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15 pages, 4296 KiB

Open AccessArticle

Multi-Channel Surface EMG Spatio-Temporal Image Enhancement Using Multi-Scale Hessian-Based Filters

by Khalil Ullah, Khalil Khan, Muhammad Amin, Muhammad Attique, Tae-Sun Chung and Rabia Riaz

Appl. Sci. 2020, 10(15), 5099; https://doi.org/10.3390/app10155099 - 24 Jul 2020

Cited by 2 | Viewed by 2522

Abstract

Surface electromyography (sEMG) signals acquired with linear electrode array are useful in analyzing muscle anatomy and physiology. Most algorithms for signal processing, detection, and estimation require adequate quality of the input signals, however, multi-channel sEMG signals are commonly contaminated due to several noise [...] Read more.

Surface electromyography (sEMG) signals acquired with linear electrode array are useful in analyzing muscle anatomy and physiology. Most algorithms for signal processing, detection, and estimation require adequate quality of the input signals, however, multi-channel sEMG signals are commonly contaminated due to several noise sources. The sEMG signal needs to be enhanced prior to the digital signal and image processing to achieve the best results. This study is using spatio-temporal images to represent surface EMG signals. The motor unit action potential (MUAP) in these images looks like a linear structure, making certain angles with the x-axis, depending on the conduction velocity of the MU. A multi-scale Hessian-based filter is used to enhance the linear structure, i.e., the MUAP region, and to suppress the background noise. The proposed framework is compared with some of the existing algorithms using synthetic, simulated, and experimental sEMG signals. Results show improved detection accuracy of the motor unit action potential after the proposed enhancement as a preprocessing step. Full article

(This article belongs to the Special Issue Biomedical Signal Processing: From a Conceptual Framework to Clinical Applications)

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16 pages, 3376 KiB

Open AccessArticle

Classification of Photoplethysmographic Signal Quality with Fuzzy Neural Network for Improvement of Stroke Volume Measurement

by Shing-Hong Liu, Jia-Jung Wang, Wenxi Chen, Kuo-Li Pan and Chun-Hung Su

Appl. Sci. 2020, 10(4), 1476; https://doi.org/10.3390/app10041476 - 21 Feb 2020

Cited by 23 | Viewed by 3522

Abstract

Photoplethysmography (PPG) has been extensively employed to acquire some physiological parameters such as heart rate, oxygen saturation, and blood pressure. However, PPG signals are frequently corrupted by motion artifacts and baseline wandering, especially for the reflective PPG sensor. Several different algorithms have been [...] Read more.

Photoplethysmography (PPG) has been extensively employed to acquire some physiological parameters such as heart rate, oxygen saturation, and blood pressure. However, PPG signals are frequently corrupted by motion artifacts and baseline wandering, especially for the reflective PPG sensor. Several different algorithms have been studied for determining the signal quality of PPG by the characteristic parameters of its waveform and the rule-based methods. The levels of signal quality usually were defined by the manual operations. Thus, whether the good PPG waveforms are enough to increase the accuracy of the measurement is still a subjective issue. The aim of this study is to use a fuzzy neural network to determine the signal quality indexes (SQI) of PPG pulses measured by the impedance cardiography. To test the algorithm performance, the beat-to-beat stroke volumes (SV) were measured with our device and the medis^® CS 2000, synchronously. A total of 1466 pulses from 10 subjects were used to validate our algorithm in which the SQIs of 1007 pulses were high, those of 71 pulses were in the middle, and those of 388 pulses were low. The total error of SV measurement was −18 ± 22.0 mL. The performances of the classification were that the sensitivity and specificity for the 1007 pulses with the high SQIs were 0.81 and 0.90, and the error of SV measurement was 6.4 ± 12.8 mL. The sensitivity and specificity for the 388 pulses with the low SQIs were 0.84 and 0.93, while the error of SV measurement was 30.4 ± 3.6 mL. The results show that the proposed algorithm could be helpful in choosing good-quality PPG pulses to increase the accuracy of SV measurement in the impedance plethysmography. Full article

(This article belongs to the Special Issue Biomedical Signal Processing: From a Conceptual Framework to Clinical Applications)

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20 pages, 451 KiB

Open AccessArticle

Prediction of Post-Intubation Tachycardia Using Machine-Learning Models

by Hanna Kim, Young-Seob Jeong, Ah Reum Kang, Woohyun Jung, Yang Hoon Chung, Bon Sung Koo and Sang Hyun Kim

Appl. Sci. 2020, 10(3), 1151; https://doi.org/10.3390/app10031151 - 8 Feb 2020

Cited by 6 | Viewed by 5372

Abstract

Tachycardia is defined as a heart rate greater than 100 bpm for more than 1 min. Tachycardia often occurs after endotracheal intubation and can cause serious complication in patients with cardiovascular disease. The ability to predict post-intubation tachycardia would help clinicians by notifying [...] Read more.

