 (19%) showed delirium. Patients whose total EEG power diminished the most during emergence were less likely to screen positive for delirium in the postanesthesia care unit. A positive slope in total power and band power evaluated by using a regression model was associated with a higher risk ratio (total, 2.83 [95% CI, 1.46 to 5.51]; alpha/beta band, 7.79 [95% CI, 2.24 to 27.09]) for delirium. Furthermore, a negative slope in multiple bands during emergence was specific for patients without delirium and allowed definition of a test for patients at low risk.

CONCLUSIONS

This study developed an easily applicable exploratory method to analyze a single frontal EEG channel and to identify patterns specific for patients at low risk for delirium.

EDITOR’S PERSPECTIVE

Response of the GE Entropy™ monitor to neuromuscular block in awake volunteers

BACKGROUND

The GE Entropy™ monitor analyses the frontal electroencephalogram (EEG) and generates two indices intended to represent the degree of anaesthetic drug effect on the brain. It is frequently used in the context of neuromuscular block. We have shown that a similar device, the Bispectral Index monitor (BIS), does not generate correct values in awake volunteers when neuromuscular blocking drugs are administered.

METHODS

We replayed the EEGs recorded during awake paralysis from the original study to an Entropy monitor via a calibrated electronic playback system. Each EEG was replayed 30 times to evaluate the consistency of the Entropy output.

RESULTS

Both State Entropy and Response Entropy decreased during periods of neuromuscular block to values consistent with anaesthesia, despite there being no change in conscious state (State Entropy <60 in eight of nine rocuronium trials and nine of 10 suxamethonium trials). Entropy values did not return to pre-test levels until after the return of movement. Entropy did not generate exactly the same results when the same EEG was replayed multiple times, which is primarily because of a cyclical state within the Entropy system itself.

CONCLUSIONS

The GE Entropy™ monitor requires muscle activity to generate correct values in an awake subject. It could therefore be unreliable at detecting awareness in patients who have been given neuromuscular blocking drugs. In addition, Entropy does not generate the same result each time it is presented with the same EEG.

Effect of sugammadex on processed EEG parameters in patients undergoing robot-assisted radical prostatectomy

BACKGROUND

Sugammadex has been associated with increases in the bispectral index (BIS). We evaluated the effects of sugammadex administration on quantitative electroencephalographic (EEG) and electromyographic (EMG) measures.

METHODS

We performed a prospective observational study of adult male patients undergoing robot-assisted radical prostatectomy. All patients received a sevoflurane-based general anaesthetic and a continuous infusion of rocuronium, which was reversed with 2 mg kg-1 of sugammadex i.v. BIS, EEG, and EMG measures were captured with the BIS Vista™ monitor.

RESULTS

Twenty-five patients were included in this study. Compared with baseline, BIS increased at 4-6 min (β coefficient: 3.63; 95% confidence interval [CI]: 2.22-5.04; P<0.001), spectral edge frequency 95 (SEF95) increased at 2-4 min (β coefficient: 0.29; 95% CI: 0.05-0.52; P=0.016) and 4-6 min (β coefficient: 0.71; 95% CI: 0.47-0.94; P<0.001), and EMG increased at 4-6 min (β coefficient: 1.91; 95% CI: 1.00-2.81; P<0.001) after sugammadex administration. Compared with baseline, increased beta power was observed at 2-4 min (β coefficient: 93; 95% CI: 1-185; P=0.046) and 4-6 min (β coefficient: 208; 95% CI: 116-300; P<0.001), and decreased delta power was observed at 4-6 min (β coefficient: -526.72; 95% CI: -778 to -276; P<0.001) after sugammadex administration. Neither SEF95 nor frequency band data analysis adjusted for EMG showed substantial differences. None of the patients showed clinical signs of awakening.

CONCLUSIONS

After neuromuscular block reversal with 2 mg kg-1 sugammadex, BIS, SEF95, EMG, and beta power showed small but statistically significant increases over time, while delta power decreased.

Always Assess the Raw Electroencephalogram: Why Automated Burst Suppression Detection May Not Detect All Episodes

BACKGROUND

Electroencephalogram (EEG)-based monitors of anesthesia are used to assess patients' level of sedation and hypnosis as well as to detect burst suppression during surgery. One of these monitors, the Entropy module, uses an algorithm to calculate the burst suppression ratio (BSR) that reflects the percentage of suppressed EEG. Automated burst suppression detection monitors may not reliably detect this EEG pattern. Hence, we evaluated the detection accuracy of BSR and investigated the EEG features leading to errors in the identification of burst suppression.

METHODS

With our study, we were able to compare the performance of the BSR to the visual burst suppression detection in the raw EEG and obtain insights on the architecture of the unrecognized burst suppression phases.