Tachycardia is defined as a heart rate greater than 100 bpm for more than 1 min. Tachycardia often occurs after endotracheal intubation and can cause serious complication in patients with cardiovascular disease. The ability to predict post-intubation tachycardia would help clinicians by notifying a potential event to pre-treat. In this paper, we predict the potential post-intubation tachycardia. Given electronic medical record and vital signs collected before tracheal intubation, we predict whether post-intubation tachycardia will occur within 10 min. Of 1931 available patient datasets, 257 remained after filtering those with inappropriate data such as outliers and inappropriate annotations. Three feature sets were designed using feature selection algorithms, and two additional feature sets were defined by statistical inspection or manual examination. The five feature sets were compared with various machine learning models such as naïve Bayes classifiers, logistic regression, random forest, support vector machines, extreme gradient boosting, and artificial neural networks. Parameters of the models were optimized for each feature set. By 10-fold cross validation, we found that an logistic regression model with eight-dimensional hand-crafted features achieved an accuracy of 80.5%, recall of 85.1%, precision of 79.9%, an F1 score of 79.9%, and an area under the receiver operating characteristic curve of 0.85. Full article

(This article belongs to the Special Issue Biomedical Signal Processing: From a Conceptual Framework to Clinical Applications)

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11 pages, 5064 KiB

Open AccessArticle

Preliminary Study of the Use of Root with Sedline^® EEG Monitoring for Assessment of Anesthesia Depth in 6 Horses

by Olga Drewnowska, Bernard Turek, Barbara Lisowska and Charles E. Short

Appl. Sci. 2020, 10(3), 1050; https://doi.org/10.3390/app10031050 - 5 Feb 2020

Cited by 6 | Viewed by 7430

Abstract

Management of equine anesthesia monitoring is still a challenge. Careful monitoring to provide guidelines for anesthesia depth assessment currently relies upon eye signs, cardiopulmonary responses, and the level of muscle relaxation. Electroencephalography, as a non-invasive brain activity monitor, may be used to complement [...] Read more.

Management of equine anesthesia monitoring is still a challenge. Careful monitoring to provide guidelines for anesthesia depth assessment currently relies upon eye signs, cardiopulmonary responses, and the level of muscle relaxation. Electroencephalography, as a non-invasive brain activity monitor, may be used to complement the routinely monitored physiologic parameters. Six horses, undergoing various surgical procedures and anesthesia protocols, were monitored with the use of a Root with Sedline EEG monitor and a routine monitor of life parameters. The life parameters were compared to the changes on the EEG density spectral array observed live during anesthesia. During all procedures the level of awareness was monitored using the EEG, with higher frequency and power of waves indicating a higher level of awareness. It was evident from this that there were variations according to the type of procedure and the anesthetic protocol. Cerebral activity was elevated during painful moments of the surgery and recovery, requiring adjustments in anesthetic concentrations. Evaluation of changes in the spectral edge frequency (SEF) could show the periods when the patient is stabilized. EEG monitoring has the potential to be used in clinical anesthesiology of horses. It was shown that this system may be used in horses under general anesthesia but is currently less effective in a standing horse for diagnostic or minor procedures. Full article

(This article belongs to the Special Issue Biomedical Signal Processing: From a Conceptual Framework to Clinical Applications)

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10 pages, 1613 KiB

Open AccessArticle

Variance of the Gait Parameters and Fraction of Double-Support Interval for Determining the Severity of Parkinson’s Disease

by Sana M. Keloth, Sridhar P. Arjunan and Dinesh K. Kumar

Appl. Sci. 2020, 10(2), 577; https://doi.org/10.3390/app10020577 - 13 Jan 2020

Cited by 11 | Viewed by 3443

Abstract

The aim of this study was to determine the gait features that are most suitable for the quantified assessment of the severity of Parkinson’s disease (PD). This study computed the mean and variance of the four phases of gait intervals, i.e., stride, swing, [...] Read more.

The aim of this study was to determine the gait features that are most suitable for the quantified assessment of the severity of Parkinson’s disease (PD). This study computed the mean and variance of the four phases of gait intervals, i.e., stride, swing, stance and double-support intervals, and lateral difference to determine the difference between three groups, i.e., control subjects and PD patients with two severity levels (early and advanced stage) of the disease, PD1 and PD2. Data from 31 subjects were used in the study. The data were obtained from the public database (16 control healthy subjects, 6 Parkinson’s disease patients with early stages, and 9 Parkinson’s disease patients with advanced stages based on the Hoehn and Yahr scale). The main outcome measure of the study was the group difference of the four gait interval parameters and the statistical significance of this difference. The results show that there was a significant increase in the variance of the four gait intervals with the severity of the disease. However, there was no significant difference in the mean values between the three groups. It was also observed that the fraction corresponding to the double-support interval was significantly higher for PD patients. This study has shown that the variance of the gait parameters and the fraction of double-support interval are associated with the severity of PD and may be suitable measures for a quantified evaluation of the disease. Full article

(This article belongs to the Special Issue Biomedical Signal Processing: From a Conceptual Framework to Clinical Applications)

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16 pages, 1962 KiB

Open AccessArticle

Stochastic Modeling and Optimal Time-Frequency Estimation of Task-Related HRV

by Rachele Anderson, Peter Jönsson and Maria Sandsten

Appl. Sci. 2019, 9(23), 5154; https://doi.org/10.3390/app9235154 - 28 Nov 2019

Cited by 2 | Viewed by 2511

Abstract

In this paper, we propose a novel framework for the analysis of task-related heart rate variability (HRV). Respiration and HRV are measured from 92 test participants while performing a chirp-breathing task consisting of breathing at a slowly increasing frequency under metronome guidance. A [...] Read more.

In this paper, we propose a novel framework for the analysis of task-related heart rate variability (HRV). Respiration and HRV are measured from 92 test participants while performing a chirp-breathing task consisting of breathing at a slowly increasing frequency under metronome guidance. A non-stationary stochastic model, belonging to the class of Locally Stationary Chirp Processes, is used to model the task-related HRV data, and its parameters are estimated with a novel inference method. The corresponding optimal mean-square error (MSE) time-frequency spectrum is derived and evaluated both with the individually estimated model parameters and the common process parameters. The results from the optimal spectrum are compared to the standard spectrogram with different window lengths and the Wigner-Ville spectrum, showing that the MSE optimal spectral estimator may be preferable to the other spectral estimates because of its optimal bias and variance properties. The estimated model parameters are considered as response variables in a regression analysis involving several physiological factors describing the test participants’ state of health, finding a correlation with gender, age, stress, and fitness. The proposed novel approach consisting of measuring HRV during a chirp-breathing task, a corresponding time-varying stochastic model, inference method, and optimal spectral estimator gives a complete framework for the study of task-related HRV in relation to factors describing both mental and physical health and may highlight otherwise overlooked correlations. This approach may be applied in general for the analysis of non-stationary data and especially in the case of task-related HRV, and it may be useful to search for physiological factors that determine individual differences. Full article

(This article belongs to the Special Issue Biomedical Signal Processing: From a Conceptual Framework to Clinical Applications)

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14 pages, 767 KiB

Open AccessArticle

Prediction of Blood Pressure after Induction of Anesthesia Using Deep Learning: A Feasibility Study

by Young-Seob Jeong, Ah Reum Kang, Woohyun Jung, So Jeong Lee, Seunghyeon Lee, Misoon Lee, Yang Hoon Chung, Bon Sung Koo and Sang Hyun Kim

Appl. Sci. 2019, 9(23), 5135; https://doi.org/10.3390/app9235135 - 27 Nov 2019

Cited by 17 | Viewed by 4594

Abstract

Anesthesia induction is associated with frequent blood pressure fluctuation such as hypotension and hypertension. If it is possible to precisely predict blood pressure a few minutes ahead, anesthesiologists can proactively give anesthetic management before patients develop hemodynamic problem. The objective of this study [...] Read more.