RESULTS

We showed that the BSR did not detect burst suppression in 13 of 90 (14%) patients. Furthermore, the time comparison between the visually identified burst suppression duration and elevated BSR values strongly depended on the BSR value being used as a cutoff. A possible factor for unrecognized burst suppression by the BSR may be a significantly higher suppression amplitude ( P = .002). Six of the 13 patients with undetected burst suppression by BSR showed intraoperative state entropy values >80, indicating a risk of awareness while being in burst suppression.

CONCLUSIONS

Our results complement previous results regarding the underestimation of burst suppression by other automated detection modules and highlight the importance of not relying solely on the processed index, but to assess the native EEG during anesthesia.

Evaluation of Anesthetic Specific EEG Dynamics during State Transitions between Loss and Return of Responsiveness

Purpose: electroencephalographic (EEG) information is used to monitor the level of cortical depression of a patient undergoing surgical intervention under general anesthesia. The dynamic state transitions into and out of anesthetic-induced loss and return of responsiveness (LOR, ROR) present a possibility to evaluate the dynamics of the EEG induced by different substances. We evaluated changes in the EEG power spectrum during anesthesia emergence for three different anesthetic regimens. We also assessed the possible impact of these changes on processed EEG parameters such as the permutation entropy (PeEn) and the cerebral state index (CSI). Methods: we analyzed the EEG from 45 patients, equally assigned to three groups. All patients were induced with propofol and the groups differed by the maintenance anesthetic regimen, i.e., sevoflurane, isoflurane, or propofol. We evaluated the EEG and parameter dynamics during LOR and ROR. For the emergence period, we focused on possible differences in the EEG dynamics in the different groups. Results: depending on the substance, the EEG emergence patterns showed significant differences that led to a substance-specific early activation of higher frequencies as indicated by the “wake” CSI values that occurred minutes before ROR in the inhalational anesthetic groups. Conclusion: our results highlight substance-specific differences in the emergence from anesthesia that can influence the EEG-based monitoring that probably have to be considered in order to improve neuromonitoring during general anesthesia.

An Ultra-Low Power Surface EMG Sensor for Wearable Biometric and Medical Applications

In recent years, the surface electromyography (EMG) signal has received a lot of attention. EMG signals are used to analyze muscle activity or to evaluate a patient's muscle status. However, commercial surface EMG systems are expensive and have high power consumption. Therefore, the purpose of this paper is to implement a surface EMG acquisition system that supports high sampling and ultra-low power consumption measurement. This work analyzes and optimizes each part of the EMG acquisition circuit and combines an MCU with BLE. Regarding the MCU power saving method, the system uses two different frequency MCU clock sources and we proposed a ping-pong buffer as the memory architecture to achieve the best power saving effect. The measured surface EMG signal samples can be forwarded immediately to the host for further processing and additional application. The results show that the average current of the proposed architecture can be reduced by 92.72% compared with commercial devices, and the battery life is 9.057 times longer. In addition, the correlation coefficients were up to 99.5%, which represents a high relative agreement between the commercial and the proposed system.

Spurious electroencephalographic activity due to pulsation artifact in the depth of anesthesia monitor

Background

The depth of anesthesia (DOA) is estimated based on the anesthesia-induced electroencephalogram (EEG) changes. However, the surgical environment, as well as the patient him/herself, generates electrical interferences that cause EEG waveform distortion.

Case presentation

A 52-year-old patient required general anesthesia due to the right femur necrotizing fasciitis. He had no history of epilepsy or head injury. His cardiovascular status was stable without arrhythmia under propofol and remifentanil anesthesia. The DOA was evaluated with Root® with SedLine® Brain Function Monitoring (Masimo Inc, Irvine, CA). The EEG showed a rhythmic, heart rate time-locked pulsation artifact, which diminished after electrode repositioning. Offline analysis revealed that the pulse wave-like interference in EEG was observed at the heart rate frequency.

Conclusions

We experienced an anesthesia case that involves a pulsation artifact generated by the superficial temporal artery contaminating the EEG signal. Numerous clinical conditions, including pulsation artifact, disturb anesthesia EEG.

Topological data analysis (TDA) enhances bispectral EEG (BSEEG) algorithm for detection of delirium