Anesthesia induction is associated with frequent blood pressure fluctuation such as hypotension and hypertension. If it is possible to precisely predict blood pressure a few minutes ahead, anesthesiologists can proactively give anesthetic management before patients develop hemodynamic problem. The objective of this study is to develop a real-time model for predicting 3-min-ahead blood pressure from the start of anesthesia induction to surgical incision. We used only vital signs and anesthesia-related data obtained during anesthesia-induction phase and designed a bidirectional recurrent neural network followed by fully connected layers. We conducted experiments on our collected data of 102 patients, and obtained mean absolute errors between 8.2 mmHg and 11.1 mmHg and standard deviation between 8.7 mmHg and 12.7 mmHg. The average elapsed time for prediction of a batch of 100 unseen data was about 26.56 milliseconds. We believe that this study shows feasibility of real-time prediction of future blood pressures, and the performance will be improved by collecting more data and finding better model structures. Full article

(This article belongs to the Special Issue Biomedical Signal Processing: From a Conceptual Framework to Clinical Applications)

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19 pages, 5395 KiB

Open AccessArticle

A New Method for Detecting Architectural Distortion in Mammograms by NonSubsampled Contourlet Transform and Improved PCNN

by Guangming Du, Min Dong, Yi Sun, Shuyi Li, Xiaomin Mu, Hongbin Wei, Lei Ma and Bang Liu

Appl. Sci. 2019, 9(22), 4916; https://doi.org/10.3390/app9224916 - 15 Nov 2019

Cited by 7 | Viewed by 3750

Abstract

Breast cancer is the leading cause of cancer death in women, and early detection can reduce mortality. Architectural distortion (AD) is a feature of clinical manifestations for breast cancer, however, due to its complex structure and low detection accuracy, which cause a high [...] Read more.

Breast cancer is the leading cause of cancer death in women, and early detection can reduce mortality. Architectural distortion (AD) is a feature of clinical manifestations for breast cancer, however, due to its complex structure and low detection accuracy, which cause a high mortality of breast cancer. In order to improve the accuracy of AD detection and reduce the mortality of breast cancer, this paper proposes a new method by combining the non-subsampled contourlet transform (NSCT) with the improved pulse coupled neural network (PCNN). Firstly, the top–bottom hat transformation and the exponential transformation are employed to enhance the image. Secondly, the NSCT is employed to expand the overall contrast of the mammograms and filter out the noise. Finally, the improved PCNN by the maximum inter-class variance threshold selection method is employed to complete the AD detection. This proposed approach is tested on the public and authoritative database—Digital Database for Screening Mammography (DDSM). The specificity of the method is 98.73%, the accuracy is 93.16%, and the F1-score is 79.80%, and the area under curve (AUC) of the receiver operating characteristic (ROC) curve is 0.93, these results clearly demonstrate that the proposed method is comparable with those methods in recent literatures. This proposed method is simple, furthermore it can achieve high accuracy and help doctors to perform computer-aided detection of AD effectively. Full article

(This article belongs to the Special Issue Biomedical Signal Processing: From a Conceptual Framework to Clinical Applications)

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16 pages, 3950 KiB

Open AccessArticle

Motion Recognition and an Accuracy Comparison of Left and Right Arms by EEG Signal Analysis

by Bu Il Jeon, Byung Jun Kang, Hyun Chan Cho and Jongwon Kim

Appl. Sci. 2019, 9(22), 4885; https://doi.org/10.3390/app9224885 - 14 Nov 2019

Cited by 4 | Viewed by 4730

Abstract

An electromyogram (EMG) is a signal for muscle output that indicates the degree of muscle contraction and relaxation. For these muscle signals to be output, certain signals must be received from the brain. To analyze these relations, electroencephalograms (EEGs) of the brain are [...] Read more.

An electromyogram (EMG) is a signal for muscle output that indicates the degree of muscle contraction and relaxation. For these muscle signals to be output, certain signals must be received from the brain. To analyze these relations, electroencephalograms (EEGs) of the brain are measured to extract brain waves that are active at that time, although it is difficult to identify or distinguish expression patterns of the brain signal through EMG output. However, the brain signal operates via a partially reached signal and transmits the results of the operation. In this study, we analyze signals transmitted in this process and confirm whether human motion can be predicted from brain signals. It is not easy to guess the exact protocol of the brain using a general method, because a biosignal is a signal that differs from person to person. However, by analyzing the signals displayed by a particular user through actions, it is possible to determine the presence or absence of a signal to distinguish muscle movements. In the course of signal transduction, the energy of the left and right brain waves changes in the form of energy or signals that cause an arm’s movement. Responding to this, we analyze the signal transmission process of brain signals and EMGs to analyze loss and generated output. We extract EEG data from brain waves and determine EMG signals from the energy characteristics; we then collect and merge the results of spectra analysis through the Common Spatial Pattern (CSP) filter and explore the basis for predicting wills during muscle signals and stimulation transmission. The active information of the data within the working time of left and right brain waves depends on the changes of the left and right brain waves. It is proposed that the appearance of similar signals at these specific timescales can help identify the operations of the arms and outputs by the left and right biceps. Full article

(This article belongs to the Special Issue Biomedical Signal Processing: From a Conceptual Framework to Clinical Applications)

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16 pages, 5618 KiB

Open AccessArticle

Physiological Control Law for Rotary Blood Pumps with Full-State Feedback Method

by Mohsen Bakouri

Appl. Sci. 2019, 9(21), 4593; https://doi.org/10.3390/app9214593 - 29 Oct 2019

Cited by 6 | Viewed by 3345

Abstract

One concern about pulsatile rotary blood pumps is their physiological controller reactions when “venous return” changes. When a patient rises from a supine to a standing position, the blood volume in the leg veins is raised, owing to vasodilation, thus venous returns to [...] Read more.

One concern about pulsatile rotary blood pumps is their physiological controller reactions when “venous return” changes. When a patient rises from a supine to a standing position, the blood volume in the leg veins is raised, owing to vasodilation, thus venous returns to the right atrium, and consequently, the left atrium is reduced. In this work, a physiological control law using a full-state feedback control method was developed in order to drive mechanical circulatory support. This strategy was used as a validated state-space pump model, to implement the controller and regulate the desired reference flow. The control law was assessed using a software model of the hemodynamical cardiovascular system interacting with the left ventricular assist device in different physiological conditions ranging from rest to exercise scenarios. Under these scenarios, heart failure disease was simulated by changing the hemodynamic parameters of the total blood volume, heart rate, cardiac contractility, and systemic peripheral resistance. The results were numerically observed during the postural changes. The rate of change in the physiological variables showed that the proposed control law can regulate the desired reference pump flow with minimal error within the acceptable clinical range in order to prevent suction and over perfusion. Full article

(This article belongs to the Special Issue Biomedical Signal Processing: From a Conceptual Framework to Clinical Applications)

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7 pages, 1202 KiB

Open AccessArticle

Smartphone-Based Point-of-Care Cholesterol Blood Test Performance Evaluation Compared with a Clinical Diagnostic Laboratory Method

by Kyongsik Yun, Jaekyu Choi, In-Uk Song and Yong-An Chung

Appl. Sci. 2019, 9(16), 3334; https://doi.org/10.3390/app9163334 - 14 Aug 2019

Cited by 2 | Viewed by 5810

Abstract

Managing blood cholesterol levels is important for the treatment and prevention of diabetes, cardiovascular disease, and obesity. An easy-to-use, portable cholesterol blood test could accelerate more frequent testing by patients and at-risk populations. We aim to evaluate the performance of smartphone-based point-of-care cholesterol [...] Read more.

Managing blood cholesterol levels is important for the treatment and prevention of diabetes, cardiovascular disease, and obesity. An easy-to-use, portable cholesterol blood test could accelerate more frequent testing by patients and at-risk populations. We aim to evaluate the performance of smartphone-based point-of-care cholesterol blood tests compared to that of hospital-grade laboratory tests. We used smartphone systems that are already familiar to many people. Because smartphone systems can be carried around everywhere, blood can be measured easily and frequently. We compared the results of cholesterol tests with those of existing clinical diagnostic laboratory methods. We found that smartphone-based point-of-care lipid blood tests were as accurate as hospital-grade laboratory tests (N = 116, R > 0.97, p < 0.001 for all three cholesterol blood tests, i.e., total cholesterol, high density lipoprotein, and triglyceride). Our system could be useful for those who need to manage blood cholesterol levels to motivate them to track and control their behavior. Full article

(This article belongs to the Special Issue Biomedical Signal Processing: From a Conceptual Framework to Clinical Applications)

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7 pages, 235 KiB

Open AccessFeature PaperArticle

Influence of Horseback Riding and Horse Simulator Riding on Heart Rate Variability: Are There Differences?

by Santos Villafaina, Carmen Cordón-González, Daniel Collado-Mateo, Juan P. Fuentes-García, J. C. Adsuar, E. Merellano-Navarro and J. A. Parraca

Appl. Sci. 2019, 9(11), 2194; https://doi.org/10.3390/app9112194 - 29 May 2019

Cited by 10 | Viewed by 4098

Abstract

This study aimed to compare the heart rate variability (HRV) in healthy young people while riding a real horse or a horse gait simulator. The sample consisted of a group of 23 healthy young adults aged 22.91 (2.37), who rode a horse for [...] Read more.