Current methods for screening and detecting delirium are not practical in clinical settings. We previously showed that a simplified EEG with bispectral electroencephalography (BSEEG) algorithm can detect delirium in elderly inpatients. In this study, we performed a post-hoc BSEEG data analysis using larger sample size and performed topological data analysis to improve the BSEEG method. Data from 274 subjects included in the previous study were analyzed as a 1st cohort. Subjects were enrolled at the University of Iowa Hospitals and Clinics (UIHC) between January 30, 2016, and October 30, 2017. A second cohort with 265 subjects was recruited between January 16, 2019, and August 19, 2019. The BSEEG score was calculated as a power ratio between low frequency to high frequency using our newly developed algorithm. Additionally, Topological data analysis (TDA) score was calculated by applying TDA to our EEG data. The BSEEG score and TDA score were compared between those patients with delirium and without delirium. Among the 274 subjects from the first cohort, 102 were categorized as delirious. Among the 206 subjects from the second cohort, 42 were categorized as delirious. The areas under the curve (AUCs) based on BSEEG score were 0.72 (1st cohort, Fp1-A1), 0.76 (1st cohort, Fp2-A2), and 0.67 (2nd cohort). AUCs from TDA were much higher at 0.82 (1st cohort, Fp1-A1), 0.84 (1st cohort, Fp2-A2), and 0.78 (2nd cohort). When sensitivity was set to be 0.80, the TDA drastically improved specificity to 0.66 (1st cohort, Fp1-A1), 0.72 (1st cohort, Fp2-A2), and 0.62 (2nd cohort), compared to 0.48 (1st cohort, Fp1-A1), 0.54 (1st cohort, Fp2-A2), and 0.46 (2nd cohort) with BSEEG. BSEEG has the potential to detect delirium, and TDA is helpful to improve the performance.

Effect of depth of anesthesia on the phase lag entropy in patients undergoing general anesthesia by propofol: A STROBE-compliant study

The PLEM100 (Inbody Co., Ltd., Seoul, Korea) is a device for measuring phase lag entropy (PLE), a recently developed index for the quantification of consciousness during sedation and general anesthesia. In the present study, we assessed changes in PLE along with the level of consciousness during the induction of general anesthesia using propofol. PLE was compared with the bispectral index (BIS), which is currently the most commonly used index of consciousness.After obtaining Institutional Review Board approval and written informed consent, we enrolled 15 patients (8 men, 7 women; mean age: 37 ± 9 years; mean height: 168 ± 8 cm; mean weight; 68 ± 11 kg) undergoing nasal bone reduction. PLE and BIS sensors were attached simultaneously, and general anesthesia was induced via target-controlled infusion (TCI) of propofol. PLE and BIS scores were recorded when the calculated effect site concentration shown on the TCI pump was equal to the target concentrations of 1.5, 2.0, 2.5, 2.8, 3.0, 3.2, 3.4, and 3.5 μg/mL (and at each 0.1 μg/mL increase, thereafter). Observer's Assessment of Alertness/Sedation (OAA/S) scores were also recorded until unconsciousness was achieved. Throughout the anesthesia period, all pairs of PLE and BIS data were collected using data acquisition software.The partial correlation coefficients between OAA/S scores and PLE, and between OAA/S scores and BIS were 0.778 (P < .001) and 0.846 (P < .001), respectively. Throughout the period of anesthesia, PLE and BIS exhibited a significant positive correlation. The partial correlation coefficient prior to the loss of consciousness was 0.838 (P < .001), and 0.669 (P < .001) following the loss of consciousness. Intra-class correlation between the 2 indices was 0.889 (P < .001) and 0.791 (P < .001) prior and following the loss of consciousness, respectively.PLE exhibited a strong and predictable correlation with both BIS and OAA/S scores. These results suggest that PLE is reliable for assessing the level of consciousness during sedation and general anesthesia.

The fundamental contribution of the electromyogram to a high bispectral index: a postoperative observational study

The electromyogram (EMG) activity has been reported to falsely increase BIS. Conversely, EMG seems necessary to constitute the high BIS indicative of an awake condition, and may play a fundamental role in calculating BIS, rather than distorting the appropriate BIS. However, exactly how EMG is associated with a high BIS remains unclear. We intended to clarify the respective contributions of EMG and various electroencephalogram (EEG) parameters to high BIS. In 79 courses of anaesthesia, BIS monitor-derived EMG parameters (EMGLOW), and other processed EEG parameters [SEF95 (spectral edge frequency 95%), SynchFastSlow (bispectral parameter), BetaRatio (frequency parameter), total power subtypes in five frequency range], were obtained simultaneously with BIS, every 3 s. These EEG parameters were used for receiver operating characteristic (ROC) analysis of detecting three BIS levels (BIS > 80, BIS > 70, and BIS > 60) to assess their diagnosabilities. A total of 218,418 data points derived from 79 cases were used for analysis. Area under the ROC curve (AUC) was calculated and optimal cut-off (threshold) was determined by Youden index. As the results, for detecting BIS > 80, the AUC of EMGLOW was 0.975 [0.974-0.977] (mean [95% confidence interval]), significantly higher than any other processed EEG parameters such as BetaRatio (0.832 [0.828-0.835]), SEF95 (0.821 [0.817-0.826]) and SynchFastSlow (0.769 [0.764-0.774]) (p < 0.05 each). The threshold of EMGLOW for detecting BIS > 80 was 35.7 dB, with high sensitivity (92.5%) and high specificity (96.5%). Our results suggest EMG contributes considerably to the diagnosis of high BIS, and is particularly essential for determining BIS > 80.