This study aimed to compare the heart rate variability (HRV) in healthy young people while riding a real horse or a horse gait simulator. The sample consisted of a group of 23 healthy young adults aged 22.91 (2.37), who rode a horse for five minutes at walking speed and spent five minutes on a horse gait simulator, while their HRV values were being recorded. Furthermore, immediately after each protocol, the HRV at rest was also recorded to observe the acute effects. We used the paired samples t-test to compare between the HRV during the horse-riding and the horse simulator-riding activities, as well as the differences in the acute effects between both situations. The findings indicate that the HRV was lower when participants were riding the horse compared with the activity on the horse simulator. However, no differences were observed immediately after the two protocols. Therefore, we state that the sympathetic tone is higher while riding a real horse than while riding a horse simulator. These differences may be due to emotional aspects and not due to differences in the physical load, considering the absence of differences in the acute effects. Full article

(This article belongs to the Special Issue Biomedical Signal Processing: From a Conceptual Framework to Clinical Applications)

10 pages, 526 KiB

Open AccessArticle

Duration of the Symptoms and Brain Aging in Women with Fibromyalgia: A Cross-Sectional Study

by Santos Villafaina, Daniel Collado-Mateo, Juan P. Fuentes-García, Francisco J. Domínguez-Muñoz and Narcís Gusi

Appl. Sci. 2019, 9(10), 2106; https://doi.org/10.3390/app9102106 - 23 May 2019

Cited by 7 | Viewed by 3289

Abstract

Fibromyalgia is a chronic syndrome that is characterized by widespread pain and an altered brain dynamic. The aim of this study was to analyze the effect of the duration of the symptoms on the cortical activity of women with fibromyalgia using electroencephalogram power [...] Read more.

Fibromyalgia is a chronic syndrome that is characterized by widespread pain and an altered brain dynamic. The aim of this study was to analyze the effect of the duration of the symptoms on the cortical activity of women with fibromyalgia using electroencephalogram power spectrum analyses in an eye-closed resting state. Twenty-nine women participated in this cross-sectional study (N: 29; age: 55.59 [9.50]). Theta, alpha, beta-1, beta-2, and beta-3 frequency bands were analyzed using EEGLAB. Theta power significantly correlated with the duration of the symptoms, but not with age. In addition, participants were divided into two groups according to number the years for which they were suffering from fibromyalgia. Participants who had a longer duration of symptoms obtained higher theta power in the frontal (Fp1, F4, F7, F8, and Fz), central (C3, C4, and Cz), and parietal (P3 and Pz) areas than those who had a shorter duration of symptoms, which may be related to brain aging. This exploratory study demonstrates for the first time that the frontal, central, and parietal areas may be influenced by the years in which they were suffering from the symptoms of fibromyalgia. This might indicate that the duration of these symptoms may have a higher impact on brain aging than the actual age of the patient. Full article

(This article belongs to the Special Issue Biomedical Signal Processing: From a Conceptual Framework to Clinical Applications)

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Review

Jump to: Research

11 pages, 4090 KiB

Open AccessReview

What Do We Know about the Use of EEG Monitoring during Equine Anesthesia: A Review

by Olga Drewnowska, Barbara Lisowska and Bernard Turek

Appl. Sci. 2019, 9(18), 3678; https://doi.org/10.3390/app9183678 - 5 Sep 2019

Cited by 6 | Viewed by 4901

Abstract

Electroencephalography is a noninvasive method used for the measurement of central nervous system bioelectrical activity. Besides its use for neurological disorders diagnostics in humans and animals, it was found to be useful as a part of the anesthetic monitoring. Introducing the electroencephalography (EEG) [...] Read more.

Electroencephalography is a noninvasive method used for the measurement of central nervous system bioelectrical activity. Besides its use for neurological disorders diagnostics in humans and animals, it was found to be useful as a part of the anesthetic monitoring. Introducing the electroencephalography (EEG) measurement intraoperatively in humans and in animals, due to its high specificity and sensitivity (limited number of wave patterns and high number of variabilities influencing them), with comparison to cardiovascular parameters might significantly increase the quality of anesthesia. The use of EEG during equine anesthesia may help to maintain a proper depth of anesthesia in this species. Due to the fact that EEG analyzers were designed for humans, there are still limitations of their use in horses, and different methods of analysis are studied. The paper introduces the physiology of EEG, its use in animals during anesthesia, and specification for horses. Full article

(This article belongs to the Special Issue Biomedical Signal Processing: From a Conceptual Framework to Clinical Applications)

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